From c9524575fcf41cf70dac39ae9214b77f4745e0c5 Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 06:59:17 -0500 Subject: [PATCH 01/39] Add files via upload --- fa23-team/Xiang_li_Lationx_analysis.ipynb | 1187 ++++++++++++++++++++- 1 file changed, 1144 insertions(+), 43 deletions(-) diff --git a/fa23-team/Xiang_li_Lationx_analysis.ipynb b/fa23-team/Xiang_li_Lationx_analysis.ipynb index 720d52a..06a9ecf 100644 --- a/fa23-team/Xiang_li_Lationx_analysis.ipynb +++ b/fa23-team/Xiang_li_Lationx_analysis.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 22, + "execution_count": 112, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 163, "metadata": {}, "outputs": [], "source": [ @@ -26,12 +26,250 @@ "latinx2011=pd.read_csv('data/Latinx2011.csv')\n", "white2021=pd.read_csv('data/White2021.csv')\n", "white2016=pd.read_csv('data/White2016.csv')\n", - "white2011=pd.read_csv('data/White2011.csv')" + "white2011=pd.read_csv('data/White2011.csv')\n", + "\n", + "all2021=pd.read_csv('data/export (1).csv')\n", + "all2016=pd.read_csv('data/export 2016.csv')\n", + "all2011=pd.read_csv('data/export 2011.csv')\n", + "asian2021=pd.read_csv('data/Asian2021.csv')\n", + "asian2016=pd.read_csv('data/Asian2016.csv')\n", + "asian2011=pd.read_csv('data/Asian2011.csv')\n", + "black2021=pd.read_csv('data/black2021.csv')\n", + "black2016=pd.read_csv('data/black2016.csv')\n", + "black2011=pd.read_csv('data/black2011.csv')\n", + "twoormore2021=pd.read_csv('data/twoormore2021.csv')\n", + "twoormore2016=pd.read_csv('data/twoormore2016.csv')\n", + "twoormore2011=pd.read_csv('data/twoormore2011.csv')\n", + "someother2021=pd.read_csv('data/someother2021.csv')\n", + "someother2016=pd.read_csv('data/someother2016.csv')\n", + "someother2011=pd.read_csv('data/someother2011.csv')\n", + "americanindian2021=pd.read_csv('data/AmericanIn2011.csv')\n", + "americanindian2016=pd.read_csv('data/AmericanIn2016.csv')\n", + "americanindian2011=pd.read_csv('data/AmericanIn2011.csv')\n" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "# * 2 (roundtrip) * 5 days/week * 50 weeks) / 60 min \n", + "def calculation(time):\n", + " return time*2*5*50/60\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8690" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all2021['White alone'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Means of transportation to work (JWTR)White aloneBlack or African American aloneAmerican Indian aloneAlaska Native aloneAmerican Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other racesAsian aloneNative Hawaiian and Other Pacific Islander aloneSome Other Race aloneTwo or More Races
0-> Boston City--Dorchester & South Boston PUM...NaNNaNNaNNaNNaNNaNNaNNaNNaN
1Bus or Trolley Bus7086.03997.0110.00.015.0818.00.0902.0275.0
2-> Boston City--Mattapan & Roxbury PUMA, Mass...NaNNaNNaNNaNNaNNaNNaNNaNNaN
3Bus or Trolley Bus2101.011139.032.00.019.0371.00.01898.0408.0
4-> Boston City--Hyde Park, Jamaica Plain, Ros...NaNNaNNaNNaNNaNNaNNaNNaNNaN
5Bus or Trolley Bus2845.02665.026.00.00.0484.00.0504.0214.0
\n", + "
" + ], + "text/plain": [ + " Means of transportation to work (JWTR) White alone \\\n", + "0 -> Boston City--Dorchester & South Boston PUM... NaN \n", + "1 Bus or Trolley Bus 7086.0 \n", + "2 -> Boston City--Mattapan & Roxbury PUMA, Mass... NaN \n", + "3 Bus or Trolley Bus 2101.0 \n", + "4 -> Boston City--Hyde Park, Jamaica Plain, Ros... NaN \n", + "5 Bus or Trolley Bus 2845.0 \n", + "\n", + " Black or African American alone American Indian alone \\\n", + "0 NaN NaN \n", + "1 3997.0 110.0 \n", + "2 NaN NaN \n", + "3 11139.0 32.0 \n", + "4 NaN NaN \n", + "5 2665.0 26.0 \n", + "\n", + " Alaska Native alone \\\n", + "0 NaN \n", + "1 0.0 \n", + "2 NaN \n", + "3 0.0 \n", + "4 NaN \n", + "5 0.0 \n", + "\n", + " American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races \\\n", + "0 NaN \n", + "1 15.0 \n", + "2 NaN \n", + "3 19.0 \n", + "4 NaN \n", + "5 0.0 \n", + "\n", + " Asian alone Native Hawaiian and Other Pacific Islander alone \\\n", + "0 NaN NaN \n", + "1 818.0 0.0 \n", + "2 NaN NaN \n", + "3 371.0 0.0 \n", + "4 NaN NaN \n", + "5 484.0 0.0 \n", + "\n", + " Some Other Race alone Two or More Races \n", + "0 NaN NaN \n", + "1 902.0 275.0 \n", + "2 NaN NaN \n", + "3 1898.0 408.0 \n", + "4 NaN NaN \n", + "5 504.0 214.0 " + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all2016" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ @@ -48,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 118, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ @@ -108,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ @@ -141,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 121, "metadata": {}, "outputs": [], "source": [ @@ -160,12 +398,21 @@ "\n", "selected_columns3 = [col for col in latinx2021.columns if any(item in col for item in Dorcherter_list)]\n", "\n", - "Dorcherter_lat_data2021 = latinx2021[selected_columns3]\n" + "Dorcherter_lat_data2021 = latinx2021[selected_columns3]\n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ @@ -192,30 +439,20 @@ "for i in estimate_columns_dor_lat:\n", " sum2021_dor_lat=sum2021_dor_lat+float(Dorcherter_lat_data2021[i][3])\n", " \n", - "\n" + "\n", + "latinx_total_2021=sum2021_rox_lat+sum2021_mat_lat+sum2021_dor_lat" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'463'" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 123, "metadata": {}, "outputs": [], "source": [ @@ -239,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 124, "metadata": {}, "outputs": [], "source": [ @@ -258,12 +495,14 @@ "sum2016_dor_lat=0\n", "\n", "for i in estimate_columns_dor_lat:\n", - " sum2016_dor_lat=sum2016_dor_lat+Dorcherter_lat_data2016[i][3]" + " sum2016_dor_lat=sum2016_dor_lat+Dorcherter_lat_data2016[i][3]\n", + "\n", + "latinx_total_2016=sum2016_rox_lat+sum2016_mat_lat+sum2016_dor_lat" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -282,12 +521,13 @@ "\n", "selected_columns3 = [col for col in latinx2011.columns if any(item in col for item in Dorcherter_list)]\n", "\n", - "Dorcherter_lat_data2011 = latinx2011[selected_columns3]" + "Dorcherter_lat_data2011 = latinx2011[selected_columns3]\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 126, "metadata": {}, "outputs": [], "source": [ @@ -306,12 +546,14 @@ "sum2011_dor_lat=0\n", "\n", "for i in estimate_columns_dor_lat:\n", - " sum2011_dor_lat=sum2011_dor_lat+Dorcherter_lat_data2011[i][3]" + " sum2011_dor_lat=sum2011_dor_lat+Dorcherter_lat_data2011[i][3]\n", + "\n", + "latinx_total_2011=sum2011_rox_lat+sum2011_mat_lat+sum2011_dor_lat" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 127, "metadata": {}, "outputs": [], "source": [ @@ -337,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 128, "metadata": {}, "outputs": [], "source": [ @@ -363,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 129, "metadata": {}, "outputs": [], "source": [ @@ -384,12 +626,13 @@ "\n", "selected_columns3 = [col for col in white2011.columns if any(item in col for item in Dorcherter_list)]\n", "\n", - "Dorcherter_white_data2011 = white2011[selected_columns3]" + "Dorcherter_white_data2011 = white2011[selected_columns3]\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 130, "metadata": {}, "outputs": [], "source": [ @@ -416,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 131, "metadata": {}, "outputs": [], "source": [ @@ -441,7 +684,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 132, "metadata": {}, "outputs": [], "source": [ @@ -459,12 +702,543 @@ "estimate_columns_dor_white = [col for col in Dorcherter_white_data2011.columns if \"Estimate\" in col]\n", "sum2011_dor_white=0\n", "for i in estimate_columns_dor_white:\n", - " sum2011_dor_white=sum2011_dor_white+float(Dorcherter_white_data2011[i][3])" + " sum2011_dor_white=sum2011_dor_white+float(Dorcherter_white_data2011[i][3])\n", + "\n", + "\n", + "white_total_2011=sum2011_rox_white+sum2011_mat_white+sum2011_dor_white\n", + "white_total_2016=sum2016_rox_white+sum2016_mat_white+sum2016_dor_white\n", + "white_total_2021=sum2021_rox_white+sum2021_mat_white+sum2021_dor_white\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "Roxbuy_list=['805','806.','804.01','806.01','801','814','803','813','815','904','819','820','821,9803']\n", + "Mattapan_list=['9811','1011.01','1011.02','1010.01','1010.02','1009']\n", + "Dorcherter_list=['907','913','912','911','914','909.01','915','910.01','902','918','916','917'\n", + " ,'901','919','920','921.01','924','923','922','1006.01','1001','1005'\n", + " ,'1006.03','1002','1003','1008','1006.01','1007']\n", + "\n", + "selected_columns = [col for col in asian2021.columns if any(item in col for item in Roxbuy_list)]\n", + "Roxbury_asian_data_2021 = asian2021[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in asian2021.columns if any(item in col for item in Mattapan_list)]\n", + "\n", + "Mattapan_asian_data_2021 = asian2021[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in asian2021.columns if any(item in col for item in Dorcherter_list)]\n", + "\n", + "Dorcherter_asian_data2021 = asian2021[selected_columns3]\n", + "\n", + "# white 2016 demographic data\n", + "Roxbuy_list=['805','806.','804.01','806.01','801','814','803','813','815','904','819','820','821,9803']\n", + "Mattapan_list=['9811','1011.01','1011.02','1010.01','1010.02','1009']\n", + "\n", + "Dorcherter_list=['907','913','912','911','914','909.01','915','910.01','902','918','916','917'\n", + " ,'901','919','920','921.01','924','923','922','1006.01','1001','1005'\n", + " ,'1006.03','1002','1003','1008','1006.01','1007']\n", + "\n", + "selected_columns = [col for col in asian2016.columns if any(item in col for item in Roxbuy_list)]\n", + "Roxbury_asian_data_2016 = asian2016[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in asian2016.columns if any(item in col for item in Mattapan_list)]\n", + "\n", + "Mattapan_asian_data_2016 = asian2016[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in asian2016.columns if any(item in col for item in Dorcherter_list)]\n", + "\n", + "Dorcherter_asian_data2016 = asian2016[selected_columns3]\n", + "\n", + "# white 2011 demographic data\n", + "Roxbuy_list=['805','806.','804.01','806.01','801','814','803','813','815','904','819','820','821,9803']\n", + "Mattapan_list=['9811','1011.01','1011.02','1010.01','1010.02','1009']\n", + "\n", + "Dorcherter_list=['907','913','912','911','914','909.01','915','910.01','902','918','916','917'\n", + " ,'901','919','920','921.01','924','923','922','1006.01','1001','1005'\n", + " ,'1006.03','1002','1003','1008','1006.01','1007']\n", + "\n", + "selected_columns = [col for col in asian2011.columns if any(item in col for item in Roxbuy_list)]\n", + "Roxbury_asian_data_2011 = asian2011[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in asian2011.columns if any(item in col for item in Mattapan_list)]\n", + "\n", + "Mattapan_asian_data_2011 = asian2011[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in asian2011.columns if any(item in col for item in Dorcherter_list)]\n", + "\n", + "Dorcherter_asian_data2011 = asian2011[selected_columns3]\n", + "\n", + "estimate_columns_rox_asian = [col for col in Roxbury_asian_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_rox_asian=0\n", + "for i in estimate_columns_rox_asian:\n", + " sum2021_rox_asian=sum2021_rox_asian+float(Roxbury_asian_data_2021[i][3])\n", + "\n", + "estimate_columns_mat_asian = [col for col in Mattapan_asian_data_2021.columns if \"Estimate\" in col] \n", + "sum2021_mat_asian=0\n", + "for i in estimate_columns_mat_asian:\n", + " try:\n", + " sum2021_mat_asian=sum2021_mat_asian+Mattapan_asian_data_2021[i][3]\n", + " except:\n", + " print(Mattapan_asian_data_2021[i][3])\n", + "\n", + "estimate_columns_dor_asian = [col for col in Dorcherter_asian_data2021.columns if \"Estimate\" in col]\n", + "sum2021_dor_asian=0\n", + "for i in estimate_columns_dor_asian:\n", + " sum2021_dor_asian=sum2021_dor_asian+float(Dorcherter_asian_data2021[i][3])\n", + "\n", + "estimate_columns_rox_asian = [col for col in Roxbury_asian_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_rox_asian=0\n", + "for i in estimate_columns_rox_asian:\n", + " sum2016_rox_asian=sum2016_rox_asian+float(Roxbury_asian_data_2016[i][3])\n", + "\n", + "estimate_columns_mat_asian = [col for col in Mattapan_asian_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_mat_asian=0\n", + "for i in estimate_columns_mat_asian:\n", + " sum2016_mat_asian=sum2016_mat_asian+Mattapan_asian_data_2016[i][3]\n", + "\n", + "estimate_columns_dor_asian = [col for col in Dorcherter_asian_data2016.columns if \"Estimate\" in col]\n", + "sum2016_dor_asian=0\n", + "for i in estimate_columns_dor_asian:\n", + " sum2016_dor_asian=sum2016_dor_asian+float(Dorcherter_asian_data2016[i][3])\n", + "\n", + "estimate_columns_rox_asian = [col for col in Roxbury_asian_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_rox_asian=0\n", + "for i in estimate_columns_rox_asian:\n", + " sum2011_rox_asian=sum2011_rox_asian+float(Roxbury_asian_data_2011[i][3])\n", + "\n", + "estimate_columns_mat_asian = [col for col in Mattapan_asian_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_mat_asian=0\n", + "for i in estimate_columns_mat_asian:\n", + " sum2011_mat_asian=sum2011_mat_asian+Mattapan_asian_data_2011[i][3]\n", + "\n", + "estimate_columns_dor_asian = [col for col in Dorcherter_asian_data2011.columns if \"Estimate\" in col]\n", + "sum2011_dor_asian=0\n", + "for i in estimate_columns_dor_asian:\n", + " sum2011_dor_asian=sum2011_dor_asian+float(Dorcherter_asian_data2011[i][3])\n", + "\n", + "asian_total_2021=sum2021_rox_asian+sum2021_mat_asian+sum2021_dor_asian\n", + "asian_total_2016=sum2016_rox_asian+sum2016_mat_asian+sum2016_dor_asian\n", + "asian_total_2011=sum2011_rox_asian+sum2011_mat_asian+sum2011_dor_asian\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "def to_float(value):\n", + " if isinstance(value, str): # If value is a string, remove commas and convert to float\n", + " return float(value.replace(',', ''))\n", + " elif isinstance(value, (int, float, numpy.int64)): # If value is already a number, just return it as a float\n", + " return float(value)\n", + " else:\n", + " raise ValueError(f\"Cannot convert to float: {value}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "# did the same thing anove for black population\n", + "# Lists of tract codes for Roxbury, Mattapan, and Dorchester\n", + "Roxbury_list = ['805', '806.', '804.01', '806.01', '801', '814', '803', '813', '815', '904', '819', '820', '821,9803']\n", + "Mattapan_list = ['9811', '1011.01', '1011.02', '1010.01', '1010.02', '1009']\n", + "Dorchester_list = ['907', '913', '912', '911', '914', '909.01', '915', '910.01', '902', '918', '916', '917',\n", + " '901', '919', '920', '921.01', '924', '923', '922', '1006.01', '1001', '1005',\n", + " '1006.03', '1002', '1003', '1008', '1006.01', '1007']\n", + "\n", + "# 2021 demographic data for Black population\n", + "selected_columns = [col for col in black2021.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_black_data_2021 = black2021[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in black2021.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_black_data_2021 = black2021[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in black2021.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_black_data_2021 = black2021[selected_columns3]\n", + "\n", + "# 2016 demographic data for Black population\n", + "selected_columns = [col for col in black2016.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_black_data_2016 = black2016[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in black2016.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_black_data_2016 = black2016[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in black2016.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_black_data_2016 = black2016[selected_columns3]\n", + "\n", + "# 2011 demographic data for Black population\n", + "selected_columns = [col for col in black2011.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_black_data_2011 = black2011[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in black2011.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_black_data_2011 = black2011[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in black2011.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_black_data_2011 = black2011[selected_columns3]\n", + "\n", + "# Calculation of sums for Black population data\n", + "estimate_columns_rox_black = [col for col in Roxbury_black_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_rox_black = 0\n", + "for i in estimate_columns_rox_black:\n", + " sum2021_rox_black = sum2021_rox_black + float(Roxbury_black_data_2021[i][3])\n", + "\n", + "estimate_columns_mat_black = [col for col in Mattapan_black_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_mat_black = 0\n", + "for i in estimate_columns_mat_black:\n", + " try:\n", + " sum2021_mat_black = sum2021_mat_black + float(Mattapan_black_data_2021[i][3])\n", + " except:\n", + " print(Mattapan_black_data_2021[i][3])\n", + "\n", + "estimate_columns_dor_black = [col for col in Dorchester_black_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_dor_black = 0\n", + "for i in estimate_columns_dor_black:\n", + " sum2021_dor_black = sum2021_dor_black + to_float(Dorchester_black_data_2021[i][3])\n", + "\n", + "# ... Repeat for 2016 and 2011 ...\n", + "estimate_columns_rox_black = [col for col in Roxbury_black_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_rox_black = 0\n", + "for i in estimate_columns_rox_black:\n", + " sum2016_rox_black = sum2016_rox_black + float(Roxbury_black_data_2016[i][3])\n", + "\n", + "estimate_columns_mat_black = [col for col in Mattapan_black_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_mat_black = 0\n", + "for i in estimate_columns_mat_black:\n", + " sum2016_mat_black = sum2016_mat_black + to_float(Mattapan_black_data_2016[i][3])\n", + "\n", + "estimate_columns_dor_black = [col for col in Dorchester_black_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_dor_black = 0\n", + "for i in estimate_columns_dor_black:\n", + " sum2016_dor_black = sum2016_dor_black + float(Dorchester_black_data_2016[i][3])\n", + "\n", + "estimate_columns_rox_black = [col for col in Roxbury_black_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_rox_black = 0\n", + "for i in estimate_columns_rox_black:\n", + " sum2011_rox_black = sum2011_rox_black + float(Roxbury_black_data_2011[i][3])\n", + "\n", + "estimate_columns_mat_black = [col for col in Mattapan_black_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_mat_black = 0\n", + "for i in estimate_columns_mat_black:\n", + " sum2011_mat_black = sum2011_mat_black + to_float(Mattapan_black_data_2011[i][3])\n", + "\n", + "estimate_columns_dor_black = [col for col in Dorchester_black_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_dor_black = 0\n", + "for i in estimate_columns_dor_black:\n", + " sum2011_dor_black = sum2011_dor_black + float(Dorchester_black_data_2011[i][3])\n", + "\n", + "\n", + "# Total sums for Black population data for 2021, 2016, and 2011\n", + "black_total_2021 = sum2021_rox_black + sum2021_mat_black + sum2021_dor_black\n", + "black_total_2016 = sum2016_rox_black + sum2016_mat_black + sum2016_dor_black\n", + "black_total_2011 = sum2011_rox_black + sum2011_mat_black + sum2011_dor_black\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [], + "source": [ + "# Lists of tract codes for Roxbury, Mattapan, and Dorchester\n", + "Roxbury_list = ['805', '806.', '804.01', '806.01', '801', '814', '803', '813', '815', '904', '819', '820', '821,9803']\n", + "Mattapan_list = ['9811', '1011.01', '1011.02', '1010.01', '1010.02', '1009']\n", + "Dorchester_list = ['907', '913', '912', '911', '914', '909.01', '915', '910.01', '902', '918', '916', '917',\n", + " '901', '919', '920', '921.01', '924', '923', '922', '1006.01', '1001', '1005',\n", + " '1006.03', '1002', '1003', '1008', '1006.01', '1007']\n", + "\n", + "# 2021 demographic data for Two or More races\n", + "selected_columns = [col for col in twoormore2021.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_twoormore_data_2021 = twoormore2021[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in twoormore2021.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_twoormore_data_2021 = twoormore2021[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in twoormore2021.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_twoormore_data_2021 = twoormore2021[selected_columns3]\n", + "\n", + "# 2016 demographic data for Two or More races\n", + "selected_columns = [col for col in twoormore2016.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_twoormore_data_2016 = twoormore2016[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in twoormore2016.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_twoormore_data_2016 = twoormore2016[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in twoormore2016.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_twoormore_data_2016 = twoormore2016[selected_columns3]\n", + "\n", + "# 2011 demographic data for Two or More races\n", + "selected_columns = [col for col in twoormore2011.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_twoormore_data_2011 = twoormore2011[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in twoormore2011.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_twoormore_data_2011 = twoormore2011[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in twoormore2011.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_twoormore_data_2011 = twoormore2011[selected_columns3]\n", + "\n", + "# Calculation of sums for Two or More races data\n", + "estimate_columns_rox_twoormore = [col for col in Roxbury_twoormore_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_rox_twoormore = 0\n", + "for i in estimate_columns_rox_twoormore:\n", + " sum2021_rox_twoormore = sum2021_rox_twoormore + float(Roxbury_twoormore_data_2021[i][3])\n", + "\n", + "estimate_columns_mat_twoormore = [col for col in Mattapan_twoormore_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_mat_twoormore = 0\n", + "for i in estimate_columns_mat_twoormore:\n", + " try:\n", + " sum2021_mat_twoormore = sum2021_mat_twoormore + Mattapan_twoormore_data_2021[i][3]\n", + " except:\n", + " print(Mattapan_twoormore_data_2021[i][3])\n", + "\n", + "estimate_columns_dor_twoormore = [col for col in Dorchester_twoormore_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_dor_twoormore = 0\n", + "for i in estimate_columns_dor_twoormore:\n", + " sum2021_dor_twoormore = sum2021_dor_twoormore + float(Dorchester_twoormore_data_2021[i][3])\n", + "\n", + "# ... Repeat for 2016 and 2011\n", + "\n", + "estimate_columns_rox_twoormore = [col for col in Roxbury_twoormore_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_rox_twoormore = 0\n", + "for i in estimate_columns_rox_twoormore:\n", + " sum2016_rox_twoormore = sum2016_rox_twoormore + float(Roxbury_twoormore_data_2016[i][3])\n", + "\n", + "estimate_columns_mat_twoormore = [col for col in Mattapan_twoormore_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_mat_twoormore = 0\n", + "for i in estimate_columns_mat_twoormore:\n", + " sum2016_mat_twoormore = sum2016_mat_twoormore + Mattapan_twoormore_data_2016[i][3]\n", + "\n", + "estimate_columns_dor_twoormore = [col for col in Dorchester_twoormore_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_dor_twoormore = 0\n", + "for i in estimate_columns_dor_twoormore:\n", + " sum2016_dor_twoormore = sum2016_dor_twoormore + float(Dorchester_twoormore_data_2016[i][3])\n", + "\n", + "estimate_columns_rox_twoormore = [col for col in Roxbury_twoormore_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_rox_twoormore = 0\n", + "for i in estimate_columns_rox_twoormore:\n", + " sum2011_rox_twoormore = sum2011_rox_twoormore + float(Roxbury_twoormore_data_2011[i][3])\n", + "\n", + "estimate_columns_mat_twoormore = [col for col in Mattapan_twoormore_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_mat_twoormore = 0\n", + "for i in estimate_columns_mat_twoormore:\n", + " sum2011_mat_twoormore = sum2011_mat_twoormore + Mattapan_twoormore_data_2011[i][3]\n", + "\n", + "estimate_columns_dor_twoormore = [col for col in Dorchester_twoormore_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_dor_twoormore = 0\n", + "for i in estimate_columns_dor_twoormore:\n", + " sum2011_dor_twoormore = sum2011_dor_twoormore + float(Dorchester_twoormore_data_2011[i][3])\n", + "\n", + "twoormore_total_2021=sum2021_rox_twoormore+sum2021_mat_twoormore+sum2021_dor_twoormore\n", + "twoormore_total_2016=sum2016_rox_twoormore+sum2016_mat_twoormore+sum2016_dor_twoormore\n", + "twoormore_total_2011=sum2011_rox_twoormore+sum2011_mat_twoormore+sum2011_dor_twoormore\n" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 164, + "metadata": {}, + "outputs": [], + "source": [ + "# Assuming you have dataframes named someother2021, someother2016, and someother2011 for the respective years\n", + "\n", + "# 2021 demographic data for Some Other races\n", + "selected_columns = [col for col in someother2021.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_someother_data_2021 = someother2021[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in someother2021.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_someother_data_2021 = someother2021[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in someother2021.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_someother_data_2021 = someother2021[selected_columns3]\n", + "\n", + "# 2016 demographic data for Some Other races\n", + "selected_columns = [col for col in someother2016.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_someother_data_2016 = someother2016[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in someother2016.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_someother_data_2016 = someother2016[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in someother2016.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_someother_data_2016 = someother2016[selected_columns3]\n", + "\n", + "# 2011 demographic data for Some Other races\n", + "selected_columns = [col for col in someother2011.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_someother_data_2011 = someother2011[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in someother2011.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_someother_data_2011 = someother2011[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in someother2011.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_someother_data_2011 = someother2011[selected_columns3]\n", + "\n", + "# Calculation of sums for Some Other races data\n", + "# ... Your summing code goes here, similar to the one for twoormore ...\n", + "\n", + "estimate_columns_rox_someother = [col for col in Roxbury_someother_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_rox_someother = 0\n", + "for i in estimate_columns_rox_someother:\n", + " sum2021_rox_someother = sum2021_rox_someother + float(Roxbury_someother_data_2021[i][3])\n", + "\n", + "estimate_columns_mat_someother = [col for col in Mattapan_someother_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_mat_someother = 0\n", + "for i in estimate_columns_mat_someother:\n", + " try:\n", + " sum2021_mat_someother = sum2021_mat_someother + Mattapan_someother_data_2021[i][3]\n", + " except:\n", + " print(Mattapan_someother_data_2021[i][3])\n", + "\n", + "estimate_columns_dor_someother = [col for col in Dorchester_someother_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_dor_someother = 0\n", + "for i in estimate_columns_dor_someother:\n", + " sum2021_dor_someother = sum2021_dor_someother + float(Dorchester_someother_data_2021[i][3])\n", + "\n", + "# ... Repeat for 2016 and 2011\n", + "estimate_columns_rox_someother = [col for col in Roxbury_someother_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_rox_someother = 0\n", + "for i in estimate_columns_rox_someother:\n", + " sum2016_rox_someother = sum2016_rox_someother + float(Roxbury_someother_data_2016[i][3])\n", + "\n", + "estimate_columns_mat_someother = [col for col in Mattapan_someother_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_mat_someother = 0\n", + "for i in estimate_columns_mat_someother:\n", + " sum2016_mat_someother = sum2016_mat_someother + Mattapan_someother_data_2016[i][3]\n", + "\n", + "estimate_columns_dor_someother = [col for col in Dorchester_someother_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_dor_someother = 0\n", + "for i in estimate_columns_dor_someother:\n", + " sum2016_dor_someother = sum2016_dor_someother + float(Dorchester_someother_data_2016[i][3])\n", + "\n", + "estimate_columns_rox_someother = [col for col in Roxbury_someother_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_rox_someother = 0\n", + "for i in estimate_columns_rox_someother:\n", + " sum2011_rox_someother = sum2011_rox_someother + float(Roxbury_someother_data_2011[i][3])\n", + "\n", + "estimate_columns_mat_someother = [col for col in Mattapan_someother_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_mat_someother = 0\n", + "for i in estimate_columns_mat_someother:\n", + " sum2011_mat_someother = sum2011_mat_someother + Mattapan_someother_data_2011[i][3]\n", + "\n", + "estimate_columns_dor_someother = [col for col in Dorchester_someother_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_dor_someother = 0\n", + "for i in estimate_columns_dor_someother:\n", + " sum2011_dor_someother = sum2011_dor_someother + float(Dorchester_someother_data_2011[i][3])\n", + "\n", + "someother_total_2021=sum2021_rox_someother+sum2021_mat_someother+sum2021_dor_someother\n", + "someother_total_2016=sum2016_rox_someother+sum2016_mat_someother+sum2016_dor_someother\n", + "someother_total_2011=sum2011_rox_someother+sum2011_mat_someother+sum2011_dor_someother" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [], + "source": [ + "# Assuming you have dataframes named americanindian2021, americanindian2016, and americanindian2011 for the respective years\n", + "\n", + "# 2021 demographic data for American Indian\n", + "selected_columns = [col for col in americanindian2021.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_americanindian_data_2021 = americanindian2021[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in americanindian2021.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_americanindian_data_2021 = americanindian2021[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in americanindian2021.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_americanindian_data_2021 = americanindian2021[selected_columns3]\n", + "\n", + "# 2016 demographic data for American Indian\n", + "selected_columns = [col for col in americanindian2016.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_americanindian_data_2016 = americanindian2016[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in americanindian2016.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_americanindian_data_2016 = americanindian2016[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in americanindian2016.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_americanindian_data_2016 = americanindian2016[selected_columns3]\n", + "\n", + "# 2011 demographic data for American Indian\n", + "selected_columns = [col for col in americanindian2011.columns if any(item in col for item in Roxbury_list)]\n", + "Roxbury_americanindian_data_2011 = americanindian2011[selected_columns]\n", + "\n", + "selected_columns2 = [col for col in americanindian2011.columns if any(item in col for item in Mattapan_list)]\n", + "Mattapan_americanindian_data_2011 = americanindian2011[selected_columns2]\n", + "\n", + "selected_columns3 = [col for col in americanindian2011.columns if any(item in col for item in Dorchester_list)]\n", + "Dorchester_americanindian_data_2011 = americanindian2011[selected_columns3]\n", + "\n", + "# Calculation of sums for American Indian data\n", + "\n", + "estimate_columns_rox_americanindian = [col for col in Roxbury_americanindian_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_rox_americanindian = 0\n", + "for i in estimate_columns_rox_americanindian:\n", + " sum2021_rox_americanindian = sum2021_rox_americanindian + float(Roxbury_americanindian_data_2021[i][3])\n", + "\n", + "estimate_columns_mat_americanindian = [col for col in Mattapan_americanindian_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_mat_americanindian = 0\n", + "for i in estimate_columns_mat_americanindian:\n", + " try:\n", + " sum2021_mat_americanindian = sum2021_mat_americanindian + Mattapan_americanindian_data_2021[i][3]\n", + " except:\n", + " print(Mattapan_americanindian_data_2021[i][3])\n", + "\n", + "estimate_columns_dor_americanindian = [col for col in Dorchester_americanindian_data_2021.columns if \"Estimate\" in col]\n", + "sum2021_dor_americanindian = 0\n", + "for i in estimate_columns_dor_americanindian:\n", + " sum2021_dor_americanindian = sum2021_dor_americanindian + float(Dorchester_americanindian_data_2021[i][3])\n", + "\n", + "# ... Repeat for 2016 and 2011\n", + "estimate_columns_rox_americanindian = [col for col in Roxbury_americanindian_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_rox_americanindian = 0\n", + "for i in estimate_columns_rox_americanindian:\n", + " sum2016_rox_americanindian = sum2016_rox_americanindian + float(Roxbury_americanindian_data_2016[i][3])\n", + "\n", + "estimate_columns_mat_americanindian = [col for col in Mattapan_americanindian_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_mat_americanindian = 0\n", + "for i in estimate_columns_mat_americanindian:\n", + " sum2016_mat_americanindian = sum2016_mat_americanindian + Mattapan_americanindian_data_2016[i][3]\n", + "\n", + "estimate_columns_dor_americanindian = [col for col in Dorchester_americanindian_data_2016.columns if \"Estimate\" in col]\n", + "sum2016_dor_americanindian = 0\n", + "for i in estimate_columns_dor_americanindian:\n", + " sum2016_dor_americanindian = sum2016_dor_americanindian + float(Dorchester_americanindian_data_2016[i][3])\n", + "\n", + "estimate_columns_rox_americanindian = [col for col in Roxbury_americanindian_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_rox_americanindian = 0\n", + "for i in estimate_columns_rox_americanindian:\n", + " sum2011_rox_americanindian = sum2011_rox_americanindian + float(Roxbury_americanindian_data_2011[i][3])\n", + "\n", + "estimate_columns_mat_americanindian = [col for col in Mattapan_americanindian_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_mat_americanindian = 0\n", + "for i in estimate_columns_mat_americanindian:\n", + " sum2011_mat_americanindian = sum2011_mat_americanindian + Mattapan_americanindian_data_2011[i][3]\n", + "\n", + "estimate_columns_dor_americanindian = [col for col in Dorchester_americanindian_data_2011.columns if \"Estimate\" in col]\n", + "sum2011_dor_americanindian = 0\n", + "for i in estimate_columns_dor_americanindian:\n", + " sum2011_dor_americanindian = sum2011_dor_americanindian + float(Dorchester_americanindian_data_2011[i][3])\n", + "\n", + "americanindian_total_2021=sum2021_rox_americanindian+sum2021_mat_americanindian+sum2021_dor_americanindian\n", + "americanindian_total_2016=sum2016_rox_americanindian+sum2016_mat_americanindian+sum2016_dor_americanindian\n", + "americanindian_total_2011=sum2011_rox_americanindian+sum2011_mat_americanindian+sum2011_dor_americanindian\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -502,7 +1276,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 135, "metadata": {}, "outputs": [ { @@ -527,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -551,7 +1325,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 137, "metadata": {}, "outputs": [ { @@ -618,6 +1392,333 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "white_mean_2016=all2016['White alone'].sum() \n", + "Bk_mean_2016=all2016['Black or African American alone'].sum()\n", + "American_In_mean_2016=all2016['American Indian alone'].sum()\n", + "Alska_mean_2016=all2016['Alaska Native alone'].sum()\n", + "Asian_mean_2016=all2016['Asian alone'].sum()\n", + "Native_H_mean_2016=all2016['Native Hawaiian and Other Pacific Islander alone'].sum()\n", + "Some_other__mean_2016=all2016[\"Some Other Race alone\"].sum()\n", + "Two_or_more_2016=all2016[\"Two or More Races\"].sum()\n", + "\n", + "white_mean_2016=calculation(white_mean_2016)\n", + "Bk_mean_2016=calculation(Bk_mean_2016)\n", + "American_In_mean_2016=calculation(American_In_mean_2016)\n", + "Alska_mean_2016=calculation(Alska_mean_2016)\n", + "Asian_mean_2016=calculation(Asian_mean_2016)\n", + "Native_H_mean_2016=calculation(Native_H_mean_2016)\n", + "Some_other__mean_2016=calculation(Some_other__mean_2016)\n", + "Two_or_more_2016=calculation(Two_or_more_2016)\n", + "\n", + "race_list2016=[\"White\",\"Black\",\"American_In\",\"Asian\",\"Some_other\",\"Two_or_more\"]\n", + "data_list2016=[white_mean_2016/white_total_2016,Bk_mean_2016/white_total_2016,American_In_mean_2016/americanindian_total_2016,Asian_mean_2016/asian_total_2016,Some_other__mean_2016/someother_total_2016,Two_or_more_2016/twoormore_total_2016]\n", + "\n", + "\n", + "\n", + "\n", + "plt.figure(figsize=(10, 8))\n", + "plt.barh(race_list2016, data_list2016, color='navy')\n", + "plt.xlabel('Communitee Time')\n", + "plt.title(\"Mean of travel time to work of different race in 2016\")\n", + "plt.gca().invert_yaxis() # Invert y-axis to match the provided graph's order\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "white_mean_2021=all2021['White alone'][0]\n", + "Bk_mean_2021=all2021['Black or African American alone'][0]\n", + "American_In_mean_2021=all2021['American Indian alone'][0]\n", + "Alska_mean_2021=all2021['Alaska Native alone'][0]\n", + "Asian_mean_2021=all2021['Asian alone'][0]\n", + "Native_H_mean_2021=all2021['Native Hawaiian and Other Pacific Islander alone'][0]\n", + "Some_other_mean_2021=all2021['Some Other Race alone'][0]\n", + "Two_or_more_2021=all2021[\"Two or More Races\"][0]\n", + "\n", + "# use the calculation function to calculate the time\n", + "white_mean_2021=calculation(white_mean_2021)\n", + "Bk_mean_2021=calculation(Bk_mean_2021)\n", + "American_In_mean_2021=calculation(American_In_mean_2021)\n", + "Alska_mean_2021=calculation(Alska_mean_2021)\n", + "Asian_mean_2021=calculation(Asian_mean_2021)\n", + "Native_H_mean_2021=calculation(Native_H_mean_2021)\n", + "Some_other_mean_2021=calculation(Some_other_mean_2021)\n", + "Two_or_more_2021=calculation(Two_or_more_2021)\n", + "\n", + "\n", + "race_list2021=[\"White\",\"Black\",\"American_In\",\"Asian\",\"Some_other\",\"Two_or_more\"]\n", + "data_list2021=[white_mean_2021/white_total_2021,Bk_mean_2021/white_total_2021,American_In_mean_2021/americanindian_total_2021,Asian_mean_2021/asian_total_2021,Some_other_mean_2021/someother_total_2021,Two_or_more_2021/twoormore_total_2021]\n", + "# graph the data Y asix is the Race_list and X asix is the data_list\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "plt.figure(figsize=(10, 8))\n", + "plt.barh(race_list2021, data_list2021, color='navy')\n", + "plt.xlabel('Communitee data_list2021')\n", + "plt.title(\"Mean of travel time to work of different race in 2021\")\n", + "plt.gca().invert_yaxis() # Invert y-axis to match the provided graph's order\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Means of transportation to work (JWTR)White aloneBlack or African American aloneAmerican Indian aloneAlaska Native aloneAmerican Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other racesAsian aloneNative Hawaiian and Other Pacific Islander aloneSome Other Race aloneTwo or More Races
0-> PUMA5 03303, MassachusettsNaNNaNNaNNaNNaNNaNNaNNaNNaN
1Bus or Trolley Bus911.09319.012.00.030.0491.00.01911.0183.0
2-> PUMA5 03304, MassachusettsNaNNaNNaNNaNNaNNaNNaNNaNNaN
3Bus or Trolley Bus4524.02174.08.00.020.0271.00.0705.0107.0
4-> PUMA5 03305, MassachusettsNaNNaNNaNNaNNaNNaNNaNNaNNaN
5Bus or Trolley Bus3119.02003.00.00.00.0364.00.0398.0133.0
\n", + "
" + ], + "text/plain": [ + " Means of transportation to work (JWTR) White alone \\\n", + "0 -> PUMA5 03303, Massachusetts NaN \n", + "1 Bus or Trolley Bus 911.0 \n", + "2 -> PUMA5 03304, Massachusetts NaN \n", + "3 Bus or Trolley Bus 4524.0 \n", + "4 -> PUMA5 03305, Massachusetts NaN \n", + "5 Bus or Trolley Bus 3119.0 \n", + "\n", + " Black or African American alone American Indian alone \\\n", + "0 NaN NaN \n", + "1 9319.0 12.0 \n", + "2 NaN NaN \n", + "3 2174.0 8.0 \n", + "4 NaN NaN \n", + "5 2003.0 0.0 \n", + "\n", + " Alaska Native alone \\\n", + "0 NaN \n", + "1 0.0 \n", + "2 NaN \n", + "3 0.0 \n", + "4 NaN \n", + "5 0.0 \n", + "\n", + " American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races \\\n", + "0 NaN \n", + "1 30.0 \n", + "2 NaN \n", + "3 20.0 \n", + "4 NaN \n", + "5 0.0 \n", + "\n", + " Asian alone Native Hawaiian and Other Pacific Islander alone \\\n", + "0 NaN NaN \n", + "1 491.0 0.0 \n", + "2 NaN NaN \n", + "3 271.0 0.0 \n", + "4 NaN NaN \n", + "5 364.0 0.0 \n", + "\n", + " Some Other Race alone Two or More Races \n", + "0 NaN NaN \n", + "1 1911.0 183.0 \n", + "2 NaN NaN \n", + "3 705.0 107.0 \n", + "4 NaN NaN \n", + "5 398.0 133.0 " + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all2011" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "white_mean_2011=all2011['White alone'].sum()\n", + "Bk_mean_2011=all2011['Black or African American alone'].sum()\n", + "American_In_mean_2011=all2011['American Indian alone'].sum()\n", + "Alska_mean_2011=all2011['Alaska Native alone'].sum()\n", + "Asian_mean_2011=all2011['Asian alone'].sum()\n", + "Native_H_mean_2011=all2011['Native Hawaiian and Other Pacific Islander alone'].sum()\n", + "Some_other_mean_2011=all2011['Some Other Race alone'][0]\n", + "Two_or_more_2011=all2011[\"Two or More Races\"][0]\n", + "\n", + "# use the calculation function to calculate the time\n", + "white_mean_2011=calculation(white_mean_2011)\n", + "Bk_mean_2011=calculation(Bk_mean_2011)\n", + "American_In_mean_2011=calculation(American_In_mean_2011)\n", + "Alska_mean_2011=calculation(Alska_mean_2011)\n", + "Asian_mean_2011=calculation(Asian_mean_2011)\n", + "Native_H_mean_2011=calculation(Native_H_mean_2011)\n", + "Some_other_mean_2011=calculation(Some_other_mean_2011)\n", + "Two_or_more_2011=calculation(Two_or_more_2011)\n", + "race_list2011=[\"White\",\"Black\",\"American_In\",\"Asian\",\"Some_other\",\"Two_or_more\"]\n", + "data_list2011=[white_mean_2011/white_total_2011,Bk_mean_2011/white_total_2011,American_In_mean_2011/americanindian_total_2011,Asian_mean_2011/asian_total_2011,Some_other_mean_2011/someother_total_2011,Two_or_more_2011/twoormore_total_2011]\n", + "\n", + "plt.figure(figsize=(10, 8))\n", + "plt.barh(race_list2021, data_list2021, color='navy')\n", + "plt.xlabel('Communitee data_list2021')\n", + "plt.title(\"Mean of travel time to work of different race in 2021\")\n", + "plt.gca().invert_yaxis() # Invert y-axis to match the provided graph's order\n", + "plt.show()\n", + "\n" + ] + }, { "cell_type": "code", "execution_count": null, From 93215116a98846deac567da3c652d430251a99cb Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 07:00:57 -0500 Subject: [PATCH 02/39] Add files via upload --- fa23-team/data/AmericanIn2011.csv | 8 ++++++++ fa23-team/data/AmericanIn2016.csv | 8 ++++++++ fa23-team/data/AmericanIn2021.csv | 8 ++++++++ fa23-team/data/black2011.csv | 8 ++++++++ fa23-team/data/black2016.csv | 8 ++++++++ fa23-team/data/black2021.csv | 8 ++++++++ fa23-team/data/export (1).csv | 8 ++++++++ fa23-team/data/export 2011.csv | 7 +++++++ fa23-team/data/export 2016.csv | 7 +++++++ fa23-team/data/someother2011.csv | 8 ++++++++ fa23-team/data/someother2016.csv | 8 ++++++++ fa23-team/data/someother2021.csv | 8 ++++++++ fa23-team/data/twoormore2011.csv | 8 ++++++++ fa23-team/data/twoormore2016.csv | 8 ++++++++ fa23-team/data/twoormore2021.csv | 8 ++++++++ 15 files changed, 118 insertions(+) create mode 100644 fa23-team/data/AmericanIn2011.csv create mode 100644 fa23-team/data/AmericanIn2016.csv create mode 100644 fa23-team/data/AmericanIn2021.csv create mode 100644 fa23-team/data/black2011.csv create mode 100644 fa23-team/data/black2016.csv create mode 100644 fa23-team/data/black2021.csv create mode 100644 fa23-team/data/export (1).csv create mode 100644 fa23-team/data/export 2011.csv create mode 100644 fa23-team/data/export 2016.csv create mode 100644 fa23-team/data/someother2011.csv create mode 100644 fa23-team/data/someother2016.csv create mode 100644 fa23-team/data/someother2021.csv create mode 100644 fa23-team/data/twoormore2011.csv create mode 100644 fa23-team/data/twoormore2016.csv create mode 100644 fa23-team/data/twoormore2021.csv diff --git a/fa23-team/data/AmericanIn2011.csv b/fa23-team/data/AmericanIn2011.csv new file mode 100644 index 0000000..266a4ee --- /dev/null +++ b/fa23-team/data/AmericanIn2011.csv @@ -0,0 +1,8 @@ +"Label (Grouping)","Census Tract 1, Suffolk County, Massachusetts!!Estimate","Census Tract 1, Suffolk County, Massachusetts!!Margin of Error","Census Tract 2.01, Suffolk County, Massachusetts!!Estimate","Census Tract 2.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 2.02, Suffolk County, Massachusetts!!Estimate","Census Tract 2.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 3.01, Suffolk County, Massachusetts!!Estimate","Census Tract 3.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 3.02, Suffolk County, Massachusetts!!Estimate","Census Tract 3.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 4.01, Suffolk County, Massachusetts!!Estimate","Census Tract 4.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 4.02, Suffolk County, Massachusetts!!Estimate","Census Tract 4.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.02, Suffolk County, Massachusetts!!Estimate","Census Tract 5.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.03, Suffolk County, Massachusetts!!Estimate","Census Tract 5.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.04, Suffolk County, Massachusetts!!Estimate","Census Tract 5.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 6.01, Suffolk County, Massachusetts!!Estimate","Census Tract 6.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 6.02, Suffolk County, Massachusetts!!Estimate","Census Tract 6.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.01, Suffolk County, Massachusetts!!Estimate","Census Tract 7.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.03, Suffolk County, Massachusetts!!Estimate","Census Tract 7.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.04, Suffolk County, Massachusetts!!Estimate","Census Tract 7.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 8.02, Suffolk County, Massachusetts!!Estimate","Census Tract 8.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 8.03, Suffolk County, Massachusetts!!Estimate","Census Tract 8.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 101.03, Suffolk County, Massachusetts!!Estimate","Census Tract 101.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 101.04, Suffolk County, Massachusetts!!Estimate","Census Tract 101.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 102.03, Suffolk County, Massachusetts!!Estimate","Census Tract 102.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 102.04, Suffolk County, Massachusetts!!Estimate","Census Tract 102.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 103, Suffolk County, Massachusetts!!Estimate","Census Tract 103, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.04, Suffolk County, Massachusetts!!Estimate","Census Tract 104.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.05, Suffolk County, Massachusetts!!Estimate","Census Tract 104.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.08, Suffolk County, Massachusetts!!Estimate","Census Tract 104.08, Suffolk County, Massachusetts!!Margin of Error","Census Tract 105, Suffolk County, Massachusetts!!Estimate","Census Tract 105, Suffolk County, Massachusetts!!Margin of Error","Census Tract 106, Suffolk County, Massachusetts!!Estimate","Census Tract 106, Suffolk County, Massachusetts!!Margin of Error","Census Tract 107.01, Suffolk County, Massachusetts!!Estimate","Census Tract 107.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 107.02, Suffolk County, Massachusetts!!Estimate","Census Tract 107.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 108.01, Suffolk County, Massachusetts!!Estimate","Census Tract 108.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 108.02, Suffolk County, Massachusetts!!Estimate","Census Tract 108.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 201.01, Suffolk County, Massachusetts!!Estimate","Census Tract 201.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 202, Suffolk County, Massachusetts!!Estimate","Census Tract 202, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.02, Suffolk County, Massachusetts!!Estimate","Census Tract 203.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.03, Suffolk County, Massachusetts!!Estimate","Census Tract 203.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 301, Suffolk County, Massachusetts!!Estimate","Census Tract 301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 302, Suffolk County, Massachusetts!!Estimate","Census Tract 302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 303, Suffolk County, Massachusetts!!Estimate","Census Tract 303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 304, Suffolk County, Massachusetts!!Estimate","Census Tract 304, Suffolk County, Massachusetts!!Margin of Error","Census Tract 305, Suffolk County, Massachusetts!!Estimate","Census Tract 305, Suffolk County, Massachusetts!!Margin of Error","Census Tract 401, Suffolk County, Massachusetts!!Estimate","Census Tract 401, Suffolk County, Massachusetts!!Margin of Error","Census Tract 402, Suffolk County, Massachusetts!!Estimate","Census Tract 402, Suffolk County, Massachusetts!!Margin of Error","Census Tract 403, Suffolk County, Massachusetts!!Estimate","Census Tract 403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 404.01, Suffolk County, Massachusetts!!Estimate","Census Tract 404.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 406, Suffolk County, Massachusetts!!Estimate","Census Tract 406, Suffolk County, Massachusetts!!Margin of Error","Census Tract 408.01, Suffolk County, Massachusetts!!Estimate","Census Tract 408.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 501.01, Suffolk County, Massachusetts!!Estimate","Census Tract 501.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 502, Suffolk County, Massachusetts!!Estimate","Census Tract 502, Suffolk County, Massachusetts!!Margin of Error","Census Tract 503, Suffolk County, Massachusetts!!Estimate","Census Tract 503, Suffolk County, Massachusetts!!Margin of Error","Census Tract 504, Suffolk County, Massachusetts!!Estimate","Census Tract 504, Suffolk County, Massachusetts!!Margin of Error","Census Tract 505, Suffolk County, Massachusetts!!Estimate","Census Tract 505, Suffolk County, Massachusetts!!Margin of Error","Census Tract 506, Suffolk County, Massachusetts!!Estimate","Census Tract 506, Suffolk County, Massachusetts!!Margin of Error","Census Tract 507, Suffolk County, Massachusetts!!Estimate","Census Tract 507, Suffolk County, Massachusetts!!Margin of Error","Census Tract 509.01, Suffolk County, Massachusetts!!Estimate","Census Tract 509.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 510, Suffolk County, Massachusetts!!Estimate","Census Tract 510, Suffolk County, Massachusetts!!Margin of Error","Census Tract 511.01, Suffolk County, Massachusetts!!Estimate","Census Tract 511.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 512, Suffolk County, Massachusetts!!Estimate","Census Tract 512, Suffolk County, Massachusetts!!Margin of Error","Census Tract 601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 602, Suffolk County, Massachusetts!!Estimate","Census Tract 602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 603.01, Suffolk County, Massachusetts!!Estimate","Census Tract 603.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 604, Suffolk County, Massachusetts!!Estimate","Census Tract 604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 606, Suffolk County, Massachusetts!!Estimate","Census Tract 606, Suffolk County, Massachusetts!!Margin of Error","Census Tract 607, Suffolk County, Massachusetts!!Estimate","Census Tract 607, Suffolk County, Massachusetts!!Margin of Error","Census Tract 608, Suffolk County, Massachusetts!!Estimate","Census Tract 608, Suffolk County, Massachusetts!!Margin of Error","Census Tract 610, Suffolk County, Massachusetts!!Estimate","Census Tract 610, Suffolk County, Massachusetts!!Margin of Error","Census Tract 611.01, Suffolk County, Massachusetts!!Estimate","Census Tract 611.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 612, Suffolk County, Massachusetts!!Estimate","Census Tract 612, Suffolk County, Massachusetts!!Margin of Error","Census Tract 701.01, Suffolk County, Massachusetts!!Estimate","Census Tract 701.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 702, Suffolk County, Massachusetts!!Estimate","Census Tract 702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 703, Suffolk County, Massachusetts!!Estimate","Census Tract 703, Suffolk County, Massachusetts!!Margin of Error","Census Tract 704.02, Suffolk County, Massachusetts!!Estimate","Census Tract 704.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 705, Suffolk County, Massachusetts!!Estimate","Census Tract 705, Suffolk County, Massachusetts!!Margin of Error","Census Tract 706, Suffolk County, Massachusetts!!Estimate","Census Tract 706, Suffolk County, Massachusetts!!Margin of Error","Census Tract 707, Suffolk County, Massachusetts!!Estimate","Census Tract 707, Suffolk County, Massachusetts!!Margin of Error","Census Tract 708, Suffolk County, Massachusetts!!Estimate","Census Tract 708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 709, Suffolk County, Massachusetts!!Estimate","Census Tract 709, Suffolk County, Massachusetts!!Margin of Error","Census Tract 711.01, Suffolk County, Massachusetts!!Estimate","Census Tract 711.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 712.01, Suffolk County, Massachusetts!!Estimate","Census Tract 712.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 801, Suffolk County, Massachusetts!!Estimate","Census Tract 801, Suffolk County, Massachusetts!!Margin of Error","Census Tract 803, Suffolk County, Massachusetts!!Estimate","Census Tract 803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 804.01, Suffolk County, Massachusetts!!Estimate","Census Tract 804.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 805, Suffolk County, Massachusetts!!Estimate","Census Tract 805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 806.01, Suffolk County, Massachusetts!!Estimate","Census Tract 806.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 808.01, Suffolk County, Massachusetts!!Estimate","Census Tract 808.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 809, Suffolk County, Massachusetts!!Estimate","Census Tract 809, Suffolk County, Massachusetts!!Margin of Error","Census Tract 810.01, Suffolk County, Massachusetts!!Estimate","Census Tract 810.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 811, Suffolk County, Massachusetts!!Estimate","Census Tract 811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 812, Suffolk County, Massachusetts!!Estimate","Census Tract 812, Suffolk County, Massachusetts!!Margin of Error","Census Tract 813, Suffolk County, Massachusetts!!Estimate","Census Tract 813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 814, Suffolk County, Massachusetts!!Estimate","Census Tract 814, Suffolk County, Massachusetts!!Margin of Error","Census Tract 815, Suffolk County, Massachusetts!!Estimate","Census Tract 815, Suffolk County, Massachusetts!!Margin of Error","Census Tract 817, Suffolk County, Massachusetts!!Estimate","Census Tract 817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 818, Suffolk County, Massachusetts!!Estimate","Census Tract 818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 819, Suffolk County, Massachusetts!!Estimate","Census Tract 819, Suffolk County, Massachusetts!!Margin of Error","Census Tract 820, Suffolk County, Massachusetts!!Estimate","Census Tract 820, Suffolk County, Massachusetts!!Margin of Error","Census Tract 821, Suffolk County, Massachusetts!!Estimate","Census Tract 821, Suffolk County, Massachusetts!!Margin of Error","Census Tract 901, Suffolk County, Massachusetts!!Estimate","Census Tract 901, Suffolk County, Massachusetts!!Margin of Error","Census Tract 902, Suffolk County, Massachusetts!!Estimate","Census Tract 902, Suffolk County, Massachusetts!!Margin of Error","Census Tract 903, Suffolk County, Massachusetts!!Estimate","Census Tract 903, Suffolk County, Massachusetts!!Margin of Error","Census Tract 904, Suffolk County, Massachusetts!!Estimate","Census Tract 904, Suffolk County, Massachusetts!!Margin of Error","Census Tract 906, Suffolk County, Massachusetts!!Estimate","Census Tract 906, Suffolk County, Massachusetts!!Margin of Error","Census Tract 907, Suffolk County, Massachusetts!!Estimate","Census Tract 907, Suffolk County, Massachusetts!!Margin of Error","Census Tract 909.01, Suffolk County, Massachusetts!!Estimate","Census Tract 909.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 910.01, Suffolk County, Massachusetts!!Estimate","Census Tract 910.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 911, Suffolk County, Massachusetts!!Estimate","Census Tract 911, Suffolk County, Massachusetts!!Margin of Error","Census Tract 912, Suffolk County, Massachusetts!!Estimate","Census Tract 912, Suffolk County, Massachusetts!!Margin of Error","Census Tract 913, Suffolk County, Massachusetts!!Estimate","Census Tract 913, Suffolk County, Massachusetts!!Margin of Error","Census Tract 914, Suffolk County, Massachusetts!!Estimate","Census Tract 914, Suffolk County, Massachusetts!!Margin of Error","Census Tract 915, Suffolk County, Massachusetts!!Estimate","Census Tract 915, Suffolk County, Massachusetts!!Margin of Error","Census Tract 916, Suffolk County, Massachusetts!!Estimate","Census Tract 916, Suffolk County, Massachusetts!!Margin of Error","Census Tract 917, Suffolk County, Massachusetts!!Estimate","Census Tract 917, Suffolk County, Massachusetts!!Margin of Error","Census Tract 918, Suffolk County, Massachusetts!!Estimate","Census Tract 918, Suffolk County, Massachusetts!!Margin of Error","Census Tract 919, Suffolk County, Massachusetts!!Estimate","Census Tract 919, Suffolk County, Massachusetts!!Margin of Error","Census Tract 920, Suffolk County, Massachusetts!!Estimate","Census Tract 920, Suffolk County, Massachusetts!!Margin of Error","Census Tract 921.01, Suffolk County, Massachusetts!!Estimate","Census Tract 921.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 922, Suffolk County, Massachusetts!!Estimate","Census Tract 922, Suffolk County, Massachusetts!!Margin of Error","Census Tract 923, Suffolk County, Massachusetts!!Estimate","Census Tract 923, Suffolk County, Massachusetts!!Margin of Error","Census Tract 924, Suffolk County, Massachusetts!!Estimate","Census Tract 924, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1001, Suffolk County, Massachusetts!!Estimate","Census Tract 1001, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1002, Suffolk County, Massachusetts!!Estimate","Census Tract 1002, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1003, Suffolk County, Massachusetts!!Estimate","Census Tract 1003, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1004, Suffolk County, Massachusetts!!Estimate","Census Tract 1004, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1005, Suffolk County, Massachusetts!!Estimate","Census Tract 1005, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1007, Suffolk County, Massachusetts!!Estimate","Census Tract 1007, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1008, Suffolk County, Massachusetts!!Estimate","Census Tract 1008, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1009, Suffolk County, Massachusetts!!Estimate","Census Tract 1009, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1102.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1102.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1103.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1103.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1202.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1202.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1204, Suffolk County, Massachusetts!!Estimate","Census Tract 1204, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1205, Suffolk County, Massachusetts!!Estimate","Census Tract 1205, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1206, Suffolk County, Massachusetts!!Estimate","Census Tract 1206, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1207, Suffolk County, Massachusetts!!Estimate","Census Tract 1207, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1301, Suffolk County, Massachusetts!!Estimate","Census Tract 1301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1302, Suffolk County, Massachusetts!!Estimate","Census Tract 1302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1303, Suffolk County, Massachusetts!!Estimate","Census Tract 1303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1403, Suffolk County, Massachusetts!!Estimate","Census Tract 1403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1404, Suffolk County, Massachusetts!!Estimate","Census Tract 1404, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1602, Suffolk County, Massachusetts!!Estimate","Census Tract 1602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1603, Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701, Suffolk County, Massachusetts!!Estimate","Census Tract 1701, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703, Suffolk County, Massachusetts!!Estimate","Census Tract 1703, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error" 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Massachusetts!!Margin of Error","Census Tract 4.02, Suffolk County, Massachusetts!!Estimate","Census Tract 4.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.02, Suffolk County, Massachusetts!!Estimate","Census Tract 5.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.03, Suffolk County, Massachusetts!!Estimate","Census Tract 5.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.04, Suffolk County, Massachusetts!!Estimate","Census Tract 5.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 6.01, Suffolk County, Massachusetts!!Estimate","Census Tract 6.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 6.02, Suffolk County, Massachusetts!!Estimate","Census Tract 6.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.01, Suffolk County, Massachusetts!!Estimate","Census Tract 7.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.03, Suffolk County, Massachusetts!!Estimate","Census Tract 7.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.04, Suffolk County, Massachusetts!!Estimate","Census Tract 7.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 8.02, Suffolk County, Massachusetts!!Estimate","Census Tract 8.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 8.03, Suffolk County, Massachusetts!!Estimate","Census Tract 8.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 101.03, Suffolk County, Massachusetts!!Estimate","Census Tract 101.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 101.04, Suffolk County, Massachusetts!!Estimate","Census Tract 101.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 102.03, Suffolk County, Massachusetts!!Estimate","Census Tract 102.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 102.04, Suffolk County, Massachusetts!!Estimate","Census Tract 102.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 103, Suffolk County, Massachusetts!!Estimate","Census Tract 103, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.04, Suffolk County, Massachusetts!!Estimate","Census Tract 104.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.05, Suffolk County, Massachusetts!!Estimate","Census Tract 104.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.08, Suffolk County, Massachusetts!!Estimate","Census Tract 104.08, Suffolk County, Massachusetts!!Margin of Error","Census Tract 105, Suffolk County, Massachusetts!!Estimate","Census Tract 105, Suffolk County, Massachusetts!!Margin of Error","Census Tract 106, Suffolk County, Massachusetts!!Estimate","Census Tract 106, Suffolk County, Massachusetts!!Margin of Error","Census Tract 107.01, Suffolk County, Massachusetts!!Estimate","Census Tract 107.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 107.02, Suffolk County, Massachusetts!!Estimate","Census Tract 107.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 108.01, Suffolk County, Massachusetts!!Estimate","Census Tract 108.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 108.02, Suffolk County, Massachusetts!!Estimate","Census Tract 108.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 201.01, Suffolk County, Massachusetts!!Estimate","Census Tract 201.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 202, Suffolk County, Massachusetts!!Estimate","Census Tract 202, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.02, Suffolk County, Massachusetts!!Estimate","Census Tract 203.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.03, Suffolk County, Massachusetts!!Estimate","Census Tract 203.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 301, Suffolk County, Massachusetts!!Estimate","Census Tract 301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 302, Suffolk County, Massachusetts!!Estimate","Census Tract 302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 303, Suffolk County, Massachusetts!!Estimate","Census Tract 303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 304, Suffolk County, Massachusetts!!Estimate","Census Tract 304, Suffolk County, Massachusetts!!Margin of Error","Census Tract 305, Suffolk County, Massachusetts!!Estimate","Census Tract 305, Suffolk County, Massachusetts!!Margin of Error","Census Tract 401, Suffolk County, Massachusetts!!Estimate","Census Tract 401, Suffolk County, Massachusetts!!Margin of Error","Census Tract 402, Suffolk County, Massachusetts!!Estimate","Census Tract 402, Suffolk County, Massachusetts!!Margin of Error","Census Tract 403, Suffolk County, Massachusetts!!Estimate","Census Tract 403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 404.01, Suffolk County, Massachusetts!!Estimate","Census Tract 404.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 406, Suffolk County, Massachusetts!!Estimate","Census Tract 406, Suffolk County, Massachusetts!!Margin of Error","Census Tract 408.01, Suffolk County, Massachusetts!!Estimate","Census Tract 408.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 501.01, Suffolk County, Massachusetts!!Estimate","Census Tract 501.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 502, Suffolk County, Massachusetts!!Estimate","Census Tract 502, Suffolk County, Massachusetts!!Margin of Error","Census Tract 503, Suffolk County, Massachusetts!!Estimate","Census Tract 503, Suffolk County, Massachusetts!!Margin of Error","Census Tract 504, Suffolk County, Massachusetts!!Estimate","Census Tract 504, Suffolk County, Massachusetts!!Margin of Error","Census Tract 505, Suffolk County, Massachusetts!!Estimate","Census Tract 505, Suffolk County, Massachusetts!!Margin of Error","Census Tract 506, Suffolk County, Massachusetts!!Estimate","Census Tract 506, Suffolk County, Massachusetts!!Margin of Error","Census Tract 507, Suffolk County, Massachusetts!!Estimate","Census Tract 507, Suffolk County, Massachusetts!!Margin of Error","Census Tract 509.01, Suffolk County, Massachusetts!!Estimate","Census Tract 509.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 510, Suffolk County, Massachusetts!!Estimate","Census Tract 510, Suffolk County, Massachusetts!!Margin of Error","Census Tract 511.01, Suffolk County, Massachusetts!!Estimate","Census Tract 511.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 512, Suffolk County, Massachusetts!!Estimate","Census Tract 512, Suffolk County, Massachusetts!!Margin of Error","Census Tract 601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 602, Suffolk County, Massachusetts!!Estimate","Census Tract 602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 603.01, Suffolk County, Massachusetts!!Estimate","Census Tract 603.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 604, Suffolk County, Massachusetts!!Estimate","Census Tract 604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 606, Suffolk County, Massachusetts!!Estimate","Census Tract 606, Suffolk County, Massachusetts!!Margin of Error","Census Tract 607, Suffolk County, Massachusetts!!Estimate","Census Tract 607, Suffolk County, Massachusetts!!Margin of Error","Census Tract 608, Suffolk County, Massachusetts!!Estimate","Census Tract 608, Suffolk County, Massachusetts!!Margin of Error","Census Tract 610, Suffolk County, Massachusetts!!Estimate","Census Tract 610, Suffolk County, Massachusetts!!Margin of Error","Census Tract 611.01, Suffolk County, Massachusetts!!Estimate","Census Tract 611.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 612, Suffolk County, Massachusetts!!Estimate","Census Tract 612, Suffolk County, Massachusetts!!Margin of Error","Census Tract 701.01, Suffolk County, Massachusetts!!Estimate","Census Tract 701.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 702, Suffolk County, Massachusetts!!Estimate","Census Tract 702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 703, Suffolk County, Massachusetts!!Estimate","Census Tract 703, Suffolk County, Massachusetts!!Margin of Error","Census Tract 704.02, Suffolk County, Massachusetts!!Estimate","Census Tract 704.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 705, Suffolk County, Massachusetts!!Estimate","Census Tract 705, Suffolk County, Massachusetts!!Margin of Error","Census Tract 706, Suffolk County, Massachusetts!!Estimate","Census Tract 706, Suffolk County, Massachusetts!!Margin of Error","Census Tract 707, Suffolk County, Massachusetts!!Estimate","Census Tract 707, Suffolk County, Massachusetts!!Margin of Error","Census Tract 708, Suffolk County, Massachusetts!!Estimate","Census Tract 708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 709, Suffolk County, Massachusetts!!Estimate","Census Tract 709, Suffolk County, Massachusetts!!Margin of Error","Census Tract 711.01, Suffolk County, Massachusetts!!Estimate","Census Tract 711.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 712.01, Suffolk County, Massachusetts!!Estimate","Census Tract 712.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 801, Suffolk County, Massachusetts!!Estimate","Census Tract 801, Suffolk County, Massachusetts!!Margin of Error","Census Tract 803, Suffolk County, Massachusetts!!Estimate","Census Tract 803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 804.01, Suffolk County, Massachusetts!!Estimate","Census Tract 804.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 805, Suffolk County, Massachusetts!!Estimate","Census Tract 805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 806.01, Suffolk County, Massachusetts!!Estimate","Census Tract 806.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 808.01, Suffolk County, Massachusetts!!Estimate","Census Tract 808.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 809, Suffolk County, Massachusetts!!Estimate","Census Tract 809, Suffolk County, Massachusetts!!Margin of Error","Census Tract 810.01, Suffolk County, Massachusetts!!Estimate","Census Tract 810.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 811, Suffolk County, Massachusetts!!Estimate","Census Tract 811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 812, Suffolk County, Massachusetts!!Estimate","Census Tract 812, Suffolk County, Massachusetts!!Margin of Error","Census Tract 813, Suffolk County, Massachusetts!!Estimate","Census Tract 813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 814, Suffolk County, Massachusetts!!Estimate","Census Tract 814, Suffolk County, Massachusetts!!Margin of Error","Census Tract 815, Suffolk County, Massachusetts!!Estimate","Census Tract 815, Suffolk County, Massachusetts!!Margin of Error","Census Tract 817, Suffolk County, Massachusetts!!Estimate","Census Tract 817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 818, Suffolk County, Massachusetts!!Estimate","Census Tract 818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 819, Suffolk County, Massachusetts!!Estimate","Census Tract 819, Suffolk County, Massachusetts!!Margin of Error","Census Tract 820, Suffolk County, Massachusetts!!Estimate","Census Tract 820, Suffolk County, Massachusetts!!Margin of Error","Census Tract 821, Suffolk County, Massachusetts!!Estimate","Census Tract 821, Suffolk County, Massachusetts!!Margin of Error","Census Tract 901, Suffolk County, Massachusetts!!Estimate","Census Tract 901, Suffolk County, Massachusetts!!Margin of Error","Census Tract 902, Suffolk County, Massachusetts!!Estimate","Census Tract 902, Suffolk County, Massachusetts!!Margin of Error","Census Tract 903, Suffolk County, Massachusetts!!Estimate","Census Tract 903, Suffolk County, Massachusetts!!Margin of Error","Census Tract 904, Suffolk County, Massachusetts!!Estimate","Census Tract 904, Suffolk County, Massachusetts!!Margin of Error","Census Tract 906, Suffolk County, Massachusetts!!Estimate","Census Tract 906, Suffolk County, Massachusetts!!Margin of Error","Census Tract 907, Suffolk County, Massachusetts!!Estimate","Census Tract 907, Suffolk County, Massachusetts!!Margin of Error","Census Tract 909.01, Suffolk County, Massachusetts!!Estimate","Census Tract 909.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 910.01, Suffolk County, Massachusetts!!Estimate","Census Tract 910.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 911, Suffolk County, Massachusetts!!Estimate","Census Tract 911, Suffolk County, Massachusetts!!Margin of Error","Census Tract 912, Suffolk County, Massachusetts!!Estimate","Census Tract 912, Suffolk County, Massachusetts!!Margin of Error","Census Tract 913, Suffolk County, Massachusetts!!Estimate","Census Tract 913, Suffolk County, Massachusetts!!Margin of Error","Census Tract 914, Suffolk County, Massachusetts!!Estimate","Census Tract 914, Suffolk County, Massachusetts!!Margin of Error","Census Tract 915, Suffolk County, Massachusetts!!Estimate","Census Tract 915, Suffolk County, Massachusetts!!Margin of Error","Census Tract 916, Suffolk County, Massachusetts!!Estimate","Census Tract 916, Suffolk County, Massachusetts!!Margin of Error","Census Tract 917, Suffolk County, Massachusetts!!Estimate","Census Tract 917, Suffolk County, Massachusetts!!Margin of Error","Census Tract 918, Suffolk County, Massachusetts!!Estimate","Census Tract 918, Suffolk County, Massachusetts!!Margin of Error","Census Tract 919, Suffolk County, Massachusetts!!Estimate","Census Tract 919, Suffolk County, Massachusetts!!Margin of Error","Census Tract 920, Suffolk County, Massachusetts!!Estimate","Census Tract 920, Suffolk County, Massachusetts!!Margin of Error","Census Tract 921.01, Suffolk County, Massachusetts!!Estimate","Census Tract 921.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 922, Suffolk County, Massachusetts!!Estimate","Census Tract 922, Suffolk County, Massachusetts!!Margin of Error","Census Tract 923, Suffolk County, Massachusetts!!Estimate","Census Tract 923, Suffolk County, Massachusetts!!Margin of Error","Census Tract 924, Suffolk County, Massachusetts!!Estimate","Census Tract 924, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1001, Suffolk County, Massachusetts!!Estimate","Census Tract 1001, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1002, Suffolk County, Massachusetts!!Estimate","Census Tract 1002, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1003, Suffolk County, Massachusetts!!Estimate","Census Tract 1003, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1004, Suffolk County, Massachusetts!!Estimate","Census Tract 1004, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1005, Suffolk County, Massachusetts!!Estimate","Census Tract 1005, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1007, Suffolk County, Massachusetts!!Estimate","Census Tract 1007, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1008, Suffolk County, Massachusetts!!Estimate","Census Tract 1008, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1009, Suffolk County, Massachusetts!!Estimate","Census Tract 1009, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1102.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1102.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1103.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1103.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1202.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1202.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1204, Suffolk County, Massachusetts!!Estimate","Census Tract 1204, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1205, Suffolk County, Massachusetts!!Estimate","Census Tract 1205, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1206, Suffolk County, Massachusetts!!Estimate","Census Tract 1206, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1207, Suffolk County, Massachusetts!!Estimate","Census Tract 1207, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1301, Suffolk County, Massachusetts!!Estimate","Census Tract 1301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1302, Suffolk County, Massachusetts!!Estimate","Census Tract 1302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1303, Suffolk County, Massachusetts!!Estimate","Census Tract 1303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1403, Suffolk County, Massachusetts!!Estimate","Census Tract 1403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1404, Suffolk County, Massachusetts!!Estimate","Census Tract 1404, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1602, Suffolk County, Massachusetts!!Estimate","Census Tract 1602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1603, Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701, Suffolk County, Massachusetts!!Estimate","Census Tract 1701, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703, Suffolk County, Massachusetts!!Estimate","Census Tract 1703, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error" 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Massachusetts!!Margin of Error","Census Tract 4.01, Suffolk County, Massachusetts!!Estimate","Census Tract 4.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 4.02, Suffolk County, Massachusetts!!Estimate","Census Tract 4.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.02, Suffolk County, Massachusetts!!Estimate","Census Tract 5.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.03, Suffolk County, Massachusetts!!Estimate","Census Tract 5.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.05, Suffolk County, Massachusetts!!Estimate","Census Tract 5.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.06, Suffolk County, Massachusetts!!Estimate","Census Tract 5.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 6.01, Suffolk County, Massachusetts!!Estimate","Census Tract 6.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 6.03, Suffolk County, Massachusetts!!Estimate","Census Tract 6.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 6.04, Suffolk County, Massachusetts!!Estimate","Census Tract 6.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.01, Suffolk County, Massachusetts!!Estimate","Census Tract 7.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.03, Suffolk County, Massachusetts!!Estimate","Census Tract 7.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.04, Suffolk County, Massachusetts!!Estimate","Census Tract 7.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 8.04, Suffolk County, Massachusetts!!Estimate","Census Tract 8.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 8.05, Suffolk County, Massachusetts!!Estimate","Census Tract 8.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 8.06, Suffolk County, Massachusetts!!Estimate","Census Tract 8.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 8.07, Suffolk County, Massachusetts!!Estimate","Census Tract 8.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 101.03, Suffolk County, Massachusetts!!Estimate","Census Tract 101.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 101.04, Suffolk County, Massachusetts!!Estimate","Census Tract 101.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 102.04, Suffolk County, Massachusetts!!Estimate","Census Tract 102.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 102.05, Suffolk County, Massachusetts!!Estimate","Census Tract 102.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 102.06, Suffolk County, Massachusetts!!Estimate","Census Tract 102.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 103, Suffolk County, Massachusetts!!Estimate","Census Tract 103, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.04, Suffolk County, Massachusetts!!Estimate","Census Tract 104.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.05, Suffolk County, Massachusetts!!Estimate","Census Tract 104.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.08, Suffolk County, Massachusetts!!Estimate","Census Tract 104.08, Suffolk County, Massachusetts!!Margin of Error","Census Tract 105, Suffolk County, Massachusetts!!Estimate","Census Tract 105, Suffolk County, Massachusetts!!Margin of Error","Census Tract 106, Suffolk County, Massachusetts!!Estimate","Census Tract 106, Suffolk County, Massachusetts!!Margin of Error","Census Tract 107.01, Suffolk County, Massachusetts!!Estimate","Census Tract 107.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 107.02, Suffolk County, Massachusetts!!Estimate","Census Tract 107.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 108.01, Suffolk County, Massachusetts!!Estimate","Census Tract 108.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 108.02, Suffolk County, Massachusetts!!Estimate","Census Tract 108.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 201.01, Suffolk County, Massachusetts!!Estimate","Census Tract 201.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 202, Suffolk County, Massachusetts!!Estimate","Census Tract 202, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.02, Suffolk County, Massachusetts!!Estimate","Census Tract 203.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.04, Suffolk County, Massachusetts!!Estimate","Census Tract 203.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.05, Suffolk County, Massachusetts!!Estimate","Census Tract 203.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 301, Suffolk County, Massachusetts!!Estimate","Census Tract 301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 302, Suffolk County, Massachusetts!!Estimate","Census Tract 302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 303.01, Suffolk County, Massachusetts!!Estimate","Census Tract 303.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 303.02, Suffolk County, Massachusetts!!Estimate","Census Tract 303.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 304, Suffolk County, Massachusetts!!Estimate","Census Tract 304, Suffolk County, Massachusetts!!Margin of Error","Census Tract 305, Suffolk County, Massachusetts!!Estimate","Census Tract 305, Suffolk County, Massachusetts!!Margin of Error","Census Tract 401, Suffolk County, Massachusetts!!Estimate","Census Tract 401, Suffolk County, Massachusetts!!Margin of Error","Census Tract 402, Suffolk County, Massachusetts!!Estimate","Census Tract 402, Suffolk County, Massachusetts!!Margin of Error","Census Tract 403, Suffolk County, Massachusetts!!Estimate","Census Tract 403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 404.01, Suffolk County, Massachusetts!!Estimate","Census Tract 404.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 406, Suffolk County, Massachusetts!!Estimate","Census Tract 406, Suffolk County, Massachusetts!!Margin of Error","Census Tract 408.01, Suffolk County, Massachusetts!!Estimate","Census Tract 408.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 501.01, Suffolk County, Massachusetts!!Estimate","Census Tract 501.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 502, Suffolk County, Massachusetts!!Estimate","Census Tract 502, Suffolk County, Massachusetts!!Margin of Error","Census Tract 503, Suffolk County, Massachusetts!!Estimate","Census Tract 503, Suffolk County, Massachusetts!!Margin of Error","Census Tract 504, Suffolk County, Massachusetts!!Estimate","Census Tract 504, Suffolk County, Massachusetts!!Margin of Error","Census Tract 505, Suffolk County, Massachusetts!!Estimate","Census Tract 505, Suffolk County, Massachusetts!!Margin of Error","Census Tract 506, Suffolk County, Massachusetts!!Estimate","Census Tract 506, Suffolk County, Massachusetts!!Margin of Error","Census Tract 507, Suffolk County, Massachusetts!!Estimate","Census Tract 507, Suffolk County, Massachusetts!!Margin of Error","Census Tract 509.01, Suffolk County, Massachusetts!!Estimate","Census Tract 509.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 510, Suffolk County, Massachusetts!!Estimate","Census Tract 510, Suffolk County, Massachusetts!!Margin of Error","Census Tract 511.01, Suffolk County, Massachusetts!!Estimate","Census Tract 511.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 512, Suffolk County, Massachusetts!!Estimate","Census Tract 512, Suffolk County, Massachusetts!!Margin of Error","Census Tract 601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 602, Suffolk County, Massachusetts!!Estimate","Census Tract 602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 603.01, Suffolk County, Massachusetts!!Estimate","Census Tract 603.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 604, Suffolk County, Massachusetts!!Estimate","Census Tract 604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 606.03, Suffolk County, Massachusetts!!Estimate","Census Tract 606.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 606.04, Suffolk County, Massachusetts!!Estimate","Census Tract 606.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 607, Suffolk County, Massachusetts!!Estimate","Census Tract 607, Suffolk County, Massachusetts!!Margin of Error","Census Tract 608, Suffolk County, Massachusetts!!Estimate","Census Tract 608, Suffolk County, Massachusetts!!Margin of Error","Census Tract 610, Suffolk County, Massachusetts!!Estimate","Census Tract 610, Suffolk County, Massachusetts!!Margin of Error","Census Tract 611.01, Suffolk County, Massachusetts!!Estimate","Census Tract 611.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 612.01, Suffolk County, Massachusetts!!Estimate","Census Tract 612.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 612.02, Suffolk County, Massachusetts!!Estimate","Census Tract 612.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 612.03, Suffolk County, Massachusetts!!Estimate","Census Tract 612.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 612.04, Suffolk County, Massachusetts!!Estimate","Census Tract 612.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 701.02, Suffolk County, Massachusetts!!Estimate","Census Tract 701.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 701.03, Suffolk County, Massachusetts!!Estimate","Census Tract 701.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 701.04, Suffolk County, Massachusetts!!Estimate","Census Tract 701.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 702.01, Suffolk County, Massachusetts!!Estimate","Census Tract 702.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 702.02, Suffolk County, Massachusetts!!Estimate","Census Tract 702.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 703.01, Suffolk County, Massachusetts!!Estimate","Census Tract 703.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 703.02, Suffolk County, Massachusetts!!Estimate","Census Tract 703.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 704.02, Suffolk County, Massachusetts!!Estimate","Census Tract 704.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 705.01, Suffolk County, Massachusetts!!Estimate","Census Tract 705.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 706, Suffolk County, Massachusetts!!Estimate","Census Tract 706, Suffolk County, Massachusetts!!Margin of Error","Census Tract 707, Suffolk County, Massachusetts!!Estimate","Census Tract 707, Suffolk County, Massachusetts!!Margin of Error","Census Tract 708.01, Suffolk County, Massachusetts!!Estimate","Census Tract 708.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 708.02, Suffolk County, Massachusetts!!Estimate","Census Tract 708.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 709.01, Suffolk County, Massachusetts!!Estimate","Census Tract 709.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 709.02, Suffolk County, Massachusetts!!Estimate","Census Tract 709.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 711.01, Suffolk County, Massachusetts!!Estimate","Census Tract 711.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 712.01, Suffolk County, Massachusetts!!Estimate","Census Tract 712.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 801, Suffolk County, Massachusetts!!Estimate","Census Tract 801, Suffolk County, Massachusetts!!Margin of Error","Census Tract 803, Suffolk County, Massachusetts!!Estimate","Census Tract 803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 804.01, Suffolk County, Massachusetts!!Estimate","Census Tract 804.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 805, Suffolk County, Massachusetts!!Estimate","Census Tract 805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 806.01, Suffolk County, Massachusetts!!Estimate","Census Tract 806.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 808.01, Suffolk County, Massachusetts!!Estimate","Census Tract 808.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 809, Suffolk County, Massachusetts!!Estimate","Census Tract 809, Suffolk County, Massachusetts!!Margin of Error","Census Tract 810.01, Suffolk County, Massachusetts!!Estimate","Census Tract 810.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 811.01, Suffolk County, Massachusetts!!Estimate","Census Tract 811.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 811.02, Suffolk County, Massachusetts!!Estimate","Census Tract 811.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 812, Suffolk County, Massachusetts!!Estimate","Census Tract 812, Suffolk County, Massachusetts!!Margin of Error","Census Tract 813.01, Suffolk County, Massachusetts!!Estimate","Census Tract 813.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 813.02, Suffolk County, Massachusetts!!Estimate","Census Tract 813.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 814, Suffolk County, Massachusetts!!Estimate","Census Tract 814, Suffolk County, Massachusetts!!Margin of Error","Census Tract 815, Suffolk County, Massachusetts!!Estimate","Census Tract 815, Suffolk County, Massachusetts!!Margin of Error","Census Tract 817, Suffolk County, Massachusetts!!Estimate","Census Tract 817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 818, Suffolk County, Massachusetts!!Estimate","Census Tract 818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 819, Suffolk County, Massachusetts!!Estimate","Census Tract 819, Suffolk County, Massachusetts!!Margin of Error","Census Tract 820, Suffolk County, Massachusetts!!Estimate","Census Tract 820, Suffolk County, Massachusetts!!Margin of Error","Census Tract 821, Suffolk County, Massachusetts!!Estimate","Census Tract 821, Suffolk County, Massachusetts!!Margin of Error","Census Tract 901, Suffolk County, Massachusetts!!Estimate","Census Tract 901, Suffolk County, Massachusetts!!Margin of Error","Census Tract 902, Suffolk County, Massachusetts!!Estimate","Census Tract 902, Suffolk County, Massachusetts!!Margin of Error","Census Tract 903, Suffolk County, Massachusetts!!Estimate","Census Tract 903, Suffolk County, Massachusetts!!Margin of Error","Census Tract 904, Suffolk County, Massachusetts!!Estimate","Census Tract 904, Suffolk County, Massachusetts!!Margin of Error","Census Tract 906, Suffolk County, Massachusetts!!Estimate","Census Tract 906, Suffolk County, Massachusetts!!Margin of Error","Census Tract 907, Suffolk County, Massachusetts!!Estimate","Census Tract 907, Suffolk County, Massachusetts!!Margin of Error","Census Tract 909.01, Suffolk County, Massachusetts!!Estimate","Census Tract 909.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 910.01, Suffolk County, Massachusetts!!Estimate","Census Tract 910.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 911, Suffolk County, Massachusetts!!Estimate","Census Tract 911, Suffolk County, Massachusetts!!Margin of Error","Census Tract 912, Suffolk County, Massachusetts!!Estimate","Census Tract 912, Suffolk County, Massachusetts!!Margin of Error","Census Tract 913, Suffolk County, Massachusetts!!Estimate","Census Tract 913, Suffolk County, Massachusetts!!Margin of Error","Census Tract 914, Suffolk County, Massachusetts!!Estimate","Census Tract 914, Suffolk County, Massachusetts!!Margin of Error","Census Tract 915, Suffolk County, Massachusetts!!Estimate","Census Tract 915, Suffolk County, Massachusetts!!Margin of Error","Census Tract 916, Suffolk County, Massachusetts!!Estimate","Census Tract 916, Suffolk County, Massachusetts!!Margin of Error","Census Tract 917, Suffolk County, Massachusetts!!Estimate","Census Tract 917, Suffolk County, Massachusetts!!Margin of Error","Census Tract 918, Suffolk County, Massachusetts!!Estimate","Census Tract 918, Suffolk County, Massachusetts!!Margin of Error","Census Tract 919, Suffolk County, Massachusetts!!Estimate","Census Tract 919, Suffolk County, Massachusetts!!Margin of Error","Census Tract 920, Suffolk County, Massachusetts!!Estimate","Census Tract 920, Suffolk County, Massachusetts!!Margin of Error","Census Tract 921.01, Suffolk County, Massachusetts!!Estimate","Census Tract 921.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 922, Suffolk County, Massachusetts!!Estimate","Census Tract 922, Suffolk County, Massachusetts!!Margin of Error","Census Tract 923, Suffolk County, Massachusetts!!Estimate","Census Tract 923, Suffolk County, Massachusetts!!Margin of Error","Census Tract 924, Suffolk County, Massachusetts!!Estimate","Census Tract 924, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1001, Suffolk County, Massachusetts!!Estimate","Census Tract 1001, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1002, Suffolk County, Massachusetts!!Estimate","Census Tract 1002, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1003, Suffolk County, Massachusetts!!Estimate","Census Tract 1003, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1004, Suffolk County, Massachusetts!!Estimate","Census Tract 1004, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1005, Suffolk County, Massachusetts!!Estimate","Census Tract 1005, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1007, Suffolk County, Massachusetts!!Estimate","Census Tract 1007, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1008, Suffolk County, Massachusetts!!Estimate","Census Tract 1008, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1009, Suffolk County, Massachusetts!!Estimate","Census Tract 1009, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1102.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1102.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1103.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1103.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1202.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1202.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1204, Suffolk County, Massachusetts!!Estimate","Census Tract 1204, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1205, Suffolk County, Massachusetts!!Estimate","Census Tract 1205, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1206, Suffolk County, Massachusetts!!Estimate","Census Tract 1206, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1207, Suffolk County, Massachusetts!!Estimate","Census Tract 1207, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1301.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1301.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1302, Suffolk County, Massachusetts!!Estimate","Census Tract 1302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1303, Suffolk County, Massachusetts!!Estimate","Census Tract 1303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1403, Suffolk County, Massachusetts!!Estimate","Census Tract 1403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1404, Suffolk County, Massachusetts!!Estimate","Census Tract 1404, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1602, Suffolk County, Massachusetts!!Estimate","Census Tract 1602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1603, Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1701.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1701.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1703.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1703.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9809, Suffolk County, Massachusetts!!Estimate","Census Tract 9809, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, 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Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1202.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1202.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1204, Suffolk County, Massachusetts!!Estimate","Census Tract 1204, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1205, Suffolk County, Massachusetts!!Estimate","Census Tract 1205, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1206, Suffolk County, Massachusetts!!Estimate","Census Tract 1206, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1207, Suffolk County, Massachusetts!!Estimate","Census Tract 1207, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1301, Suffolk County, Massachusetts!!Estimate","Census Tract 1301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1302, Suffolk County, Massachusetts!!Estimate","Census Tract 1302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1303, Suffolk County, Massachusetts!!Estimate","Census Tract 1303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1403, Suffolk County, Massachusetts!!Estimate","Census Tract 1403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1404, Suffolk County, Massachusetts!!Estimate","Census Tract 1404, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1602, Suffolk County, Massachusetts!!Estimate","Census Tract 1602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1603, Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701, Suffolk County, Massachusetts!!Estimate","Census Tract 1701, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703, Suffolk County, Massachusetts!!Estimate","Census Tract 1703, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error" 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1001, Suffolk County, Massachusetts!!Estimate","Census Tract 1001, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1002, Suffolk County, Massachusetts!!Estimate","Census Tract 1002, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1003, Suffolk County, Massachusetts!!Estimate","Census Tract 1003, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1004, Suffolk County, Massachusetts!!Estimate","Census Tract 1004, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1005, Suffolk County, Massachusetts!!Estimate","Census Tract 1005, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1007, Suffolk County, Massachusetts!!Estimate","Census Tract 1007, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1008, Suffolk County, Massachusetts!!Estimate","Census Tract 1008, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1009, Suffolk County, Massachusetts!!Estimate","Census Tract 1009, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1102.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1102.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1103.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1103.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1202.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1202.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1204, Suffolk County, Massachusetts!!Estimate","Census Tract 1204, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1205, Suffolk County, Massachusetts!!Estimate","Census Tract 1205, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1206, Suffolk County, Massachusetts!!Estimate","Census Tract 1206, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1207, Suffolk County, Massachusetts!!Estimate","Census Tract 1207, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1301, Suffolk County, Massachusetts!!Estimate","Census Tract 1301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1302, Suffolk County, Massachusetts!!Estimate","Census Tract 1302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1303, Suffolk County, Massachusetts!!Estimate","Census Tract 1303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1403, Suffolk County, Massachusetts!!Estimate","Census Tract 1403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1404, Suffolk County, Massachusetts!!Estimate","Census Tract 1404, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1602, Suffolk County, Massachusetts!!Estimate","Census Tract 1602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1603, Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701, Suffolk County, Massachusetts!!Estimate","Census Tract 1701, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703, Suffolk County, Massachusetts!!Estimate","Census Tract 1703, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error" +"Total:","99","±60","111","±100","50","±45","58","±60","107","±98","138","±130","30","±25","72","±50","18","±30","122","±112","33","±31","204","±95","148","±169","87","±53","153","±118","287","±214","278","±122","95","±58","36","±27","63","±67","18","±18","213","±74","88","±76","176","±127","263","±103","26","±20","161","±73","107","±100","41","±29","47","±36","0","±12","6","±11","33","±45","102","±130","44","±33","0","±12","61","±38","0","±12","2","±11","195","±153","24","±26","0","±12","0","±12","155","±99","52","±44","28","±40","110","±88","350","±149","92","±57","103","±70","115","±65","35","±33","26","±24","87","±66","1","±2","61","±61","52","±73","62","±68","1","±2","0","±12","6","±9","0","±12","73","±100","25","±36","22","±24","217","±76","0","±12","97","±87","124","±55","25","±30","40","±43","55","±54","62","±65","50","±31","143","±120","11","±16","318","±90","282","±60","347","±117","88","±78","229","±126","454","±120","558","±188","733","±167","520","±189","407","±120","452","±124","159","±109","307","±217","168","±80","249","±102","752","±319","657","±180","653","±173","845","±202","1,224","±366","994","±210","968","±262","1,151","±287","1,260","±238","536","±150","976","±250","1,187","±228","873","±208","102","±87","195","±134","224","±208","127","±58","397","±136","628","±177","689","±132","774","±210","543","±236","852","±166","1,042","±404","1,751","±313","1,438","±226","749","±264","689","±256","1,260","±174","1,769","±281","1,934","±295","1,064","±152","1,650","±348","2,015","±315","1,876","±444","1,420","±413","151","±104","114","±137","1,291","±613","1,293","±241","3,109","±608","2,062","±328","1,500","±249","2,002","±250","769","±351","585","±146","551","±129","618","±192","642","±239","110","±79","458","±155","44","±45","144","±57","45","±41","20","±26","107","±87","146","±78","368","±175","226","±143","223","±113","129","±95","62","±34","47","±49","27","±28","36","±34","73","±75","71","±47","1,409","±290","1,055","±203","677","±185","445","±96","465","±120","474","±82","1,224","±238","1,812","±597","3,001","±428","170","±108","50","±51","111","±63","113","±45","131","±116","196","±131","186","±86","197","±116","294","±116","268","±223","189","±55","285","±176","26","±30","25","±29","130","±128","25","±22","437","±275","115","±139","57","±91","41","±51","0","±12","0","±12","77","±80","23","±39","32","±44","0","±12","0","±12","72","±55","0","±12","24","±39","33","±19","0","±12","4","±5","0","±12","0","±12","0","±12","0","±12" +"    Car, truck, or van - drove alone","23","±27","55","±67","10","±16","35","±32","91","±84","89","±101","0","±12","8","±17","0","±12","63","±98","23","±27","117","±89","130","±168","29","±36","15","±23","61","±100","16","±20","0","±12","6","±14","0","±17","0","±12","20","±23","0","±12","0","±17","26","±23","6","±9","15","±21","9","±14","0","±12","10","±14","0","±12","0","±12","20","±29","0","±12","7","±11","0","±12","0","±12","0","±12","0","±12","53","±77","0","±12","0","±12","0","±12","47","±53","13","±20","0","±12","81","±62","104","±70","92","±57","32","±51","12","±14","0","±12","6","±10","18","±29","0","±17","33","±43","0","±12","0","±17","0","±12","0","±12","6","±9","0","±12","29","±44","0","±12","11","±18","118","±49","0","±12","52","±55","27","±25","0","±12","15","±24","15","±23","43","±58","19","±21","68","±67","0","±12","50","±50","0","±12","114","±50","16","±25","55","±44","178","±65","148","±61","156","±89","258","±132","54","±29","61","±43","35","±33","54","±54","43","±36","64","±50","251","±176","242","±105","333","±106","437","±139","639","±231","334","±113","522","±192","638","±221","560","±130","182","±69","577","±156","603","±268","317","±105","38","±33","97","±67","55","±61","23","±24","183","±76","251","±113","276","±82","315","±107","176","±170","315","±90","451","±216","825","±215","714","±184","333","±157","340","±152","584","±128","721","±175","932","±211","524","±119","918","±226","849","±196","769","±222","504","±255","71","±47","82","±99","952","±576","718","±171","1,325","±345","1,073","±257","601","±167","1,027","±187","406","±218","316","±105","290","±81","244","±110","346","±123","77","±67","222","±139","35","±42","126","±32","41","±40","15","±23","28","±24","48","±31","121","±77","93","±87","33","±22","59","±50","17","±17","26","±39","7","±11","6","±12","38","±50","28","±28","947","±207","683","±152","388","±156","268","±64","331","±120","236","±92","883","±210","1,101","±417","1,780","±380","122","±95","7","±14","49","±39","27","±23","16","±18","82","±74","60","±57","74","±61","130","±82","162","±191","72","±65","189","±142","19","±28","17","±18","65","±76","5","±7","108","±109","90","±119","57","±91","10","±16","0","±12","0","±12","57","±76","13","±31","0","±12","0","±12","0","±12","62","±53","0","±12","13","±21","16","±18","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12" +"    Car, truck, or van - carpooled","9","±15","19","±21","5","±11","0","±12","16","±23","0","±17","0","±12","0","±17","0","±12","0","±12","0","±12","14","±22","0","±12","0","±12","0","±12","0","±17","0","±17","0","±12","0","±12","0","±17","0","±12","0","±17","0","±12","8","±12","0","±17","0","±12","0","±12","0","±12","2","±5","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±17","0","±12","0","±12","0","±12","7","±9","29","±44","0","±12","0","±12","17","±25","0","±17","0","±17","0","±12","0","±12","0","±12","14","±20","1","±2","0","±12","0","±12","0","±17","1","±2","0","±12","0","±12","0","±12","0","±17","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±17","0","±17","0","±12","0","±12","0","±17","0","±12","0","±12","0","±12","8","±15","0","±12","11","±17","72","±50","27","±28","8","±16","0","±12","21","±19","7","±12","22","±25","0","±12","0","±12","0","±12","0","±17","31","±38","100","±63","58","±42","44","±45","9","±15","30","±32","19","±29","100","±67","66","±44","0","±12","10","±15","66","±45","0","±12","0","±12","0","±12","9","±14","53","±49","78","±68","30","±23","110","±83","113","±93","89","±49","97","±55","142","±73","119","±96","0","±17","64","±54","127","±79","96","±110","161","±95","87","±60","125","±86","79","±64","132","±118","0","±17","6","±9","0","±12","69","±65","98","±63","453","±247","197","±119","137","±84","117","±74","24","±39","34","±37","65","±43","45","±50","9","±15","24","±22","38","±44","0","±12","0","±17","0","±12","0","±12","0","±12","27","±42","46","±52","0","±17","14","±21","6","±12","0","±12","0","±17","0","±17","0","±12","0","±17","0","±12","147","±115","152","±126","34","±74","26","±29","45","±29","39","±43","78","±69","29","±45","339","±189","0","±17","0","±12","23","±26","18","±21","6","±9","0","±12","41","±40","9","±17","21","±34","8","±12","60","±56","0","±17","0","±12","0","±12","0","±17","7","±11","82","±89","0","±17","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","6","±15","0","±12","0","±12","10","±17","0","±12","6","±12","4","±6","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12" 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Massachusetts!!Margin of Error","Census Tract 703.02, Suffolk County, Massachusetts!!Estimate","Census Tract 703.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 704.02, Suffolk County, Massachusetts!!Estimate","Census Tract 704.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 705.01, Suffolk County, Massachusetts!!Estimate","Census Tract 705.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 706, Suffolk County, Massachusetts!!Estimate","Census Tract 706, Suffolk County, Massachusetts!!Margin of Error","Census Tract 707, Suffolk County, Massachusetts!!Estimate","Census Tract 707, Suffolk County, Massachusetts!!Margin of Error","Census Tract 708.01, Suffolk County, Massachusetts!!Estimate","Census Tract 708.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 708.02, Suffolk County, Massachusetts!!Estimate","Census Tract 708.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 709.01, Suffolk County, Massachusetts!!Estimate","Census Tract 709.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 709.02, Suffolk County, Massachusetts!!Estimate","Census Tract 709.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 711.01, Suffolk County, Massachusetts!!Estimate","Census Tract 711.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 712.01, Suffolk County, Massachusetts!!Estimate","Census Tract 712.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 801, Suffolk County, Massachusetts!!Estimate","Census Tract 801, Suffolk County, Massachusetts!!Margin of Error","Census Tract 803, Suffolk County, Massachusetts!!Estimate","Census Tract 803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 804.01, Suffolk County, Massachusetts!!Estimate","Census Tract 804.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 805, Suffolk County, Massachusetts!!Estimate","Census Tract 805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 806.01, Suffolk County, Massachusetts!!Estimate","Census Tract 806.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 808.01, Suffolk County, Massachusetts!!Estimate","Census Tract 808.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 809, Suffolk County, Massachusetts!!Estimate","Census Tract 809, Suffolk County, Massachusetts!!Margin of Error","Census Tract 810.01, Suffolk County, Massachusetts!!Estimate","Census Tract 810.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 811.01, Suffolk County, Massachusetts!!Estimate","Census Tract 811.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 811.02, Suffolk County, Massachusetts!!Estimate","Census Tract 811.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 812, Suffolk County, Massachusetts!!Estimate","Census Tract 812, Suffolk County, Massachusetts!!Margin of Error","Census Tract 813.01, Suffolk County, Massachusetts!!Estimate","Census Tract 813.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 813.02, Suffolk County, Massachusetts!!Estimate","Census Tract 813.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 814, Suffolk County, Massachusetts!!Estimate","Census Tract 814, Suffolk County, Massachusetts!!Margin of Error","Census Tract 815, Suffolk County, Massachusetts!!Estimate","Census Tract 815, Suffolk County, Massachusetts!!Margin of Error","Census Tract 817, Suffolk County, Massachusetts!!Estimate","Census Tract 817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 818, Suffolk County, Massachusetts!!Estimate","Census Tract 818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 819, Suffolk County, Massachusetts!!Estimate","Census Tract 819, Suffolk County, Massachusetts!!Margin of Error","Census Tract 820, Suffolk County, Massachusetts!!Estimate","Census Tract 820, Suffolk County, Massachusetts!!Margin of Error","Census Tract 821, Suffolk County, Massachusetts!!Estimate","Census Tract 821, Suffolk County, Massachusetts!!Margin of Error","Census Tract 901, Suffolk County, Massachusetts!!Estimate","Census Tract 901, Suffolk County, Massachusetts!!Margin of Error","Census Tract 902, Suffolk County, Massachusetts!!Estimate","Census Tract 902, Suffolk County, Massachusetts!!Margin of Error","Census Tract 903, Suffolk County, Massachusetts!!Estimate","Census Tract 903, Suffolk County, Massachusetts!!Margin of Error","Census Tract 904, Suffolk County, Massachusetts!!Estimate","Census Tract 904, Suffolk County, Massachusetts!!Margin of Error","Census Tract 906, Suffolk County, Massachusetts!!Estimate","Census Tract 906, Suffolk County, Massachusetts!!Margin of Error","Census Tract 907, Suffolk County, Massachusetts!!Estimate","Census Tract 907, Suffolk County, Massachusetts!!Margin of Error","Census Tract 909.01, Suffolk County, Massachusetts!!Estimate","Census Tract 909.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 910.01, Suffolk County, Massachusetts!!Estimate","Census Tract 910.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 911, Suffolk County, Massachusetts!!Estimate","Census Tract 911, Suffolk County, Massachusetts!!Margin of Error","Census Tract 912, Suffolk County, Massachusetts!!Estimate","Census Tract 912, Suffolk County, Massachusetts!!Margin of Error","Census Tract 913, Suffolk County, Massachusetts!!Estimate","Census Tract 913, Suffolk County, Massachusetts!!Margin of Error","Census Tract 914, Suffolk County, Massachusetts!!Estimate","Census Tract 914, Suffolk County, Massachusetts!!Margin of Error","Census Tract 915, Suffolk County, Massachusetts!!Estimate","Census Tract 915, Suffolk County, Massachusetts!!Margin of Error","Census Tract 916, Suffolk County, Massachusetts!!Estimate","Census Tract 916, Suffolk County, Massachusetts!!Margin of Error","Census Tract 917, Suffolk County, Massachusetts!!Estimate","Census Tract 917, Suffolk County, Massachusetts!!Margin of Error","Census Tract 918, Suffolk County, Massachusetts!!Estimate","Census Tract 918, Suffolk County, Massachusetts!!Margin of Error","Census Tract 919, Suffolk County, Massachusetts!!Estimate","Census Tract 919, Suffolk County, Massachusetts!!Margin of Error","Census Tract 920, Suffolk County, Massachusetts!!Estimate","Census Tract 920, Suffolk County, Massachusetts!!Margin of Error","Census Tract 921.01, Suffolk County, Massachusetts!!Estimate","Census Tract 921.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 922, Suffolk County, Massachusetts!!Estimate","Census Tract 922, Suffolk County, Massachusetts!!Margin of Error","Census Tract 923, Suffolk County, Massachusetts!!Estimate","Census Tract 923, Suffolk County, Massachusetts!!Margin of Error","Census Tract 924, Suffolk County, Massachusetts!!Estimate","Census Tract 924, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1001, Suffolk County, Massachusetts!!Estimate","Census Tract 1001, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1002, Suffolk County, Massachusetts!!Estimate","Census Tract 1002, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1003, Suffolk County, Massachusetts!!Estimate","Census Tract 1003, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1004, Suffolk County, Massachusetts!!Estimate","Census Tract 1004, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1005, Suffolk County, Massachusetts!!Estimate","Census Tract 1005, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1007, Suffolk County, Massachusetts!!Estimate","Census Tract 1007, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1008, Suffolk County, Massachusetts!!Estimate","Census Tract 1008, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1009, Suffolk County, Massachusetts!!Estimate","Census Tract 1009, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1102.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1102.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1103.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1103.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1202.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1202.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1204, Suffolk County, Massachusetts!!Estimate","Census Tract 1204, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1205, Suffolk County, Massachusetts!!Estimate","Census Tract 1205, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1206, Suffolk County, Massachusetts!!Estimate","Census Tract 1206, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1207, Suffolk County, Massachusetts!!Estimate","Census Tract 1207, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1301.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1301.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1302, Suffolk County, Massachusetts!!Estimate","Census Tract 1302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1303, Suffolk County, Massachusetts!!Estimate","Census Tract 1303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1403, Suffolk County, Massachusetts!!Estimate","Census Tract 1403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1404, Suffolk County, Massachusetts!!Estimate","Census Tract 1404, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1602, Suffolk County, Massachusetts!!Estimate","Census Tract 1602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1603, Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1701.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1701.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1703.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1703.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9809, Suffolk County, Massachusetts!!Estimate","Census Tract 9809, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9819, Suffolk County, Massachusetts!!Estimate","Census Tract 9819, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error" +"Total:","54","±58","185","±129","149","±127","188","±128","183","±117","111","±79","163","±170","74","±74","107","±90","56","±51","82","±46","77","±84","72","±59","0","±13","59","±65","159","±144","20","±22","293","±183","138","±93","84","±75","171","±105","238","±170","90","±68","126","±78","38","±36","90","±62","270","±341","203","±91","78","±65","83","±60","259","±111","92","±70","198","±109","49","±58","27","±36","17","±20","51","±36","9","±14","0","±13","9","±15","13","±19","0","±13","0","±13","28","±33","0","±13","24","±28","45","±74","138","±91","196","±255","0","±13","0","±13","128","±100","289","±212","94","±111","124","±161","112","±81","0","±19","40","±41","63","±62","52","±48","48","±51","37","±32","36","±42","88","±77","137","±136","162","±232","105","±115","32","±34","27","±23","0","±13","0","±13","65","±84","0","±13","0","±13","9","±24","54","±62","235","±101","3","±6","102","±119","202","±93","62","±82","0","±13","26","±45","23","±39","14","±29","0","±13","0","±13","57","±71","4","±7","0","±13","0","±13","95","±69","21","±34","82","±114","93","±74","375","±291","0","±13","279","±221","80","±66","164","±130","373","±202","149","±102","221","±104","524","±145","428","±112","476","±237","303","±110","849","±483","87","±47","289","±149","0","±13","90","±103","562","±243","506","±256","255","±137","684","±248","578","±170","1,287","±359","893","±348","1,356","±695","1,223","±442","1,205","±346","1,244","±276","449","±131","606","±209","1,200","±322","796","±225","81","±73","191","±99","214","±125","298","±168","357","±156","525","±159","701","±179","631","±160","426","±207","995","±277","1,084","±284","1,270","±403","1,473","±346","608","±296","683","±192","1,449","±329","1,401","±308","2,765","±592","1,326","±318","1,421","±298","1,476","±352","1,813","±602","813","±387","99","±81","32","±33","805","±324","1,395","±359","2,507","±482","2,461","±621","1,651","±445","1,939","±458","493","±249","408","±361","160","±228","641","±168","538","±157","713","±191","537","±161","82","±89","287","±194","10","±16","144","±108","31","±25","61","±47","58","±38","266","±125","563","±162","276","±203","140","±93","313","±233","126","±94","139","±120","70","±70","97","±80","79","±80","359","±193","970","±290","1,295","±285","975","±362","532","±175","592","±179","383","±254","990","±258","1,828","±410","2,964","±602","110","±85","255","±175","102","±90","97","±94","258","±109","97","±58","199","±174","190","±104","363","±117","13","±21","93","±125","169","±92","30","±83","303","±254","433","±381","26","±41","0","±13","82","±91","122","±107","63","±67","106","±126","0","±19","89","±117","26","±27","27","±32","90","±82","84","±83","41","±93","4","±9","0","±13","0","±13","0","±13","0","±13","0","±13","17","±30","0","±13","0","±13","0","±13","0","±13","0","±13","12","±18","0","±13","0","±13" +"    Car, truck, or van - drove 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\ No newline at end of file diff --git a/fa23-team/data/export (1).csv b/fa23-team/data/export (1).csv new file mode 100644 index 0000000..53d3d46 --- /dev/null +++ b/fa23-team/data/export (1).csv @@ -0,0 +1,8 @@ +Means of transportation to work (JWTRNS),Total,White alone,Black or African American alone,American Indian alone,Alaska Native alone,"American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races",Asian alone,Native Hawaiian and Other Pacific Islander alone,Some Other Race alone,Two or More Races + -> Total,27541,8690,12803,35,0,0,1247,0,2915,1851 +" -> Total -> Total Boston City--Dorchester & South Boston PUMA, Massachusetts",9281,5285,2038,0,0,0,650,0,784,524 +Bus,9281,5285,2038,0,0,0,650,0,784,524 +" -> Total -> Total Boston City--Mattapan & Roxbury PUMA, Massachusetts",12616,1406,8364,35,0,0,433,0,1225,1153 +Bus,12616,1406,8364,35,0,0,433,0,1225,1153 +" -> Total -> Total Boston City--Hyde Park, Jamaica Plain, Roslindale & West Roxbury PUMA; Massachusetts",5644,1999,2401,0,0,0,164,0,906,174 +Bus,5644,1999,2401,0,0,0,164,0,906,174 diff --git a/fa23-team/data/export 2011.csv b/fa23-team/data/export 2011.csv new file mode 100644 index 0000000..4349090 --- /dev/null +++ b/fa23-team/data/export 2011.csv @@ -0,0 +1,7 @@ +Means of transportation to work (JWTR),White alone,Black or African American alone,American Indian alone,Alaska Native alone,"American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races",Asian alone,Native Hawaiian and Other Pacific Islander alone,Some Other Race alone,Two or More Races +" -> PUMA5 03303, Massachusetts",,,,,,,,, +Bus or Trolley Bus,911,9319,12,0,30,491,0,1911,183 +" -> PUMA5 03304, Massachusetts",,,,,,,,, +Bus or Trolley Bus,4524,2174,8,0,20,271,0,705,107 +" -> PUMA5 03305, Massachusetts",,,,,,,,, +Bus or Trolley Bus,3119,2003,0,0,0,364,0,398,133 diff --git a/fa23-team/data/export 2016.csv b/fa23-team/data/export 2016.csv new file mode 100644 index 0000000..d340197 --- /dev/null +++ b/fa23-team/data/export 2016.csv @@ -0,0 +1,7 @@ +Means of transportation to work (JWTR),White alone,Black or African American alone,American Indian alone,Alaska Native alone,"American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races",Asian alone,Native Hawaiian and Other Pacific Islander alone,Some Other Race alone,Two or More Races +" -> Boston City--Dorchester & South Boston PUMA, Massachusetts",,,,,,,,, +Bus or Trolley Bus,7086,3997,110,0,15,818,0,902,275 +" -> Boston City--Mattapan & Roxbury PUMA, Massachusetts",,,,,,,,, +Bus or Trolley Bus,2101,11139,32,0,19,371,0,1898,408 +" -> Boston City--Hyde Park, Jamaica Plain, Roslindale & West Roxbury PUMA; Massachusetts",,,,,,,,, +Bus or Trolley Bus,2845,2665,26,0,0,484,0,504,214 diff --git a/fa23-team/data/someother2011.csv b/fa23-team/data/someother2011.csv new file mode 100644 index 0000000..6b88acb --- /dev/null +++ b/fa23-team/data/someother2011.csv @@ -0,0 +1,8 @@ +"Label (Grouping)","Census Tract 1, Suffolk County, Massachusetts!!Estimate","Census Tract 1, Suffolk County, Massachusetts!!Margin of Error","Census Tract 2.01, Suffolk County, Massachusetts!!Estimate","Census Tract 2.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 2.02, Suffolk County, Massachusetts!!Estimate","Census Tract 2.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 3.01, Suffolk County, Massachusetts!!Estimate","Census Tract 3.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 3.02, Suffolk County, Massachusetts!!Estimate","Census Tract 3.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 4.01, Suffolk County, Massachusetts!!Estimate","Census Tract 4.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 4.02, Suffolk County, Massachusetts!!Estimate","Census Tract 4.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.02, Suffolk County, Massachusetts!!Estimate","Census Tract 5.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.03, Suffolk County, Massachusetts!!Estimate","Census Tract 5.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 5.04, Suffolk County, Massachusetts!!Estimate","Census Tract 5.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 6.01, Suffolk County, Massachusetts!!Estimate","Census Tract 6.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 6.02, Suffolk County, Massachusetts!!Estimate","Census Tract 6.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.01, Suffolk County, Massachusetts!!Estimate","Census Tract 7.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.03, Suffolk County, Massachusetts!!Estimate","Census Tract 7.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 7.04, Suffolk County, Massachusetts!!Estimate","Census Tract 7.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 8.02, Suffolk County, Massachusetts!!Estimate","Census Tract 8.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 8.03, Suffolk County, Massachusetts!!Estimate","Census Tract 8.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 101.03, Suffolk County, Massachusetts!!Estimate","Census Tract 101.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 101.04, Suffolk County, Massachusetts!!Estimate","Census Tract 101.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 102.03, Suffolk County, Massachusetts!!Estimate","Census Tract 102.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 102.04, Suffolk County, Massachusetts!!Estimate","Census Tract 102.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 103, Suffolk County, Massachusetts!!Estimate","Census Tract 103, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.04, Suffolk County, Massachusetts!!Estimate","Census Tract 104.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.05, Suffolk County, Massachusetts!!Estimate","Census Tract 104.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 104.08, Suffolk County, Massachusetts!!Estimate","Census Tract 104.08, Suffolk County, Massachusetts!!Margin of Error","Census Tract 105, Suffolk County, Massachusetts!!Estimate","Census Tract 105, Suffolk County, Massachusetts!!Margin of Error","Census Tract 106, Suffolk County, Massachusetts!!Estimate","Census Tract 106, Suffolk County, Massachusetts!!Margin of Error","Census Tract 107.01, Suffolk County, Massachusetts!!Estimate","Census Tract 107.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 107.02, Suffolk County, Massachusetts!!Estimate","Census Tract 107.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 108.01, Suffolk County, Massachusetts!!Estimate","Census Tract 108.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 108.02, Suffolk County, Massachusetts!!Estimate","Census Tract 108.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 201.01, Suffolk County, Massachusetts!!Estimate","Census Tract 201.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 202, Suffolk County, Massachusetts!!Estimate","Census Tract 202, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.02, Suffolk County, Massachusetts!!Estimate","Census Tract 203.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 203.03, Suffolk County, Massachusetts!!Estimate","Census Tract 203.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 301, Suffolk County, Massachusetts!!Estimate","Census Tract 301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 302, Suffolk County, Massachusetts!!Estimate","Census Tract 302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 303, Suffolk County, Massachusetts!!Estimate","Census Tract 303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 304, Suffolk County, Massachusetts!!Estimate","Census Tract 304, Suffolk County, Massachusetts!!Margin of Error","Census Tract 305, Suffolk County, Massachusetts!!Estimate","Census Tract 305, Suffolk County, Massachusetts!!Margin of Error","Census Tract 401, Suffolk County, Massachusetts!!Estimate","Census Tract 401, Suffolk County, Massachusetts!!Margin of Error","Census Tract 402, Suffolk County, Massachusetts!!Estimate","Census Tract 402, Suffolk County, Massachusetts!!Margin of Error","Census Tract 403, Suffolk County, Massachusetts!!Estimate","Census Tract 403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 404.01, Suffolk County, Massachusetts!!Estimate","Census Tract 404.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 406, Suffolk County, Massachusetts!!Estimate","Census Tract 406, Suffolk County, Massachusetts!!Margin of Error","Census Tract 408.01, Suffolk County, Massachusetts!!Estimate","Census Tract 408.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 501.01, Suffolk County, Massachusetts!!Estimate","Census Tract 501.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 502, Suffolk County, Massachusetts!!Estimate","Census Tract 502, Suffolk County, Massachusetts!!Margin of Error","Census Tract 503, Suffolk County, Massachusetts!!Estimate","Census Tract 503, Suffolk County, Massachusetts!!Margin of Error","Census Tract 504, Suffolk County, Massachusetts!!Estimate","Census Tract 504, Suffolk County, Massachusetts!!Margin of Error","Census Tract 505, Suffolk County, Massachusetts!!Estimate","Census Tract 505, Suffolk County, Massachusetts!!Margin of Error","Census Tract 506, Suffolk County, Massachusetts!!Estimate","Census Tract 506, Suffolk County, Massachusetts!!Margin of Error","Census Tract 507, Suffolk County, Massachusetts!!Estimate","Census Tract 507, Suffolk County, Massachusetts!!Margin of Error","Census Tract 509.01, Suffolk County, Massachusetts!!Estimate","Census Tract 509.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 510, Suffolk County, Massachusetts!!Estimate","Census Tract 510, Suffolk County, Massachusetts!!Margin of Error","Census Tract 511.01, Suffolk County, Massachusetts!!Estimate","Census Tract 511.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 512, Suffolk County, Massachusetts!!Estimate","Census Tract 512, Suffolk County, Massachusetts!!Margin of Error","Census Tract 601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 602, Suffolk County, Massachusetts!!Estimate","Census Tract 602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 603.01, Suffolk County, Massachusetts!!Estimate","Census Tract 603.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 604, Suffolk County, Massachusetts!!Estimate","Census Tract 604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 606, Suffolk County, Massachusetts!!Estimate","Census Tract 606, Suffolk County, Massachusetts!!Margin of Error","Census Tract 607, Suffolk County, Massachusetts!!Estimate","Census Tract 607, Suffolk County, Massachusetts!!Margin of Error","Census Tract 608, Suffolk County, Massachusetts!!Estimate","Census Tract 608, Suffolk County, Massachusetts!!Margin of Error","Census Tract 610, Suffolk County, Massachusetts!!Estimate","Census Tract 610, Suffolk County, Massachusetts!!Margin of Error","Census Tract 611.01, Suffolk County, Massachusetts!!Estimate","Census Tract 611.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 612, Suffolk County, Massachusetts!!Estimate","Census Tract 612, Suffolk County, Massachusetts!!Margin of Error","Census Tract 701.01, Suffolk County, Massachusetts!!Estimate","Census Tract 701.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 702, Suffolk County, Massachusetts!!Estimate","Census Tract 702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 703, Suffolk County, Massachusetts!!Estimate","Census Tract 703, Suffolk County, Massachusetts!!Margin of Error","Census Tract 704.02, Suffolk County, Massachusetts!!Estimate","Census Tract 704.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 705, Suffolk County, Massachusetts!!Estimate","Census Tract 705, Suffolk County, Massachusetts!!Margin of Error","Census Tract 706, Suffolk County, Massachusetts!!Estimate","Census Tract 706, Suffolk County, Massachusetts!!Margin of Error","Census Tract 707, Suffolk County, Massachusetts!!Estimate","Census Tract 707, Suffolk County, Massachusetts!!Margin of Error","Census Tract 708, Suffolk County, Massachusetts!!Estimate","Census Tract 708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 709, Suffolk County, Massachusetts!!Estimate","Census Tract 709, Suffolk County, Massachusetts!!Margin of Error","Census Tract 711.01, Suffolk County, Massachusetts!!Estimate","Census Tract 711.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 712.01, Suffolk County, Massachusetts!!Estimate","Census Tract 712.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 801, Suffolk County, Massachusetts!!Estimate","Census Tract 801, Suffolk County, Massachusetts!!Margin of Error","Census Tract 803, Suffolk County, Massachusetts!!Estimate","Census Tract 803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 804.01, Suffolk County, Massachusetts!!Estimate","Census Tract 804.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 805, Suffolk County, Massachusetts!!Estimate","Census Tract 805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 806.01, Suffolk County, Massachusetts!!Estimate","Census Tract 806.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 808.01, Suffolk County, Massachusetts!!Estimate","Census Tract 808.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 809, Suffolk County, Massachusetts!!Estimate","Census Tract 809, Suffolk County, Massachusetts!!Margin of Error","Census Tract 810.01, Suffolk County, Massachusetts!!Estimate","Census Tract 810.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 811, Suffolk County, Massachusetts!!Estimate","Census Tract 811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 812, Suffolk County, Massachusetts!!Estimate","Census Tract 812, Suffolk County, Massachusetts!!Margin of Error","Census Tract 813, Suffolk County, Massachusetts!!Estimate","Census Tract 813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 814, Suffolk County, Massachusetts!!Estimate","Census Tract 814, Suffolk County, Massachusetts!!Margin of Error","Census Tract 815, Suffolk County, Massachusetts!!Estimate","Census Tract 815, Suffolk County, Massachusetts!!Margin of Error","Census Tract 817, Suffolk County, Massachusetts!!Estimate","Census Tract 817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 818, Suffolk County, Massachusetts!!Estimate","Census Tract 818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 819, Suffolk County, Massachusetts!!Estimate","Census Tract 819, Suffolk County, Massachusetts!!Margin of Error","Census Tract 820, Suffolk County, Massachusetts!!Estimate","Census Tract 820, Suffolk County, Massachusetts!!Margin of Error","Census Tract 821, Suffolk County, Massachusetts!!Estimate","Census Tract 821, Suffolk County, Massachusetts!!Margin of Error","Census Tract 901, Suffolk County, Massachusetts!!Estimate","Census Tract 901, Suffolk County, Massachusetts!!Margin of Error","Census Tract 902, Suffolk County, Massachusetts!!Estimate","Census Tract 902, Suffolk County, Massachusetts!!Margin of Error","Census Tract 903, Suffolk County, Massachusetts!!Estimate","Census Tract 903, Suffolk County, Massachusetts!!Margin of Error","Census Tract 904, Suffolk County, Massachusetts!!Estimate","Census Tract 904, Suffolk County, Massachusetts!!Margin of Error","Census Tract 906, Suffolk County, Massachusetts!!Estimate","Census Tract 906, Suffolk County, Massachusetts!!Margin of Error","Census Tract 907, Suffolk County, Massachusetts!!Estimate","Census Tract 907, Suffolk County, Massachusetts!!Margin of Error","Census Tract 909.01, Suffolk County, Massachusetts!!Estimate","Census Tract 909.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 910.01, Suffolk County, Massachusetts!!Estimate","Census Tract 910.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 911, Suffolk County, Massachusetts!!Estimate","Census Tract 911, Suffolk County, Massachusetts!!Margin of Error","Census Tract 912, Suffolk County, Massachusetts!!Estimate","Census Tract 912, Suffolk County, Massachusetts!!Margin of Error","Census Tract 913, Suffolk County, Massachusetts!!Estimate","Census Tract 913, Suffolk County, Massachusetts!!Margin of Error","Census Tract 914, Suffolk County, Massachusetts!!Estimate","Census Tract 914, Suffolk County, Massachusetts!!Margin of Error","Census Tract 915, Suffolk County, Massachusetts!!Estimate","Census Tract 915, Suffolk County, Massachusetts!!Margin of Error","Census Tract 916, Suffolk County, Massachusetts!!Estimate","Census Tract 916, Suffolk County, Massachusetts!!Margin of Error","Census Tract 917, Suffolk County, Massachusetts!!Estimate","Census Tract 917, Suffolk County, Massachusetts!!Margin of Error","Census Tract 918, Suffolk County, Massachusetts!!Estimate","Census Tract 918, Suffolk County, Massachusetts!!Margin of Error","Census Tract 919, Suffolk County, Massachusetts!!Estimate","Census Tract 919, Suffolk County, Massachusetts!!Margin of Error","Census Tract 920, Suffolk County, Massachusetts!!Estimate","Census Tract 920, Suffolk County, Massachusetts!!Margin of Error","Census Tract 921.01, Suffolk County, Massachusetts!!Estimate","Census Tract 921.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 922, Suffolk County, Massachusetts!!Estimate","Census Tract 922, Suffolk County, Massachusetts!!Margin of Error","Census Tract 923, Suffolk County, Massachusetts!!Estimate","Census Tract 923, Suffolk County, Massachusetts!!Margin of Error","Census Tract 924, Suffolk County, Massachusetts!!Estimate","Census Tract 924, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1001, Suffolk County, Massachusetts!!Estimate","Census Tract 1001, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1002, Suffolk County, Massachusetts!!Estimate","Census Tract 1002, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1003, Suffolk County, Massachusetts!!Estimate","Census Tract 1003, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1004, Suffolk County, Massachusetts!!Estimate","Census Tract 1004, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1005, Suffolk County, Massachusetts!!Estimate","Census Tract 1005, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1007, Suffolk County, Massachusetts!!Estimate","Census Tract 1007, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1008, Suffolk County, Massachusetts!!Estimate","Census Tract 1008, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1009, Suffolk County, Massachusetts!!Estimate","Census Tract 1009, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1102.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1102.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1103.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1103.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1202.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1202.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1204, Suffolk County, Massachusetts!!Estimate","Census Tract 1204, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1205, Suffolk County, Massachusetts!!Estimate","Census Tract 1205, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1206, Suffolk County, Massachusetts!!Estimate","Census Tract 1206, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1207, Suffolk County, Massachusetts!!Estimate","Census Tract 1207, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1301, Suffolk County, Massachusetts!!Estimate","Census Tract 1301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1302, Suffolk County, Massachusetts!!Estimate","Census Tract 1302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1303, Suffolk County, Massachusetts!!Estimate","Census Tract 1303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1403, Suffolk County, Massachusetts!!Estimate","Census Tract 1403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1404, Suffolk County, Massachusetts!!Estimate","Census Tract 1404, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1602, Suffolk County, Massachusetts!!Estimate","Census Tract 1602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1603, Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701, Suffolk County, Massachusetts!!Estimate","Census Tract 1701, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703, Suffolk County, Massachusetts!!Estimate","Census Tract 1703, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error" 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Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1701.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1701.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1703.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1703.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9809, Suffolk County, Massachusetts!!Estimate","Census Tract 9809, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9819, Suffolk County, Massachusetts!!Estimate","Census Tract 9819, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error" 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Massachusetts!!Estimate","Census Tract 917, Suffolk County, Massachusetts!!Margin of Error","Census Tract 918, Suffolk County, Massachusetts!!Estimate","Census Tract 918, Suffolk County, Massachusetts!!Margin of Error","Census Tract 919, Suffolk County, Massachusetts!!Estimate","Census Tract 919, Suffolk County, Massachusetts!!Margin of Error","Census Tract 920, Suffolk County, Massachusetts!!Estimate","Census Tract 920, Suffolk County, Massachusetts!!Margin of Error","Census Tract 921.01, Suffolk County, Massachusetts!!Estimate","Census Tract 921.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 922, Suffolk County, Massachusetts!!Estimate","Census Tract 922, Suffolk County, Massachusetts!!Margin of Error","Census Tract 923, Suffolk County, Massachusetts!!Estimate","Census Tract 923, Suffolk County, Massachusetts!!Margin of Error","Census Tract 924, Suffolk County, Massachusetts!!Estimate","Census Tract 924, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1001, Suffolk County, Massachusetts!!Estimate","Census Tract 1001, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1002, Suffolk County, Massachusetts!!Estimate","Census Tract 1002, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1003, Suffolk County, Massachusetts!!Estimate","Census Tract 1003, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1004, Suffolk County, Massachusetts!!Estimate","Census Tract 1004, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1005, Suffolk County, Massachusetts!!Estimate","Census Tract 1005, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1007, Suffolk County, Massachusetts!!Estimate","Census Tract 1007, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1008, Suffolk County, Massachusetts!!Estimate","Census Tract 1008, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1009, Suffolk County, Massachusetts!!Estimate","Census Tract 1009, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1102.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1102.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1103.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1103.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1202.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1202.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1204, Suffolk County, Massachusetts!!Estimate","Census Tract 1204, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1205, Suffolk County, Massachusetts!!Estimate","Census Tract 1205, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1206, Suffolk County, Massachusetts!!Estimate","Census Tract 1206, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1207, Suffolk County, Massachusetts!!Estimate","Census Tract 1207, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1301, Suffolk County, Massachusetts!!Estimate","Census Tract 1301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1302, Suffolk County, Massachusetts!!Estimate","Census Tract 1302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1303, Suffolk County, Massachusetts!!Estimate","Census Tract 1303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1403, Suffolk County, Massachusetts!!Estimate","Census Tract 1403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1404, Suffolk County, Massachusetts!!Estimate","Census Tract 1404, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1602, Suffolk County, Massachusetts!!Estimate","Census Tract 1602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1603, Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701, Suffolk County, Massachusetts!!Estimate","Census Tract 1701, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703, Suffolk County, Massachusetts!!Estimate","Census Tract 1703, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error" +"Total:","24","±26","20","±32","16","±26","0","±95","0","±95","19","±22","8","±12","9","±19","53","±60","62","±50","32","±29","57","±48","69","±66","8","±12","39","±42","144","±93","26","±30","21","±20","44","±35","0","±95","17","±20","45","±30","50","±51","110","±76","53","±59","13","±22","19","±20","0","±95","9","±21","0","±95","14","±23","16","±26","0","±95","13","±22","16","±30","28","±35","0","±95","0","±95","13","±20","39","±47","22","±24","21","±27","6","±10","15","±18","123","±119","0","±95","0","±95","43","±44","725","±387","532","±241","62","±80","273","±190","283","±114","406","±134","605","±272","706","±328","212","±145","710","±442","166","±134","0","±95","0","±95","0","±95","17","±28","0","±95","34","±33","11","±18","0","±95","20","±26","21","±26","0","±95","0","±95","62","±65","36","±44","40","±52","20","±33","52","±62","0","±95","0","±95","70","±73","21","±25","34","±80","40","±44","36","±28","37","±31","53","±91","5","±9","56","±57","74","±55","85","±102","85","±51","21","±31","42","±41","53","±54","58","±58","7","±14","64","±51","0","±95","24","±39","37","±35","40","±62","6","±12","0","±95","60","±49","33","±38","24","±38","39","±30","14","±22","74","±98","112","±97","19","±21","0","±95","36","±32","30","±39","24","±39","72","±70","42","±50","21","±34","150","±197","20","±24","35","±47","13","±21","22","±36","36","±35","34","±40","35","±45","94","±109","118","±117","38","±39","13","±20","88","±129","35","±28","0","±95","0","±95","6","±12","58","±46","46","±47","18","±21","113","±84","67","±58","27","±32","39","±47","91","±128","61","±76","74","±68","38","±42","52","±37","5","±10","16","±17","57","±42","56","±47","53","±54","50","±36","29","±32","23","±25","80","±100","97","±125","27","±33","78","±70","0","±95","33","±31","37","±38","0","±95","143","±159","15","±22","246","±160","147","±103","80","±98","1,935","±586","573","±211","142","±152","278","±182","754","±357","328","±279","502","±259","286","±225","148","±146","274","±158","149","±130","56","±59","0","±95","93","±120","257","±345","0","±95","207","±157","151","±242","69","±74","30","±26","25","±36","121","±78","0","±95","0","±95","60","±117","0","±95","0","±95","0","±95","0","±95","11","±27","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95" +"    Car, truck, or van - drove alone","9","±14","20","±32","16","±26","0","±95","0","±95","8","±12","8","±12","0","±95","0","±95","17","±27","32","±29","57","±48","29","±35","0","±95","0","±95","78","±69","5","±8","0","±95","0","±95","0","±95","0","±95","15","±2","0","±95","0","±95","0","±95","0","±95","5","±10","0","±95","9","±21","0","±95","1","±2","0","±95","0","±95","13","±22","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","12","±19","0","±95","6","±10","3","±5","11","±18","0","±95","0","±95","11","±15","100","±111","69","±69","20","±29","83","±74","83","±118","16","±26","163","±117","184","±126","46","±58","200","±160","57","±53","0","±95","0","±95","0","±95","0","±95","0","±95","3","±8","0","±95","0","±95","0","±95","6","±10","0","±95","0","±95","1","±3","0","±95","0","±95","0","±95","13","±20","0","±95","0","±95","49","±70","0","±95","0","±95","18","±29","29","±26","22","±23","0","±95","5","±9","5","±11","0","±95","14","±22","29","±30","21","±31","20","±23","27","±32","17","±26","0","±95","38","±41","0","±95","13","±21","37","±35","12","±20","0","±95","0","±95","7","±11","22","±34","0","±95","8","±13","0","±95","10","±19","56","±47","0","±95","0","±95","25","±28","25","±38","0","±95","26","±32","28","±44","0","±95","39","±46","0","±95","25","±39","0","±95","22","±36","16","±22","12","±19","0","±95","26","±35","69","±93","38","±39","0","±95","58","±83","35","±28","0","±95","0","±95","0","±95","10","±16","23","±38","11","±19","40","±41","16","±27","8","±12","0","±95","51","±67","44","±72","19","±24","38","±42","0","±95","0","±95","6","±10","8","±13","35","±37","34","±43","16","±25","14","±22","23","±25","80","±100","55","±70","12","±20","66","±68","0","±95","26","±30","30","±34","0","±95","0","±95","0","±95","115","±85","84","±91","19","±30","680","±247","142","±89","49","±56","72","±49","370","±223","66","±84","288","±157","210","±163","96","±92","71","±44","75","±78","25","±29","0","±95","19","±26","11","±18","0","±95","20","±32","151","±242","18","±31","18","±29","25","±36","52","±33","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95" +"    Car, truck, or van - carpooled","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","30","±48","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","13","±22","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","141","±95","50","±60","0","±95","36","±42","0","±95","0","±95","77","±97","102","±80","12","±18","56","±90","34","±61","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","11","±18","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","22","±34","0","±95","0","±95","0","±95","0","±95","18","±28","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","14","±21","0","±95","11","±18","0","±95","0","±95","0","±95","0","±95","20","±31","0","±95","24","±38","11","±17","0","±95","12","±24","0","±95","19","±21","0","±95","0","±95","5","±10","0","±95","13","±22","0","±95","0","±95","12","±18","14","±23","10","±21","13","±21","0","±95","4","±8","22","±35","10","±17","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","6","±12","30","±33","0","±95","0","±95","73","±81","19","±32","0","±95","29","±45","0","±95","0","±95","37","±54","0","±95","8","±14","0","±95","0","±95","10","±14","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","42","±56","0","±95","12","±20","0","±95","0","±95","7","±15","0","±95","0","±95","2","±6","0","±95","0","±95","0","±95","267","±128","163","±119","0","±95","43","±61","192","±142","102","±105","52","±80","0","±95","0","±95","25","±33","47","±81","0","±95","0","±95","0","±95","0","±95","0","±95","53","±78","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95","0","±95" 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Massachusetts!!Margin of Error","Census Tract 821, Suffolk County, Massachusetts!!Estimate","Census Tract 821, Suffolk County, Massachusetts!!Margin of Error","Census Tract 901, Suffolk County, Massachusetts!!Estimate","Census Tract 901, Suffolk County, Massachusetts!!Margin of Error","Census Tract 902, Suffolk County, Massachusetts!!Estimate","Census Tract 902, Suffolk County, Massachusetts!!Margin of Error","Census Tract 903, Suffolk County, Massachusetts!!Estimate","Census Tract 903, Suffolk County, Massachusetts!!Margin of Error","Census Tract 904, Suffolk County, Massachusetts!!Estimate","Census Tract 904, Suffolk County, Massachusetts!!Margin of Error","Census Tract 906, Suffolk County, Massachusetts!!Estimate","Census Tract 906, Suffolk County, Massachusetts!!Margin of Error","Census Tract 907, Suffolk County, Massachusetts!!Estimate","Census Tract 907, Suffolk County, Massachusetts!!Margin of Error","Census Tract 909.01, Suffolk County, Massachusetts!!Estimate","Census Tract 909.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 910.01, Suffolk County, Massachusetts!!Estimate","Census Tract 910.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 911, Suffolk County, Massachusetts!!Estimate","Census Tract 911, Suffolk County, Massachusetts!!Margin of Error","Census Tract 912, Suffolk County, Massachusetts!!Estimate","Census Tract 912, Suffolk County, Massachusetts!!Margin of Error","Census Tract 913, Suffolk County, Massachusetts!!Estimate","Census Tract 913, Suffolk County, Massachusetts!!Margin of Error","Census Tract 914, Suffolk County, Massachusetts!!Estimate","Census Tract 914, Suffolk County, Massachusetts!!Margin of Error","Census Tract 915, Suffolk County, Massachusetts!!Estimate","Census Tract 915, Suffolk County, Massachusetts!!Margin of Error","Census Tract 916, Suffolk County, Massachusetts!!Estimate","Census Tract 916, Suffolk County, Massachusetts!!Margin of Error","Census Tract 917, Suffolk County, Massachusetts!!Estimate","Census Tract 917, Suffolk County, Massachusetts!!Margin of Error","Census Tract 918, Suffolk County, Massachusetts!!Estimate","Census Tract 918, Suffolk County, Massachusetts!!Margin of Error","Census Tract 919, Suffolk County, Massachusetts!!Estimate","Census Tract 919, Suffolk County, Massachusetts!!Margin of Error","Census Tract 920, Suffolk County, Massachusetts!!Estimate","Census Tract 920, Suffolk County, Massachusetts!!Margin of Error","Census Tract 921.01, Suffolk County, Massachusetts!!Estimate","Census Tract 921.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 922, Suffolk County, Massachusetts!!Estimate","Census Tract 922, Suffolk County, Massachusetts!!Margin of Error","Census Tract 923, Suffolk County, Massachusetts!!Estimate","Census Tract 923, Suffolk County, Massachusetts!!Margin of Error","Census Tract 924, Suffolk County, Massachusetts!!Estimate","Census Tract 924, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1001, Suffolk County, Massachusetts!!Estimate","Census Tract 1001, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1002, Suffolk County, Massachusetts!!Estimate","Census Tract 1002, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1003, Suffolk County, Massachusetts!!Estimate","Census Tract 1003, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1004, Suffolk County, Massachusetts!!Estimate","Census Tract 1004, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1005, Suffolk County, Massachusetts!!Estimate","Census Tract 1005, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1007, Suffolk County, Massachusetts!!Estimate","Census Tract 1007, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1008, Suffolk County, Massachusetts!!Estimate","Census Tract 1008, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1009, Suffolk County, Massachusetts!!Estimate","Census Tract 1009, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1102.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1102.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1103.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1103.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1202.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1202.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1204, Suffolk County, Massachusetts!!Estimate","Census Tract 1204, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1205, Suffolk County, Massachusetts!!Estimate","Census Tract 1205, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1206, Suffolk County, Massachusetts!!Estimate","Census Tract 1206, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1207, Suffolk County, Massachusetts!!Estimate","Census Tract 1207, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1301, Suffolk County, Massachusetts!!Estimate","Census Tract 1301, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1302, Suffolk County, Massachusetts!!Estimate","Census Tract 1302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1303, Suffolk County, Massachusetts!!Estimate","Census Tract 1303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1403, Suffolk County, Massachusetts!!Estimate","Census Tract 1403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1404, Suffolk County, Massachusetts!!Estimate","Census Tract 1404, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1602, Suffolk County, Massachusetts!!Estimate","Census Tract 1602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1603, Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701, Suffolk County, Massachusetts!!Estimate","Census Tract 1701, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703, Suffolk County, Massachusetts!!Estimate","Census Tract 1703, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error" +"Total:","17","±27","17","±19","21","±37","0","±12","18","±21","97","±71","65","±58","47","±47","87","±72","67","±57","45","±43","34","±31","95","±67","14","±12","114","±72","224","±214","87","±65","93","±63","139","±131","16","±25","51","±44","33","±30","74","±89","43","±33","92","±59","17","±15","26","±27","0","±12","56","±38","21","±33","10","±15","19","±20","28","±35","48","±38","42","±27","4","±6","34","±33","0","±12","27","±22","20","±25","33","±32","73","±57","0","±12","6","±9","51","±57","8","±13","24","±22","22","±27","621","±296","1,256","±412","60","±58","255","±102","264","±140","542","±227","1,573","±391","922","±251","396","±214","959","±536","132","±107","6","±10","11","±11","22","±31","0","±17","24","±28","46","±43","92","±68","94","±101","8","±16","0","±12","76","±52","53","±44","97","±52","111","±121","21","±28","205","±135","28","±28","17","±24","0","±12","68","±45","84","±79","81","±80","10","±11","65","±45","44","±38","1","±3","9","±10","73","±50","46","±38","106","±77","43","±43","51","±44","194","±162","35","±30","15","±22","37","±43","95","±75","31","±47","25","±27","188","±170","129","±127","62","±46","60","±56","28","±44","29","±22","52","±37","16","±25","28","±42","88","±70","171","±92","49","±35","74","±56","215","±134","48","±56","57","±52","107","±132","16","±27","46","±41","0","±17","35","±38","15","±22","37","±61","68","±62","52","±47","11","±18","57","±55","118","±108","41","±39","23","±35","34","±46","107","±150","36","±33","0","±17","50","±50","30","±42","0","±17","116","±135","12","±14","8","±10","69","±52","157","±119","95","±69","32","±33","7","±11","82","±101","6","±9","150","±88","54","±31","102","±57","161","±108","103","±68","34","±27","42","±29","35","±46","51","±49","17","±19","64","±66","0","±17","32","±48","38","±43","36","±38","0","±12","41","±38","28","±31","41","±37","91","±96","48","±60","132","±92","1,786","±657","1,265","±384","237","±134","489","±162","935","±263","481","±278","402","±186","404","±199","184","±182","75","±94","293","±277","80","±75","25","±37","146","±113","125","±112","36","±32","53","±75","142","±165","46","±55","25","±25","0","±12","73","±77","33","±46","4","±11","0","±12","0","±12","0","±12","0","±12","0","±12","10","±17","25","±35","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12" +"    Car, truck, or van - drove alone","15","±26","7","±11","16","±35","0","±12","11","±19","31","±49","12","±14","7","±11","37","±47","27","±32","7","±11","15","±19","0","±12","9","±14","54","±42","37","±43","17","±33","15","±23","7","±12","0","±17","0","±12","0","±17","22","±29","0","±17","5","±11","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","12","±18","0","±12","10","±16","0","±12","0","±12","9","±15","0","±12","4","±6","0","±17","0","±12","7","±17","0","±12","0","±12","30","±48","8","±13","6","±9","4","±6","186","±96","235","±109","12","±14","34","±33","0","±12","55","±40","176","±106","194","±100","104","±104","87","±90","30","±49","6","±10","11","±11","0","±12","0","±17","16","±24","11","±16","24","±27","22","±35","0","±12","0","±12","17","±19","0","±17","0","±17","24","±45","21","±28","43","±68","9","±15","0","±12","0","±12","10","±15","39","±57","13","±21","0","±12","24","±29","15","±19","0","±12","0","±12","6","±9","6","±10","0","±12","8","±15","10","±15","101","±90","0","±12","12","±21","0","±12","66","±61","0","±12","25","±27","15","±25","90","±92","0","±12","22","±36","14","±23","8","±11","19","±22","16","±25","28","±42","21","±34","20","±24","15","±17","33","±36","104","±69","34","±38","16","±14","27","±36","16","±27","19","±30","0","±17","0","±12","15","±22","0","±17","34","±39","46","±43","11","±18","7","±12","56","±66","27","±33","23","±35","25","±39","66","±139","36","±33","0","±17","0","±17","0","±12","0","±17","116","±135","6","±10","3","±5","29","±33","86","±88","70","±63","11","±16","0","±12","82","±101","0","±12","17","±26","29","±20","32","±27","28","±31","0","±17","0","±12","8","±12","0","±12","24","±40","17","±19","0","±12","0","±17","32","±48","17","±26","7","±13","0","±12","23","±19","28","±31","31","±30","69","±95","28","±33","73","±69","680","±230","302","±165","68","±63","130","±76","276","±130","306","±188","191","±105","149","±81","106","±83","59","±90","236","±271","49","±60","0","±12","24","±38","10","±14","9","±11","0","±17","67","±88","46","±55","9","±14","0","±12","39","±36","33","±46","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","10","±17","4","±7","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12" +"    Car, truck, or van - carpooled","0","±12","0","±12","0","±12","0","±12","0","±12","0","±17","21","±36","0","±17","10","±16","0","±12","0","±12","0","±12","10","±15","0","±12","0","±12","0","±17","0","±17","0","±12","0","±12","0","±17","0","±12","0","±17","0","±12","0","±17","0","±17","0","±12","0","±12","0","±12","6","±11","0","±12","0","±12","0","±12","0","±12","10","±17","0","±12","0","±12","0","±12","0","±12","0","±12","0","±17","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","136","±140","175","±136","0","±12","14","±22","14","±22","105","±96","60","±55","103","±80","27","±44","72","±73","4","±9","0","±12","0","±12","0","±12","0","±17","0","±12","18","±30","0","±12","0","±12","0","±12","0","±12","10","±16","0","±17","0","±17","0","±12","0","±12","0","±17","0","±12","0","±12","0","±12","0","±12","0","±12","14","±31","0","±12","19","±25","0","±12","0","±12","3","±5","0","±12","0","±12","13","±21","0","±12","0","±12","0","±17","0","±12","3","±7","0","±12","0","±12","0","±12","0","±12","0","±17","0","±12","0","±12","20","±31","14","±21","0","±12","0","±12","0","±12","0","±12","0","±17","7","±11","0","±12","6","±11","30","±32","0","±12","0","±12","21","±35","0","±12","0","±17","0","±17","0","±12","0","±12","0","±17","0","±17","6","±10","0","±12","11","±17","43","±71","0","±17","0","±12","0","±12","0","±17","0","±12","0","±17","15","±25","0","±12","0","±17","0","±17","0","±12","5","±9","0","±12","13","±22","0","±12","8","±12","7","±11","0","±17","6","±9","0","±12","0","±12","12","±19","0","±12","0","±17","0","±12","0","±12","0","±12","0","±17","0","±17","0","±12","0","±17","0","±12","0","±17","15","±25","0","±12","0","±12","0","±12","10","±16","0","±17","0","±17","32","±40","252","±163","244","±190","56","±51","82","±47","135","±96","0","±12","87","±73","25","±31","0","±17","16","±24","40","±64","0","±17","0","±12","20","±22","29","±47","7","±11","0","±17","0","±17","0","±12","16","±27","0","±12","34","±48","0","±12","4","±11","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12","0","±12" 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Massachusetts!!Estimate","Census Tract 703.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 703.02, Suffolk County, Massachusetts!!Estimate","Census Tract 703.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 704.02, Suffolk County, Massachusetts!!Estimate","Census Tract 704.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 705.01, Suffolk County, Massachusetts!!Estimate","Census Tract 705.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 706, Suffolk County, Massachusetts!!Estimate","Census Tract 706, Suffolk County, Massachusetts!!Margin of Error","Census Tract 707, Suffolk County, Massachusetts!!Estimate","Census Tract 707, Suffolk County, Massachusetts!!Margin of Error","Census Tract 708.01, Suffolk County, Massachusetts!!Estimate","Census Tract 708.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 708.02, Suffolk County, Massachusetts!!Estimate","Census Tract 708.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 709.01, Suffolk County, Massachusetts!!Estimate","Census Tract 709.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 709.02, Suffolk County, Massachusetts!!Estimate","Census Tract 709.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 711.01, Suffolk County, Massachusetts!!Estimate","Census Tract 711.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 712.01, Suffolk County, Massachusetts!!Estimate","Census Tract 712.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 801, Suffolk County, Massachusetts!!Estimate","Census Tract 801, Suffolk County, Massachusetts!!Margin of Error","Census Tract 803, Suffolk County, Massachusetts!!Estimate","Census Tract 803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 804.01, Suffolk County, Massachusetts!!Estimate","Census Tract 804.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 805, Suffolk County, Massachusetts!!Estimate","Census Tract 805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 806.01, Suffolk County, Massachusetts!!Estimate","Census Tract 806.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 808.01, Suffolk County, Massachusetts!!Estimate","Census Tract 808.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 809, Suffolk County, Massachusetts!!Estimate","Census Tract 809, Suffolk County, Massachusetts!!Margin of Error","Census Tract 810.01, Suffolk County, Massachusetts!!Estimate","Census Tract 810.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 811.01, Suffolk County, Massachusetts!!Estimate","Census Tract 811.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 811.02, Suffolk County, Massachusetts!!Estimate","Census Tract 811.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 812, Suffolk County, Massachusetts!!Estimate","Census Tract 812, Suffolk County, Massachusetts!!Margin of Error","Census Tract 813.01, Suffolk County, Massachusetts!!Estimate","Census Tract 813.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 813.02, Suffolk County, Massachusetts!!Estimate","Census Tract 813.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 814, Suffolk County, Massachusetts!!Estimate","Census Tract 814, Suffolk County, Massachusetts!!Margin of Error","Census Tract 815, Suffolk County, Massachusetts!!Estimate","Census Tract 815, Suffolk County, Massachusetts!!Margin of Error","Census Tract 817, Suffolk County, Massachusetts!!Estimate","Census Tract 817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 818, Suffolk County, Massachusetts!!Estimate","Census Tract 818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 819, Suffolk County, Massachusetts!!Estimate","Census Tract 819, Suffolk County, Massachusetts!!Margin of Error","Census Tract 820, Suffolk County, Massachusetts!!Estimate","Census Tract 820, Suffolk County, Massachusetts!!Margin of Error","Census Tract 821, Suffolk County, Massachusetts!!Estimate","Census Tract 821, Suffolk County, Massachusetts!!Margin of Error","Census Tract 901, Suffolk County, Massachusetts!!Estimate","Census Tract 901, Suffolk County, Massachusetts!!Margin of Error","Census Tract 902, Suffolk County, Massachusetts!!Estimate","Census Tract 902, Suffolk County, Massachusetts!!Margin of Error","Census Tract 903, Suffolk County, Massachusetts!!Estimate","Census Tract 903, Suffolk County, Massachusetts!!Margin of Error","Census Tract 904, Suffolk County, Massachusetts!!Estimate","Census Tract 904, Suffolk County, Massachusetts!!Margin of Error","Census Tract 906, Suffolk County, Massachusetts!!Estimate","Census Tract 906, Suffolk County, Massachusetts!!Margin of Error","Census Tract 907, Suffolk County, Massachusetts!!Estimate","Census Tract 907, Suffolk County, Massachusetts!!Margin of Error","Census Tract 909.01, Suffolk County, Massachusetts!!Estimate","Census Tract 909.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 910.01, Suffolk County, Massachusetts!!Estimate","Census Tract 910.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 911, Suffolk County, Massachusetts!!Estimate","Census Tract 911, Suffolk County, Massachusetts!!Margin of Error","Census Tract 912, Suffolk County, Massachusetts!!Estimate","Census Tract 912, Suffolk County, Massachusetts!!Margin of Error","Census Tract 913, Suffolk County, Massachusetts!!Estimate","Census Tract 913, Suffolk County, Massachusetts!!Margin of Error","Census Tract 914, Suffolk County, Massachusetts!!Estimate","Census Tract 914, Suffolk County, Massachusetts!!Margin of Error","Census Tract 915, Suffolk County, Massachusetts!!Estimate","Census Tract 915, Suffolk County, Massachusetts!!Margin of Error","Census Tract 916, Suffolk County, Massachusetts!!Estimate","Census Tract 916, Suffolk County, Massachusetts!!Margin of Error","Census Tract 917, Suffolk County, Massachusetts!!Estimate","Census Tract 917, Suffolk County, Massachusetts!!Margin of Error","Census Tract 918, Suffolk County, Massachusetts!!Estimate","Census Tract 918, Suffolk County, Massachusetts!!Margin of Error","Census Tract 919, Suffolk County, Massachusetts!!Estimate","Census Tract 919, Suffolk County, Massachusetts!!Margin of Error","Census Tract 920, Suffolk County, Massachusetts!!Estimate","Census Tract 920, Suffolk County, Massachusetts!!Margin of Error","Census Tract 921.01, Suffolk County, Massachusetts!!Estimate","Census Tract 921.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 922, Suffolk County, Massachusetts!!Estimate","Census Tract 922, Suffolk County, Massachusetts!!Margin of Error","Census Tract 923, Suffolk County, Massachusetts!!Estimate","Census Tract 923, Suffolk County, Massachusetts!!Margin of Error","Census Tract 924, Suffolk County, Massachusetts!!Estimate","Census Tract 924, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1001, Suffolk County, Massachusetts!!Estimate","Census Tract 1001, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1002, Suffolk County, Massachusetts!!Estimate","Census Tract 1002, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1003, Suffolk County, Massachusetts!!Estimate","Census Tract 1003, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1004, Suffolk County, Massachusetts!!Estimate","Census Tract 1004, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1005, Suffolk County, Massachusetts!!Estimate","Census Tract 1005, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1006.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1006.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1007, Suffolk County, Massachusetts!!Estimate","Census Tract 1007, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1008, Suffolk County, Massachusetts!!Estimate","Census Tract 1008, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1009, Suffolk County, Massachusetts!!Estimate","Census Tract 1009, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1010.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1010.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1011.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1011.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1101.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1101.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1102.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1102.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1103.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1103.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1104.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1104.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1105.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1105.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1106.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1106.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1201.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1201.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1202.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1202.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1203.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1203.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1204, Suffolk County, Massachusetts!!Estimate","Census Tract 1204, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1205, Suffolk County, Massachusetts!!Estimate","Census Tract 1205, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1206, Suffolk County, Massachusetts!!Estimate","Census Tract 1206, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1207, Suffolk County, Massachusetts!!Estimate","Census Tract 1207, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1301.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1301.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1302, Suffolk County, Massachusetts!!Estimate","Census Tract 1302, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1303, Suffolk County, Massachusetts!!Estimate","Census Tract 1303, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1304.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1304.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.05, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.05, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.06, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.06, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1401.07, Suffolk County, Massachusetts!!Estimate","Census Tract 1401.07, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1402.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1402.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1403, Suffolk County, Massachusetts!!Estimate","Census Tract 1403, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1404, Suffolk County, Massachusetts!!Estimate","Census Tract 1404, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1601.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1601.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1602, Suffolk County, Massachusetts!!Estimate","Census Tract 1602, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1603, Suffolk County, Massachusetts!!Estimate","Census Tract 1603, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1604, Suffolk County, Massachusetts!!Estimate","Census Tract 1604, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1605.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1605.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1606.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1606.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1701.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1701.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1701.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1702, Suffolk County, Massachusetts!!Estimate","Census Tract 1702, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1703.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1703.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1703.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1704, Suffolk County, Massachusetts!!Estimate","Census Tract 1704, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.03, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.03, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1705.04, Suffolk County, Massachusetts!!Estimate","Census Tract 1705.04, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1706.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1706.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1707.02, Suffolk County, Massachusetts!!Estimate","Census Tract 1707.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1708, Suffolk County, Massachusetts!!Estimate","Census Tract 1708, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1802, Suffolk County, Massachusetts!!Estimate","Census Tract 1802, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1803.01, Suffolk County, Massachusetts!!Estimate","Census Tract 1803.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1804, Suffolk County, Massachusetts!!Estimate","Census Tract 1804, Suffolk County, Massachusetts!!Margin of Error","Census Tract 1805, Suffolk County, Massachusetts!!Estimate","Census Tract 1805, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9801.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9801.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9803, Suffolk County, Massachusetts!!Estimate","Census Tract 9803, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9807, Suffolk County, Massachusetts!!Estimate","Census Tract 9807, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9809, Suffolk County, Massachusetts!!Estimate","Census Tract 9809, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9810, Suffolk County, Massachusetts!!Estimate","Census Tract 9810, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9811, Suffolk County, Massachusetts!!Estimate","Census Tract 9811, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9812.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9812.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9813, Suffolk County, Massachusetts!!Estimate","Census Tract 9813, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.01, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9815.02, Suffolk County, Massachusetts!!Estimate","Census Tract 9815.02, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9816, Suffolk County, Massachusetts!!Estimate","Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9817, Suffolk County, Massachusetts!!Estimate","Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9818, Suffolk County, Massachusetts!!Estimate","Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9819, Suffolk County, Massachusetts!!Estimate","Census Tract 9819, Suffolk County, Massachusetts!!Margin of Error","Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate","Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error" +"Total:","29","±28","36","±44","219","±126","197","±125","95","±102","40","±34","391","±289","200","±225","99","±58","33","±27","56","±66","5","±9","119","±70","55","±54","27","±33","106","±64","51","±25","124","±51","65","±64","54","±52","94","±45","77","±77","79","±50","92","±62","78","±44","229","±183","0","±13","114","±60","202","±215","88","±45","207","±140","15","±14","44","±39","55","±71","67","±60","116","±86","174","±135","27","±26","71","±53","48","±45","216","±230","26","±30","58","±62","34","±44","10","±17","59","±51","42","±52","79","±87","0","±13","12","±17","180","±132","144","±107","36","±40","63","±59","73","±46","77","±91","1,033","±485","1,148","±363","372","±211","188","±143","299","±127","513","±312","1,265","±552","847","±394","939","±479","483","±318","440","±219","55","±40","11","±14","61","±58","26","±41","202","±150","89","±82","11","±18","34","±44","35","±42","204","±144","187","±194","49","±56","130","±99","73","±89","0","±13","28","±34","0","±13","36","±38","34","±40","14","±22","77","±43","31","±29","68","±41","59","±41","143","±127","26","±40","0","±13","77","±70","9","±11","75","±108","36","±45","108","±120","56","±52","124","±96","94","±81","82","±59","107","±131","72","±73","152","±107","53","±43","78","±64","102","±86","111","±92","0","±13","130","±72","146","±99","71","±74","217","±175","62","±48","94","±56","116","±76","107","±73","160","±140","58","±53","95","±101","104","±78","529","±284","297","±253","227","±184","145","±100","125","±96","78","±68","63","±50","177","±139","107","±88","201","±129","215","±173","194","±113","398","±416","256","±178","67","±64","480","±295","396","±284","46","±65","222","±205","153","±112","706","±639","211","±191","73","±52","172","±124","255","±149","144","±131","187","±125","14","±15","0","±13","302","±245","151","±133","251","±244","160","±104","121","±157","237","±168","35","±66","29","±47","6","±16","167","±119","86","±56","123","±89","371","±208","171","±93","201","±123","87","±65","173","±153","61","±48","104","±66","33","±25","205","±118","323","±208","240","±129","85","±46","59","±40","34","±34","91","±62","167","±109","113","±74","248","±249","133","±147","176","±179","57","±61","133","±113","43","±40","148","±72","150","±105","254","±168","301","±158","676","±450","600","±281","1,036","±462","813","±259","93","±78","477","±195","1,291","±472","780","±281","516","±477","618","±261","80","±87","614","±562","57","±63","99","±101","175","±101","299","±197","81","±76","70","±71","352","±217","373","±294","366","±202","1,287","±599","564","±389","222","±266","81","±71","33","±51","29","±26","193","±201","0","±13","10","±24","0","±13","0","±13","0","±13","0","±13","0","±13","8","±20","0","±13","0","±13","0","±13","0","±13","0","±13","0","±13","0","±13","0","±13" +"    Car, truck, or van - drove 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\ No newline at end of file From d0821fa7053e944c29d49f4f0cf32e9519a19c1d Mon Sep 17 00:00:00 2001 From: xyyy9 Date: Tue, 28 Nov 2023 20:38:00 -0500 Subject: [PATCH 03/39] Nov28 - update code for Q4 --- fa23-team/1128Q4.ipynb | 284 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 284 insertions(+) create mode 100644 fa23-team/1128Q4.ipynb diff --git a/fa23-team/1128Q4.ipynb b/fa23-team/1128Q4.ipynb new file mode 100644 index 0000000..82b1a17 --- /dev/null +++ b/fa23-team/1128Q4.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Xinyu Zhang 1128 Q4\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Year': ['2020', '2021'],\n", + " 'Predicted Disparity': array([31.44729234, 26.29339372]),\n", + " 'Actual Disparity': [33.8064428953237, 33.24782967557695]}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Data from Q1\n", + "data = {\n", + " 2013: 78.86815850290287,\n", + " 2014: 56.2361385890797,\n", + " 2015: 51.53462970291224,\n", + " 2016: 46.096841259665666,\n", + " 2017: 46.772274667615136,\n", + " 2018: 45.2665483914447,\n", + " 2019: 39.66561655259187,\n", + " 2020: 33.8064428953237,\n", + " 2021: 33.24782967557695\n", + "}\n", + "\n", + "years = np.array(list(data.keys())).reshape(-1, 1)\n", + "disparities = list(data.values())\n", + "\n", + "# Train data (2013 to 2019)\n", + "train_years = years[:-2]\n", + "train_disparities = disparities[:-2]\n", + "\n", + "# Test data (2020 and 2021)\n", + "test_years = years[-2:]\n", + "actual_disparities = disparities[-2:]\n", + "\n", + "# Create and train the model\n", + "model = LinearRegression()\n", + "model.fit(train_years, train_disparities)\n", + "\n", + "# Predict disparities for 2020 and 2021\n", + "predicted_disparities = model.predict(test_years)\n", + "\n", + "# Compare predictions with actual data\n", + "comparison = {\n", + " \"Year\": [\"2020\", \"2021\"],\n", + " \"Predicted Disparity\": predicted_disparities,\n", + " \"Actual Disparity\": actual_disparities\n", + "}\n", + "\n", + "comparison\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Extracting the years and values for plotting\n", + "years = list(data.keys())\n", + "actual_disparities = list(data.values())\n", + "\n", + "# Generate predictions for all years for a smoother curve\n", + "predicted_years = np.arange(2013, 2022).reshape(-1, 1)\n", + "lr_predicted_all_years = model.predict(predicted_years)\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(years, actual_disparities, label=\"Actual Disparity\", marker='o', color='blue')\n", + "plt.plot(predicted_years, lr_predicted_all_years, label=\"Linear Regression Predicted Disparity\", linestyle='--', color='red')\n", + "\n", + "# Adding data labels for the years 2020 and 2021\n", + "for i in range(len(test_years)):\n", + " plt.text(test_years[i], actual_disparities[-2:][i], f\"{actual_disparities[-2:][i]:.2f} hrs\", horizontalalignment='center', verticalalignment='bottom', color='blue')\n", + " plt.text(test_years[i], predicted_disparities[-2:][i], f\"{predicted_disparities[-2:][i]:.2f} hrs\", horizontalalignment='center', verticalalignment='top', color='red')\n", + "\n", + "# Adding labels and title\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Disparity in Hours\")\n", + "plt.title(\"Disparity in Travel Time Between Black and White Riders (2013-2021) with Linear Regression\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage difference in disparity between actual and predicted values for 2020: 6.98%\n", + "Percentage difference in disparity between actual and predicted values for 2021: 20.92%\n" + ] + } + ], + "source": [ + "# Calculate the percentage difference between actual and predicted values\n", + "percentage_2020 = abs((actual_disparities[-2] - predicted_disparities[-2]) / actual_disparities[-2]) * 100\n", + "percentage_2021 = abs((actual_disparities[-1] - predicted_disparities[-1]) / actual_disparities[-1]) * 100\n", + "\n", + "print(f\"Percentage difference in disparity between actual and predicted values for 2020: {percentage_2020:.2f}%\")\n", + "print(f\"Percentage difference in disparity between actual and predicted values for 2021: {percentage_2021:.2f}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'Year': ['2020', '2021'],\n", + " 'Predicted White Riders Time': array([344.784137 , 347.74828187]),\n", + " 'Predicted Black Riders Time': array([382.28133646, 382.36029788]),\n", + " 'Actual White Riders Time': [337.29556074766356, 339.5608513189448],\n", + " 'Actual Black Riders Time': [371.10200364298726, 372.80868099452175]}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Data for average travel time to work for Black and White riders from 2014 to 2021\n", + "data = {\n", + " '2014, RAC1P=1': 324.8910675381264,\n", + " '2014, RAC1P=2': 381.1272061272061,\n", + " '2015, RAC1P=1': 331.9309787626962,\n", + " '2015, RAC1P=2': 383.46560846560845,\n", + " '2016, RAC1P=1': 333.647201105736,\n", + " '2016, RAC1P=2': 379.74404236540164,\n", + " '2017, RAC1P=1': 336.20532145711996,\n", + " '2017, RAC1P=2': 382.9775961247351,\n", + " '2018, RAC1P=1': 338.7404149751917,\n", + " '2018, RAC1P=2': 384.0069633666364,\n", + " '2019, RAC1P=1': 341.0427958446251,\n", + " '2019, RAC1P=2': 380.708412397217,\n", + " '2020, RAC1P=1': 337.29556074766356,\n", + " '2020, RAC1P=2': 371.10200364298726,\n", + " '2021, RAC1P=1': 339.5608513189448,\n", + " '2021, RAC1P=2': 372.80868099452175\n", + "}\n", + "\n", + "# Separate data for White and Black riders\n", + "white_data = {int(year.split(\",\")[0]): time for year, time in data.items() if \"RAC1P=1\" in year}\n", + "black_data = {int(year.split(\",\")[0]): time for year, time in data.items() if \"RAC1P=2\" in year}\n", + "\n", + "# Prepare the dataset\n", + "years = np.array(list(white_data.keys())).reshape(-1, 1)\n", + "white_times = list(white_data.values())\n", + "black_times = list(black_data.values())\n", + "\n", + "# Train data (2014 to 2019)\n", + "train_years = years[:-2]\n", + "train_white_times = white_times[:-2]\n", + "train_black_times = black_times[:-2]\n", + "\n", + "# Test data (2020 and 2021)\n", + "test_years = years[-2:]\n", + "\n", + "# Create and train the models\n", + "white_model = LinearRegression()\n", + "black_model = LinearRegression()\n", + "white_model.fit(train_years, train_white_times)\n", + "black_model.fit(train_years, train_black_times)\n", + "\n", + "# Predict travel times for 2020 and 2021\n", + "predicted_white_times = white_model.predict(test_years)\n", + "predicted_black_times = black_model.predict(test_years)\n", + "\n", + "# Generate predictions for all years for a smoother curve\n", + "predicted_years = np.arange(2014, 2022).reshape(-1, 1)\n", + "predicted_white_all_years = white_model.predict(predicted_years)\n", + "predicted_black_all_years = black_model.predict(predicted_years)\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(years, white_times, label=\"Actual White Riders\", marker='o', color='blue')\n", + "plt.plot(years, black_times, label=\"Actual Black Riders\", marker='o', color='green')\n", + "plt.plot(predicted_years, predicted_white_all_years, label=\"Predicted White Riders\", linestyle='--', color='red')\n", + "plt.plot(predicted_years, predicted_black_all_years, label=\"Predicted Black Riders\", linestyle='--', color='purple')\n", + "\n", + "# Adding data labels for predicted times in 2020 and 2021\n", + "for i, year in enumerate(test_years.flatten()):\n", + " plt.text(year, predicted_white_times[i], f\"{predicted_white_times[i]:.2f} min\", horizontalalignment='center', color='red')\n", + " plt.text(year, predicted_black_times[i], f\"{predicted_black_times[i]:.2f} min\", horizontalalignment='center', color='purple')\n", + " plt.text(year, white_times[-2:][i], f\"{white_times[-2:][i]:.2f} min\", horizontalalignment='center', color='blue')\n", + " plt.text(year, black_times[-2:][i], f\"{black_times[-2:][i]:.2f} min\", horizontalalignment='center', color='green')\n", + "# Adding labels and title\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Average Travel Time to Work (min)\")\n", + "plt.title(\"Average Travel Time to Work for White and Black Riders (2014-2021)\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "# Show the plot\n", + "plt.show()\n", + "\n", + "# Outputting the new predictions\n", + "predictions_comparison = {\n", + " \"Year\": [\"2020\", \"2021\"],\n", + " \"Predicted White Riders Time\": predicted_white_times,\n", + " \"Predicted Black Riders Time\": predicted_black_times,\n", + " \"Actual White Riders Time\": white_times[-2:],\n", + " \"Actual Black Riders Time\": black_times[-2:]\n", + "}\n", + "\n", + "predictions_comparison" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9239c6b97154ff00f6613f35750bfc17764cdfde Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 21:01:52 -0500 Subject: [PATCH 04/39] Add files via upload --- fa23-team/q4_analisys.ipynb | 930 ++++++++++++++++++++++++++++++++++++ 1 file changed, 930 insertions(+) create mode 100644 fa23-team/q4_analisys.ipynb diff --git a/fa23-team/q4_analisys.ipynb b/fa23-team/q4_analisys.ipynb new file mode 100644 index 0000000..85570da --- /dev/null +++ b/fa23-team/q4_analisys.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3172: DtypeWarning: Columns (219,221,223,225) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + "/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3172: DtypeWarning: Columns (106,107,108) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + "/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3172: DtypeWarning: Columns (107,108,109) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + "/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3172: DtypeWarning: Columns (107,108) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + "/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3172: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n" + ] + } + ], + "source": [ + "df_2011= pd.read_csv('data/psam_p25_2011.csv')\n", + "df_2012= pd.read_csv('data/psam_p25_2012.csv')\n", + "df_2013= pd.read_csv('data/psam_p25_2013.csv')\n", + "df_2014= pd.read_csv('data/psam_p25_2014.csv')\n", + "df_2015= pd.read_csv('data/psam_p25_2015.csv')\n", + "df_2016= pd.read_csv('data/psam_p25_2016.csv')\n", + "df_2017= pd.read_csv('data/psam_p25_2017.csv')\n", + "df_2018= pd.read_csv('data/psam_p25_2018.csv')\n", + "df_2019= pd.read_csv('data/psam_p25_2019.csv')\n", + "df_2020= pd.read_csv('data/psam_p25_2020.csv')\n", + "df_2021= pd.read_csv('data/psam_p25_2021.csv')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete NAN in JWMNP\n", + "df_2013 = df_2013.dropna(subset=['JWMNP'])\n", + "df_2014 = df_2014.dropna(subset=['JWMNP'])\n", + "df_2015 = df_2015.dropna(subset=['JWMNP'])\n", + "df_2016 = df_2016.dropna(subset=['JWMNP'])\n", + "df_2017 = df_2017.dropna(subset=['JWMNP'])\n", + "df_2018 = df_2018.dropna(subset=['JWMNP'])\n", + "df_2019 = df_2019.dropna(subset=['JWMNP'])\n", + "df_2020 = df_2020.dropna(subset=['JWMNP'])\n", + "df_2011 = df_2011.dropna(subset=['JWMNP'])\n", + "df_2021 = df_2021.dropna(subset=['JWMNP'])\n", + "df_2012= df_2012.dropna(subset=['JWMNP'])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "dataframes = {\n", + " '2011': df_2011,\n", + " '2012': df_2012,\n", + " '2013': df_2013,\n", + " '2014': df_2014,\n", + " '2015': df_2015,\n", + " '2016': df_2016,\n", + " '2017': df_2017,\n", + " '2018': df_2018,\n", + "}\n", + "\n", + "average_jwmnp_values = {}\n", + "\n", + "for year, df in dataframes.items():\n", + " # filter JWTRNS=02\n", + " df_filtered = df[df['JWTR'] == 2]\n", + "\n", + " for rac1p_value in [1,2,3,4,6,7,8,9]:\n", + " condition = df_filtered['RAC1P'] == rac1p_value\n", + " average_jwmnp = df_filtered[condition]['JWMNP'].mean()\n", + " average_jwmnp_values[f\"{year}, RAC1P={rac1p_value}\"] = average_jwmnp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "dataframes_latinx = {\n", + " '2011': df_2011,\n", + " '2012': df_2012,\n", + " '2013': df_2013,\n", + " '2014': df_2014,\n", + " '2015': df_2015,\n", + " '2016': df_2016,\n", + " '2017': df_2017,\n", + " '2018': df_2018,\n", + "}\n", + "\n", + "average_jwmnp_values_latinx = {}\n", + "\n", + "for year, df in dataframes_latinx.items():\n", + " # filter JWTRNS=02\n", + " df_filtered = df[df['JWTR'] == 2]\n", + "\n", + " for latinx_value in [1]:\n", + " condition = df_filtered['HISP'] == latinx_value\n", + " average_jwmnp = df_filtered[condition]['JWMNP'].mean()\n", + " average_jwmnp_values_latinx[f\"{year}, HISP={latinx_value}\"] = average_jwmnp" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average JWMNP for 2011, RAC1P=1: 39.14512922465209\n", + "Average JWMNP for 2011, RAC1P=2: 46.8818407960199\n", + "Average JWMNP for 2011, RAC1P=3: 39.166666666666664\n", + "Average JWMNP for 2011, RAC1P=4: nan\n", + "Average JWMNP for 2011, RAC1P=6: 39.41256830601093\n", + "Average JWMNP for 2011, RAC1P=7: nan\n", + "Average JWMNP for 2011, RAC1P=8: 40.782758620689656\n", + "Average JWMNP for 2011, RAC1P=9: 40.0546875\n", + "Average JWMNP for 2012, RAC1P=1: 38.60902830910482\n", + "Average JWMNP for 2012, RAC1P=2: 45.952225841476654\n", + "Average JWMNP for 2012, RAC1P=3: 40.833333333333336\n", + "Average JWMNP for 2012, RAC1P=4: nan\n", + "Average JWMNP for 2012, RAC1P=6: 40.73809523809524\n", + "Average JWMNP for 2012, RAC1P=7: nan\n", + "Average JWMNP for 2012, RAC1P=8: 39.827118644067795\n", + "Average JWMNP for 2012, RAC1P=9: 39.52564102564103\n", + "Average JWMNP for 2013, RAC1P=1: 38.89210233592881\n", + "Average JWMNP for 2013, RAC1P=2: 45.56709956709957\n", + "Average JWMNP for 2013, RAC1P=3: 27.857142857142858\n", + "Average JWMNP for 2013, RAC1P=4: nan\n", + "Average JWMNP for 2013, RAC1P=6: 40.58795180722892\n", + "Average JWMNP for 2013, RAC1P=7: nan\n", + "Average JWMNP for 2013, RAC1P=8: 40.674193548387095\n", + "Average JWMNP for 2013, RAC1P=9: 39.547486033519554\n", + "Average JWMNP for 2014, RAC1P=1: 38.98692810457516\n", + "Average JWMNP for 2014, RAC1P=2: 45.735264735264735\n", + "Average JWMNP for 2014, RAC1P=3: 31.666666666666668\n", + "Average JWMNP for 2014, RAC1P=4: nan\n", + "Average JWMNP for 2014, RAC1P=6: 41.0\n", + "Average JWMNP for 2014, RAC1P=7: 40.0\n", + "Average JWMNP for 2014, RAC1P=8: 39.375757575757575\n", + "Average JWMNP for 2014, RAC1P=9: 40.21025641025641\n", + "Average JWMNP for 2015, RAC1P=1: 39.831717451523545\n", + "Average JWMNP for 2015, RAC1P=2: 46.01587301587302\n", + "Average JWMNP for 2015, RAC1P=3: 33.63636363636363\n", + "Average JWMNP for 2015, RAC1P=4: nan\n", + "Average JWMNP for 2015, RAC1P=6: 40.78600823045267\n", + "Average JWMNP for 2015, RAC1P=7: 32.5\n", + "Average JWMNP for 2015, RAC1P=8: 38.611111111111114\n", + "Average JWMNP for 2015, RAC1P=9: 40.37019230769231\n", + "Average JWMNP for 2016, RAC1P=1: 40.03766413268832\n", + "Average JWMNP for 2016, RAC1P=2: 45.56928508384819\n", + "Average JWMNP for 2016, RAC1P=3: 35.0\n", + "Average JWMNP for 2016, RAC1P=4: nan\n", + "Average JWMNP for 2016, RAC1P=6: 41.44465648854962\n", + "Average JWMNP for 2016, RAC1P=7: 29.0\n", + "Average JWMNP for 2016, RAC1P=8: 39.438692098092645\n", + "Average JWMNP for 2016, RAC1P=9: 40.83116883116883\n", + "Average JWMNP for 2017, RAC1P=1: 40.3446385748544\n", + "Average JWMNP for 2017, RAC1P=2: 45.95731153496821\n", + "Average JWMNP for 2017, RAC1P=3: 36.5\n", + "Average JWMNP for 2017, RAC1P=4: nan\n", + "Average JWMNP for 2017, RAC1P=6: 40.39741219963032\n", + "Average JWMNP for 2017, RAC1P=7: 29.0\n", + "Average JWMNP for 2017, RAC1P=8: 40.4475\n", + "Average JWMNP for 2017, RAC1P=9: 42.05020920502092\n", + "Average JWMNP for 2018, RAC1P=1: 40.648849797023004\n", + "Average JWMNP for 2018, RAC1P=2: 46.08083560399637\n", + "Average JWMNP for 2018, RAC1P=3: 43.888888888888886\n", + "Average JWMNP for 2018, RAC1P=4: nan\n", + "Average JWMNP for 2018, RAC1P=6: 41.51171171171171\n", + "Average JWMNP for 2018, RAC1P=7: 29.0\n", + "Average JWMNP for 2018, RAC1P=8: 39.697916666666664\n", + "Average JWMNP for 2018, RAC1P=9: 42.04761904761905\n", + "Average JWMNP for 2019, RAC1P=1: 40.925135501355015\n", + "Average JWMNP for 2019, RAC1P=2: 45.685009487666036\n", + "Average JWMNP for 2019, RAC1P=3: 47.5\n", + "Average JWMNP for 2019, RAC1P=4: nan\n", + "Average JWMNP for 2019, RAC1P=6: 41.812274368231044\n", + "Average JWMNP for 2019, RAC1P=7: 26.25\n", + "Average JWMNP for 2019, RAC1P=8: 40.45118733509235\n", + "Average JWMNP for 2019, RAC1P=9: 42.61666666666667\n", + "Average JWMNP for 2020, RAC1P=1: 40.475467289719624\n", + "Average JWMNP for 2020, RAC1P=2: 44.53224043715847\n", + "Average JWMNP for 2020, RAC1P=3: 56.666666666666664\n", + "Average JWMNP for 2020, RAC1P=4: nan\n", + "Average JWMNP for 2020, RAC1P=6: 41.554455445544555\n", + "Average JWMNP for 2020, RAC1P=7: 15.0\n", + "Average JWMNP for 2020, RAC1P=8: 40.20963172804532\n", + "Average JWMNP for 2020, RAC1P=9: 41.9\n", + "Average JWMNP for 2021, RAC1P=1: 40.74730215827338\n", + "Average JWMNP for 2021, RAC1P=2: 44.737041719342606\n", + "Average JWMNP for 2021, RAC1P=3: 48.333333333333336\n", + "Average JWMNP for 2021, RAC1P=4: nan\n", + "Average JWMNP for 2021, RAC1P=6: 40.429193899782135\n", + "Average JWMNP for 2021, RAC1P=7: nan\n", + "Average JWMNP for 2021, RAC1P=8: 39.647230320699705\n", + "Average JWMNP for 2021, RAC1P=9: 41.77814569536424\n" + ] + } + ], + "source": [ + "dataframes = {\n", + " '2019': df_2019,\n", + " '2020': df_2020,\n", + " '2021': df_2021\n", + "\n", + "}\n", + "# latinx : HISP\n", + "for year, df in dataframes.items():\n", + " # filter JWTRNS=02\n", + " df_filtered = df[df['JWTRNS'] == 2]\n", + "\n", + " for rac1p_value in [1, 2,3,4,6,7,8,9]:\n", + " condition = df_filtered['RAC1P'] == rac1p_value\n", + " average_jwmnp = df_filtered[condition]['JWMNP'].mean()\n", + " average_jwmnp_values[f\"{year}, RAC1P={rac1p_value}\"] = average_jwmnp\n", + " \n", + "for key, value in average_jwmnp_values.items():\n", + " print(f\"Average JWMNP for {key}: {value}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average JWMNP for 2011, HISP=1: 41.02212008043666\n", + "Average JWMNP for 2012, HISP=1: 40.56586826347306\n", + "Average JWMNP for 2013, HISP=1: 40.742208135235074\n", + "Average JWMNP for 2014, HISP=1: 41.000756048387096\n", + "Average JWMNP for 2015, HISP=1: 41.60057609217475\n", + "Average JWMNP for 2016, HISP=1: 41.67589576547231\n", + "Average JWMNP for 2017, HISP=1: 42.04327701920852\n", + "Average JWMNP for 2018, HISP=1: 42.17130374798804\n", + "Average JWMNP for 2019, HISP=1: 42.21195012938132\n", + "Average JWMNP for 2020, HISP=1: 41.51996805111821\n", + "Average JWMNP for 2021, HISP=1: 41.54132606721163\n" + ] + } + ], + "source": [ + "dataframes_latinx = {\n", + " '2019': df_2019,\n", + " '2020': df_2020,\n", + " '2021': df_2021\n", + "\n", + "}\n", + "# latinx : HISP\n", + "for year, df in dataframes_latinx.items():\n", + " # filter JWTRNS=02\n", + " df_filtered = df[df['JWTRNS'] == 2]\n", + "\n", + " for rac1p_value in [1]:\n", + " condition = df_filtered['HISP'] == rac1p_value\n", + " average_jwmnp = df_filtered[condition]['JWMNP'].mean()\n", + " average_jwmnp_values_latinx[f\"{year}, HISP={rac1p_value}\"] = average_jwmnp\n", + " \n", + "for key, value in average_jwmnp_values_latinx.items():\n", + " print(f\"Average JWMNP for {key}: {value}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "341.85100067030544" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_travel_time = {}\n", + "for key, average_jwmnp in average_jwmnp_values.items():\n", + " # Using the formula (Average JWMNP * 2 * 5 * 50) / 60\n", + " yearly_travel_time = (average_jwmnp * 2 * 5 * 50) / 60\n", + " total_travel_time[key] = yearly_travel_time\n", + "total_travel_time\n", + "latinx_travel_time = {}\n", + "for key, average_jwmnp in average_jwmnp_values_latinx.items():\n", + " # Using the formula (Average JWMNP * 2 * 5 * 50) / 60\n", + " yearly_travel_time = (average_jwmnp * 2 * 5 * 50) / 60\n", + " latinx_travel_time[key] = yearly_travel_time\n", + "latinx_travel_time\n", + "# add latinx_travel_time to total_travel_time\n", + "total_travel_time.update(latinx_travel_time)\n", + "total_travel_time['2011, HISP=1']\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'2011, HISP=1': 341.85100067030544,\n", + " '2012, HISP=1': 338.04890219560883,\n", + " '2013, HISP=1': 339.51840112695896,\n", + " '2014, HISP=1': 341.6729670698925,\n", + " '2015, HISP=1': 346.6714674347896,\n", + " '2016, HISP=1': 347.29913137893595,\n", + " '2017, HISP=1': 350.3606418267376,\n", + " '2018, HISP=1': 351.4275312332337,\n", + " '2019, HISP=1': 351.7662510781776,\n", + " '2020, HISP=1': 345.9997337593184,\n", + " '2021, HISP=1': 346.1777172267636}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latinx_travel_time" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculating the difference in total travel time between RAC1P=2 and RAC1P=1 for each year\n", + "travel_time_difference = {}\n", + "tavel_time_diff_Latinx_White = {}\n", + "travel_time_diff_Latinx_black = {}\n", + "travel_time_diff_Latinx_american_indian = {}\n", + "travel_time_diff_Latinx_alaska_native = {}\n", + "travel_time_diff_Latinx_asian = {}\n", + "travel_time_diff_Latinx_hawaiian = {}\n", + "travel_time_diff_Latinx_other = {}\n", + "travel_time_diff_Latinx_two_or_more = {}\n", + "\n", + "\n", + "for year in range(2011, 2022): # Looping through the years 2017 to 2021\n", + " white = f\"{year}, RAC1P=1\"\n", + " black = f\"{year}, RAC1P=2\"\n", + " american_indian = f\"{year}, RAC1P=3\"\n", + " alaska_native = f\"{year}, RAC1P=4\"\n", + " asian = f\"{year}, RAC1P=6\"\n", + " hawaiian = f\"{year}, RAC1P=7\"\n", + " other = f\"{year}, RAC1P=8\"\n", + " two_or_more = f\"{year}, RAC1P=9\"\n", + " latinx= f\"{year}, HISP=1\"\n", + "\n", + "\n", + "\n", + " # Calculate the difference if both keys exist in the dictionary\n", + " if white in total_travel_time and black in total_travel_time:\n", + " difference = total_travel_time[black] - total_travel_time[white]\n", + " travel_time_difference[year] = difference\n", + "\n", + " if latinx in total_travel_time and white in total_travel_time:\n", + " difference = total_travel_time[latinx] - total_travel_time[white]\n", + " tavel_time_diff_Latinx_White[year] = difference\n", + "\n", + " if latinx in total_travel_time and black in total_travel_time:\n", + " difference = total_travel_time[latinx] - total_travel_time[black]\n", + " travel_time_diff_Latinx_black[year] = difference\n", + "\n", + " if latinx in total_travel_time and american_indian in total_travel_time:\n", + " difference = total_travel_time[latinx] - total_travel_time[american_indian]\n", + " travel_time_diff_Latinx_american_indian[year] = difference\n", + "\n", + " if latinx in total_travel_time and alaska_native in total_travel_time:\n", + " difference = total_travel_time[latinx] - total_travel_time[alaska_native]\n", + " travel_time_diff_Latinx_alaska_native[year] = difference\n", + "\n", + " if latinx in total_travel_time and asian in total_travel_time:\n", + " difference = total_travel_time[latinx] - total_travel_time[asian]\n", + " travel_time_diff_Latinx_asian[year] = difference\n", + " \n", + " if latinx in total_travel_time and hawaiian in total_travel_time:\n", + " difference = total_travel_time[latinx] - total_travel_time[hawaiian]\n", + " travel_time_diff_Latinx_hawaiian[year] = difference\n", + "\n", + " if latinx in total_travel_time and other in total_travel_time:\n", + " difference = total_travel_time[latinx] - total_travel_time[other]\n", + " travel_time_diff_Latinx_other[year] = difference\n", + "\n", + " if latinx in total_travel_time and two_or_more in total_travel_time:\n", + " difference = total_travel_time[latinx] - total_travel_time[two_or_more]\n", + " travel_time_diff_Latinx_two_or_more[year] = difference\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-56.60006055101417" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "difference.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Extracting years and differences\n", + "years = list(travel_time_difference.keys())\n", + "differences = list(travel_time_difference.values())\n", + "\n", + "# Plotting the line graph\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Difference in Total Travel Time between Black and White Riders (2014-2021)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Travel Time Difference')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + "\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PhtZpC97FuFPJOeaT/b9/DttmQ4Fa/vB4f1/6C3m+e6PYltf68/wi1uMxbB73h62X5Rn3mD9uViHTHxS2zXsUsayksLLlPWEts+2SZ7pq5PwWi+riODEmrP406f40N0RVPsqZTiS2hPXmsI1UPWx4YQd5geMibJCUCOMa+AfqDvwfhxHKHIf3JbgB/6SbZ9kOmFzAtPFhyz+lgDId/BPBHqBl2PDwA+SDvDueX2a6P/4/eYY3IefkdncMO8JnhH1ZRRjfiJzELpYdubDPMHQgbycsMQobH7raszN8+xfnRcEJa+jq90agRYTpQndkHNA3z7gxRJGwFhFXO//z30b+K95F7uMxLCe0DqmFbJuNxHgHPc/+scY/XpLyjLvan/+iAqa9zB+/LHxf389jdN8xVALbLuK+U0i5lDzDU8O2/+0RpusVNn4DeX5Y+GU+9sfflmd4R3/91xB2Vz9PmQv8aRfEuN6h4zMbOCHC+M7kXGgYmmfcOAo5P/pl3iXClw/RJax3+GXy1sqZ5g8PJab/zjM+dJ67NM/w0B3qewpYXg1gPnmSYAI6z0fx2YXPOyWWaQuZZ2HHeFTHW0GfbUlsC7xGRBzej7WmeM9ThT6bp0tiG+RZXmHn7tSw9RkdYdra5NxB/XOecaG7oLuIcEef3OeLtGLEHdomd+QZ/oU//GD//Uj//dgCjqFnC/l8HZHPGUeGjW9TwDbLt05El7CGvkc+Ic8FtLAyT/hlHolxm4WWPzHP8Fp459d1/vh1efcFv1wKEb7L8WrDLY0077DxH1LAscz+nWdD67SbCBdC/TKhJCdfzYX9OG5CSfQTxZy+NTl32vIlgXgt/DrgtULmUT9smxZ6IYsKkrCW9XbJM921/jRZ+OePKKZZTuwJ6wR/mlejKV+iz7CG2Rj2f6HPCZWgQXh9s37kCqib7ZybRU7rV/nqZfvy1QX3peBdtV/gnIvUChrOe1D6S7yrEykFzOde539SeUzz/x6aZ/i5eHdlNuJdLS+SmXUAjse7avtsAbFuwDvxQQzNSkdpqnNuaYThoWqiNfHueJSGUB9yzzivUYpcnHMf4N0hBO9qcYlyzi3Du5peB69p/CA975wrqGuXQvn7xxd4X7A984yegnci62xmR0SYPPQs1yt59vWSOkbLi93AgxGGf453UQa8HwabIpT52P+b93j/M942n+KcW0lkU/F+/B5iRTxzWoBPXYSuGpxzP5HTWF7evhiH+X//Vch8J/t/i3M+CXWlcWJogJmFWjXdAjyMd+4IH18bryYJhD175z/3eB5eIhbp88E5t5ucdQ2PN4VgzvNlrohjvCQVd1v8A+8OXnPgabxqlm3x7lJeX8IxRnvu3gk8FGHaHeS0jhrpOxy8x5d+ijDtp+xfVzL5nmP1j40j8RKu0LPfn/p/e4WVq0XOMVRYDAWdM74BfvXflvQ+HTrnPOyc21NAmdCzucX9DTPYf656lXnP0G/HeySlMd5nPcQ5F0u3Zt3xLmiB12VHLv5xcHfe4WFK4jz7rovcvQt4tUbAu0NdUkLzirlrK/Pat3gJ73fBL0TYZng1MiBy930h4Z9RQqxxlDdBbhcz6xq2vEeccz9GM10xhfaZqPbHWPthLc9CX7h9zayw5pVDCfSB5CQuITvwrroXNv+ORcy/ftj8I/mqgOGh/rwa5hl+rP93pou+K59QrAnAr1ZwGzahHbikO8iOuI7OuT3+l0Jz8q/nfvMb8Ah9ac4spOgMvDt5kZKtaJd1EnAJ3vOyLclpwTpcq+LOv4Tk3b/zMbOjgSvw9plEck6C4XKth3NujZl9jHe3+iK855VD82tJzo/4yeRWEsdoebLc+a3yhXPOZZvZOrztWdAPh9CFhLzHQWgbDTOz8wpZdqiRggPxqu7EIq2Qcel4n+m+Y8Nv5CPUTdk7fqMpkYQa0CnO+WQW/h1LM+vonFuCV726EfCev88twOtLsrFzbj3eMVwD+D3PBbIj/eEO+L6Q81/omA2PN6jzfKkpzjFewoq1LZxzu8xsKF610LP8wVnAxc65YvcUsJ/n7h+dc9sKGFfQ+oSOpcJa4E2n4BZ8i/IJ3oWuY8yshn8x5li8Y+CzsIsFC/Gq9YaXO4ac47aw+Ar6DMFb70RKcJ/2f7Af7b990sweLaBoqOG64v6GqeW/8loO9PcvTsUi9FmvjnRxwvcFXrXyXL+/S/A8W9h35rvA34GzzGuwbCKQ7p9Pi6uJ/3djoaUi+zfeRcjdwEUucl/CVVEg28X//TYN75z4Dd6+UppC+0yTQkv5SithDT9xbSilZeQVytDr+K+iRCqz3hXc8lVo/jXxEq7izJ9IP3B9oTsyeVvLCi0rUufbBQnFWo39iHU/FLSOUPB6loRG5LR8XViH3qErwk2LsxAzm4BXLTZkD95+HroK3Ahv/WJuhbmErS1spJndAPyTnM6jQ41D7Pbf18f7Mo+0HpPxEtbBZva3sB9Fg/E+gwXOue/zTFMSx2h5UliimFVEmdD4vMdBaBsd4L+KUpxtVNixERoXfmyEX/1sFsX8Y47JObfdzL7CS6pOxHt+N3Q3Nc3/m46XxPbC+1INjc97ZygUrxH7+S+o83yp2M9jvETsz7Zwzn1nZg/iPWYE8KBfC6NYSuDcXZzvttCx9Hsh0xZ2TBYltP+Hahx8Ts5d1NBdVZxzzsw+B/6Elwx+Rk6S/Itzbnkhyyjr7/RG5CRmjaMoX9yW+Sc551JhX82MbniPH/QBnjKzAX5iH60iP2v/Qsw6IG//liV1ni3we985l25mtwG34T0/fSaAmS3C65PzSf9iYSxCvS7Esp0ws7vxLqRl4d3J/ryAoqELRIV9xuHbIvBuL/2LDwVd5DnHOfdFIdMGsl38Vog/wHs0YglwunNuZ2HTlIDQ/KM6fkurSnCoI9hfC6nKUdJC6/Kwc86ieE2MMI/Cmq8Ozf/NKOc/piRXLkahWOdHGWtqgLGWlkhXTfebmQ3A+8GThffMazJQ0znX2PkdkOM1tAM5PxKDUuD+7DeJfh9ejI8Ah+CtR6Ow9QhVmYy0Hq/jnWwSyX1nIFQdOO/dVSiZY7SyC22j66PcRmllGBNAwyhiSirmcvJWCw79Tc/zt6DxeePNjHIbpkSYtiKc5wtVAsd44MwsgdyPbhxnZsX63VLBzt1R8xOM0IWx3nn+fpqn+GcFlNufKsmlIfwzPjyaY3F/F+ic2+5fDDkNryp6X7xnSstKSZ1nC+2GxTk3Fq8F+5vxqrFvBrrgtR7/o5n9Oca4QzelGkQ7gZmNJqetm8udc1MLKR5K/gurBRI+LtYaR6UhHu+CZ6RXgd2aBbVdzOsC6328Goq/4NUuKNbjZDEK3dyM6g5/iSesfrXMfv7bvCfL0hTauG0q6PyLWm7bYkxT0lV9y7sNeM+tQeGfU6jaTaF3IAsQqqb5jHPuDufcz87lez4rmjszQRuEd/y/75y72jn3o8vfH1iB6+G8uyb/899eCPuenT4av/GcCJMFdQxVJGWxjaL5ggs/NsK/uEozrrwJaW+8q8KhPn/3JbTm9aV8TJ7pQkLx1vO/iGNRmfbR/TrGy4nxeM8ErsT7YX0CcGMx5xXUuTt0LEX7w7I4QsdGb7867bF4x863ecqFEtZefrlQn5uFVQcOQqj7JSjjY9F5d5Wu899eZ2axtLdR5Gft/0aOVAWyrM6zOOeWOefudc6dinc3uw/ePlQNeMzMornDGxJ6DjGqKuFmdj05FwKudc49V8QkoWcoDyrkYlWor1xHzjPbgXHOLY/1InNQ28XM6gLv4HVPtAovWY2lRuf+CO0zUT3/XBp3WC8npzrDS4UVLGGhakIpVkDn3yU0/65m1roU5l+QUOe9saxXKNZGZnZMoSUrEedV3Qk9M9inkKJ9/b9z8wwPJbuFXa0NJbt5fwh4E5q1pfQalCpJRa1HXXKeny5I6C7quX7Hzxf472e5yNXLSvsYrQxC2+jUUlzGiVGM23dsOK8xmtCPqQHFWF40xxV4VRmzgAPN7Ay86nVfOOf2+nGswesyqBte9zK1gDXOubxfxF/jPSNmxL4dgzrPl4b9Ocazw8oFcrfRzM7Eayk2G6+l3Wv8UXeYWfdizDKoc3foWCrsGdXCjslohBLOnngXDevinYf35in3DV5bHT3xqg/XzTN9WSn0nOC8mnmhC1XFOefsF+fcDLxnTavj3Y2PVuizbm5mnQoo05MIj+OVwHm2WJxzWX4SdQZ+P714yUu0Qs/qtiuqoJldSU5DeDc55/4dxfxD7ZHUJ6eBsLxO9v/OdgU/Y15uBbVd/N9h0/H2yfV4yWqsVcL3R5L/d1E0hUs0YTWzU8hpZXeWc+7tkpx/EV7Fq9PdEK9+foHMrDiNA3yMd5U3noJbEt6f+RdkKt4XTJHrFeKcW0ROovtPP5mIyMxq+3crKotQFYrUSC2omtnJ5FxV/m+e0aEW9BoUMv/Qw++HFTD+bipGdbKi1mM0RT9D+Q5eS9SN8U6MhVUHhtI/RiuD5/Guhh5kZiMLK7gf2+hEM8vXKqyZdSSnRdNX84ye6P+9obBEzjwN8gyO5rgK3bUPJROh/SMtT7F0vO+tW/z3+aoy+vN5zX97p5kVuB+bWTW/2mlIUOf50rA/x/jmsP8blFRA0fLv8Dzjv33QOZfunJuE9yhCDeDFYnxvBXXuDh1L5/jHWC7+sVjcBpdCQsdBPXKe0c1Xw82/qDvHLxe6APBHGf9IhejOCRP9v6lm1q2wmZXSsRg6/i/waw9FYx5etzYQocEa/+LPTYVMP9H/W5zzbJH8u7sF2U3OXe1Yjq3QM5aFJrlmNgyvH2SAO51z90Uzc+e1UhtqDPVvEebbipzfHmV5k6xEBLVd/H3hdbybO5uAk51zP8QU/P4LJdr5WiCPZL8TVjOrb2anmNnLeD9ga+N94eftFqFUOa+Vs1DDDDeZ2dPhV7j8xKyXmT2Od+Us1vnvAf6K92PyQjObFn6V18yqm1kPM/snXrccJcI5tw6vj0Lw1usRM9tXXcTMWprZKP9B+nDX4HV90Rv42MxOCFUbMLN4MzvMnyaDkm3iPGiP4NXVrw28Z2Y9YN86DwJe8ct95F9FDRc6WE+I9MPC96H/d6SZXRL6AjCzNmY2Ce8EUZzW8spaaD1ON7ObzWtwAjNramb34x1LhT5X4JzbhXfCA7gT7xm5veS/EBAqX6rHaGXgfwmN998+Zmb3mFnozhBmdoCZnWxmL5I/qYzWZuB1MzstdPfMzHrhtSBZE+84yPsZ3ot3rmgCfGFm54ffJff3/xF4dxgG5pk2dFydGukiUh6hH975uqvJ876g8SE34T0i0MmP99TQhTv/x15HMxuFd2V33w+toM7zMapvZk0KeYWqQRf7GHdeV0yhZ6SGl+K6FOQZvJpa35NzcQK8vkRX4Z1rInX1UJigzt1T8Krv1cRr/fUEf7lxZnY63jl0cyHTR+NHcqrVhZ75LeiRrE/zlAvi+dXQOeEcK7ja/rN4F95rATPM7HIzqxcaaWYtzGyImaXj9RtZ0t4CFuNdvLq5iLLAvm5rxvhvLzGz+0KJpZk1B/6DV8OroK5y9uc8G43nzew5/zf7votVZpYETMLb1juI7XG+z/HOl4lmlrchqdD8B+F9ngbc75y7Pca4/+H/HWRm/wzFbmYH490hPABvuz1dwPL3nR/JXXW5QZ5zZ76cyMwS8kwfugFUO8+0MTc2GNR2MbN4vBsLp+I1qDbAOZe3xmFRsdfPs11C265unu0S8eKHeReZQheCotvfXHSdu04kp0PiVf5rNd5B58Je2Xgn5yYFzCeFCJ0tFzUurMxyiug4He/LLTsspq14P1yywoYti3XZYWWH4yWCoXltx/vS3xu+LfJMkxRpeAzbxvB+xIZv6014V4xD7ydGmG6AXy5UZifel9ruPPNqG81+EEWcaf641P35DKOMIzSfMRHGHe1/5qH124x3Eg69nw80izBddXI6/c7G6wh+uf9K9MvUwKs2GJrXXrwfOaH3txa0HWLZz6JYfxdpGbFsY7y7UOHHbugZYIf3g3FiQds4bB798uxL70YRe3GO0aTQuBLYdgXuO9FsRyDVH55W3P28sHng/UAKdfAeemXiHcvh221mjOsd2i//L2w/305O5+TO3+cjdhSOV13yxzz7/jryfw8MyzNdE7xzpPM/4z/87ZPvOMBrwTQ0n21A9TzjW+VZ1mGFrO9ReK2vhsru9uPdlWceJ0aYtszP80V8dkl5Yi7slRY2XbGPcbwLpeHH6HL/dV1YmYjT78+2wHusyPnbv2uE6U4PW58+MWzD/Tl3p+bdthHmP4aCv4sPxju2QsvaQs5xswQYVdT8o1i/18PmvxuoXUC5U/LsL1cWMs8C948I55WotxleIz+h42sP3nG6HK8bnvByzfDuvoRizcI7DrfmWYfbY9xWoZjzfVYF7Iu7gTbRHsd4F87D97Pw4+4aCvl+oPjn2YifQ54y08Kmz8bb/7flWdbFxdj3ZvjTX1rA+IywZawq4tWzgHnckifO8N+/a4FDC4kv2nNnUiHHQFGvAo+RQuIKZLvg3cgKldlRxHK/KuIYKuoVcX8Ehvrj06PdXrHeYa1OTktXoY6VM/CuRI0G2jvnBjvvrmAgnHPj8J5xegrviyAOr07+H3itYN1IWMfZxZj/c0BnvM7Df8A7gdbDO4mmAbf740uM81yPt5NNwTu518Y74c/Fq8p0V4Tp3sW7wzDOL7cLrwrOZrw7WPcCRzrnVpRkvEFzzs3B+4EwHu8KaXW8A/lrvKoTxzjvebi80+3BS8BewNvGDfEau2qL/8yJ86pU9SfnSmi2P+8PgTOd1wJfRTEY707UQrwfDYZ3tXSYc+6yKOcxk9ytzxVUHXif0j5GKzrnPVN0FV4DMy8CK/DuztTCa8HvLby7gMWtxbIe76LOQ3gXHmvg3U17GujuCugo3Hl9nR4OXIX3uW/Ee35mL/Ad3ud5uh9z+HTr8KodvY73BdqUnOMqr0/Jeb7tC5enlXnn3O/kVLnbQMH93OKc+wrvh/Hf8c53W/HOf9vxzgUT8JLVfHdpgzjPl5L9OcbvxNt23/nThT6zBqUVrHlVL0PPct3inPsubxnnPWr0lB/TJIuyamSQ527/mOqOd5HgD7zvpFV431FHUTLd/4Xvx1+7gvtt/4LcLcmW+R1W5z22dBLwHt4P7BZ4+1ZinnJr8J7vHYJXg28tOdXYF+E9QnE+3mdaGp7H+5yqE0OflM65v+L9IJ+N97vL8D6fM5xzE4qYtljn2SjdhPf9+h7eMVAD7wLpz8BzwBHOuReKMd9n/b8XFDA+PNcoqPXcQlvR9X83nITX/c5GvO/EDLzz+KHOuQK/C8qxoLZL+HJrFbHcYnX/GIXQvvJsoaXCmJ/pioiIiIiIRM3MauH1b98ArzbaqmAjkvLMzBrjXbjbire/FFRFPpfS6odVREREREQqMed1BXQP3t3a64KNRiqAa/BqLfwz2mQVdIdVRERERESKyW9cZzH+o1TOuY0BhyTlkN8w1Aq8R0o7FPLoQj75+oISERERERGJhnNul5ml4j1z3JaK0VuClL22eM/Xfh5Lsgq6wypVWJMmTVxSUlLQYVQY27Zto27dukUXlDKlz6X80WdSPulzKX/0mcTmm2++WeecK62GcETKLd1hlSorKSmJr7/+OugwKoy0tDRSUlKCDkPy0OdS/ugzKZ/0uZQ/+kxiY2aVqlcHkWip0SUREREREREpl5SwioiIiIiISLmkhFVERERERETKJSWsIiIiIiIiUi4pYRUREREREZFySQmriEgJmzp1KldffTW9evWiXr16mBlDhw4tdJqsrCyeeeYZevfuTcOGDalduzbt27dn8ODBLF68OKrlpqamYmaFvvr165dvujVr1nDjjTdy6KGHcsABB9C4cWOOPPJI7r//frZs2VKsbSAiIiJSEtStjYhICRs3bhzz588nISGBxMREFi1aVGj5rVu3ctZZZzFjxgy6d+/OsGHDqFWrFr/99huffvopixcvplOnTkUud+DAgRTUt/ALL7xARkYGAwYMyDV8+fLlHHPMMaxZs4aUlBQGDBjAzp07+eCDD7jxxht58cUX+fLLL6ldu3bU6y8iIiJSUpSwioiUsPHjx5OYmEhycjLp6en06dOn0PIjR45kxowZPPHEE4wcOTLf+D179kS13IEDBzJw4MB8wzdt2sQ///lPatSoQWpqaq5x999/P2vWrGHMmDHcfvvt+4ZnZWVx8sknM2PGDF599VX+/Oc/RxWDiIiISElSlWARkRLWp08fOnbsiJkVWXbu3LlMnjyZwYMHR0xWAapXr75f8bzwwgvs2LGDc845hyZNmuQal5GRAcCf/vSnXMPj4+M5/fTTAVi7du1+LV9ERESkuHSHVUQkQJMnTwbgwgsvJDMzk+nTp7Ny5UoaN25M3759SU5O3u9lPP300wCMGDEi37hDDjmE9957j7fffpvDDz983/Ds7Gzeffdd4uLi6Nu3737HICIiIlIcSlhFRAL01VdfAbBixQo6dOjA+vXr940zM6688komTJhAfHx8seY/a9Ysvv/+ezp16hSxavKNN97I//73P2699VZmzpzJEUccwe7du/nggw9YtWoVzzzzTK5EVkRERKQsqUqwiEiA1qxZA8CoUaNISUlh4cKFbNmyhY8++ogOHTrw2GOPMXbs2GLP/6mnngLg8ssvjzi+WbNmfPnll5x99tnMmDGDBx54gAkTJvDTTz9x/vnn079//2IvW0RERGR/KWEVEQlQdnY2AF26dGHKlCl06dKFhIQE+vXrx9SpU4mLi+PBBx9k9+7dMc87MzOT//73vxEbWwpZvnw5vXv35vvvv+edd94hMzOTP/74g8cff5yXXnqJo446imXLlu3PKoqIiIgUmxJWEZEANWjQAIAzzzwzX7Xfbt260a5dO7Zs2cLChQtjnveLL77I9u3bIza2FJKamsr333/Pa6+9xoABA6hXrx4tWrRg5MiR3HXXXaxevZo77rgj5mWLiIiIlAQlrCIiAercuTOQk7jm1bBhQwB27NgR87xDjS0V1Prwli1bSE9Pp1GjRnTt2jXf+NAzr998803MyxYREREpCUpYRUQCFHpGdMGCBfnG7dq1iyVLlgCQlJQU03xnz57N/Pnz6dSpEykpKRHLhKoZb968OWKV41B3NjVq1Ihp2SIiIiIlRQmriEiABg0aRKtWrZgyZQpz5szJNW7s2LFkZmbSp08fWrRosW94ZmYmixYt4o8//ihwvqHGliJ1ZRPSuHFjDjroIPbu3ZuvYaedO3cybtw4APr16xfzeomIiIiUBHVrIyJSwqZNm8a0adMAWLVqFeB1LxNq+KhJkyY88MADANStW5eJEydyxhln0KtXL8455xxat27N7Nmz+eyzz2jWrBlPPvlkrvm/8cYbDB8+nGHDhkVsTGnz5s1MmTKFmjVrMmzYsEJjnTBhAqeffjrjxo3jww8/pGfPnuzYsYN3332XFStWkJyczN///vf92yAiIiIixaSEVUSkhM2bN49JkyblGpaRkUFGRgYAbdu23ZewApx00knMmTOHsWPH8tFHH5GZmUmLFi244ooruPXWW2nVqlVMy3/ppZfYtm0bF1xwQYGNLYX079+fr776ivvvv5/09HQeeeQR4uPjad++PTfffDM33nhjgc/XioiIiJQ2c84FHYNIIHr06OG+/vrroMOoMNLS0gp8FlKCo8+l/NFnUj7pcyl/9JnExsy+cc71CDoOkbKmZ1hFRCqiZS/BtCRO/L0vTEvy3ouIiIhUMqoSLCJS0Sx7CeaMgKztGMD2Fd57gHZDgoxMREREpETpDquISEUzfzRkbc89LGu7N1xERESkElHCKiJS0Wz/JbbhIiIiIhWUElYRkYqmRoPIw+u0KdMwREREREqbElYRkYpk3RzYnQkWn3t4fB3odlcwMYmIiIiUEiWsIiIVxa718Nl5UPdA6PEY1GnLvo7JDhykBpdERESk0lHCKiJSEbhs+OJi2LkKTpgKHUfAwOWkt5wBzXrDqg9h746goxQREREpUUpYRSq4qVOncvXVV9OrVy/q1auHmTF06NBCp8nKyuKZZ56hd+/eNGzYkNq1a9O+fXsGDx7M4sWLo1ruypUrueqqqzjmmGNo0aIFNWvWpFWrVvTq1YvnnnuOPXv25Jvm119/5a677uK8884jOTmZuLg4zIylS5cWa92rlB/vhT/ehSMfgsZh/cabwWF3eonskscDC09ERESkNKgfVpEKbty4ccyfP5+EhAQSExNZtGhRoeW3bt3KWWedxYwZM+jevTvDhg2jVq1a/Pbbb3z66acsXryYTp06Fbncn3/+mZdeeoljjjmGgQMH0qhRI9avX8+7777LJZdcwgsvvMAHH3xAtWo5p5mvv/6aW265BTOjXbt21K9fn02bNu3vJqj8Vs2A726FthdB8hX5xzc/EZr385La5BFQPaHsYxQREREpBUpYRSq48ePHk5iYSHJyMunp6fTp06fQ8iNHjmTGjBk88cQTjBw5Mt/4SHdGI+nZsycbN24kLi53RY09e/Zw8sknM3PmTF5//XXOP//8feN69OjBJ598Qrdu3ahXrx4pKSmkp6dHtbwqa/vv8MWFcEBnOPpJ745qJF3Hwoc9YfEjcMhNZRujiIiISClRlWCRCq5Pnz507NgRKyiRCTN37lwmT57M4MGDIyarANWrV49quTVq1MiXrIamHzhwIABLlizJNS4xMXFf1WWJQvYe+Hww7NkKvaYWfue06XHQcgAsvB/2bC67GEVERERKke6wilQhkydPBuDCCy8kMzOT6dOns3LlSho3bkzfvn1JTk7e72VkZWXxzjvvANC1a9f9nl+VNn80rP0Mer4E9Q8uunzXO+H9o2DRQ3DYbaUenoiIiEhpU8IqUoV89dVXAKxYsYIOHTqwfv36fePMjCuvvJIJEyYQHx9f0CzyWbduHY888gjOOdauXcuHH37I0qVLueiiizjzzDNLfB2qjF/f9O6WdrwSki6KbprGPSDxLFj0IHS+Gmo0LN0YRUREREqZqgSLVCFr1qwBYNSoUaSkpLBw4UK2bNnCRx99RIcOHXjssccYO3ZsTPNct24dd9xxB3feeSePP/44P//8MzfccAMTJ04shTWoIrZmwKxh0KgHHDE+tmkPuwP2ZMLCB0snNhEREZEypIRVpArJzs4GoEuXLkyZMoUuXbqQkJBAv379mDp1KnFxcTz44IPs3r076nl26dIF5xx79+5lxYoVjB8/nqeeeorevXuzYcOG0lqVyitrJ3x6LmBwwn8hvmZs0zfsBgeeCz89BDvXlUaEIiIiImVGCatIFdKgQQMAzjzzzHzVfrt160a7du3YsmULCxcujHne8fHxtGnThmuvvZYnn3ySL7/8kttu03OUMfvmWtj4LRz3PCS0K948DhsDe7d5VYpFREREKjAlrCJVSOfOnYGcxDWvhg29Zx537NixX8sZMGAAAGlpafs1nypn2Quw9Ck4+CZI3I/nfxscAm0v9Lq42bG65OITERERKWNKWEWqkP79+wOwYMGCfON27dq1rxuapKSk/VrOb7/9BkC1amrXLWqbFsCcK6DZiV6fqvvrsNsheyf8eO/+z0tEREQkIEpYRaqQQYMG0apVK6ZMmcKcOXNyjRs7diyZmZn06dOHFi1a7BuemZnJokWLcrUoDF6frllZWfmWsXXrVq699loATj/99FJYi0pozxb47FyofgAc/zLElUCiX68TtPszLHkctv+2//MTERERCYBuf4hUcNOmTWPatGkArFq1CoBZs2aRmpoKQJMmTXjggQcAqFu3LhMnTuSMM86gV69enHPOObRu3ZrZs2fz2Wef0axZM5588slc83/jjTcYPnw4p5xyCoMGDdo3/M477+Tzzz+nZ8+etGnThjp16rBy5UreffddNm3aRM+ePbn55pvzxRuKC2DRokUA/P3vf+eAAw4A4LLLLuOEE04okW1TITgHsy+DLUug78dQu2XJzfvQ22DZi/DDPXDUIyU3XxEREZEyooRVpIKbN28ekyZNyjUsIyODjIwMANq2bbsvYQU46aSTmDNnDmPHjuWjjz4iMzOTFi1acMUVV3DrrbfSqlWrqJZ7+eWXk5CQwJw5c0hLS2P79u00bNiQI488kvPPP59LLrkkYpXgvLECvP766/v+T0lJqVoJ6+JH4Zf/Qrd7oHlKyc47oR10uAR+fhoOvhHqtinZ+YuIiIiUMnPOBR2DSCB69Ojhvv7666DDqDDS0tJISUkJOozKZd0c+OgEaHEKnPgmWOxPaRT5uWz7BaZ3hHbD4Jinih+rRE3HSvmkz6X80WcSGzP7xjnXI+g4RMqanmEVkcItewmmJXHi731hWpL3XvbfrvXw2XlQuzUcN6lYyWpU6raBDpdDxnOwNaN0liEiIiJSSpSwikjBlr0Ec0bA9hUYDrav8N4rad0/Lhu+uBh2roITXoWajUp3eYf8w2vI6fs7S3c5IiIiIiVMCauIFGz+aMjanntY1nZvuBTfD/fAH+/CkQ9B4zKo3VWnFSRfCctfgM0/lf7yREREREqIElYRKdj2X2IbLkVbNQO+vw3aXgTJV5Tdcg+5CeJq6S6riIiIVChKWEUkv6zdsOAuoIBG2Wo2LdNwKo3tv8MXF8IBneHoJ8Gs7JZdqxl0vhpWvAybfii75YqIiIjsByWsIpLbutnw3pHw3S3Q6GiIr52ngMGutbDkyYiTSwGy98Dng2HPVug1FaonlH0MB/0NqtWF78eU/bJFREREikEJq4h49myBr6+BD46DPZug91tw6mw4+mmo0xaHQZ223vtWA+CrK2Du/0F2VtCRVwzzR8Paz+CYp6H+wcHEULMxdL4OVk6FjfOCiUFEREQkBkpYRQR+exvePgQWPwIdr4LTf4DEM71x7YbAwOWkt5oBA5dD8qXQ+03odDUsehA+GwR7twUafrn365uw8H7oeCUkXRRsLAeNgur14bvbg41DREREJApKWEWqsh2r4bMLIP0MqH4AnPQ5HPUIVK9X+HRx1aDHBDhyAvw2HT7sDdt/K5uYK5qtGTBrGDTqAUeMDzoaqNEQuvwf/PYWrP8q6GhERERECqWEVaQqcg5+fg7ePgh+fQMOuxNO/RaaHhfbfDpfDb2nw5bF8P4xqmaaV9ZO+PRcsDivv9X4mkFH5OlyLdRoBN/dFnQkIiIiIoVSwipS1WxZCjP6w+xLoP4hMGA+HHYrxNco3vxan+bdmbU4+PAE+O1/JRtvRfbNtbDxWzjueUhICjqaHNXrwcE3wh/vwdovgo5GREREpEBKWEWqiuw98ON98M5hsOFrOOoJ6J8O9bvs/7wbdoVTZkO9LvDJWbDoYe8ublW27AVY+hQcfBO0PiPoaPLr9FevqxvdZRUREZFyTAmrBM7MzjWzf5vZp2a22cycmb1YxDTxZnaZmX1iZhvNbIeZZZjZFDPrVFaxVxjrv4b3joJ5N0HLAXD6j9BxpHdXtKTUbuklwK3PgrnXwdd/hey9JTf/imTTAphzBTQ7EbqODTqayKrV9ZLp1R/D6vSgoxERERGJSAmrlAe3AH8FugNFttxjZgnAB8DTwAHAJOBh4HPgGEAJa8jebfDNKPjgGNi1Bnq9Dr1fhzqtS2d51ep6fYwedCMseQzSz4Q9m0tnWeXVni3w2bletdvjX/EaqCqvkq/wLjR8d6vuiIuIiEi5VI5/SUkVcj3wK7AUOBGYWUT5J4G+wBXOuSfzjjSz6iUeYUX0+/teX6nblkPySOh+L9RoUPrLtTg4/D44IBm+ugo+OB5S/gd125b+soPmHMy+DLYsgb4fQ+0WQUdUuGq14eB/wDdXw6qPoOVJQUckIiIikovusErgnHMznXNLnCv6Fo+ZHQFcBEyJlKz689tT0jFWKDvXwhdDIe1UiK8F/T+Bo58om2Q1XPLl0Odd2L7Sa0F43ZyyXX4QFj8Kv/wXut4FzVOCjiY6yZdDnQN1l1VERETKJSWsUtFc5P992czqm9lQM7vZzEaYWXKgkQXNOa+hn7cP8pKmQ2+DAfOgWa/gYmrRH06eBfF14OMT4ZepwcVS2tbNgW9HQaszvBZ4K4r4mnDoLbB+Nvz+btDRiIiIiOSihFUqmqP8v22Bn4EXgLvxqgkvNrNHzSy+qJmY2bm//PILvXr1ol69epgZQ4cOjVh2+fLlmFmBrwsuuCDmlcjKyuKZZ56hd+/eNGzYkNq1a9O+fXsGDx7M4sWLC512165dHHrooZgZiYmJ3sCty2DmKTDrz3BAJ69P1a53lI9+P+sf5LUg3PAI+Ow8r6XiynYnb9d6b91qt4bjJpVsY1Zlof1wqNsOvr+t8n02IiIiUqHpGVapaJr5fx8EpuE12PQrXmNLTwBXAWuBMZEmNrMRwAjg4LVr17J161aaNm3Kli1bWL16NWlpafmmWbVqFQAdOnTghBNOyDe+Xbt2EacryI4dOxg9ejTffvstycnJ9OvXjxo1arBu3TrS09N57bXXOO644wqc/rHHHiMjIwPwktef/3clSVuewxFHRv1r+L36WTBvLRB9TNHYunVrTOuZV1z12+lc+z6az7uJPxans7j+9bjK8Lixy+awDf+g4a4/+LbJv9ky67syXfz+fi4hLaqfT5cN97HgvbtYVzv/fi7RK6nPREqWPpfyR5+JiETFOaeXXuXmBaQADnixgPE/+eMXAPF5xnUDsoDNQI0iltPnkEMOcdnZ2W7mzJkOcEOGDHGRLFu2zAFu2LBhEcfH6qKLLnKAe+KJJyKO3717d4HTzpw505mZe/zxxx3gWjeu7txLOJd2pnNbfymR+Apb9n7LznZu/m1ezB/1cW7Xhv2fZ9C+H+etz+LHAll8iXwuzjmXtce5tzo69/ZhzmVnlcw8q6gS+0ykROlzKX/0mcQG+NqVg99qeulV1q8KVm9NhE3+3+nOuazwEc65+cAyvK5uDipsJs65mbVq1cLMSiXIgsydO5fJkyczePBgRo4cGbFM9eqR7zpu3ryZ1NRU+vVN4YpjvDusuCw44b/Q+02oe2BphV1yzLyqyse9AGs/hw+Ogy0/Bx1V8a2a4VWjbXuR10VMRRZXDQ69HTZ9X7mfNRYREZEKRVWCpaL5CTianMQ1r43+39olveDff/+dJ598kvXr19O4cWOOO+44unbtGtM8Jk+eDMCFF15IZmYm06dPZ+XKlTRu3Ji+ffuSnFxwu1HXXHMNGzes5dkLHSz0e/6p3RzanFfsdQpMu6FeNzefnu31EdtrGjSrYNVQt/8OX1wIB3SGo5/0kvGKru0F8OPd8P0YOHAQxBX5OLiIiIhIqVLCKhXNR8DFwKF5R5hZTaCj/3Z5SS/4ww8/5MMPP8w1LCUlhUmTJtGmTZuo5vHVV18BsGLFCjp06MD69ev3jTMzrrzySiZMmEB8fO5E4Y3/Ps+kSZN45nJo06wmHD0T6EOFbjetWS84+UtIOx1m9INj/gPthgQdVXSy98Dng2HvNuiXBtUTgo6oZMTFw2Fj4LPzYcXL3oUFERERkQBV4F+7UkW9BvwODDazo/OMuxWoD8x0zq0qqQXWqVOHW2+9lW+++YaNGzeyceNG0tPT6dOnD2lpafTr149t27ZFNa81a9YAMGrUKFJSUli4cCFbtmzho48+okOHDjz22GOMHTs2ZwLnWP3V44y4PJUB3YxL//IPGDC/4vTxWZQDkr1ub5r0hFlD4bsxFaOV2vmjYe1ncPRTXivIlcmBg6BBV/j+DsjeG3Q0IiIiUsUpYZXAmdlAM5toZhOBm/zBx4WGmdkDobLOuW1AKl7DS5+a2ctm9oCZfQqMBtYAkR8OLaZmzZpx5513csQRR9CgQQMaNGhA7969+eCDDzjmmGNYunQpzzzzTFTzys7OBqBLly5MmTKFLl26kJCQQL9+/Zg6dSpxcXE8+OCD7N69G7atgLTTufyKq9ibHcczkz+AbndBtRKv7Rysmo2gz/vQPhUW3AFfDIWsnUFHVbBf34SF90PHKyHpoqLLVzQWB13vhK1LvX59RURERAKkhFXKg+7AMP91ij+sfdiwc8MLO+c+xHuOdTrQH7gGr1/WJ4DDnXNLyiLoatWqcdlllwHwySefRDVNgwYNADjzzDPzVfvt1q0b7dq1Y8uWLSx8ZzS8fQjPT/2Y6XPh4X8/Q6uD+5do/OVKfA2vSnC3e2DFZJjRH3auDTqq/LZmwKxh0KgHHDE+6GhKT+s/QaMjYcGdkLU76GhERESkClPCKoFzzo1xzlkhr6QI08x3zp3rnGvqnKvhnGvjnLvSOfd7WcbetGlTgKirBHfu3BnISVzzalivFgA75j8ATXszF++ZzmHDh2NmuV4Av/322773mzZt2o81KQfM4JCbvFaPN3wDHxwLmYuCjipH1k749FzvDuQJr0J8zaAjKj1mcNidsG05ZDwXdDQiIiJShanRJZH98OWXXwLQvn37qMr379+fF154gQULFuQesXcHu+bezpKffgAg6bTHoMcVHLfmv2zdFfm60rPPPkudOnW48MILAahZs5IkUG3Ogzpt4JM/ed3e9HoNWvQNOir45lrY+C2cOB0SkoKOpvS1GgCNj4UfxkH7YRBfK+iIREREpArSHVaRIsydO3ffs6fhPv74Y8aP96qFDh2auzXVzMxMFi1axB9//JFr+KBBg2jVqhVTpkxhzpw53sDVM+Gdroy9+34yt0OfE0+gxVFXghmDBw/mmWeeifgCaNiw4b73tWtX/Gdbp06dytVXX02vs2+gXup27PxNDD23P/z8bL6yy5cvz3fXOfx1wQUX7Fcsl1122b55LZ1xPyx9Cg6+CVqfgXOO9957j6uvvpru3bvTsGFDatWqRefOnbnuuutYvXr1fi27XDCDbmNh+6+w9OmgoxEREZEqSndYpUoys4GNGzcmNTWVVau8BoVnzZpFamoqAE2aNOGBB7y2nkaNGsWSJUvo2bMniYmJAHz33XfMmDEDgLFjx9KzZ89c83/jjTcYPnw4w4YNY+LEifuG161bl4kTJ3LGGWfQq1cvzjkxkdbVM5i9vBaf/eg18PTk0/8p5bUvv8aNG8f8+fNJSEggMTGRRYsWQa0WMPsy2LIEut3tVckN061bNwYOHJhvXocemq/no6hNnz6dZ599loSEBLZu3Qrf3wqHnAhdvRacd+3axYABA6hRowa9e/emf//+ZGVlMWPGDB5++GFeeeUVPv30Uzp27FjEksq55v2gWW/44W7ocFnla/BLREREyj0lrFJVdV+/fj2TJk3aNyAjI4OMjAwA2rZtuy9hvfjii3njjTf46quvePfdd9mzZw/Nmzfn/PPP569//Su9evWKacEn9e/PnGljGXvnbXz0ZQaZO+Jo0bIJV1xxBrfeeiutWrUqubWsYMaPH09iYiLJycn7ug6ieQp0bAA/3gdblsJxz0O1Ovum6d69O2PGjCmxGNauXcvll1/O4MGDWfXHb6R/8hlUOwCOfwXivFNmfHw848aN46qrrqJhw4b7ps3Ozuaqq67iySefZNSoUUyfPr3E4gqEmZekf3QiLHkcDhoVdEQiIiJSxShhlSrJOTemR48et3/99ddFlr300ku59NJLY5p/amrqvru1uWxbCV//hW4bpzP1tiPgmGeg0eExzTvEVYT+SmPUp0+f/AMtDno8Cgd0grmjYNsvcOKbpRbDiBEjAHj0kUcYdNLB3sAjH4LaLfaVqV69OqNHj843bVxcHLfddhtPPvkkaWlppRZjmWrWG1r0hx/vheQRUD0h6IhERESkClHCKlJalr0E80fD9l+gzoHQLAV+fR1cFhz+AHS+dt8dOymCGXS5DhLawxcXwfvHQPunAPj999958sknWb9+PY0bN+a4446ja9euxVrMxIkTmTZtGtOmTaPx+ldgl9+1TpNjop5H9erVAa/bo0rjsDvhw56w+BGvJWcRERGRMlKJflGJlCPLXoI5IyBru/d++y+w/HmodyikvOklXhK7xD9B/08h/Qz4xOue98MPP+TDDz/MVSwlJYVJkybRpk2bqGe9YsUKrr32WoYOHcpZx7eAj86DGo2ADTGF+J//eM8gn3rqqTFNV641PQ5aDoCF90Onq6B6vaAjEhERkSpCrQSLlIb5o3OS1XB7NytZ3V+NDodT5lCncTtuPRu+ef3vbNy4kY0bN+577jUtLY1+/fpF3T9udnY2w4YNIyEhgQn3j4HPzofaraHeQTGF9tVXX3HHHXdwwAEHMG7cuGKsXDnW9U7YvQEWPRR0JCIiIlKFKGEVKQ3bfylg+MqyjaOyqtOaZufO4s5r/8QRO+6jwdLbaVDvAHr37s0HH3zAMcccw9KlS/d1/1OU8ePHk56eztNPPUnDhVfDzlVwwqsxVdlevHgxZ555Jnv27OHFF1+kQ4cOxV278qlxD0g8CxY9CLs3Bh2NiIiIVBFKWEVKQ50CqqIWNFxiVz0Ber0OXUbB4gnwyVmwZwvVqlXjsssuA+CTTz4pcjaLFy9m9OjRDB8+nNPazoc/3vUaWWrcI+pQFi9eTJ8+fdiwYQOvvPIKf/rTn4q7VuXbYXfCnkxY+GDQkYiIiEgVoYRVpDR0uwvi6+QeFl/HGy4lJy4ejvgXHPUY/PEefNgLtv9K06ZNAaKqEvzjjz+ya9cunnvuOeywW7AhYJ2uwsxIT08HoGPHjpgZ06ZNyzf9woULSUlJYd26dbz66qsMGjSoRFexXGnYFdqcBz89BDvXBR2NiIiIVAFqdEmkNLQb4v3d10pwGy9ZDQ2XktXxSqjbHj4/H94/mi/TTwGgffuinxdOSkri0mEXwq9vQFxNr9prnNfS79tvv82qVas477zzqFevHklJSbmm/f777+nfvz+ZmZm8/vrrnH766SW+auXOYWPgl6leA0yH3xd0NCIiIlLJKWEVKS3thihBLUVz586le/fuxMX5FUVanQInfc7HE/ox/rGJAAwdOjTXNJmZmfzxxx/Ur1+fli1bAtC96yE8M2QlbIyHU2ZB/ZyGllJSUli1ahV33303ycnJueY1b948+vfvz/bt23nzzTc55ZRTSm9ly5P6B0PSRV4XN11GQe3mQUckIiIilZgSVhEpN0J9oAKsWrUKgFmzZpGamgpAkyZNeOCBBwAYNWoUS5YsoWfPniQmJgLw3XffMWPGGgDGngs9G84Cd5zXjyvwxhtvMHz4cIYNG8bEiRO9hc4fDWs/g54v5UpWC7Nx40b69evHhg0b6NevH7NmzWLWrFn5yl133XU0aNCgGFuinDv0NljxMvx4Lxw5PuhoREREpBJTwioi5ca8efOYNGlSrmEZGRlkZGQA0LZt230J68UXX8wbb7zBV199xbvvvsuePXto3rw5559/Pn+98nJ6VXsKvr0BtiyGHo/sq+aby8ppXtXWjld6dw2jlJmZyYYNXv+sH3/8MR9//HHEcqmpqZUzYa3XCdr9GZY8DgfdAHVaBx2RiIiIVFJKWEWk3BgzZgxjxoyJquyll17KpZdeWnAB1xcO6Ag/3A1bM+CEV0lNTd13t5atGfBlKjTqAUdEvkuYlpYWcXhSUhLOuajirLQOvQ2WvQg/3ANHPRJ0NCIiIlJJKWEVkcrJ4ryGrg7oCHNGwIfHQ4fLYdFDXkNYcdXAqnv9rcbXDDraiiehHXS4BH5+Gg6+EeqqyyYREREpeerWRkQqt/ap0OcD2Loc5l4P21cADrL3gMuCtZ8HHGAFdsgt3t8F44KNQ0RERCotJawiUvk1T4EaDfIPz97lNbokxVP3QEgeARnPeVWsRUREREqYElYRqRp2/BF5+PZfyjaOyubgm73q1d/fGXQkIiIiUgkpYRWRqqFOAc9YFjRcolOnFSRfCctfgM0/BR2NiIiIVDJKWEWkauh2F8TXyT0svo43XPbPITdBXC3dZRUREZESp4RVRKqGdkPg6KegTlvAvL9HP+UNl/1Tqxl0vhpWvAybfgg6GhEREalE1K2NiFQd7YYoQS0tB/0NFj8G34+BXq8GHY2IiIhUErrDKiIi+69mY+hyHaycChvnBR2NiIiIVBJKWEVEpGR0GQXV68N3twcdiYiIiFQSSlhFRKRk1GgAXf4PfnsL1n8VdDQiIiJSCShhFRGRktPlWqjRCL67LehIREREpBJQwioiIiWnej04+Eb44z1Y+0XQ0YiIiEgFp4RVRERKVqe/el3d6C6riIiI7CclrCIiUrKq1YWDb4LVH8Pq9KCjERERkQpMCauIiJS85Cugdiv47lZwLuhoREREpIJSwioiIiWvWm045B+w9lNY9VHQ0YiIiEgFpYRVRERKR4fLoM6BussqIiIixaaEVURECjR16lSuvvpqevXqRb169TAzhg4dGt3E8TW5bHIb7NTZWFwcS5cujXq5KSkpmFmhr0svvbTQeaxbt46WLVtiZpxwwglRL1tERETKj2pBByAiIuXXuHHjmD9/PgkJCSQmJrJo0aKop50+fTrPvvo5CbWMrTtdTHdZU1NTSUlJiTju3//+Nxs2bGDAgAGFzmPkyJFs3bo16mWKiIhI+aOEVURECjR+/HgSExNJTk4mPT2dPn36RDXd2rVrufzyyxk8eDCrls8jffZP3rOsHTtGNX1qamrE4T/99BN33HEHzZs356yzzipw+ueff57XX3+dxx57jKuuuiqqZYqIiEj5oyrBIiJSoD59+tCxY0fMLKbpRowYAcCjjz4KtZp7A396CFz2fsXz1FNPATB8+HCqV68escwvv/zCNddcw6WXXlrkXVgREREp35SwiohIiZo4cSLTpk3jySefpHHjxoCf7G5eDL9MLfZ8d+3axfPPP4+Zcfnll0cs45wjNTWV+vXr8+CDDxZ7WSIiIlI+KGEVEZESs2LFCq699lqGDh2av8ruAcnw/RjIzirWvF9//XXWrVtH//79ad++fcQyDz30EGlpaTz77LPUq1evWMsRERGR8kMJq4iIlIjs7GyGDRtGQkICEyZMyF+g8zWweSGseLlY8w9VBw5VN87rxx9/5B//+AdXXHEF/fv3L9YyREREpHxRwioiIiVi/PjxpKen8/TTT9OwYcP8BVqeAg26wvd3QPbemOa9ZMkS0tLSCmxsac+ePVx88cW0bNmSf/7zn8VdBRERESlnlLCKiMh+W7x4MaNHj2b48OGcdtppkQtZHHS9E7YuhWUvxDT/ohpbuueee/j222957rnnSEhIiDl+ERERKZ+UsIqIyH778ccf2bVrF8899xxmluuVnp4O4LU2fOBApi3qAAvuhKzdUc179+7dTJo0qdDGlubOnYtzjpSUlFzLbteuHQCff/45ZkaDBg1KZH1FRESkbKgfVhER2W9JSUlceumlEce9/fbbrFq1ivPOO4969eqRdEJ3WHc1ZDwHHUcWOe833niDtWvXctJJJxXY2NJJJ51EkyZN8g3funUrU6ZMoXnz5pxxxhnUqVMnpvUSERGRYClhFRGR/da9e3eeeeaZiONSUlJYtWoVd999N8nJyeAcfDgZfhjHunqns27jVpo0aRIx4YSc6sAjRxac3P7lL3+JOHz58uVMmTKF5OTkAuMTERGR8ksJq4iIFGjatGlMmzYNgFWrVgEwa9YsUlNTAWjSpAkPPPBAbDM1855lnXESj4y9nDsefY/bb7+dMWPG5Cu6dOlSZs6cSfPmzfnTn/60H2siIiIiFZESVhERKdC8efOYNGlSrmEZGRlkZGQA0LZt29gTVoDm/aBZb1j7aaHFnn76aZxzBTa2JCIiIpWbGl0SEZECjRkzBudcga/ly5cXOY+0tDScc1514BAz6DqWMWdtw/34r4h3VwHuu+8+nHPcc889xYo/KSkJ5xyfffZZsaYXERGRYClhFRGRYDTrDfUOgW//BpPjYFoSLHsp6KhERESkHFGVYBERCcayl2Drz0C29377Cpgzwvu/3ZDAwhIREZHyQ3dYRUQkGPNHQ/bO3MOytnvDRURERFDCKiIiQdn+S2zDRUREpMpRwioiIsGo06aA4YllG4eIiIiUW0pYRUQkGN3ugvg6+YdXqwdZO/MPFxERkSpHCauIiASj3RA4+imo0xYw72+HEbD5B/j0PMjaHXSEIiIiEjC1EiwiIsFpNyR/i8CNDoevroRZQ6HnZIjTV5WIiEhVpV8BIiJSvnS8AvZuh2//D+Jrw7HPgalCkIiISFWkhFVERMqfg0bB3m3w/W3ec65HPQZmQUclIiIiZUwJq4iIlE+H3uL1y/rjvd6d1iP+paRVRESkilHCKiIi5ZMZdLvbqx7803ioVhe6jQ06KhERESlDSlhFRKT8MoMjx3t3Wn8Y5yWth9wUdFQiIiJSRpSwiohI+WZxcNQT3p3W+TdDtTrQ+ZqgoxIREZEyoGYXRUSk/IuLh+MmQeLZ8M21sPSZoCMK1NSpU7n66qvp1asX9erVw8wYOnRo1NNfdtllmBlmxtKlS6OebtOmTdx///0MGTKEgw8+mGrVqmFmfPTRR0VOu2rVKq6//no6d+5M7dq1adiwIUcccQQ33aQ75iIiUjDdYRURkYohrhoc/zJ8cjbMGeE1xJS3D9cqYty4ccyfP5+EhAQSExNZtGhR1NNOnz6dZ599loSEBLZu3RrTcpcvX86NN94IQGJiIk2aNGH16tVFTvf5559zxhlnsH37dk477TTOPvtsduzYwdKlS3nllVe49957Y4pDRESqDiWsIiJSccTXhF6vQfrp8OUwqFYbDjwn6KjK3Pjx40lMTCQ5OZn09HT69OkT1XRr167l8ssvZ/DgwaxatYr09PSYltu2bVs++ugjDj/8cBo1akRqaiqTJk0qdJpVq1Zx1llnUb9+fWbPnk2nTp1yjd+zZ09MMYiISNWiKsEiIlKxVKsNvd+CxkfD5xfAb+8EHVGZ69OnDx07dsRi7OZnxIgRADz66KPFWm7Dhg3p168fjRo1inqau+++m/Xr1/PEE0/kS1YBqlevXqxYRESkatAdVhERqXiqJ0DKu/BxX/hsEJz4NrToG3RU5drEiROZNm0a06ZNo3HjxmW23JdffpmGDRtyyimn8OOPP/Lxxx+zfft2OnTowKmnnkpCQkKZxSIiIhWPElYREamYatSHPu/DxynwyZ+gzwfQtGfQUZVLK1as4Nprr2Xo0KGcddZZZbbcZcuWsW7dOo466iiuv/56Hn744VzjGzduzPPPP89pp51WZjGJiEjFoirBIiJScdVqAn0/gtqtIG0AbPgm6IjKnezsbIYNG0ZCQgITJkwo02WvWbMGgLlz5/LUU0/xyCOPsGbNGn7//Xf++c9/kpmZyaBBg1i4cGGZxiUiIhWHElYREanYareAvh9DjYYw42TYtCDoiMqV8ePHk56eztNPP03Dhg3LdNnZ2dkAZGVlcdttt/GXv/yFpk2b0rJlS/72t79xzTXXsHPnTh566KEyjUtERCoOJawiIlLx1T0Q+s2A+Fowoz9sXhx0ROXC4sWLGT16NMOHDw+k2m2DBg32/X/22WfnGx8aNmfOnLIKSUREKhglrCIiUjkktPfutLpsmNEPti4POqLA/fjjj+zatYvnnnsOM8v1CnVpE2pteNq0aSW+/A4dOlCtmtdcRnjyGhK647tjx44SX7aIiFQOanRJREQqj/pdvGdaP07xWhA+6VOo0zroqAKTlJTEpZdeGnHc22+/zapVqzjvvPOoV68eSUlJJb78GjVq0KtXL2bOnMmCBQto3rx5rvELFnjVt9u1a1fiyxYRkcpBCauIiFQuDbv6rQf38+609kuH2s2Lnq4S6t69O88880zEcSkpKaxatYq7776b5OTkXOPWrVvHunXraNKkCU2aNNmvGK6++mpmzpzJbbfdxrHHHkvdunUB2LRpE2PHjgXgwgsv3K9liIhI5aWEVUREKp/GR0HKOzDzFJh5EvRLg5qNgo6qxIT6UwVYtWoVALNmzSI1NRWAJk2a8MADDxR7/o888gh33HEHt99+O2PGjMk17oYbbmDdunUAfPbZZwDcf//9vPjiiwAMHDiQgQMH7it/9tlnM3z4cJ577jkOO+wwBgwYQFZWFv/73//47bffGDRoEEOHDi12rCIiUrkpYRURkcqp2Qlw4puQdoaXuPb9yOu7tRKYN28ekyZNyjUsIyODjIwMANq2bbtfCWthpk6dyooVK3IN++CDD/b9n5SUlCthBXj22Wfp2bMnTz75JBMnTsQ5x8EHH8zNN9/MlVdeSVycmtQQEZHIzDkXdAwigejRo4f7+uuvgw6jwkhLSyMlJSXoMCQPfS5R+O1t+GQgNDnGqypcrW6pLk6fSfmkz6X80WcSGzP7xjnXI+g4RMqaLmmKiEjl1vp0OH4yrJsF6WdB1s6gIyr/lr0E05Jgcpz3d9lLQUckIiJVlBJWERGp/NqcB8dOhNUz4NNzIWt30BGVX8tegjkjYPsKwHl/54xQ0ioiIoFQwioiIlVDu4vhqMfh97fhiyGQvTfoiMqneX+HrO25h2Vth/mjg4lHRESqNDW6JCIiVUfHkZC1A+ZeD1/WhuMmgunaLQDbVsCP/4Qdv0Uev/2Xso1HREQEJawiIlLVdLkO9m6D726BanW8u65mQUcVnM2L4cd7YdkL3naolgB7t+YvV6dN2ccmIiJVni4rS+DM7Fwz+7eZfWpmm83MmdmLMUz/jD+NM7Pk0oxVRCqJQ0fDwTfD0idh7iioii3mb/oePr8Q3j4IVrwMHa+EM3+Go56A+Dp5ChscemsgYYqISNWmO6xSHtwCdAO2Ar8CXaKd0MzOBC71p00olehEpHLqdpf3bOZPD3ld3XQbF3REZWP9V/DDXfDrm97d1C43QJdRULu5N77dEO/v/NFeNeBazWDnalj3OSRfGlzcIiJSJSlhlfLgerxEdSlwIjAzmonMrCnwNDAFaOFPKyISHTM4Yjzs3e4lcNXqwiE3Bx1V6VnzCSy4C1Z9ANUbwKG3Q+droGaj/GXbDclJXAHm3+Jto1YDvBaXRUREyogSVgmcc25fgmqxPUf2lP/3L8BrJRmTiFQRZt4zrFk7YP4/vKqwXa4NOqqS4xys+hAWjIO1n0LNptD9Xq/6b/V60c/nsNvhjw9gzkhochzUSSy9mEVERMLoGVapkMwsFRgIjHTOrQ82GhGpCKZOncrVV19Nr169qFevHmbG0KFDIS4ejn0ODhwEc6+DpU+zcuVKrrrqKo455hhatGhBzZo1adWqFb169eK5555jz549MS8/KyuLZ555ht69e9OwYUNq165N+/btGTx4MIsXL85VduLEiZhZga8nnnii8IW5bK/K7/tHw8xTYGsGHPkwnLUcDv57bMkqQFx16PkSZO+GWX/25i8iIlIGdIdVKhwzaws8DLzonHszxmlHACMAmjdvTlpaWskHWElt3bpV26sc0ucSvZtuuomff/6Z2rVr07RpU7Zs2cLq1av3bT9zIzm05q80mjOSGb9cxPPPT+Oggw7i6KOP5oADDmDz5s3MmTOHSy65hH//+9/cf//9xMfH51tOpM9kx44djB49mm+//Zbk5GT69etHjRo1WLduHenp6bz22mscd9xx+8ovWrQIgOOPP57k5PxtyZlZ5M/dZdFsZzpttrxIwt5l7IhvxS/1/49VdU7G/VED/phT7O0H0CLhKrqsvp+f376KlQkX7Ne8ypqOlfJHn4mIREMJq1QoZhYHTMJrZOmaWKd3zj2FX5W4R48eLiUlpUTjq8zS0tLQ9ip/9LlE7+mnnyYxMZHk5GTS09Pp06cPzZs3z7399p4A6WdwYdZkLl4whbik3M9r7tmzh5NPPpm0tDTWr1/P+eefn285kT6TIUOG8O233/LEE08wcuTIfNPs2bOH6tWr73u/fPlyAC677DJSU1OLXrnsPbDsRfjxHtiyBOodBIe8QO22F9A5rhqdi55DdNyJ8FkGHX77Dx16joBGR5TUnEudjpXyR5+JiERDVYIlZmbW1MyuMLOHzeyZPMOPNrPapbj46/EaV7rcObexFJcjIpVMnz596NixY+HPylerDb3fpEbz44j7cgj89k6u0dWrV2fgwIEALFmyJKrlzp07l8mTJzN48OCIyWpovsWStRMWPwZvJcPsS7yGo06YCqcvgHZDIa6Er0ubwdFPQc1m8MVFXoNVIiIipUh3WCUmZnYpMAGoBRjggMv80c2BWXhVbp8thWV3Au4CnnPOvVNUeRGRYqmeACnvwIx+8Ok5kPI2tOgHeM+hvvOOd/rp2rVrVLObPHkyABdeeCGZmZlMnz6dlStX0rhxY/r27Ruxym/IvHnzeOihh9i5cyetW7emT58+JCYmwp6tXh+yCx+Anau8hpCOetxrxTe2xutiV7MRHPc8zOgPc/8Pjn68dJcnIiJVmhJWiZqZnYRXnfY74HbgFOCK0Hjn3AIz+wGvMaQST1iBg4GawHAzG15AmSX+3ZOznXPTSiEGEakKatSHPu+z7rUTeOSvp+KSLmbt9lp8+OGHLF26lIsuuogzzzwzqll99dVXAKxYsYIOHTqwfn1OO3FmxpVXXsmECRMiPg/78MMP53ofHx/PZeccwUNn/0wttwGa94XjJ0OzlNJPVMO16AsH3QAL7/eS5MQ/ld2yRUSkSlHCKrH4O/AHcKJzbrOZHR6hzHfAcRGGl4TlFJwIn47XF+urwGa/rIhI8dVszLqDn+aOIb2A5wAvwbzhhhu4++67o57NmjVrABg1ahQDBw5k3LhxJCYmMnv2bK644goee+wxmjZtypgxY/ZN065dO/79739z8sknk5iYSObqDD6behs3/2saT776FZvXt2LyK19A09I63Uah61hY9RHMvhQafwe1WwYXi4iIVFp6hlVi0QP4n3NucyFlfsVLHEucc26ec+6ySC/gJ7/YP/xh80ojBhGpWrp0PwG3bSV7X0tixWP1GX/333jqqafo3bs3GzZsiGoe2dleFzBdunRhypQpdOnShYSEBPr168fUqVOJi4vjwQcfZPfu3fumOfHEE/nrX/9Kp8QE6iy8hZZzjuG81tOY+ehpNGxQj5dn/M783+uUyjpHLb6m19XN3m0wK1Vd3YiISKlQwiqxqAFsK6JMAyArlpma2UAzm2hmE4Gb/MHHhYaZ2QMxRyoiUlLqJBJ/8gzatEjg2k4TeXL87Xz55ZfcdtttUU3eoEEDAM4888x81X67detGu3bt2LJlCwsXLswZsXU5zLkS3moHiydAm3Ph9B84cND/OO10ryryJ598UhJrt3/qHwRHPAirPoCf/h10NCIiUgmpSrDEYjlwZBFljiHnbme0ugPD8gxr778AVgA3xDhPEZGSk9AO+n0MH/VmQA3vGlq0/Ud27tyZOXPm7Etc82rYsCHg9dXK5p/gh3tg+Ytg8dA+FQ7+OyS031e+adOmAGzbVtT1wzKSPBJ+fwfm3QjN+0DD6BqjEhERiYbusEos3gR6mdl5kUb6DSF1BV6LZabOuTHOOSvklRTFPFL8sktjWbaISNTqdYa+H/HbWq8rl2pxLqrJ+vfvD8CCBQvyjdu1a9e+7nGSVt8N/zsIfvkvdLoa/pQBRz+ZK1kFmD17NgDt27fPN79AmMExz0KNhvDFENi7I+iIRESkElHCKrH4J/AL8LKZTcFvXMnM/uq/fwpYAqhemIhUaHPnziUrK//TDVurtePaNzsDcHqXVbBj9b5xmZmZLFq0KFcrwACDBg2iVatWTJkyhTlz5uQaN/bmK8jMzKTPwdBiVxocfBOctZyv3RCo0zpX2ezsbO655x5mzZpFkyZNOPXUU0tobUtAraZw7ETIXADzbiqyuIiISLRUJVii5pzbaGYnAs8D4XdZJ/h/PwUucs6Vk3pqIiI5pk2bxrRp0wBYtWoVALNmzSI1NRWAJk2a8MADXnXfO++8k88//5yePXvSpk0b6tSpw8qVK3n33XfZtGkTPY86jJtPXwozT4J+M6FmY9544w2GDx/OKaecwqBBg/Ytt27dukycOJEzzjiDXr16cc4559C6QTazP/uAzxZsoll948n7roaTx3h3KYGjjmrOoYceSrdu3WjdujWZmZl8/vnnLFiwgDp16vDSSy9Rr169Mtt2UWl1KnS6xnvmttWpXnc3IiIi+0kJq8TEOfcLkGJmXfHusDYGMoEvnXPfBBqciEgh5s2bx6RJk3INy8jIICMjA4C2bdvuS1gvv/xyEhISmDNnDmlpaWzfvp2GDRty5JFHcv7553PJJZdQbV06pJ0OM0+Bvh8XuuyTTjqJObNnM3b0X/no3alkbt1Li4ZxXDH4WG69bxKt2nbKVf6GG25gzpw5zJgxgw0bNhAXF0ebNm34y1/+wqhRo8pPdeC8Dr8PVs+AL4fDad9BrWZBRyQiIhWcORfdMzgilU2PHj3c119/HXQYFUZaWhopKSlBhyF56HMJ2G/vwKcDofHR0Od9qFY3/2fisuHXN2HBONg4F+oc6DWk1P4SqFY7qMhLz6bv4b2joOXJ0PtN7xnXckDHSvmjzyQ2ZvaNc65H0HGIlDXdYZWomVltoCmwyjm3O8L4mkBzYI1zbmdZxyciUuZanwY9X4bPz4f3joa9Wzhx+68wrQ10HQtx8fDD3ZD5AyQkwzHPQNLFEF8j6MhLT4PDoPt9MPc6WPokdLwi6IhERKQCU6NLEovb8LqsSShgfF1gEfCPMotIRCRobQZBhxGw+UfYvhLDwfYV8OUwr9VcgJ6T4YyF0OHSyp2shnS+GlqeAnNHQebCosuLiIgUQAmrxGIA8JFzbkOkkf7wj4AzyjQqEZGg/f5uhIEOajb1nuVMuhDiqlClJouDY5+DanXhi4sga1fQEYmISAWlhFVikQQsLqLMYr+ciEjVsf2XyMN3rfOSt6qodkuvf9aN8+C7W4OORkREKqgq+i0qxVQdyC6ijANqlUEsIiLlR502sQ2vKhL/BMlXwML7YVXhLSmLiIhEooRVYpEBnFhEmRRgRemHIiJSjnS7C+Lr5B4WX8cbXtUd8S+o1xlmDYNd64OORkREKhglrBKLt4AjzezGSCPN7CbgCGBaWQYlIhK4dkPg6KegTlscBnXaeu/bDQk6suBVq+M1OrVrDcwZCepOT0REYlCFWoCQEvAAMAS4x8zOBz4AfgNaA6cA3YFfgH8GFaCISGDaDYF2Q0hX35L5NToCut4F826EjOegwyVBRyQiIhWEElaJmnNuo5mlAJOBY/Hupjog1Cv8F8BQ59zGQAIUEZHy66D/gz/eg2+ugaa9oF7HoCMSEZEKQAmrxMQ5txzoaWZH4CWtDYBNwJfOubnBRSYiIuWaxcFxk+Cdrl7/tCd/DnHVg45KRETKOSWsUix+cqoEVUREolcnEY5+Gj47F76/A7qNCzoiEREp59TokoiIiJSdNoOg/SXww92w5pOgoxERkXJOd1glJmZWHTgLOBpoCMRHKOacc5eWaWAiIlJxHPmwl6x+cTGcNh9qNAg6IhERKaeUsErUzKwV8CHQhZyGliJxgBJWERGJrHoC9HwRPjwevroKjp8cdEQiIlJOKWGVWPwLOAh4GXgaWAnsDTQiERGpmJocA4eNge9uhVanq89aERGJSAmrxOJk4BPnnH5ViIjI/jv4Zvjjffj6KmjaExLaBR2RiIiUM2p0SWJRC5gddBAiIlJJxMXDcS94/8+6GLJVaUdERHJTwiqxWAC0DToIERGpRBKS4KjHYe3n8MM9QUcjIiLljBJWicX9wJ/M7OCgAxERkUok6SJIGgIL7oB1XwYdjYiIlCN6hlVisQaYDnxhZg8D3wCbIhV0zqlzPRERiV6PR2HtZ/DFEBgwD6ofEHREIiJSDihhlVik4XVZY8Ct/v8FidQ/q4iISGQ16sNxL8LHJ8I318CxzwUdkYiIlANKWCUWd1J4kioiIlJ8zU6AQ0bDgrHQcgC0PT/oiEREJGBKWCVqzrkxQccgIiKV3KG3wh8fwJyR0OQ4qHtg0BGJiEiA1OiSiIiIlB9x1aHnS+D2+l3dZAUdkYiIBEgJq8TMzKqb2almdr2Z3Ro2vJaZNTMz7VciIlJ8B3SAHv+GNemw6IGgoxERkQApsZCYmNmpwHLgbeBfwJiw0d2BP4DBZR2XiIhUMu2GQZvzYP4tsOGboKMREZGAKGGVqJlZD2AaXsNL1wOTw8c7574ElgFnl3lwIiJSuZjBUU9A7Rbw+UWwd1vQEYmISACUsEosbgW2Az2ccxOAJRHKfAV0K9OoRESkcqrZCI57HrYsgbmjgo5GREQCoIRVYnE8MM05t6qQMiuBlmUUj4iIVHbN+8DBN8LSp+DXN4OORkREypgSVolFArCuiDJ10H4lIiIl6bA7oeERMPtS2PFH0NGIiEgZUmIhsfgNOKSIMt2BjNIPRUREqoz4Gl5XN3u3w6xUcNlBRyQiImVECavE4l3gFDM7IdJIMxsA9AT+V6ZRiYhI5Ve/CxwxHlZ9AD9NCDoaEREpI0pYJRb3AJuAD8zsPuBgADM73X//Kl63Ng8GFqGIiFQYU6dO5eqrr6ZXr17Uq1cPM2Po0KERyy5ZsoT7XttI3weacODx11OjRnWaN2/OWWedxcyZM4u1/KysLJ555hl69+5Nw4YNqV27Nu3bt2fw4MEsXrw4X/mlS5cyfPhwEhMTqVGjBi1btuTiiy/m559/LtbyRUSkaNWCDkAqDufcb2Z2MvBf4G9ho94CDPgZOMc5V9RzriIiIowbN4758+eTkJBAYmIiixYtKrDsrbfeypQpUzj4oM6cduQ2GtWvy097j+Wt6W/z1ltv8fDDD3PNNddEveytW7dy1llnMWPGDLp3786wYcOoVasWv/32G59++imLFy+mU6dO+8p//fXX9O3bly1bttCvXz8uvPBCVqxYwSuvvMJbb71FWloahx9++H5tDxERyU8Jq8TEOTfXzDoDpwPHAY2BTOBL4E3n3N4g4xMRkYpj/PjxJCYmkpycTHp6On369Cmw7Kmnnsrf//53Lyn8/X1IOxU6tSX9uo856aST+Nvf/sZ5551Hy5bRNVQ/cuRIZsyYwRNPPMHIkSPzjd+zZ0+u95deeilbtmzhwQcf5Prrr983/LPPPiMlJYXhw4fz7bffYmZRrr2IiERDVYIlamb2HzO73jmX5Zx7yzl3s3NuhHPub86515SsiohILPr06UPHjh2jSvJSU1Nz7mC2OgU6XweL/82JydtISUlh9+7dfPHFF1Etd+7cuUyePJnBgwdHTFYBqlevvu//jIwMvvvuO5o1a8a1116bq9wJJ5zAGWecwfz58/n000+jWr6IiERPd1glFhcB44MOQkREhO73wOqPYfZwqsd3BaBateh+1kyePBmACy+8kMzMTKZPn87KlStp3Lgxffv2JTk5OVf5Vau87seTkpKIi8t/rb99+/YAfPzxx/Tu3bvYqyQiIvkpYZVYLAeaBR2EiIgI8bWg52RWvHAkH388gzp16kSdLH711VcArFixgg4dOrB+/fp948yMK6+8kgkTJhAfHw9AkyZN9pV3zuW7I5yR4fXm9tNPP+33aomISG6qEiyxmAwMMLOGQQciIiKyq3ZHhkxMZNeebMb85WQaNozu62nNmjUAjBo1ipSUFBYuXMiWLVv46KOP6NChA4899hhjx47dV75Tp0507NiR1atXM2FC7i51vvjiC/73P683t40bN5bQmomISIgSVonFPcDXwEwzO8PMmgcdkIiIVE1ZWVlcfPHFfD43g8F9WnDDEe9C5o9RTZudnQ1Aly5dmDJlCl26dCEhIYF+/foxdepU4uLiePDBB9m9e/e+aZ544glq1KjBddddt6+RpwsuuICUlBQOO+wwgIjVhUVEZP/ozCqx2InXOnBX4E3gdzPLivBS40siIlJqsrKyGDp0KK+++irnn38+L775FVa9Hnx+EWTtKnL6Bg0aAHDmmWfuq/Yb0q1bN9q1a8eWLVtYuHDhvuF9+/blyy+/5JxzzmHevHk8/PDDzJs3j/vuu4+bb74ZgGbN9NSMiEhJ0zOsEotPARd0ECIiUnXt2bOHIUOG8Oqrr3LRRRfx/PPPe0nnsf+B9DNh/mg44oFC59G5c2fmzJmzL3HNK1S1eMeOHbmGH3744bz22mv5yt92220AHHXUUcVYIxERKYwSVomacy4l6BhERKTq2r17N+effz5vvvkmf/7zn3nuuedyquG2PgM6XgWL/gWtToUW/QucT//+/XnhhRdYsGBBvnG7du1iyZIlgNcqcFH27NnDyy+/TPXq1Tn33HOLtV4iIlIwVQkWERGRcm/Xrl2cffbZvPnmm1x66aW5k9WQw++HegfBrGGwaz2ZmZksWrSIP/74I1exQYMG0apVK6ZMmcKcOXNyjRs7diyZmZn06dOHFi1a7Bu+bds2srKycpXdu3cv11xzDUuXLmXUqFG5youISMnQHVYpFjOrC3QCEpxz6ildRERiNm3aNKZNmwbk9HU6a9YsUlNTAa87mQce8Kr3XnHFFbzzzjs0adKE1q1bc+edd+abX0pKCinHT4b3j4bZl/PGz2cy/JJLGDZs2L55AtStW5eJEydyxhln0KtXL8455xxat27N7Nmz+eyzz2jWrBlPPvlkrnnPnDmTyy67jP79+5OYmMjWrVt57733+Pnnnzn33HNztSosIiIlRwmrxMTMEoGHgTOBeLxnWqv5404AngKucs6lBRWjiIhUDPPmzWPSpEm5hmVkZOzr17Rt27b7EtZly5YBsG7duojJakhKyhjodg98ewOsbVBguZNOOok5c+YwduxYPvroIzIzM2nRogVXXHEFt956K61atcpVvlOnThx//PGkp6ezZs0a6tSpQ/fu3bnjjju46KKL8vXNKiIiJUMJq0TNzFoCs4HmwFtAM+C4sCKz/WGDgbSyjk9ERCqWMWPGMGbMmKjKpqWlRT/jLtfD7++SGj+F1MyfoF6niNN369aNqVOnRjXLTp06RWxwSURESpeeYZVY3I6XkJ7knDsH+DB8pHNuD15LwscHEJuIiIjH4uC4SRBfE2aeCtPacuLvfWFaEix7KejoREQkBkpYJRanAW8552YWUuYXoFUh40VEREpfndaQdDFsWwbbf8FwsH0FzBmhpFVEpAJRwiqxaA4sKaLMHqBuGcQiIiJSuF/fzD8sa7vXV6uIiFQISlglFhuAA4so0wlYVQaxiIiIFG77L7ENFxGRckcJq8Tic+BPZhaxozkz6wicChRWZVhERKRs1GkT23ARESl3lLBKLO4HagHpZjYAqANen6z+++lANvCv4EIUERHxdbsL4uvkHhZfxxsuIiIVgrq1kag552ab2UjgceB/YaM2+3/3Apc4534o8+BERETyajfE+zt/NG77CgwgaWjOcBERKfd0h1Vi4pz7D3AoMAGYA/wMzAUeA7o659T0ooiIlB/thsDA5aS3nAGNesAf70HWrqCjEhGRKOkOqxTIzP4ELHLOLQ4f7pxbAlwfTFQiIiLFYAbd74UZ/WHJE9Dl2qAjEhGRKOgOqxTmDeCC0BszyzCzawKMR0REpPha9IMW/eGHcbBnS9DRiIhIFJSwSmH2ANXD3icBDQKJREREpCR0uxt2rYNFDwYdiYiIREEJqxTmF+AEM4sPG+aCCkZERGS/NT4KDjwXFj4AO9cGHY2IiBRBCasU5mXgRGCDmWX4w673qwYX9vo5wJhFREQK120cZO2AH+4OOhIRESmCElYpzFjgH8B3eHdWHWBRvLRfiYhI+VWvM7QfDkseg20rgo5GREQKocRCCuSc2+ucu9c518s51wEvGR3vnGtX1Cvo2EVERAp12O2Awfdjgo5EREQKoYRVCmRmXc2sWdigScC8gMIREREpOXUSofPVsOx52PRD0NGIiEgBlLBKYb4Frgh73xa1EiwiIpXFwTdBtQT47pagIxERkQIoYZXCZAPhLQSn4HVtIyIiUvHVbAwH3Qi/ToN1XwYdjYiIRKCEVQrzK9A96CBERERKTedroVZzmHcTOPXcJiJS3lQLOgAp16YDfzWzhcAf/rBUM0spYjrnnOtXmoGJiIiUiOoJcOit8PVf4Y/3odWpQUckIiJhlLBKYUYDNYDT8fpjdXhVgpOKmE6XqEVEpOLocDks/BfMvxlangymCmgiIuWFzshSIOfcFufcFc65A51z8Xjd2oxxzsUV8Yovat4iIiLlRnwN6DoWNs6DFf8NOhoREQmjhFVikQ4sDzoIERGREpd0ITTo6rUYnL0n6GhERMSnhFWi5pzr45x7Pug4RERESpzFQbe7YevP8POzQUcjIiI+JawiIiIiAK1Og6YnwPd3wN7tQUcjIiKo0SUphJll4DWg1N85t8x/Hw3nnOtQiqGJiIiUPDPodg981At+mgCH3BR0RCIiVZ7usEph4si9j8ThNbxU1Ev7lYiIVEzNToBWZ8CP98HujUFHIyJS5ekOqxTIOZdU2HsREZFKqdtd8G53L2ntfm/Q0YiIVGm6EyYiIiISrmFXSBoCPz0M238LOhoRkSpNCavEzMzizKyJmTU2U+/qIiJSCXW9A1wWLBgbdCQiIlWakg2JipnVMLNrzGw2sBNYDawBdprZLDP7i5lVDzZKERGREpLQHpJHws/PwOYlQUcjIlJlKWGVIplZM2AWMB44Cu/Z51ADS9WAY4AJwBdm1qQY8z/XzP5tZp+a2WYzc2b2YgFlO5rZ381shpmtNLPdZrbazN40sz7FXUcREZF8DrkF4mvBd7cGHYmISJWlhFWi8TxwOPATcBmQDNQG6vj/jwAWA0cCE4sx/1uAvwLdgaIeFhoL3As0B94B/gV8DpwOzDCza4qxfBERkfxqN4fO18MvU2DD3KCjERGpkpSwSqHM7ATgZGAmcKRz7j/OuQzn3C7n3E7//2eAI4B0YICZ9YxxMdcDnYB6wJVFlH0POMI5d4hzbqRz7mbn3DlAP2APcL+ZtYxx+SIiIpEddAPUbAzz/xF0JCIiVZISVinKYCALuNQ5t6OgQv64SwAHnB/LApxzM51zS5xzLoqyE51z30YYng6kATWAWBNmERGRyGrUh4P/AX+8D6tnBh2NiEiVo4RVitID+NI5t7yogs65ZXjPuh5d2kEVYI//d29AyxcRkcqo01VQJxHm3QxFX1sVEZESVC3oAKTcaw+8GkP5+cC5pRRLgcysLV614O3AJ4WUG4H3zC3NmzcnLS2tTOKrDLZu3artVQ7pcyl/9JmUT/v7ubSocSFd1t/PgvfuYl3tE0ousCpMx4qIREMJqxTlAGBDDOU34j2LWmbMrCbwElATuNE5t7Ggss65p4CnAHr06OFSUlLKJMbKIC0tDW2v8kefS/mjz6R82u/PJfsEeGc6h2ZNht43Q1x8icVWVelYEZFoqEqwFKUWsVWx3YuXOJYJM4sHXgCOB6YAD5TVskVEpAqJqwbd7oLNC2H5C0FHIyJSZShhlWiUywd2/GT1ReA84L/A0GgabhIRESmWxLOh0VHw3e2QtTPoaEREqgRVCZZoXG9mw6Ms26A0Awkxs+p41YDPAyYDf3bOZZXFskVEpIoyg+73wox+sOQJ6HJd0BGJiFR6SlglGg2ILREt1bucZlYD747qWcDzwHDnXHZpLlNERASAFn2hxUnww13Q4RKoXqbNNoiIVDlKWKUo7YIOIJzfwNLrwGnAs8AIJasiIlKmut0N7x8FCx+ErmOCjkZEpFJTwiqFcs6tKO1lmNlAYKD/toX/9zgzm+j/v845d4P//xN4yeo64DfgNjPLO8s051xaKYUrIiJVXeMe0OY8WPQvr4/WWs2CjkhEpNJSwirlQXdgWJ5h7f0XwAoglLCG7vg2AW4rZJ5pJRSbiIhIfl3HwsrX4Ye74ciHgo5GRKTSUivBEjjn3BjnnBXySgorm1JEWXPOjQlubUREpEqo1xnaXwJLHoety4OORkSk0lLCKiIiIlIch90GFgffjwk6EhGRSksJq4iIiEhx1EmETlfDsudh04KgoxERqZSUsIqIiIgU18E3eV3bzB8ddCQiIpWSElYRERGR4qrZCA6+EX57C9Z+EXQ0IiKVjhJWERERkf3R+Vqo1Rzm3QTOBR2NiEilooRVisXMupjZ2WZ2cdCxiIiIBKpaXTj0Nlj7KfzxXtDRiIhUKkpYJSZm1t3MvgZ+AKYCE8PGnWhm283szKDiExERCUSHyyChPcy7GVx20NGIiFQaSlglambWCUgDOgMPA+/mKfIJsAE4t2wjExERCVh8Deg6FjbNhxVTgo5GRKTSUMIqsbgdqAEc45wbBXwVPtI554BZwFEBxCYiIhKsthdAg67w3S2QtTvoaEREKgUlrBKLfsDrzrkfCymzEmhVRvGIiIiUHxYH3e6BrRmQ8WzQ0YiIVApKWCUWDYFfiyhjeHdhRUREqp5WA6BpL/j+Tti7LehoREQqPCWsEovVQHIRZQ7Bu8sqIiJS9ZhB93tg5yr4aULQ0YiIVHhKWCUWM4AzzaxzpJFmdhReteH3yzQqERGR8qTp8dD6TPjxPti1IehoREQqNCWsEot7gL3AJ2Z2Jf6zqmZ2iP9+OrAFeCC4EEVERMqBbnfBns1e0ioiIsWmhFWi5pz7CRiE94zqI8BleM+sfgc86g8/xzn3S2BBioiIlAcNDoOkobB4Amz/LehoREQqLCWsEhPn3HtAO2AU8F/gI+B14G9AsnNuRoDhiYiIlB9d7wCXBQvuDDoSEZEKq1rQAUjF45zbBDzsv0RERCSShHaQfAUseQy6/B/U6xR0RCIiFY7usIqIiIiUlkNGQ3wt+O7WoCMREamQdIdVYmZmTYCDgESgeqQyzrnnyzQoERGR8qh2c+/u6oI7YcON0OjIoCMSEalQlLBK1MysFvAv4BK8BpYiFgMcoIRVREQE4KD/gyWPwrx/QF/1/CYiEgslrBKL+4ErgYXAFOA3vG5uREREpCDV63lVg+eOglUzoEXfoCMSEakwlLBKLM7H68LmKOfcnqCDERERqTA6XgmLxsP8m6H5l2AWdEQiIhWCGl2SWNQFPlSyKiIiEqP4WnDYHbB+Dvw6LehoREQqDCWsEosfgJZBByEiIlIhtbsY6h0E80dDtp6oERGJhhJWicUDwNlmpo7kREREYhVXDbrdBZsXwrIXgo5GRKRC0DOsEjXn3Ktm1hL41MweA+YCmQWU/aRMgxMREakIEgdC46Ph+9sh6UKvqrCIiBRICavEqiHes6y3FVEuvgxiERERqVjMoPu98HFfWPI4dLk+6IhERMo1JawSNTO7GbgdWI/Xrc3vqFsbERGR2DTvAy1Ohh/ugg6Xet3eiIhIREpYJRYjgAzgSOdcxKrAIiIiEoXud8N7PWDhv6DrHUFHIyJSbqnRJYlFC+AtJasiIiL7qdGR0OZ8WPQv2Lkm6GhERMotJawSiwygQdBBiIiIVApdx0LWTlgwLuhIRETKLSWsEovHgTPNrEXQgYiIiFR49Tp5z7AufQK2Lgs6GhGRckkJq8RiOpAOfGFmqWZ2mJm1ifQKOlAREZEK4dDbwOLhu9uDjkREpFxSo0sSi2WAAwx4tpByDu1bIiIiRavTGjpdAwvvh4P/Bg0OCzoiEZFyRXdYJRbP+69JYf9Her0QVIAiIiJBmDp1KldffTW9evWiXr16mBlDhw4tdJovvviC0047jUb9nqJ2qqPrkcfz0EMPkZWVFfVyx4wZg5kV+urQoUOuaebNm8eYMWM4/vjjadmyJTVq1KB169ZceOGFzJ07t1jrLyJSWnQXTKLmnEsNOgYREZHyaNy4ccyfP5+EhAQSExNZtGhRoeXffPNNBg0aRK1atRg8eDCNspcw/f1Puf766/n888959dVXo1puSkpKgeOmT5/O3LlzGTBgQK7hV1xxBbNnz+bII4/knHPOISEhgXnz5vHKK68wdepUpkyZwjnnnBPV8kVESpsSVhEREZH9NH78eBITE0lOTiY9PZ0+ffoUWHbz5s1cfvnlxMfHk5aWRo8ePWDvNsZO7UDfsduZOnUqr7zyChdccEGRy01JSYmYtGZlZfHss97TOyNGjMg1bsiQIbz44oskJyfnGv7SSy8xdOhQRowYwRlnnEGNGjWiWHMRkdKlKsEiIiIi+6lPnz507NgRMyuy7NSpU1m7di0XXHCBl6wCVKtLrSNvZ9zALQA8/vjj+xXPO++8w6+//sqxxx5L165dc427+uqr8yWr4CWyHTt2ZP369Xz//ff7tXwRkZKiO6xSIDP7D14DSv9wzq3230fDOecuLcXQREREKqwZM2YAcOqpp+Ye0eEyevd4gDo1l/HFF1+wa9cuatasWaxlPPXUU0D+u6tFqV69OgDVquknooiUDzobSWFS8RLW+4DV/vtoOEAJq4iISAQ//fQTAJ06dco9Iq461Q4fR7umF/HDr3vJyMjgoIMOinn+v/76K++++y7169dn8ODBUU/35Zdf8uOPP9K6dWsOPfTQmJcrIlIalLBKYdr5f3/N815ERESKKTMzE4D69evnH9l2MPUPuAzYzqb1a4HYE9Znn32WrKwshg4dSp06daKaZsOGDfz5z38GvOdx4+PjY16uiEhpUMIqBXLOrfCrAU8D3nLOrQg4JBERkcrN4qBuW2Ah/PYW0DumybOzs/c1tjRy5Mioptm2bRtnnXUWS5Ys4cYbb+S8886LMWgRkdKjRpekKKlA94BjEBERqTRCd1ZDd1rzytzh/TxrsHoi7N0W07zfffddVq5cybHHHsthhx1WZPlt27Zx+umn89lnnzFq1Cjuu+++mJYnIlLalLCKiIiIlKHOnTsDsHjx4nzj9u7dy7Jly6hWLZ729dfDTw/HNO9QY0vR3F3dsmULAwYMID09nRtvvJF//etfMS1LRKQsKGEVERERKUN9+/YF4L333ss37pNPPmH79u307Hk8NZP+BD/eB7vWRzXf33//nbfffjuqxpYyMzM5+eST+fTTTxk9erTurIpIuaWEVURERKQMnXvuuTRp0oRXXnmFr7/+et/wnTt3cssttwBw5ZVXQre7YM8W+PE+tm/fzqJFi/jll18KnG+osaWLL76Y2rVrF1hu48aN9O/fny+//JI77riDcePGldzKiYiUMDW6JNHobmZ/jmUC59zzpRWMiIhIeTNt2jSmTZsGwKpVqwCYNWsWqampADRp0oQHHngAgHr16vH0009z7rnnkpKSwgUXXECjRo146623+Omnnzj33HO9O6Rm0O5iWPxv5qw+ij4DzufEE08kLS0t3/LDG1sqqu/Vc845h6+//poOHTqQnZ3NmDFj8pUZOHAg3bt3L9a2EBEpSUpYJRpn+a9YKGEVEZEqY968eUyaNCnXsIyMDDIyMgBo27btvoQVvIQwPT2du+66i9dee42dO3eSnJzMgw8+yDXXXIOZeQUPuwNWvAwZueed1/vvv8+KFSuiamxp2bJlAPz888/ccccdEcskJSUpYRWRckEJq0Rjvv8SERGRCMaMGRPxTmVhjj/+eN55553CCyUkQfKVpCx5FJe5COp1jlhswIABOOeiWu7y5ctjilNEJEhKWCUa05xzdwYdhIiISJV06GhY8iS8ewRk7YA6bbznW9sNCToyEZFSp4RVREREpDz740MgG7J2ee+3r4A5/nOqSlpFpJJTK8EiIiIi5dn80eD25B6Wtd0bLiJSySlhFRERESnPthfQlU1Bw0VEKhElrFKUdGB50EGIiIhUWXXaxDZcRKQSUcIqhXLO9VGfqiIiIgHqdhfE18k9LK6mN1xEpJJTwioiIiJSnrUbAkc/BXXaAgYWD7VaQNJFQUcmIlLqlLCKiIiIlHfthsDA5XBRNhzzH6+l4F/fCDoqEZFSp4RVREREpCJJGgL1usB3t0F2VtDRiIiUKiWsIiIiIhVJXDwcdgdk/gArXgk6GhGRUqWEVURERKSiaXMuNOgG34+B7D1FFhcRqaiUsIqIiIhUNBYHXcfC1qWwTI35i0jlpYRVREREpCJqfQY0Phq+vxOydgUdjYhIqVDCKgUys2wzyyrGa2/QsYuIiFR6ZtB1HGz/BZY+HXQ0IiKlolrQAUi59gnggg5CRERECtCiPzQ7EX646//bu/N4q+p6/+OvD5OAiogoDiiDU6gYGlnOR3JAHNLCtLS0NNJ+1cUhhxwj7TqUZeattFvesuKmXkeoNOWIgWmaBag4IOgNU/E6xyh8f3+sffBwOBzOPpxz1tp7v56Px3ps9ho/a389cN5+1lobtv0CdOudd0WS1K4MrFqjlFJd3jVIkqQWRGT3sv5xP3j2P2DYWXlXJEntykuCJUmSKtlm+8IWh8CTl8Oyt/OuRpLalYFVbRIR60fEbhGxb961SJJU83a9FJb8H8y+Ju9KJKldGVhVlogYGBG3Am8AjwJTGi3bJyKejIi6nMqTJKk2bTISBh4Fs78DS17PuxpJajcGVrVaRGwBPAx8HLgbeAiIRqs8DGwGHNv51UmSVON2nQDL3oHZ3827EklqNwZWleNiskB6UErpE8C9jRemlJYBDwJ751CbJEm1re9wGHQsPH0NLH4172okqV0YWFWOMcCdKaUpLazzIrBlJ9UjSZIaG/5NWL4Inrg870okqV0YWFWOAcCza1lnGbB+J9QiSZKa6rMDDDkx+4qbhfPzrkaS1pmBVeV4Hdh6LevsALzcCbVIkqTm7HIRsAJmXZp3JZK0zgysKsc04MiI2Ly5hRGxPTCaRk8OliRJnWyDwbDtF2HOT+HduXlXI0nrxMCqclwF9AQeiIhDgd6w8jtZDwXuAlYAPp5QkqQ87Xw+dOkGsybkXYkkrRMDq1otpfQw8CVgMNnX2pxVWvR26f0Q4OSU0hO5FChJkjK9t4TtvwxzfwFvP513NZLUZgZWlSWl9DNgF+AHwCPAHOCvwH8Au6aUfpVjeZIkqcFO50DXXjDj4rwrkaQ265Z3Aao8KaVngdPzrkOSJLWg52aw43h44jJ44xuw8a55VyRJZbPDqlaLiJ3yrkGSJJVh2JnQfSOYeVHelUhSmxhYVY5ZEfFwRHw5IvrlXYwkSVqLHhvDsLPgH3fAa4/kXY0klc3AqnL8AdgduBZ4KSJujojDI6JrznVJkqQ12fHfYL3+MOPCvCuRpLIZWNVqKaVDga2Bc4HngE8Cd5CF16sj4oN51idJkprRfUPY6Vx4+R54dWre1UhSWQysKktK6eWU0lUppV2AkcB1QADjgb9GxN8iYnw5+4yIsRFxbUQ8GBFvR0SKiJvWss1eETE5Il6PiEURMSMixtvtlSSpGdufBr22gL9fACnlXY0ktZqBVW2WUvprSulrwJbA0cDtwE7Ad8rc1QXAV4ARwPy1rRwRHwemAvsBtwE/BHoA3wMmlnlsSZKqX7fesPP5sOBBePnevKuRpFYzsKo99AY2K03dyDqu5Tgd2AHoA5zW0ooR0Qe4AVgO1KWUTk4pfZ0s7D4EjI2I48o8viRJ1W/bU2D9QXZZJVUUA6vaJDKjI+I3wD+BHwN7AvcBnytnXymlKSmlZ1Nq1b+eY4FNgYkppUcb7WMxWacW1hJ6JUmqSV3Xg10ugtf/AvPvyrsaSWoVA6vKEhE7R8SVwD+AScCxpT9fCAxOKR2UUvpVB5YwqvT6+2aWTQUWAntFxHodWIMkSZVpyOdgw+2zJwanFXlXI0lrZWBVq0XEY8AM4Cyyy4B/CuydUtoxpfTtlNI/OqGMHUuvzzRdkFJ6D5hLdlny0E6oRZKkytKlGwz/Jrw5A168Oe9qJGmtuuVdgCrKCOBe4Ebg9tJluJ1to9LrW2tY3jC/b3MLI2IcMA5gwIAB1NfXt2dtVe3dd9/18yogx6V4HJNiclwaSQMY2W0IXR7+On95vj8ppwfsOyaSWsPAqnJsnVJ6Ke8i1kVK6XrgeoCRI0emurq6fAuqIPX19fh5FY/jUjyOSTE5Lk3873fhwU+w/6B/wNATcynBMZHUGl4SrFYrSFht6KButIblDfPf7PhSJEmqUAOPgn4fgpmXwPKleVcjSWtkh1VtEhEDga2AZh9ulFKa2kGHfhoYSfY1OI81qakbMAR4D3i+g44vSVLli4BdL4X6Q+H5n8H2p+ZdkSQ1y8CqskTEwcD3gA+sZdWOuiHmfuB4YDTwmybL9iN7GNTUlNKSDjq+JEnVYYtDYNO9Yda3YMiJ0K1X3hVJ0mq8JFitFhEfBe4me6DRD4Eg+yqZG4DZpfd3ARM6sIxbgNeA4yJiZKPaegKXlt7+qAOPL0lSdWjosi56CZ77Sd7VSFKz7LCqHOcBi4EPp5ReioivAlNSShMiIoBvAmcA55ez04g4Cjiq9Hbz0uueEXFj6c+vpZTOAkgpvR0RXyQLrvURMRF4HTiS7CtvbgH+u22nJ0lSjRlQB5sfCE98G7Y9BbpvkHdFkrQKO6wqx57AnU0evtQFIGUuAp4iC67lGAGcWJoOKc0b2mje2MYrp5RuB/Yn6+5+EvgqsIwsLB+XUkplHl+SpNq167dgyQJ45tq8K5Gk1RhYVY6NgBcbvV8KrN9knWlk95K2WkrpkpRStDANbmabaSmlMSmljVNKvVJKw1NK30spLS/3pCRJqmn9PwpbHg5PXglL38y7GklahYFV5XgV2LjJ+22brNMd8KkNkiRVkg9+C5a9CbOvzrsSSVqFgVXleIZVA+qfgYMiYgeAiNic7BLdZ3OoTZIktdXGI2CbY2D292Dxa3lXI0krGVhVjt8D+0dEv9L7a8i6qY9HxF/InhS8KfD9fMqTJEltNvybsHwhPHVl3pVI0koGVpXjJ2T3py6D7D5S4BhgLrAL8E/gtJTSL3KrUJIktc1Gw2DQ8fDMD2HRP/OuRpIAA6vKkFJ6O6X0cErpnUbzbksp7VJ68NGwlNL1edYoSZLWwfCLYcWy7GtuJKkADKxqtYj4WUScnncdkiSpg2y4LWz7BXjuJ/CvF/KuRpIMrCrLZ4DN8i5CkiR1oJ0vAAJmXZp3JZJkYFVZ5mFglSSpuq2/NWx3Kjz/c3jbB/9LypeBVeX4NXBoRGy81jUlSVLl2vk86LIezPpm3pVIqnEGVpXj34FHgSkRcXhEDMi7IEmS1AF6bQ47fhXm/RrefCLvaiTVMAOrWhQRn4uIXUtvFwOHAbsCdwAvRcTyZqb3citYkiS1j2Ffh24bwMyL865EUg3rlncBKrwbgYuBGcCDQMq1GkmS1DnW2wQ+cEZ2WfDrf4V+u+ddkaQaZGBVawRASqku5zokSVJn+sDp8My1MONCqJuUdzWSapCXBEuSJKl5PTaCnc6GlybDgul5VyOpBhlYJUmStGY7fAV6DoAZF+RdiaQa5CXBao2+EbFNORuklF7sqGIkSVIn6rY+7PwNeOzf4OX7YfNReVckqYYYWNUa/1aaWivhf1uSJFWP7cbBU1dlXdYB0yAi74ok1QhDhVrjbeDNvIuQJEk56doTdrkQHvkSvPQ72GpM3hVJqhEGVrXG91JKE/IuQpIk5Wjo5+HJK7Iu65ajIXwUiqSO5980kiRJWrsu3WH4JfDG4/C/t+VdjaQaYWCVJElS6wz6DPT5AMy8CFYsz7saSTXAwCpJkqTW6dIVdp0Abz0JL/wm72ok1QADqyRJklpv60/CxiNg5iWwYlne1UiqcgZWtSil1MUHLkmSpJWiC+z6LXh3Djz/X3lXI6nKGVglSZJUni0Pg00+ArMmwPIleVcjqYoZWCVJklSeCPjgpbDwf+G56/OuRlIVM7BKkiSpfAM+BpvVwROXwXsL865GUpUysEqSJKl8Edm9rItfgWeuy7saSVXKwCpJkqS22Wwf2GI0PHk5LHs772okVSEDqyRJktrug5fC0tdh9vfzrkRSFTKwSpIkqe36fQgGHg2zvwtLXs+7GklVxsAqSZKkdbPrN2HZO/DUd/KuRFKVMbBKkiRp3fQdDoOOg6evgUWv5F2NpCpiYJUkSdK6G34JrFiSPYBJktqJgVWSJEnrrs8OMOREePZHsPAfeVcjqUoYWCVJktQ+drkQWAGzLs27EklVwsAqSZKkVpk0aRIHH3wwAwcOpFevXgwdOpRjjjmGhx56KFthg8Gw7TiY85/w7vOrbX/jjTcSEUQEBxxwwMo/N566du3a7LGnT5/OmDFj6NevH7169WLXXXfl+9//PsuXL+/AM5aUt255FyBJkqTiO+ecc7jyyivZZJNNOOqoo+jfvz/PPfccd9xxB7feeiu/+MUvOOGEE2Dnb8Dz/wkzJ8CeN66yjxEjRnDxxRcDMG/ePAYPHrxy2YMPPsj999/PoYceutqx77jjDj75yU/Ss2dPjj32WPr168ddd93F6aefzrRp07j55ps78tQl5cjAKkmSpBa9/PLLfOc732HAgAHMmDGDzTbbbOWyKVOmMGrUKC666KIssPbeErb/f/D092Cnc2GjD6xcd8SIEYwYMQKA+vp66urqVi7bc889ARg3btwqx3777bf54he/SNeuXamvr2fkyJEAfOtb32LUqFHccsstTJw4keOOO66Dzl5SnrwkWJIkSS164YUXWLFiBR/5yEdWCasABxxwABtuuCELFix4f+ZO50DX3jDz4lbtf+bMmfz5z39mq6224rDDDltl2S233MKCBQs47rjjVoZVgJ49e3Lppdm9sj/60Y/aeGaSis7AKkmSpBZtv/329OjRg0ceeYTXXnttlWVTp07lnXfe4cADD3x/Zs9NYcfx8OJv4Y2/r3X/119/PQAnn3zyavew3n///QCMHj16te32228/evfuzfTp01myZEmZZyWpEhhYJUmS1KJ+/fpxxRVX8Morr7DTTjsxbtw4zjvvPD71qU9x8MEHc9BBB/GTn/xk1Y2GnQnd+8KMi1rc96JFi7jpppvo2rUrp5xyymrLn376aQB22GGH1ZZ169aNIUOG8N577/H886s/5ElS5fMeVkmSJK3V+PHjGTx4MF/4whe44YYbVs7fbrvtOOmkk1a7VJgefWHYWTDjAnjtEei/R7P7/e1vf8ubb77JYYcdxtZbb73a8rfeeguAjTbaqNntG+a/+eab5Z+UpMKzwypJkqS1uvLKKxk7diwnnXQSc+bM4V//+hePPfYYQ4cO5fjjj+fss89efaMd/w3W65+F1jVouBz4S1/6UkeVLqmCGVglSZLUovr6es455xyOPPJIrr76aoYOHUrv3r3Zfffdue2229hqq6347ne/u/plud03gJ3Og5fvhVceWG2/TzzxBNOnT2fgwIGMGTOm2WM3dFAbOq1NNczv27dv209QUmEZWCVJktSiu+++G8ieCNxU79692WOPPVixYgWPP/746htvfxr02gJmXAgprbKopYctNdhxxx0BeOaZZ1Zb9t577zF37ly6devG0KFDyzonSZXBwCpJkqQWNTyBd5WvrmmkYX6PHj1WX9itF+x8ASx4EP55z8rZS5cu5Ze//CVdu3bl5JNPXuOxR40aBcDvf//71ZZNnTqVhQsXstdee7Heeuu1+nwkVQ4DqyRJklq07777AllHdP78+ass+93vfse0adPo2bMne+21FwDLli1j9uzZzJkzJ1tp21Ng/UHZvaylLmt9fT1vvPEGhx56aLMPW2owduxY+vfvz8SJE3n00UdXzl+8eDEXXJDdG3vaaae127lKKhafEixJkqQWjR07lgMPPJA//vGPDBs2jKOPPprNN9+cp556irvvvpuUEpdffjmbbLIJAPPnz2fYsGEMGjSIefPmQdcesMvF8PAXYP6dMPDjKy8zHjduXIvH7tOnDzfccANjx46lrq6O4447jn79+nHnnXfy9NNPM3bsWI499tiO/ggk5cTAKkmSpBZ16dKFyZMnc9111zFx4kRuu+02Fi5cSL9+/RgzZgxf+9rXOPjgg1veyZDPwpOXw4wLeert7Zg5c2aLD1tq7KijjuKBBx7gsssu49Zbb2Xx4sVst912XH311Xzta18jItrpTCUVjYFVkiRJa9W9e3fGjx/P+PHj17ru4MGDSU0esESXbjD8mzD90wzrPZMpU6ZQV1fX6uPvvffeTJ48ubyiJVU872GVJElS5xj0Keg1EB76LPu/NApuHwxzf5V3VZIKzA6rJEmSOse838CSBZDeIwAWvgCPlO5hHXJ8npVJKig7rJIkSeocfz8fVixZdd7yhdl8SWqGgVWSJEmdY+GL5c2XVPMMrJIkSeocvbcpb76kmmdglSRJUuf44GXQtfeq87r0yOZLUjMMrJIkSeocQ46HPa6H3oNIRBZWu/XJnh4sSc0wsEqSJKnzDDkejprHA1veD/veBktfg7m/yLsqSQVlYJUkSVI+tjwU+n0YZl0KK5blXY2kAjKwSpIkKR8RMPwS+Nc8u6ySmmVglSRJUn7sskpqgYFVkiRJ+bHLKqkFBlZJkiTlyy6rpDUwsEqSJClfdlklrYGBVZIkSfmzyyqpGQZWSZIk5c8uq6RmGFglSZJUDHZZJTVhYJUkSVIx2GWV1ISBVZIkScVhl1VSIwZWSZIkFYddVkmNGFglSZJULHZZJZUYWCVJklQsdlkllRhYJUmSVDx2WSVhYJUkSVIR2WWVhIFVkiRJRWWXVap5BlZJkiQVk11WqeYZWCVJklRcdlmlmmZglSRJUnHZZZVqmoFVkiRJxWaXVapZBlZJkiQVm11WqWYZWCVJklR8dlmlmmRgVcWKiMMi4p6I+EdELIqI5yPi5ojYM+/aJElSO7PLKtUkA6sqUkRcAdwN7A78HrgG+CvwcWBaRJyQY3mSJKkjbHko9Btpl1WqIQZWVZyI2Bw4C3gF2CmldEpK6dyU0ljgECCACXnWKEmSOoBdVqnmGFhViQaR/bf7cErp1cYLUkpTgHeATfMoTJIkdbAtx9hllWqIgVWV6FlgKbBHRPRvvCAi9gM2BP6YR2GSJKmD2WWVaoqBVRUnpfQ6cA4wAHgyIq6PiH+PiN8C9wD3Al/Ks0ZJktSB7LJKNSNSSnnXILVJRBwF/AzYuNHs54CLU0q/XsM244BxAAMGDPjQxIkTO7rMqvHuu++ywQYb5F2GmnBciscxKSbHpXjWdUz6LX6IXV//BrM3OouX1z+sHSsrpgMOOOCxlNLIvOuQOpuBVRUpIs4Gvg38APgh8DLwAeDfgYOBq1JKZ7e0j5EjR6ZHH320o0utGvX19dTV1eVdhppwXIrHMSkmx6V41nlMUoI/7AFLXoMjnoEu3duttiKKCAOrapKXBKviREQdcAVwZ0rpjJTS8ymlhSmlvwJHA/OBMyNiaI5lSpKkjuS9rFJNMLCqEh1eep3SdEFKaSHwCNl/27t1ZlGSJKmTeS+rVPUMrKpE65Ve1/TVNQ3zl3ZCLZIkKS92WaWqZ2BVJXqw9DouIrZqvCAiDgX2BhYD0zu7MEmS1MnsskpVzcCqSnQL2fesDgCeioj/iogrIuJOYBIQwLkppf/Ls0hJktQJ7LJKVc3AqoqTUloBjAFOB54ke9DSmcBHgcnAISmla/KrUJIkdaqVXdbL7LJKVcbAqoqUUlqWUvp+SumjKaU+KaVuKaXNUkqHp5Tuybs+SZLUiVZ2WefC3F/mXY2kdmRglSRJUuXzXlapKhlYJUmSVPnsskpVycAqSZKk6mCXVao6BlZJkiRVB7usUtUxsEqSJKl62GWVqoqBVZIkSdXDLqtUVQyskiRJqi52WaWqYWCVJElSdbHLKlUNA6skSZKqj11WqSoYWCVJklR97LJKVcHAKkmSpOpkl1WqeAZWSZIkVSe7rFLFM7BKkiSpetlllSqagVWSJEnVyy6rVNEMrJIkSapudlmlimVglSRJUnWLgOEX22WVKpCBVZIkSdVvy8Og34fsskoVxsAqSZKk6ue9rFJFMrBKkiSpNthllSqOgVWSJEm1wS6rVHEMrJIkSaoddlmlimJglSRJUu2wyypVFAOrJEmSaotdVqliGFglSZJUW+yyShXDwCpJkqTaY5dVqggGVkmSJNUeu6xSRTCwSpIkqTbZZZUKz8AqSZKk2mSXVSo8A6skSZJql11WqdAMrJIkSapddlmlQjOwSpIkqbbZZZUKy8AqSZKk2maXVSosA6skSZJkl1UqJAOrJEmSZJdVKiQDqyRJkgR2WaUCMrBKkiRJYJdVKiADqyRJktTALqtUKAZWSZIkqYFdVqlQDKySJElSYw1d1icus8sq5czAKkmSJDXW0GV993mYe1Pe1Ug1zcAqSZKkmnTfffdx9NFHs/nmm7Peeuux5ZZbcsghhzB58uRGXda138va4n4aefbZZ7niiisYNWoUW2+9NT169GDAgAF8/OMfZ8qUKR15qlLFMrBKkiSp5px99tkceOCBPProoxx55JGceeaZHHbYYSxYsID6+vpWd1nXup9GLrzwQs4991xeeeUVxowZw5lnnsnee+/NpEmTGDVqFD/4wQ869JylStQt7wIkSZKkznTDDTdw1VVXceKJJ3L99dfTo0ePVZYvW1bqqDbusg45Abp0b9t+SkaPHs0555zDbrvttsr8Bx54gIMOOoivf/3rHHPMMWyxxRbtdKZS5bPDKkmSpJqxZMkSzj//fLbZZptmQyZA9+6lYNpCl7Ws/ZScdNJJq4VVgP3335+6ujqWLl3K9OnT235yUhWywypJkqSace+997JgwQLGjx9Ply5dmDRpErNmzaJnz57sscce7LnnnqtusIYua9n7WYuGcNutm7+eS435EyFJkqSa8Ze//AWAnj17sttuuzFr1qxVlu+3337ccsstbLrpptmMCNjlYph6ZNZl3fbzbdtPC1544QXuu+8+evfuzX777beupyhVFS8JliRJUs149dVXAbjqqquICB588EHeeecdZsyYwcEHH8zUqVM55phjVt1oq8Nh491XeWJwm/bTjCVLlnD88cezZMkSLrnkEjbeeOP2PWGpwhlYJUmSVDNWrFgBZJfe3nnnneyzzz5ssMEGDB8+nNtuu42BAwfywAMP8NBDD72/UTP3srZpP00sX76cz372s0ybNo1jjz2Ws846q8POW6pUBlZJkiTVjL59+wKw2267MXjw4FWW9e7dm0MOOQSARx55ZNUNm3RZ27yfkuXLl3PCCSdw880386lPfYqbbrqJiFinc5OqkYFVkiRJNWPHHXcE3g+uTTVckrto0aJVFzTpsrZ5P2Rfd/PpT3+aiRMn8pnPfIZf//rXPmxJWgMDqyRJkmrGxz72MSKCJ598cuVlvY01PDxpyJAhq2/cqMv6sQP2a9N+li5dyjHHHMPNN9/M5z73OX75y1/StWvXdjgzqToZWCVJklQzBg0axBFHHMGLL77INddcs8qye+65hz/84Q/07duX0aNHA1k3dPbs2cyZM2eVLuugFQ+WtR/IHrB09NFHc8cdd3DyySfz85//nC5d/HVcaonXHkiSJKmmXHfddTz++OOcccYZTJo0id122425c+dy++2307VrV37605+y0UYbATB//nyGDRvGoEGDmDdv3ipd1uuu/WOr9wNw6qmnMnnyZPr3789WW23FhAkTVqutrq6Ourq6TvokpOIzsEqSJKmmDBw4kMcee4wJEyZw5513MnXqVPr06cMRRxzBeeedxx577LHmjRu6rFOPZOCy+rL2M3fuXABee+21ZsNqAwOr9D4DqyRJkmrOpptuyrXXXsu1117b4nqDBw8mpbTqzEZd1k0PP6FV+wGor69fh4ql2uRF85IkSVI5Gj8x+H+2gF93gdsHw9xf5V2ZVHXssEqSJEnlWvY2ELD0/7L3C1+AR8Zlfx5yfG5lSdXGDqskSZJUrr+fDzS5VHj5wtJ8Se3FwCpJkiSVa+GL5c2X1CYGVkmSJKlcvbcpb76kNjGwSpIkSeX64GXQtfeq87r2zuZLajcGVkmSJKlcQ46HPa6H3oOAyF73uN4HLkntzKcES5IkSW0x5HgDqtTB7LBKkiRJkgrJwCpJkiRJKiQDqyRJkiSpkAyskiRJkqRCMrBKkiRJkgrJwCpJkiRJKiQDqyRJkiSpkAyskiRJkqRCMrBKkiRJkgrJwCpJkiRJKiQDqyRJkiSpkAyskiRJkqRCMrBKkiRJkgrJwCpJkiRJKiQDqyRJkiSpkAyskiRJkqRCMrBKkiRJkgrJwCpJkiRJKiQDqyRJkiSpkAyskiRJkqRCipRS3jVIuYiIBcALeddRQfoDr+VdhFbjuBSPY1JMjkvxOCblGZRS2jTvIqTOZmCV1CoR8WhKaWTedWhVjkvxOCbF5LgUj2MiqTW8JFiSJEmSVEgGVkmSJElSIRlYJbXW9XkXoGY5LsXjmBST41I8jomktfIeVkmSJElSIdlhlSRJkiQVkoFVkiRJklRIBlZJkiRJUiEZWKUqERGbRMQpEXFbRDwXEYsi4q2I+FNEnBwRzf68R8ReETE5Il4vbTMjIsZHRNdm1u0bEV+PiF9FxJMR8V5EpIg4cA37jogYHRHXRsTfIuKNiFgcEU9HxPcjYkB7fw5FU8RxWcPx+kfEP0vb/Wldzrnoij4mEbF5RHyv9HOyqPRz89eIuLw9zr+oijwuETEkIn4cEbMjYmFEvBIRD0XEuIjo0V6fQdF00piMiIhLImJa6e+gpRExPyJ+ExG7t1Bb14g4vbTvRaVjTY6IvdrzM5CUPx+6JFWJiDgV+BHwT2AK8CIwAPgEsBFwK3BMavRDHxEfL81fDPw38DpwBLAjcEtK6ZgmxxgBPF56+w+ge+kYB6WU/thMTT2BRcBSYCrwd6ArMArYFXgF2Del9Ow6fwAFVcRxWUOdtwIHAxsA01JK+7ThdCtCkcckIvYG7gZ6A5OBp4FewHbAzimlwW0/82Ir6rhExIdL9fQCfg/MAvqUjrMVcA8wOlXhL1SdNCZ/Bj4CPAY8DLwLjCD7++g94NiU0v802SaA3wJjyX5G7gL6AccCPYFPppTuaKePQVLeUkpOTk5VMJGFwCOALk3mb072S0Yi+0e8YX4f4FVgCTCy0fyewPTS+sc12dfGwMeAfqX3N5bWO3ANNXUHzgc2bjK/C/Dj0rZ35f3Z1dq4NFPj50rrn1Z6/VPen1stjknp+K8B84AdmlnePe/PrkbHZVJpnRObzF8feKK0bL+8P78KHpOvAts1c+zjS+u/BvRosuzTpWXTgJ6N5n+4dOxXgQ3z/vycnJzaZ/KSYKlKpJTuTyndlVJa0WT+y2ThEKCu0aKxwKbAxJTSo43WXwxcUHp7WpN9vZFSui+l9Hora1qWUrospfRGk/krgAnN1FR1ijgujUXENsAPgP8Eflfu9pWowGPyDWAT4NSU0jPN1L2sjH1VnAKPy9DS651N9vUv4L7S203L2F/F6KQxuTal9Fwzx/4V8CzZz8TwJosb9nFBad8N2/yFrKu7aakWSVXAwCrVhoZfdN9rNG9U6fX3zaw/FVgI7BUR63ViTbUm13EpXVZ3I/AWcMa67q9K5DkmnwbeAP4QETtFxFcj4pyIGBsRG6zjvitdnuPyROn1sMYzI6J3qYaFwEPreIxK1BljstoxSrea7FXa14PNbNPwP95GNbNMUgUysEpVLiK6kV3yCav+ErFj6bW5Ts57wFygG+93F9rbF5qpqWYUZFzGk3VHTk4pvd0O+6toeY5JRAwB+gPPAd8jC0k/AC4HbgbmRcSYtu6/khXgZ+UCsns4b4yIOyPi8oj4D2A22X2TY1NKL63jMSpKZ4xJRHwU2AmYT3bfcINtyZ6F8Hxpn001PBNhh7UdQ1JlMLBK1e9yYBdgckrpD43mb1R6fWsN2zXM79veBZUeYnIx8A7vXyZWa3Idl4jYCfg28OPUygcz1YA8x2Sz0uvuwDjgK6V5WwJnl2q4NSKGrcMxKlWuPysppdlk90ZOJ7uf8xyyS1I3B24C/rwu+69QHTomEdEP+EXp7ekppeXtfQxJlcPAKlWxiPgacCZZJ+CzOZcDQETsQPZEx+7ACSmlOTmX1OnyHpeI6A78kqxrdHZnH7+I8h4T3v/3uCswIaV0XUppQUrpnymlq8i6rT3JuuI1owDjQkTsRhZWewH7AhsCWwMXkV1K/3BEbLTmPVSXjh6TiFgfuAPYHrgypXRzex9DUmUxsEpVKiK+AlwDPAkc0MxDRhr+L/SaftFqmP9mO9a0A9lXI/Qje1LknWvZpOoUZFzOA3YDPp9Sencd9lMVCjImjbe9rZnlDfP2WIdjVJQijEvp0tffkj3E54iU0p9SSu+mlP6RUrocuJYsWJ3e1mNUko4ek1JYnQTsA1ydUjqnmdU6/d8uSfkysEpVKCLGk/0iNYvsl4qXm1nt6dLravf5lH5JG0L2oIvn26mmYUA92X16x6SUbm2P/VaSAo3L7kAA9RGRGiay+8sA9i7Ne3MdjlERCjQmc3j/wTJvNrO84UnbvdbhGBWjQOPyAbLvwH1qDTVMKb1+aB2OURE6ekwiYkOyBybtT9ZZPXMNpcwBlgNDS/tsavvS62r30UqqTAZWqcpExDlkD235G9kvFa+uYdX7S6+jm1m2H9AbmJ5SWtIONQ0nC6v9gE+kGvxC94KNy71kX2PTdPrv0vJXSu9/0ezWVaJIY5JSWsr7TzzdpZlVGubNbWZZVSnSuAANT7Ptv4blDV9ns3QdjlF4HT0mpUuq7yG75PqyNXRWgZVfkTO9tK99m1nl0Ca1SKp0bf0CVycnp+JNwIVkX6b+KNBvLev2ARZQxhe8N7OPG0vrHdjCOiPIvvh9IXBI3p+R49LidoNL2/0p78+sFscEOLq0zjRg/Ubz+5J1tRLwubw/u1oaF7LA+kZpnVOaLOsLPFVa9uW8P7tKHRNgY+AvpWUXtbKmTzf6WenZaP6HS8d+FeiT92fn5OTUPlOklJBU+SLiRLJfvpaTXbbV3BMU56WUbmy0zVHALcBiYCLwOnAk2VcT3AJ8KjX5SyIivsP73YZ9yL5i4B6yB/gA3J5Sur207sZkX9PRD7gP+NMayv9+SunNVp5qRSniuLRQ62CyDt60lNI+rTvDylPkMYmInwGfJxuH35E9hOlwYCvg1tJxVpR90hWgqONSquvnZJfR3wc8ThayjiTrsP4ZqEvtcDVK0XTGmETEFLKv15pD9tTl5tyeUvpbo22C7N7isWQPf7oL2AQ4liwcfzLV4JU8UtXKOzE7OTm1zwRcQvZ/nFua6pvZbm9gMlkXYREwk+wBIl3XcJx5aznGJY3WHdyKmhIwOO/Pr5bGpYVaG8arqjusRR4TslB0ClnH6V9kVyY8Cvw/oEven10Nj8t+wP+QhdplwLvAY8C5NOrwVdvUGWPSivFIwEnNbNettM+ZpWO8UTrmXnl/bk5OTu072WGVJEmSJBWSD12SJEmSJBWSgVWSJEmSVEgGVkmSJElSIRlYJUmSJEmFZGCVJEmSJBWSgVWSJEmSVEgGVkmSJElSIRlYJUmSJEmFZGCVJNW0iPifiEgRcUYL63w4IpZFxNyI6NOZ9UmSVMsipZR3DZIk5SYiNgFmAv2AD6eUZjZZ3ht4HNgO2D+l9KfOr1KSpNpkh1WSVNNSSv8HfB7oAfwqItZrssp3gR2Ayw2rkiR1LgOrJKnmpZT+APwQGA78e8P8iBgDnAo8BlwSEd0i4ssR8eeIeDsiFkbE4xHxlYhY7d/UiDgpIm6NiOcjYlFpm2kRcUJzdUREfeny5B4RcVFEPB0RSyLixg45cUmSCs5LgiVJAiKiJ1kwHQYcBPwdmAVsCOwOPA/cBRwCPA3UA4uBA4BdgZtSSp9tss9FwBOl/fwT2AQYA2wFXJpSurDJ+vXA/sDdwIeB3wGvAq+mlL7bzqcsSVLhdcu7AEmSiiCltDgijgceBv4LmAEMAL6cUno6Ii4hC6s/BManlJYDRERX4HrgCxFxS0rpjka73SWlNKfxcSKiB1kQPTcifpxSmt9MOYNK277WvmcpSVJl8ZJgSZJKUkp/Ay4k64AeCkxKKf2odLnvV4GXgdMbwmppm+XAmUACjm+yv1XCamneUuA6sv9p/LE1lHKhYVWSJDuskiQ19R3gdGBz4OuleTuQPUX4WeCCiGhuu0VklxOvFBHbAOeQBdNtgF5NttlqDTU80pbCJUmqNgZWSZIaSSmtiIglpbeLSq+blF63By5uYfMNGv4QEUPJgufGwIPAPcBbwHJgMHAi0PSJxA1ebkvtkiRVGwOrJElr91bp9baU0idauc0ZZEH38ymlGxsviIhPkwXWZiWfiChJEuA9rJIktcZs4E3goxHRvZXbbFd6vbWZZfu3R1GSJFU7A6skSWuRUnoPuBbYAvhBRDS9F5WI2CIidmo0a17pta7JeocAp3RMpZIkVRcvCZYkqXW+BXwQOBU4IiLuB+YDm5Hd27o3cD7wZGn9/wA+D9wcEbcALwG7AKOB3wLHdmr1kiRVIAOrJEmtkFJaFhFHAScAJwGHkz1kaQEwl+zrcH7VaP0ZEXEAcClwGNm/uX8HPkF2ebGBVZKktQif6yBJkiRJKiLvYZUkSZIkFZKBVZIkSZJUSAZWSZIkSVIhGVglSZIkSYVkYJUkSZIkFZKBVZIkSZJUSAZWSZIkSVIhGVglSZIkSYVkYJUkSZIkFdL/B6X/A2+9O36aAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# did the same for latinx and white\n", + "years = list(tavel_time_diff_Latinx_White.keys())\n", + "differences = list(tavel_time_diff_Latinx_White.values())\n", + "\n", + "# Plotting the line graph\n", + "plt.rcParams[\"figure.figsize\"] = (10,10)\n", + "plt.rcParams.update({'font.size': 20})\n", + "\n", + "\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Difference in Total Travel Time between Latinx and White Riders (2011-2021)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Travel Time Difference')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + " \n", + "# make the output bigger\n", + "plt.rcParams[\"figure.figsize\"] = (10,10)\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#did the same for latinx and black\n", + "years = list(travel_time_diff_Latinx_black.keys())\n", + "differences = list(travel_time_diff_Latinx_black.values())\n", + "\n", + "# Plotting the line graph\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Difference in Total Travel Time between Latinx and Black Riders (2011-2021)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Travel Time Difference')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + " \n", + "# make the output bigger\n", + "#plt.rcParams[\"figure.figsize\"] = (10,10)\n", + "# ADJUST THE SIZE OF title and labels\n", + "\n", + "\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# did the same for latinx and american indian\n", + "years = list(travel_time_diff_Latinx_american_indian.keys())\n", + "differences = list(travel_time_diff_Latinx_american_indian.values())\n", + "plt.rcParams[\"figure.figsize\"] = (15,10)\n", + "plt.rcParams.update({'font.size': 20})\n", + "\n", + "# Plotting the line graph\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Difference in Total Travel Time between Latinx and American Indian Riders (2011-2021)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Travel Time Difference')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + "\n", + "# make the output bigger\n", + "#plt.rcParams[\"figure.figsize\"] = (10,10)\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# did the same for latinx and alaska native\n", + "years = list(travel_time_diff_Latinx_alaska_native.keys())\n", + "differences = list(travel_time_diff_Latinx_alaska_native.values())\n", + "\n", + "# Plotting the line graph\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Difference in Total Travel Time between Latinx and Alaska Native Riders (2011-2021)')\n", + "plt.xlabel('Year')\n", + "\n", + "plt.ylabel('Travel Time Difference')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + "\n", + "# make the output bigger\n", + "#plt.rcParams[\"figure.figsize\"] = (10,10)\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#did the same for latinx and asian\n", + "years = list(travel_time_diff_Latinx_asian.keys())\n", + "differences = list(travel_time_diff_Latinx_asian.values())\n", + "\n", + "# Plotting the line graph\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Difference in Total Travel Time between Latinx and Asian Riders (2011-2021)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Travel Time Difference')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + " \n", + "# make the output bigger\n", + "#plt.rcParams[\"figure.figsize\"] = (10,10)\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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pcyDeBW8dUDXGhn87xrQpYcs/MkaZPfB++U0HmsY44L8g+gdD6FecVyOGN/Tn6YAHE9hn3+R3YOP1/h5KRHsmMN/89mGaP24rYYlc2PhT/PHbw7d/IY/J0L4YEjE8VLuwnrAvCmHjQzVeDugfMW4IcSTYBcTVxt//W8j7C26Bx3gCywmtw6B8ts16EmyhEHF8rPbPl9YR467x5/97jGkv9scvDj/Wi3iOZp9DSdh2UY+dfMqlRgwfFLb974kyXZ+w8euISDj9MhP98XdHDG/vr/9qwlpNRJQ5k8J9IIXOzyzg4Cjj9yTnh5FzI8YNJZ/ro19mgl/m5ojhowra3nidljjytnoa5w8PJdLDI8aHrnMXRQwPtQB4KMbyqgKziUjaCeg6H8e+C593aiLT5jPP/M7xuM63WPs2GdsCeMIfvxKv59g6YfvmpWRsg4jl5XftHhS2PndEmbYGOTXU50eMO88fvoMoLSbIfb1IK8JxEd6yL9orvFVCQsdQWIyLo4ybQsR3DbwfD9bhJWHD/fGnhI2vSU5rurhbtOEl9KEfKw6JMj60fpHHc2j/Jbp9Yy4vGcd4nPs1NWJc07Bt6ojSysMvN4ro52b48dYvynTtyPnRZEnEuGRdH78hopVaWLlQS4D9E9lm+WzL6uTUrh9QyHlc50+fCewVMa5u2HrF/NEvbB5r41jeEMpGgl2i2yVsmkrk1I5/F+c0qWHxxJVg+9OFfiA8pqCyxXoPdpj1Yf/ne59bEp2C12Tgfy7GPQPOue/I6fUu2v0C4HWXH00qXq3IXOfcf2PM/w+8X90q++Wjedj5ey3COP/vPhHDT8X7MFpPzk39+TKzPYCD8H6FfCVGrOvwvhADHB7PfBMw1jm3MMrwULPhangX8eJwqv/3ZefcysiRzrkvyOns4PTI8UXlnFuM94FcE+ia7Pkn6HXnXKxHVeXLPz6m4v0K2Tti9Lt4F9Q9zax7lMlD9yKOiTjWk3WOlhY7gSejDP8W7wsKeF9+NkQpM9H/G3m+n4+3zd91zv1FdGPxvqzvbXE8gzKKr51z30QOdM7NI+eeo1MjRg/0/z6Rz3zf9v8W5nrylf/3kNAAMzO8L4P/Ak/jf8kNG18Dr6UOeF/yQ8Nr4tX8ZxF9/+Cc20nOuobHm0ow1/kSV8A5nkyF3Ra3A3Pwaohfwmvm2QqvFviGJMcY77V7O/BUlGm34bWKgOif4eDdzjYvYhzOua/JOf6Loj7etor1SvS+1cgYNwCto9yTnOfcxasNro+XSE2KMv5AvJr95f75FG8cO8jpxOmgeKcrrASWV5zn+wf+fd0rzWwTXsXI1f64mSTeO3LoePzeRXm0kv/d7d0Y06aSnOvjE867xzWa0H23hflsi2Y3cnrDTviRZWbWGXjIf/usy3sPd3hfRvk93Sj0KLXaicZQGgW8Xe7H+36YgZegF6fQMVPg8VieH0of+oLQ38zyJFZhQgn/7uTtVW4bXq1GfvNvX8D864bNP5ppMYaHHitRP2L4Af7fyS7+R5OFYq0NLLPYfUaFDuhYsRZW1HV0zqWb15FRY/KuZ5H5nYmEPsjyfHCEmYT3AR8tOYx3WYcDF+Ld792UnB7uw8XsjKWEFNhropntB1yOd8y0IPdFMSTXejjnVpvZRLzWAGfjfciH5teUnA/Vt8ktGedoabLEOfdv5EDnXJaZrcHbnnNjTBv64SPyPAhto4Fmdlo+yw51IrM7XhOtRKTlM24K3j7NPjfM67ws1PHNZ6FOaqIIdeZTmOvJd/g1HmbW3jm3AO9LegPgc/+YmwvsY2a7OufW4p3DVYG/I37Q6+EPd8CcfK5/oXM2PN6grvPFpjDneJIVals453aY2bl4TdlP8AdnAuc55wr9JJEiXrt/dc5tiTEu1vqEzqUpxDYF737moujnnEuLNdLv+GtxfjPwrznn4MXcCK/2L1IzvCQvZApek/nwBPqQsHFfEfHjWMT4aHF0xEsi++LVyNUm97N4Q3EkRRKWV5zne6xpXwGu9H8sTES8x2O0R5ol6/qY3+f6Z3idSr1uZs/h/Ugxw+V9tFa8wn9YWh+zVBT+95lxeNeIGcAthYyhXAlyu5jXmeRt/tvbXPE/RnU9XuumAn+gLKkEO/yCsK6Elhn6daGm/ypItDJr8/lVLTT/angJYmHmT7Qv5L5QjVdk74uhZf0ZxzJDQrFWpgixFkGsdYTY65kMDcjpKX95PuWW+X8bFWYhZvYMXjPpkHS84zz0AdAAb/0S7qU9yf7Jb6SZ3Qw8Ss4XiUy8i0noA7su3hesaOvxNl6CfYaZDQ779f4MvH0w1zk3J2KaZJyjpUl+iW1mAWVC4yPPg9A22sV/FaQw2yi/cyM0LvzcCP/lNtojRiIlHJNzbquZTcP7AncI3v3noS/haf7fKXhJdx+8D/fQ+Mjav1C8RuLXv6Cu88WiiOd4UhRlWzjnfjazJ8n5QvWk38qlUJJw7S7MZ1voXPqb2PI7J4udeU96+D/gpLDBO/Bqb0LXqkZ41/bIbTMVb/s1M7N2/o9d2eeuc26tmf0C7GtmDfyWE7HOXczsTLznAYe2YxZeJ487/Pe1/RiScswmY3nFfL5n/3BiZo2Bo/BaEl2I18fHSwnOryjHY1Kuj+T/3WQw3u1KvfESt1uA7Wb2HV5fB6MSqGwKxRoS948Rfs/sX+AlVwvwmghvj1I0/Ae3/J4gE9oWpeIxw/5nw81RRv3lnOsVZXhousC2i5kdA4zG+zx7xjn3eEHTJEFo3Qp8OlBJNRHf1/+7rAi/OiUqtG5PO+csjteoKPPIr1v30Pw/inP+Q5K5cgkKxTo7zlgHBRhrcYn2y3uRmdkAvC9omXj3ybQDqjnndnXONXHONcH70IO8v4CXtJjHs5ntjde5hOE1vdwbbz0ahK1HqAlttPX4AO/C04LcNS+h5uGRtdeQnHO0vAttoxvi3EZpJRgTQP04YmpdyOVENjWNrOWaUsD4yHg3xrkNU6NMWxau8/lKwjkeODOrTe5beQ40s0J9jylj1+6Sdglecr0Vr9Ov3Z1z1Z1zjcK2TSghy7VtnHNb8WqxAA7xb+3oi/eFOTR8ij9dHzOrDuwfNjybmTXCSxir4DVT7ol3v2T9sDiGRYujMEp6eUXlnFvlvOcHn+gPetbifEZvkiTl+ujyeYSS81onHYx3684zeB2UVQX64XVYOTfeR4n5wiv56sUzgZnVxbvlYx+8yq3DXOzb7TaRk0zm18ohNC7RFmfFpTbRbyWJWfEU5HYxs0PxPq+qAK8B1+dXPolCFcZrCypY7Am230z3UP/t18W9vDChndyyjM6/oOW2KsQ0yW76Xdqtw/v1GfLfT6GLc741vDGEmu2+7Jy71zn3h3N57r2K55fdoJ2Cdz34r3PuGufcr1E+9GKuh/N+sf/Ef3sWZN/7vx9+Z1VRJgvqHCpLSmIbxfNhF35uhH+AFmdckQl06Et66Jnr2Qm4ec+yj/olnZx46/hfCBJRno7RIp3jpcQwvE6T/sL7wnYw8J9Cziuoa3foXIrnvAtKaNvc75wb7pxbFj7SvGdg59dEMvzc3csv+61zLiPK+P3xahVXOe85uuEG4H3x/xU42zkXrWlwMvdRSS8vKZzXh8abeInnsAKKRyrK8Vgi10fn+Z9z7jrnXHe84+kyvO94bUlsncPvuy6wqb6Z1cJrpt4Tr3PAw5xzMVuQ+teQ3/y3e+cz6738v3mewx0E59yQRH4gD3K7mNnBeH04VcdraXNJlGt3cQkdMwXev18SNdiXkNOM8K0SWF5IqNlYqt/5TXHNv7OZNS+G+ccSeoh7IusVirWBme2fb8lyxHn3IoXuee2XT9H+/t+ZEcNDyXl+v1SHkvOfoo00s1YUXwduyVTQetQi5/7/WEK11KeaWRW83q3B69VxSZTyxX2OlgehbXRUMS7jkDjGZZ8bzuv8KfTFakAhlhfPeQVe53CZwO5mdizeL+lTQ1/SnXOr8R6B1gXvcTDVgdXOud8i5jMdr/MTI/HtGNR1vjgU5RzPCisXSM2dmR2H90SCLLyeuK/1R91byFq7oK7doXMpv3us8zsnS0K+2wavg6/8WoWFtz6J1rIk2vhoHbuF4vjZRbldzz8W+0cOL4KSXl4yhZ6skGpmhyUwXVGOx0Cuj8659c65keR06Bb3+eLXiIeSozb5lfW/l4zHa56+Fi+JXBDHYkJ9/kTt4NNvtdHHfzsxWpnSLMjt4vch8ileU/LxeE84ya+1cdL4HaaGavQjfwzMo1gTbDM7kpxeuL9zzn1anMuL8B5ec4T6wN35FTSzwnQ4MRHvV/QUYvc0XpT5xzIWr/O1AtcrxP9VOJSYP+onP1GZWQ2/Nqi8CDV5HBSth2UzOwKvcyTwfgkLF+q9sl4+89/o/903xvgHKQVNyeJQ0HrcQcH3AH+G17Psrnj3Y+fXPByK/xwtD17H+9LUycwuy69gEbbRIWaWp9doM2tPTg+z70WMHuX/vTm/L1bmqRcxOJ7zKtQqIvQFP3R8pEUUm4L3OXan/z7Pl3R/Pu/7b+8zs5jHsZlV9pshhwR1nS8ORTnHN4X9Xy9ZAcXLzHYDXvbfPumcm+I3jf0Ar9buzUJ8bgV17Q6dSyf751gu/rlY1A7OiirmtvHvzx5awPTf4P041hLvkVgQdu76zUjn4fXOfrw/OFonW6E49onxw84leC0akqWkl5c0zuuR/mP/7Z35lY0QOh4PNLM8x52ZtcXrSyWaYr0+mlkl/3iLJXTvdaLn/lT/b898ll0V7/rSD+97zRHOuV/inH+oxV5H/8fhSJfg9XexDfgwznmWCkFuFzPrgvcs+jp4vfmfFqWFSXHqjnes/0vsDrCzJT3BNrO6Znakmb2D94W7Bt4JGPmYl2Ll/0oV6gjlVjN7ycw6hMVZw8z6mNnz5Jxsicw/Ha+XSQecZWbjwn9FN7MqZtbTzB6lgJ46E1zuGrxnxIK3Xs+aWXbzHDNramY3mllkwnItXicdfYGJZnZw6N41M0sxs339aRaRvMchlAbP4t3LUQP43Mx6QvY6nwKM8cv9zzk3KWLa0EXj4GhfhHyhR3ZcZmYX+hcfzKylmY3GSzIT6qkyIKH1OMbMbvN/qcPMGpnZY3jnUr73nDjvESYf+G/vw2sGlEHeHy5C5Yv1HC0PnPeoi1Dzt+fM7KHw+83MbBczO8LM3iRvEhyvTXiPfjk69KXSzPrgPbYv9NzXyH34MN61oiEw1cxOD2+F4B//l+LVjpwYMW3ovDoq2o9eEUIJc57Hb0W8jzU+5Fa85oQd/HiPCv3Q6P8I0N7MbsT7VTr7S1dQ1/kE1TWzhvm8Qs3iC32OO+/RcqF7bi8oxnWJ5WW8lnBzyJ08XIbXPHFvch4RE6+grt3v4jV/rIbXC//B/nIrmddpzwfk/kEjCKFtc5eZnWBek/BQ79rj8W79idV7Os65TeR8Ae3ll50eUSz041iPsPeR/od37u0DPBP6sc7M6pjZYGAEcdwLmYCSXl6yhZLcQ0LHVUH85uWh/T3WzI4N+254EF5CsyPGtMV9fawDLDSzO/zvqKHjsJJ59+A+4JeL+oiwfIQeSxm18y5/OW/jtXj6FxjgnIts4RiTc+4ncj4zR5nZ0aH5mtn5eH1hAAzzW2JFLr9a+DWcnI6/Kse4tkdOHz5t+A8b9SKmTygHDHK7mNmeeJ2p1ce7Vpzgf+eMN/ZKEdslfNvtGjEultDxMjWuWnNXuIeJj8I7oXbifbitxGsyGHogfOiVhfdh0jDGfFKJ8vD6gsaFlVnil0nNp8ydfhyhmDbjfdHKDBu2ONFlh5W9AO/iE5rXVrwLcEbYMBcxTetowxPYNob3pTt8W2/A+/U19H5UlOkG+OVCZbbjNZXZGTGvVgkcC/nFmeaPG1SUfRhnHKH5DIkybj9/n4fWbxPeL2Sh97OB3aJMVwVYGHYsr/aXswRo4ZepitdMKjSvDLwvZaH3d8XaDokcZ3Gsv4u2jES2MV4tX/i5G7qH3eF9wR0VaxuHzePQiGNpQhyxF+YcbR0al4RtF/PYiWc74tXQOLwecgt1nOc3D7xfTJ+L2K4b8c7l8O02OcH1Dh2XN4Ud51vxPjhD81wN7BVj+nZ4iUL4sb+GvJ8DAyOma4h3jXT+Pl7hb5885wFe7VZoPluAKhHjm0Usa9981rcXXm+4obI7/Xh3RMzjkCjTlvh1voB91zoi5vxeaWHTFfocx/thN/wcXeK/rg8rE3X6omwLvFoN52//zlGmOyZsffolsA2Lcu0eFLlto8x/CLE/i/fCO7dCy/qXnPNmAXBjQfOP47iIeq2Jpyxe7+kLw8bvJOf7RYa//kvyWw7eM+dD038RZfzZYePXABbHfJy/j0KfDZ/j1abH2s6haVrHu/8Ku7yiHONJ3q/f+uX+GzF8FLHP7ab+cRd+fQt9DqwGLsovborv+lgvYl/sjDLfP/C/jyW4PbP8OGtHGd83bP7byMlzor2mxVhGHbwflULz2YL3nTv0fjxQOca0g8i93gVe22Mc9wW9Wie43QLbLsCrYWXWFbDsmws4h/J95bP+oR/gLohnexW1BrsKOT3N7epvpEV4zVTuANo6585wXq1rIJxzQ/Hu0RuJdwEJPVZiBd6vXv8hp81/Yeb/Gt4jBJ7Cq5nJxDuA1uJ9ON/jj08a57kB72B/F+9LYw28C9xMvKZtD0SZbgJeDc5Qv9wOvAvYJrwawoeBHs65pcmMN2jOey7eXng/SszHO24z8E7ywcD+LsqviM77dfZQ4A28bVwfr3O5VviPuHPefd6HkVOjl+XP+0vgOOfc/cW5bkl2Bl5N3294j1kxvA/rgc65i+Ocx2Ry9/4Yq3l4tuI+R8s651ymc+5KvA6d3gSW4tV+VcfrufNjvFqEwrYSWov3I9RTeD+UVsWrrXwJ6Oq8WvRocS0EugFX4u339Xi/CmcAP+Ptz2P8mMOnW4PXvOwDvA52GpFzXkX6mpz7f6e6iOZgzrm/8RIB8D50Yz1nHOfcNKAj3mNepuIlifXwvmRNx+uh9hDnXJ6atCCu88WkKOf4fXjb7md/utA+q1dcwZrXUeKT/ts7nXM/R5Zx3q1nI/2YRlveWxKiCvLa7Z9TXfF+1FiB95m0Eu8zqhcl9zjTqJz36KwDgOfJeYzlNvzH4bn4nugwJcb/0YZ97fxvsFFiuRG4FO92kR14Pzj+hNdr8DF4+yxpSnp5xeBR/+8RZhbz8UrhnHMr8I67J/E+X1LwflB5Ba9Z7B8FTF9c18dNwLH+fH/E+7zYBS8pm4aXZ3R1EZ3wFcR5fcJMxPvefEKUIuG5UXWi966dby/bzmvF0Rvvejsbsn8k/B6v5c3xLqfTv7IiyO2S6+klBSy7dp6pi8i8R+Kl4v3wFLVVZp5pYlzTREREREREyhUzOxXvtqpPnXPR7gcWyWZm1+D9CD/SOZdvfzjZ0yjBFhERERGRisC///gnvI789nZ5nz4hAmTfez4faA7sGW8r35J4TJeIiIiIiEjgnPcYtlvxbi25I+BwpHQ7G+956y8kcgttft3fi4iIiIiIlCvOuQn+0yNqmVnlMnhPtJQMh9fJ54hEJlIT8QQ1bNjQtW7dOugw8tiyZQu1atUKOgwJgPZ9xaT9XnFp31dc2vcVl/Z9xVVa9/2MGTPWOOeidmhW0akGO0GtW7dm+vTIxzkGLy0tjdTU1KDDkABo31dM2u8Vl/Z9xaV9X3Fp31dcpXXfm1m5eupQMukebBEREREREZEkUIItIiIiIiIikgRKsEVERERERESSQAm2iIiIiIiISBIowRYRERERERFJAiXYIiIiIiIiYdauXcvLL7/MSSedRLt27ahRowZ169bl4IMP5pVXXiErKytX+QULFvDII4/Qv39/dt99d6pWrUrjxo054YQTmDx5ctRlLFu2jAceeIDTTjuNdu3aUalSJcyMhQsXFjru0aNHs99++1G7dm3q1q1Lamoqn3zySczymZmZDBs2jM6dO1OjRg0aNGjA0UcfzdSpUwsdQ0WnBFtERERERCTMe++9xyWXXMIPP/zA/vvvz/XXX88pp5zC3Llzufjiizn99NNxzmWXv+uuu7j11ltZtWoVRx99NDfddBMHHXQQn376Kf379+eZZ57Js4zp06dz55138v777+Oco27dukWK+eabb2bQoEGsWLGCSy65hHPPPZc5c+Zw3HHH8eyzz+Yp75zjzDPP5MYbb2Tnzp1cffXVnHTSSXz11Vf07duXjz76qEjxVFR6DraIiIiIiEiYDh068PHHH3PMMcdQqVJOneSDDz7Ifvvtx/vvv88HH3zAKaecAsBRRx3FLbfcQrdu3XLNZ8qUKRx++OEMHjyY0047jaZNm2aP69mzJ1999RVdunShTp06pKamMmXKlELFO3XqVJ544gn22GMPpk2bRv369QEYPHgwPXr04Oabb+bYY4+ldevW2dOMGTOGsWPH0rt3byZOnEj16tUBuPzyyzn44IO55JJL6N+/P7vsskuhYqqoVIMtIiIiIiISpn///hx33HG5kmuAJk2acPnllwOQlpaWPXzQoEF5kmuAQw45hNTUVHbu3Jmn2XWLFi3o06cPderUKXK8L7zwAgB33HFHdnIN0Lp1a6666ip27NjBa6+9lmua559/HoChQ4dmJ9cAvXr14owzzuCff/5h7NixRY6tolGCLSIiIiIiEqcqVaoAULlyfI2BEy1fGJMmTQK8mvRIAwYMyFUGYPv27UydOpWaNWvSp0+fuKaR+CjBFhERERERiUNGRgavv/46ED2ZjbR06VImTpxIzZo16du3b7HEtGXLFpYvX07t2rVzNUEPad++PQDz58/PHvbHH3+QmZlJ27Ztoyb+0aaR+OgebBERERERkTjceuutzJ07l6OPPpojjzwy37I7duzgnHPOYceOHTz66KO5mm4n08aNGwFidpIWGr5hw4YiTSPxUQ22iIiIiIhIAZ555hmeeOIJOnbsyBtvvJFv2czMTM477zy+/fZbzjjjDG6++eYSilKCpgRbREREREQkH88++yzXXXcde+21F5MnT6ZBgwYxy2ZmZnLuuefy3nvvcfrpp/Pmm29iZsUWW6i2OVQrHSk0vF69ekWaRuKjBFtERERERCSGp556imuuuYZ99tmHyZMn06RJk5hl09PTOeussxgzZgxnn302b7/9drF2bgZQq1YtmjdvzubNm1mxYkWe8QsWLAC8R4+F7LHHHqSkpLBo0SIyMjLimkbiowRbREREREQkikceeYQbbriBrl27MnnyZHbbbbeYZXfu3Mlpp53Ge++9x/nnn88bb7xBSkpKicTZv39/AD7//PM84yZMmJCrDED16tXp3bs3W7du5euvv45rGomPEmwREREREZEI999/P7feeis9evRg4sSJNGzYMGbZHTt2cNJJJ/HRRx9x0UUX8dprr+V5hnYyrFixgt9//z1P0+7Qs7kfeOAB1q9fnz18yZIljBgxgmrVqnHBBRfkmuaKK64A4M4772T79u3Zw6dNm8a7775Lo0aNOOWUU5K+DuWdehEXEREREREJM3r0aO6++25SUlLo06cPzzzzTJ4yrVu3ZtCgQYCX4H722Wc0bNiQ5s2bc9999+Upn5qaSmpqaq5hoekBfv/9dwBuueUWdtllFwB69OiRa5rbbruN0aNH89prr+Watnfv3tx44408+eSTdO7cmVNPPZWdO3fy7rvvsm7dOoYPH07r1q1zLfvMM8/kgw8+YOzYsXTr1o3jjjuOtWvX8u6775KZmclLL71EnTp14t9oAijBFhERERERyWXx4sWA12HZU089FbXMIYcckp3khsqvWbMmanIdEplgjx49Ok+ZDz74IPv//O73jvTEE0+w7777MmLECEaOHEmlSpXo3r07gwcP5thjj81T3sx455136N27N6+++irDhw+nevXq9O3blzvvvJPevXvHvWzJoQRbREREREQkzJAhQxgyZEjc5dPS0gq1HOdcQvMdNWoUo0aNill+0KBBuWq2C1K5cmVuuOEGbrjhhrinkfzpHmwREREREZHSZPFbMK41h/zdH8a19t5LmaAabBERERERkdJi8Vvw46WQuRUD2LrUew/Q5pwgI5M4qAZbRERERESktJh9B2RuzT0sc6s3XEo9JdgiIiIiIiKlxdY/ExsupYoSbBERERERkdKi+m7Rh9dsWbJxSKEowRYRERERESkNstLBqoB393WOlJrQ5YFAQpLEKMEWEREREREpDX5/CrYtgz2vh5qtcBjUbAX7jVQHZ2WEEmwREREREZGgbVkKc4ZA8+Ohx5Nw4hKmNJsEJy5Rcl2GKMEWEREREREJknMw7Wowg57Dg45GikDPwRYREREREQnSsg/h70+g2+NQS52ZlWWqwRYREREREQlK+r8w/Vqo1wX2vC7oaKSIVIMtIiIiIiISlJ/vhm1/Q5/3oZLSs7JONdgiIiIiIiJBWDcT5j8D7S6DhvsHHY0kgRJsERERERGRkpaVCT9eBtUaQdeHgo5GkkRtEEREREREREragudh3XTo/TZUrRd0NJIkqsEWEREREREpSVv/htm3Q5PDodWZQUcjSaQEW0REREREpCTNvAGydkKv57xnX0u5oQRbRERERESkpPz9Ofz5f7D3HbBLu6CjkSRTgi0iIiIiIlISMrbCtCuhzp6w13+CjkaKgTo5ExERERERKQlzh8KWxXDoZEipFnQ0UgxUgy0iIiIiIlLcNvwCvz0GbQZC49Sgo5FiogRbRERERESkOLksmHYFVKkD3R4LOhopRmoiLiIiIiIiUpwWjYJ/vob9X4bqjYKORoqRarBFRERERESKy/Z/4KfB0OhgaHtB0NFIMVOCLSIiIiIiUlxm/QfSN0GvF8CUfpV32sMiIiIiIiLFYdUUr3l4p8FQb++go5ESoARbREREREQk2TJ3wLTLoVYb2OfOoKOREqJOzkRERERERJLtt8dg0++Q+hlUrhl0NFJCVIMtIiIiIiKSTP8uhLlDoeVp0GxA0NFICVKCLSIiIiIikizOwbSrIKUadH8q6GikhKmJuIiIiIiISLIsfRdWfgE9hkPNZkFHIyVMNdgiIiIiIiLJsHMDzLweGvSE9lcEHY0EQDXYIiIiIiIiyTD7dtjxj9exWaWUoKORAKgGW0REREREpKjW/AALXoAO10CD7kFHIwFRgi0iIiIiIlIUWRnw42VQoxl0vj/oaCRAaiIuIiIiIiJSFPOegQ2zoc/7UGWXoKORAKkGW0REREREpLC2/Alz7oZmx0CLk4KORgKmBFtERERERKSwZlwLLgt6PgtmQUcjAVMTcRERERERkcJY9pH36voI1G4ddDRSCqgGW0REREREJFHpm2H6NVB3H+h4Q9DRSCmhBFtERERERCRRc4bA1r9gvxehUpWgo5FSQgm2iIiIiIhIItbPhnlPwR6XQKPeQUcjpYgSbBERERERkXhlZXrPvK7aALo+HHQ0UsqokzMREREREZF4/fESrP0BDnwDqjUIOhopZVSDLSIiIiIiEo9tK2HWrdC4P7Q+J+hopBRSgi0iIiIiIhKPmTdC5jbo9ZyeeS1RBZ5gm9muZnaxmX1oZgvNbJuZbTSzb8zsIjOLGqOZ9Tazz8xsnT/Nz2Z2vZml5LOsY80szZ//ZjP7wcwGFt/aiYiIiIhIubDiC1j6Dux1G9TZM+hopJQqDfdgnwY8D6wAJgN/Ao2Bk4GXgQFmdppzzoUmMLMTgPeB7cC7wDrgOGAYcJA/z1zM7GpgOLAWeBPYCZwKjDKzfZ1zNxfXCoqIiIiISBmWsQ2mXQm7tIe9bw06GinFSkOCPR84HvjUOZcVGmhmtwM/AqfgJdvv+8PrAC8BmUCqc266P/wuYBJwqpmd6ZwbEzav1sDjeIl4T+fcEn/4fcA04CYze985913xrqqIiIiIiJQ5vz4Em/+A/hMhpXrQ0UgpFngTcefcJOfc+PDk2h++EnjBf5saNupUoBEwJpRc++W3A3f6b6+IWMyFQDXg2VBy7U+zHnjQf3t50dZERERERETKnY2/w68PQ+tzoUn/oKORUi7wBLsA6f7fjLBhoaP68yjlvwK2Ar3NrFqc00yIKCMiIiIiIgLOwbTLIaUWdHs86GikDCi1CbaZVQbO99+GJ8ahHgXmR07jnMsAFuM1fW8b5zQrgC1ACzOrWcSwRURERESkvFj8OqyeAt0egRqNg45GygAL6zusVDGzx4GbgM+cc8eEDZ8PtAfaO+cWRpnuW6A30Dt0T7WZ7QSqAFX8JDxymuVAM6CZn3BHjr8UuBSgcePGPcaMGRNZJHCbN2+mdu3aQYchAdC+r5i03ysu7fuKS/u+4tK+D0blrI3st3og21Ja8FPDZyD6w42KVWnd9/369ZvhnOsZdBylUWno5CwPM7sWL7n+HTgv4HBwzo0ERgL07NnTpaamBhtQFGlpaZTGuKT4ad9XTNrvFZf2fcWlfV9xad8H5IeLwW2h6uHvkFpv30BC0L4ve0pdE3H/cVpPA78C/Zxz6yKKbPT/1o0xi9DwDYWYZmOM8SIiIiIiUlGs/hr+eAU63ggBJddSNpWqBNvMrsd7VvVcvOR6ZZRi8/y/HaJMXxlog9cp2qI4p2kK1AKWOee2Fjp4EREREREp+zJ3eh2b1WoF+94ddDRSxpSaBNvMbgGGAbPwkuvVMYpO8v8eFWVcX6AmMNU5tyPOaQZElBERERERkYrq9ydg46/Q81moXCvoaKSMKRUJtpndBTwMzAAOdc6tyaf4WGANcKaZZd9Yb2bVgaH+2+cjpnkN2AFcbWatw6apD9zuv30BERERERGpuDYvgrn3we4nQ/Njg45GyqDAOzkzs4HAfUAm8DVwrZlFFlvinBsF4JzbZGaX4CXaaWY2BlgHHI/3OK6xwLvhEzvnFpvZYOAZYLqZvQvsBE4FWgBPhHocFxERERGRCsg5mHYVWGXo8XTQ0UgZFXiCjXfPNEAKcH2MMlOAUaE3zrlxZnYIcAdwClAdWAjcCDzjojx7zDk33MyWADfjPV+7El5Hanc650YnY0VERERERKSM+mssrPgcuj8FNVsEHY2UUYEn2M65IcCQQkz3LXB0gtOMB8YnuiwRERERESnHdm6EGddB/W7Q4aqgo5EyLPAEW0REREREJFA/3wnbVkLfj6CSUiQpvFLRyZmIiIiIiEgg1k6H+SO8mutdewUdjZRxSrBFRERERKRiysqAHy+DGk2g89CCy4sUQO0fRERERESkYpo/AtbPhIPehap1g45GygHVYIuIiIiISMWzdZl373XTo6DlaUFHI+WEEmwREREREal4ZlwHLgN6jQCzoKORckJNxEVEREREpGJZ/gn89QF0eRBqtw06GilHVIMtIiIiIiIVR8YWmH411N0LOt4UdDRSzqgGW0REREREKo4598GWpXDYV5BSNehopJxRDbaIiIiIiFQMG+bA709C2wthtz5BRyPlkBJsEREREREp/1yW98zrqvWg26NBRyPllJqIi4iIiIhI+ffHK7DmOzhgFFTbNehopJxSDbaIiIiIiJRv21fDrFtgt0OgzflBRyPlmBJsEREREREp32beBBmbodcLeua1FCsl2CIiIiIiUn6tnAhL3oROt0DdjkFHI+WcEmwRERERESmfMrfDtCug9h6w9+1BRyMVgDo5ExERERGR8unXR+DfBdDvC6hcI+hopAJQDbaIiIiIiJQ/m+bDLw9Cq7Og6eFBRyMVhBJsEREREREpX5zzmoan1IDuTwYdjVQgaiIuIiIiIiLly5K3YNUk6PUc1GgSdDRSgagGW0REREREyo+d62HmjbDr/tDusqCjkQpGNdgiIiIiIlJ+zLoVdq6D/b4EU32ilCwdcSIiIiIiUj78MxUWjoQ9r4P6XYKORiogJdgiIiIiIlL2ZaXDj5dBzd1h33uDjkYqKDURFxERERGRsu/3YbBxLvQdB1VqBx2NVFCqwRYRERERkbJt8xKYMwRanOC9RAKiBFtERERERMou52D6NV6HZj2eCToaqeDURFxERERERMquZR/C359At8ehVsugo5EKTjXYIiIiIiJSNqX/C9OvhXpdvJ7DRQKmGmwRERERESmbfr4Ltv0Nfd6HSkptJHiqwRYRERERkbJn3UyYPxzaXw4N9w86GhFACbaIiIiIiJQ1WZneM6+rNYIuDwYdjUg2taMQEREREZGyZcHzsG469H4bqtYLOhqRbKrBFhERERGRsmPr3zD7dmhyOLQ6M+hoRHJRgi0iIiIiImXHzOshayf0eg7Mgo5GJBcl2CIiIiIiUjb8PQH+fA/2uRN2aRd0NCJ5KMEWEREREZHSL2MrTLsK6nSEToODjkYkKnVyJiIiIiIipd/cobBlMRw6GVKqBR2NSFSqwRYRERERkdJtwy/w22PQZiA0Tg06GpGYlGCLiIiIiEjp5bJg2uVQpQ50ezzoaETypSbiIiIiIiJSei0aBf98A/u/AtUbBh2NSL5Ugy0iIiIiIqXT9n/gp8HQqA+0HRR0NCIFUoItIiIiIiKl00+DIX0T9HoeTKmLlH46SkVEREREpPRZlQaLR3uP5Kq3d9DRiMRFCbaIiIiIiJQumTu8js1qtYF97gw6GpG4qZMzEREREREpXX57DDbNg9QJULlm0NGIxE012CIiIiIiUnr8uxDmDoWWp0Gzo4KORiQhSrBFRERERKR0cA6mXQkp1aD7U0FHI5IwNREXEREREZHSYekYWPkl9BgONZsFHY1IwlSDLSIiIiIiwdu5AWbeAA16Qvsrgo5GpFBUgy0iIiIiIsGbfTvs+AdSP4NKKUFHI1IoqsEWEREREZFgrfkBFrwAHa6BBt2Djkak0JRgi4iIiIhIcLIy4MfLoEYz6Hx/0NGIFImaiIuIiIiISHDmPQ0bZkOf96HKLkFHI1IkqsEWEREREZFgbPkT5twDzY6FFicFHY1IkSnBFhERERGRYMy41nv2da9nwSzoaESKTE3ERURERESk5C37yHt1fQRqtQo6GpGkUA22iIiIiIiUrPTNMP0aqLcvdLwh6GhEkkY12CIiIiIiUrLmDIGtf8FBY6BSlaCjEUka1WCLiIiIiEjJWT8b5j0F7S6FRr2DjkYkqZRgi4iIiIhIycjK9J55XbUBdH046GhEkk5NxEVEREREpGT8MRLW/gAHvgFV6wcdjUjSqQZbRERERESK37aVMOs2aHwotD4n6GhEioUSbBERERERKX4zb4TMbdDrOT3zWsotJdgiIiIiIlK8VnwBS9+BvW+HOh2Cjkak2CjBFhERERGR4pOxDaZdCbu0h71uCToakWKlTs5ERERERKT4/PIgbP4D+k+ElOpBRyNSrFSDLSIiIiIixWPjb/DbI9D6XGjSP+hoRIqdEmwREREREUk+52DaFVC5NnR/IuhoREqEmoiLiIiIiEjyLX4dVk+B/UZC9d2CjkakRKgGW0REREREkmvHWvjpZmjYG/a4KOhoREqMEmwREREREUmun/4DOzfAfi+AKeWQikNHu4iIiIiIJM/qr2HRq9DxRqi3b9DRiJQoJdgiIiIiIpIcmTth2uVQqxXse3fQ0YiUOHVyJiIiIiIiyfH7E7DxVzjkE6hcK+hoREqcarBFRERERKToNi+CuffB7idD82OCjkYkEEqwRURERESkaJyDaVeBVYYeTwcdjUhg1ERcRERERESK5q+xsOJz6P4U1GwRdDQigVENtoiIiIiIFN7OjTDjOqjfHTpcHXQ0IoFKuAbbzBoBpwCdgFrOuYvDhrcB5jjntiU1ShERERERKZ1+vhO2rYS+H0OllKCjEQlUQgm2mV0EPANUBwxwwMX+6MbAd8ClwCtJjFFEREREREqjtdNg/gjocBXs2jPoaEQCF3cTcTM7HBgJzAdOAp4PH++cmwv8ApyYxPhERERERKQ0ysqAHy+DGk2g89CgoxEpFRKpwb4FWAEc4pzbZGbdopT5GTgwKZGJiIiIiEjpNX8ErP8JDv4/qFo36GhESoVEOjnrCXzinNuUT5llQJOihSQiIiIiIqXa1mXevddNB8DupwYdjUipkUiCXRXYUkCZekBmoaMREREREZHSb8Z14DKg1wgwCzoakVIjkSbiS4AeBZTZH5hX6GhERERERKR0W/4J/PUBdHkQarcJOhqRUiWRGuyPgD5mdlq0kWZ2AdAZeD8ZgYmIiIiISCmTsQWmXQV194KONwUdjUipk0gN9qPAmcA7ZnYqUBfAzK4G+gAnAwuA4ckOUkRERERESoE598HWP+GwryGlatDRiJQ6cSfYzrn1ZnYI8DoQXov9jP/3a+Bs51xB92mLiIiIiEhZs2EO/P4k7HER7HZw0NGIlEqJ1GDjnPsTSDWzzniP49oV2Ah875ybUQzxiYiIiIhI0FyW98zrqvWg6yNBRyNSaiWUYIc4537Ge+a1iIiIiIiUd3+8DGu+gwNGQbVdg45GpNSKu5MzM6thZi3NLOrNFmZWzR9fPXnhiYiIiIhIoLavhp9ugd1Soc35QUcjUqol0ov43XiP4KodY3wt4Hfg9qIGJSIiIiIipcTMmyBzC/R6Xs+8FilAIgn2AOB/zrl10Ub6w/8HHJuMwEREREREJGArJ8KSN2GvW6Fux6CjESn1EkmwWwPzCygz3y8nIiIiIiJlWeZ2mHYF1N4D9rot6GhEyoREOjmrAmQVUMYBugdbRERERKSs++Vh+HcB9PsCKtcIOhqRMiGRGuxFwCEFlEkFlhY6GhERERERCd6m+fDrQ9DqLGh6eNDRiJQZiSTYHwM9zOw/0Uaa2a1Ad2BcEuISEREREZEgOOc1DU+pAd2fDDoakTIlkSbijwPnAA+Z2enAF8ByoDlwJNAV+BN4NMkxioiIiIhISVnyFqya5PUaXqNJ0NGIlClxJ9jOufVmlgq8DRyAV1vtgFBf/VOBc51z65Mco4iIiIiIlIQd62DmjbDr/tDu0qCjESlzEqnBxjm3BOhtZt3xkux6wAbge+fczGQHJyIiIiIiJWjWrbBzHez3JVgid5OKCCSYYIf4yXTSEmozOxWvA7WuQBdgF+At59y5Ucq2BhbnM7t3nXNnxljOQOAqYC8gE/gJeNw590lR4hcRERERKfP+mQp/vAQdb4L6XYKORqRMKlSCXQzuxEusNwPLgHieYj+b6B2qzY1W2MweB27y5/8SUBU4ExhvZtc4555NPGwRERERkXIgKx1+vAxq7g77Dgk6GpEyK6EE28yqACcA+wH1gZQoxZxz7qIE47gBL/FdiFeTPTmOaWY554bEM3Mz642XXP8B9ArdJ25mjwEzgMfN7BO/CbyIiIiISMXy+zDYOBf6fgRVagcdjUiZFXeCbWbNgC/xapctn6IOSCjBds5lJ9Rm+c260C73/z4Q3gmbc26JmY0A7gIuAO4pjoWLiIiIiJRam5fAnCHQ4gRocXzQ0YiUaYnUYD8BdALewWti/ReQURxBxamZmV0G7AqsBb5zzv0co2x//+/nUcZNwEuw+6MEW0REREQqEudg+tVeh2Y9hgcdjUiZl0iCfQTwlXPunOIKJkGH+69sZpYGDHTO/Rk2rBbes7o3O+dWRJnPAv9vh2KKU0RERESkdFr2Ifz9KXR7AmrtHnQ0ImWeOefiK2i2BXjWOXdLsQbkPWt7MrF7Ed8NuBqvg7NF/uDOwBCgH9593F2dc1v88s2A5cBy51yLKPOrAuwEdjrnqsWI6VLgUoDGjRv3GDNmTKHXr7hs3ryZ2rV1v0xFpH1fMWm/V1za9xWX9n3FVVz7PiVrK/utHkh6pbrMaPQizqJ1ryRBKq3nfb9+/WY453oGHUdplEgN9lygVXEFEi/n3Grg7ojBX5nZEcA3wP7AxcDTSVzmSGAkQM+ePV1qamqyZp00aWlplMa4pPhp31dM2u8Vl/Z9xaV9X3EV276fcT2sXEu1wz7hkIb7J3/+UmQ678ueRJ4e/xhwvJntVVzBFIVzLgN42X/bN2zURv9v3RiThoZvKIawRERERERKn3UzYf5waH85KLkWSZpEarBXA+OBqWb2NN7jrTZEK+ic+6rooRXKP/7fWmGxbDGz5UBzM2sa5T7s9v7f+SURoIiIiIhIoLIyvWdeV9sNujwYdDQi5UoiCXYa3iO4DK/X7fxu3g7qBo4D/L+LIoZPAs4DjgJeixg3IKyMiIiIiEj5tuB5WDcder8DVesFHY1IuZJIgn0f+SfVJcLMugOznHNZEcMPBW7w374ZMdkLeAn2HWY2LvQsbDNrDVwF7CBv4i0iIiIiUr5s/Rtm3w5NjoBWZwQdjUi5E3eC7ZwbUlxBmNmJwIn+2yb+3wPNbJT//xrn3M3+/08C7c1sKrDMH9aZnGdd3+Wcmxo+f+fcVDN7ErgR+NnMxgJVgTOABsA1zrklyVwnEREREZFSZ+b1kLUTeo0As6CjESl3EqnBLk5dgYERw9r6L4ClQCjBfgM4CeiF17y7CrAK+D+8x4h9HW0BzrmbzGwOXo31pUAWMBN4zDn3SdLWRERERESkNPp7Avz5HnS+H3ZpF3Q0IuVSwgm2/9zoQ4FOQG3n3P3+8OpAHbza5qx8ZpGHXzs+JM6yrwCvJDL/sGlHAaMKM62IiIiISJmVsRWmXQV1OkKnwUFHI1JuJfKYLszsKGAJ8CnwBLmT4q7ACrxm1yIiIiIiUlrMHQpbFkOvFyClWtDRiJRbcSfYZtYTGIfX0dkNwNvh451z3wOL8Zpvi4iIiIhIabDhF/jtMWg7CBofEnQ0IuVaIjXYdwFbgZ7OuWeABVHKTAO6JCMwEREREREpIpcF0y6HKnWg62NBRyNS7iWSYB8EjHPOrcynzF9A06KFJCIiIiIiSbHoNfjnG+j2GFRvGHQ0IuVeIgl2bWBNAWVqJjhPEREREREpDtv/gZ/+A436QNsLgo5GpEJIJBleDuxdQJmuwKJCRyMiIiIiIsnx02DI+Bf2e0HPvBYpIYkk2BOAI83s4GgjzWwA0BvQM6VFRERERIK0Kg0Wj/YeyVV3r6CjEakwEkmwHwI2AF+Y2SPAXgBmdoz//j28x3Q9mewgRUREREQkTpk7vI7NarWBve8IOhqRCqVyvAWdc8vN7Ajg/4Dwp9N/DBjwB3Cyc66g+7RFRERERKS4/PoobJoHqROgcs2goxGpUOJOsAGcczPNbE/gGOBAYFdgI/A98JFzLiP5IYqIiIiISFz+XQi/PAAtT4dmRwUdjUiFE3eCbWavAnOcc8Pwaq0/LraoREREREQkMc7BtCshpRr0eCroaEQqpETuwT4b2K24AhERERERkSJYOgZWfgldHoQaTYOORqRCSiTBXoISbBERERGpQMaOHcs111xDnz59qFOnDmbGueeeG7XsX3/9xZVXXsn+++9PkyZNqFatGs2aNaNPnz689tprpKen55nmq6++4rzzzmOfffZh1113pXr16rRp04bjjz+eiRMnxh/ozg0w8wZo0JPR39Zkv/32o3bt2tStW5fU1FQ++STvg36cc3z++edcc801dO3alfr161O9enX23HNPrr/+elatWhX/8kUESOwe7LeBy82svnNufXEFJCIiIiJSWgwdOpTZs2dTu3ZtWrRowe+//x6z7B9//MFbb73F/vvvz4knnkiDBg1Yu3YtEyZM4MILL+SNN97giy++oHLlnK/gkyZNYtKkSey///7079+fWrVq8eeff/Lxxx8zfvx47rzzTg499NCCA519O+z4h5snHc4TIy6kRYsWXHLJJezcuZMxY8Zw3HHHMXz4cK6++ursSXbs2MGAAQOoWrUqffv25bDDDiMzM5NJkybx9NNPM2bMGL7++mvat29fpG0oUpEkkmA/BPQEJpvZncA055x+1hIRERGRcmvYsGG0aNGCdu3aMWXKFPr16xezbO/evVm/fj2VKuVuJJqens4RRxzB5MmT+eCDDzj99NOzx916660MGTIkz7yWL19O9+7defDBB+natWv+Qa75ARa8wNTtp/HEiDfZY489mDZtGvXr1wdg8ODB9OjRg5tvvpljjz2W1q1bA5CSksLQoUO58sors8sCZGVlceWVV/Liiy9y4403Mn78+PyXLyLZEmkivh2v9/DOwEfA32aWGeWlnsRFREREpFzo168f7du3x8wKLFu1atU8yTVAlSpVOPHEEwFYsGBBrnHVq1ePOq/mzZvTu3dvsrKy+Pvvv2MvNCsDfrwMajTjhUnesu+4445cCXPr1q256qqr2LFjB6+99lquuCLLAlSqVIm7774bgLS0tNjLFpE8Ekmwvwa+Aqb4f2O9vk5yjCIiIiIiZVZmZiafffYZAJ07d45rmtWrV/PDDz9QrVo1WrZsGbvgvKdhw2zo+QyT0ryv4UcdlffxXAMGDAC8JunxqFKlCkCu5uwiUrC4zxjnXGoxxiEiIiIiUi6sWbOGZ599Fucc//zzD19++SULFy7k7LPP5rjjjos6zfTp0/nkk0/IyMhg2bJljB8/no0bNzJ8+HDq1q0bfUFb/oSf74Zmx7Kl/hEsX76c2rVr07Rp3h7EQ/dRz58/P651ePXVV4HoybqIxKafpEREREREkmjNmjXce++92e/NjJtvvpkHH3ww5jTTp0/PNc0uu+zCa6+9xnnnnRe7mfaMa72/vZ5l48ZNADGT8dDwDRs2FBj/tGnTuPfee9lll10YOnRogeVFJEciTcSzmVktM+tmZn2SHZCIiIiISFnWsWNHnHNkZGSwdOlShg0bxsiRI+nbty/r1q2LOs3ll1+Oc45t27bx66+/csEFF3D++edz+eWXR1/Iso+8175DoFarpMU+f/58jjvuONLT03nzTa/DNBGJX0IJtpm1MLP3gfXAdGBy2LiDzexXM0tNaoQiIiIiImVQSkoKLVu25LrrruPFF1/k+++/z+48LJbq1avTqVMnnn76aS677DJefPFFpkyZkrtQ+maYfg3U2xc6Xg/k1FBv3Lgx6nxDw+vVqxdz2fPnz6dfv36sW7eOMWPGcPzxx8e3oiKSLe4E28yaAj8AJwCfAN8B4d0p/gDsBpyRzABFRERERMq6UCdjifTKHZpm1qxZuUfMuQe2/gW9XoBKXmdktWrVonnz5mzevJkVK1bkmVeo9/IOHTpEXdZvv/1Gamoqa9as4b333uOUU06JO04RyZFIDfY9eAn04c65k4Evw0c659LxehA/KHnhiYiIiIiUfcuXLwcS65U7NE1KSkrOwPWzvJ7D210KjXrnKt+/f38APv/88zzzmjBhQq4y4ebMmUNqairr1q3jgw8+4IQTTog7RhHJLZEE+2jgY+fc5HzK/Ak0K1pIIiIiIiJlz8yZM8nMzMwzfPPmzVx33XUAHHPMMbnG/fjjj1Hn9ccff2R3inbAAQd4A7My4cfLWLG1Hr9XvzBPc/DQ/doPPPAA69evzx6+ZMkSRowYQbVq1bjgggtyTTNr1iz69evHv//+y0cffZQnPhFJTCK9iDcGFhRQJh2oVfhwRERERERKj3HjxjFu3DgAVq5cCcB3333HoEGDAGjYsCGPP/44APfddx/ffvstvXv3pmXLltSsWZO//vqLCRMmsGHDBnr37s1tt92Wa/5HHHEEu+22G926dWP33XcnIyODP/74g88//5yMjAyuueYaevbs6RX+YySs/ZHbJvRh9MUH8Nprr2XHAdC7d29uvPFGnnzySTp37sypp57Kzp07effdd1m3bh3Dhw+ndevW2eXXr1/PoYceyrp16zj00EP57rvv+O677/Jsg+uvvz7fe7dFJEciCfY6YPcCynQAVhY+HBERERGR0mPWrFmMHj0617BFixaxaNEiAFq1apWdYF9yySXUrl2bH3/8kbS0NLZu3Ur9+vXp0aMHp59+OhdeeGGeJuL33XcfX3zxBd9//z3jx48nMzOTxo0bc+KJJ3LxxRdz5JFHevdtb1sJs26DxodC7RZ4d2bm9cQTT7DvvvsyYsQIRo4cSaVKlejevTuDBw/m2GOPzVV248aN2b2aT5w4kYkTJ0ad56BBg5Rgi8QpkQT7W+B4M2vinMuTRJtZe+Ao4M1kBSciIiIiEqQhQ4YwZMiQuMoec8wxCTexvvbaa7n22msLLjjzBsjcBr2eY9ShHRg1alTMooMGDcpVsx1L69atcc7FH6yIFCiRe7AfA6oDU8xsAFATsp+JPQAYD2QBTyQ9ShERERGRimbxWzCuNYf83Q+WjoFmx0Kd6L2Ai0jpEHcNtnPuBzO7DHge7zFdIZv8vxnAhc65X5IYn4iIiIhIxbP4LfjxUsjcmvNc3BWfe8PbnBNkZCKSj0RqsHHOvQrsAzwD/Aj8AcwEngM6O+feSnqEIiIiIiIVzew7IHNr7mGZW73hIlJqxazBNrPjgd+dc/PDhzvnFgA3FHdgIiIiIiIV0o51sHVp9HFb/yzZWEQkIfnVYH8InBl6Y2aLzCyOHhhERERERCRh63+GHy6BcS1il6nZsuTiEZGE5XcPdjpQJex9a6BecQYjIiIiIlKhZGXAsnEwfzis/gpSakDrc6FWG/hlaO5m4ik1ocsDgYUqIgXLL8H+EzjYzFKcc5n+MPXjLyIiIiJSVNv/gT9eggXPw9ZlUKs1dHsM2l4I1Rp4ZWq1hNl34Lb+idVs6SXX6uBMpFTLL8F+B7gLWGdma/1hN5jZBQXM0znn9khKdCIiIiIi5cm6mV5t9ZJ3IGsHNDkMeo6AZsdApZTcZducA23OYUpaGqmpqYGEKyKJyS/Bvh/YBhwDNMOrvTb/lZ+CxouIiIiIVBxZ6fDn+15ivWYqVK4Fe1wEHa6Gup2Cjk5Ekihmgu2cywAe9l+YWRYwzDl3XwnFJiIiIiJSdm1bBQtfhIUvwLYVULsddB8GbS+AqnWDjk5EikF+j+nqDKx0zq32B40GZpVEUCIiIiIiZdaaH7za6j//z6u9bnoU7PcyNDsKLL+H+IhIWZdfE/GfgHuBUI11K9SLuIiIiIhIXpk7vIR63nBYNw0q7wLtroAOV0GdDkFHJyIlJL8EOwsI72khFUgrzmBERERERMqUrX97TcAXvgjbV0OdjtDzWWhzPlTZJejoRKSE5ZdgLwO6llAcIiIiIiJlg3NeZ2XzhsNf74PLhObHQodrvF7BTX3+ilRU+SXY44Grzew3YIU/bJCZpRYwT+ecOzQJsYmIiIiIlB6Z273Ha80fDut/gir1YM9rvWbgtdsGHZ2IlAL5Jdh3AFXxHtN1CN5julr7r/y4ZAQmIiIiIlIqbPkTFjwPf7wEO9ZC3b2h1wvQ5lzvkVsiIr78HtP1L3B56L3/mK4hekyXiIiIiJR7zsHqKV5t9bJx3rDmJ8Ce18BuqWoGLiJR5VeDHWkKsKSY4hARERERCV7GVljylpdYb5gDVRtAp8HQ/gqo1Sro6ESklIs7wXbO9SvOQEREREREArN5MSx4Dv54BXauh/pdYf9XoNVZULlG0NGJSBmRSA22iIiIiEj54Rysmuj1Br58PFgl2P1k6HAtNDpIzcBFJGExE2wzW4TXYdlhzrnF/vt4OOfcHkmJTkREREQk2dI3w+LXYf6zsOk3qNYI9r4d2l8ONVsEHZ2IlGH51WBXIneP4JHvY9FPfSIiIiJS+vy7EOaPgEWvQvomaNATDhgNrU6HlOpBRyci5UB+vYi3zu+9iIiIiEip57JgxRdep2V/TwBLgZane72B77q/moGLSFLpHmwRERERKX/SN8GiUV4z8H8XQPUmsM/d0P4yqNE06OhEpJxKOME2s0pAA7zm4uudc1lJj0pEREREpDA2/u4l1YtHQ8Zm2PUA6D0Edj8VUqoGHZ2IlHNxJdhmVhW4HDgH6Aak+KMyzWwG8CYw0jmXXixRioiIiIjEkpUJKybAvGdg5ZdQqSq0OhM6XAO79gw6OhGpQApMsM1sN2AC0JW8HZhVBvYH9gMGmdkA59yaZAcpIiIiIpLHzg3wx6uwYARsXgQ1mkPnodDuEqi+W9DRiUgFFE8N9ut4tda/A48DacByvGS7GdAfuAnoAYwCji2GOEVEREREPBvm+s3A34DMrdDoYOjyEOx+ElSqEnR0IlKB5Ztgm9nBwBHAZOBY59y2iCKLgEVm9hbwGTDAzHo756YWS7QiIiIiUjFlZcDy8V5v4Ksme4/VanW21xt4/a5BRyciAhRcg30GkAlcFCW5zuac22ZmFwILgNMBJdgiIiIiUnQ71sIfL8P852Drn1CzJXR9GPa4GKrtGnR0IiK5FJRg9wS+d84tKWhGzrnFZvYd3v3YIiIiIiKFt362V1u95C3I3A6N+0GPp6D5cVBJT5oVkdKpoKtTW+C9BOY3Gzi18OGIiIiISIWVlQ7LxsG84fDP15BSE9oMhA5XQ719go5ORKRABSXYuwDrEpjfeqBO4cMRERERkQpn+2pY+BIseB62LYdabaDb47DHhVC1ftDRiYjEraAEuzqQkcD8MoBqhQ9HRERERCqMtdO9ZuBLx0DWTmhyOPR6HpodDZVSgo5ORCRh8dzA4oo9ChERERGpGDJ3wl9jvWbga7+HyrVhj0u8ZuB1OwYdnYhIkcSTYN9gZhfEOb96RYhFRERERMqrbSth4Yuw4AXYvhJ2aQ89nvbusa5aN+joRESSIp4Eux6JJc6q8RYRERERcA7W/uDVVv/1nteJWbOjocM10PQIsEpBRygiklQFJdhtSiQKERERESk/MnfA0ne9+6vXTYcqdaD9ldD+KqjTPujoRESKTb4JtnNuaUkFIiIiIiJl3NblXk/gC0fCjn+gTifoOQLanAdVdgk6OhGRYqd2OSIiIiIFGDt2LNdccw19+vShTp06mBnnnntuvtNMnTqVo48+mgYNGlCjRg06d+7MU089RWZmZtTyq1at4pprrqFNmzZUq1aNRo0acdJJJzFz5syEYm3dujVmlu/r/vvvjzrtwoULueSSS2jTpg3Vq1enYcOGHHDAATzxxBOxF+gcrP4avjkdPmoFvzwIDQ+E/l/CMb9AhyuVXItIhRHPPdgiIiIiFdrQoUOZPXs2tWvXpkWLFvz+++/5lv/oo4845ZRTqF69OmeccQYNGjRg/Pjx3HDDDXz77be89957ucovWbKE3r17s2LFCvbbbz9OPvlk/vnnHz744AM+/fRTxo8fT7Vq8T0J9frrr2fDhg15hjvnePDBB8nIyGDAgAF5xn/wwQecffbZVKlShWOPPZY2bdqwceNG5s2bxwcffMBNN92Ue4KMbbD0bZj/LKyfBVXqQccbvKbgtXWXoYhUTEqwRURERAowbNgwWrRoQbt27ZgyZQr9+vWLWXbTpk1ccsklpKSkkJaWRs+ePQG4//776d+/P2PHjmXMmDGceeaZ2dNcd911rFixgmuvvZannnoKMwPgzjvvpGfPnlxwwQW88sorccV6/fXXRx3+3//+l4yMDLp165YdU8jcuXM5++yz2Wuvvfjss89o0qRJrvHp6ek5b7b8CQueg4Uvwc51UHcf2O9FaH0OVK4VV4wiIuWVmoiLiIjEqSSaCW/atIkHH3yQrl27Uq9ePerWrcu+++7LXXfdxT///JNwzDt27OCJJ56gV69e1KlTh1q1atGhQwcGDhwYdX4zZ87ktNNOo3HjxlStWpWWLVty5ZVXsmrVqoSXXZ7069eP9u3bZye++Rk7diz//PMPZ555Zq5Etnr16gwdOhSA559/Pnv49u3bmTBhApUqVWLo0KG5ltGhQwcuvPBCVqxYwVdffVWkdRg5ciQAl112WZ5xt99+Ozt37uStt97Kk1wDVKlcGVZNhq9Oho/bwG+PQeNUOHQyHP0ztLtUybWICKrBFhERiVtxNxPeuHEj++23H/Pnz8+utQT46quvGDp0KKNGjeLpp5+OO96VK1dyxBFHMGfOHA466KDsWtU///yT//73vwwePJhGjRpll//kk084+eSTycjI4LjjjqNDhw78/vvvvPDCC4wfP55vv/2Wli1bJrDFKqZJkyYBcNRRR+UZ17dvX2rWrMnUqVPZsWMH1apVY926daSnp7Pbbruxyy5571Vu27YtQML3YodbtWoV48ePp3bt2px99tm5xm3atIlPP/2ULl260KlTJ3788Ue++eYbMjMz6dShDUe0W0XVJS/AxrlQbVfo9B9ofwXU0rEgIhJJCbaIiEiciruZ8MiRI5k/fz4XXHABr776aq75DRo0iNGjRzN+/HhOPvnkAmPNysri9NNPZ968eXz88cccd9xxucY758jKysp+v337di6++GLS09N5//33cy3jnXfe4eyzz+bqq6/m448/LnDZFd28efMAr/Y5UuXKlWnTpg2//PILixYtolOnTtSvX5+UlBTWrFnD5s2bqV27dq5pFi1aBMBff/1V6JheffVV0tPTGTRoUJ4kfsaMGWRlZdG6dWtOP/30PD/8tNwVxt7VgV7HvgqtzoTKNQodh4hIeacm4iIiInEqzmbCkJNIRSbDAMcffzzg1XLHY9y4cXz99dfccMMNUednZqSkpGS/nzp1KqtWraJnz555EvizzjqLLl268Mknn7B0qZ7gWZDQPqpbt27U8aHhoY7IatSoQb9+/cjKyuLuu+/OVXbhwoXZP7Zs3ry5UPE453j55ZcBuPTSS/OMX716NQDjx49n4v8+5+27urNuJCx5uhKDz9qTP9fC0UPXsabucUquRUQKUKgE28w6mtlJZnZesgMSEREpDxJpJhyy9957A/Dpp5/mmeaTTz4BoHv37nEt/+233wa85HjVqlW88sorPPTQQ7z22mssX748T/mVK1cCOc2RI7Vt2xbnXPZ6SXI99dRT1K1bl2HDhnHggQdy8803M3DgQLp27coee+wBENcPO9H873//Y9GiRXTv3j1P52YAWTu3AJCZmcmIc/7lrK7LqL/fnbS6+E8efft3Tj75ZNasWcNLL71U+BUUEakgEmoibmZdgZeBbmGD3/DHHQJMAM5wzo1PVoAiIiJlUaLNhAEuvvhi3nnnHV555ZXs+6YBvv76a3799VceeOABevfuHdfyp02bBsCPP/7I9ddfz9atW7PHValShbvvvps777wze1jDhg0BWLx4cdT5hWrXQ+slsYVqqGO1NggNr1evXvawvffemxkzZnDffffx5ZdfMmPGDJo1a8YNN9zAkUceSZ8+fXKVT0Soc7M8tdebFsD8Z6n3m5c4m8EJV74MHc6FlJxHgp100kl88MEH/Pjjj4VavohIRRJ3DbaZdQDSgD2Bp/GS6XBfAeuAU5MVnIiISFmVaDNh8JqPT5o0icsuu4wff/yRYcOGMWzYMKZPn87RRx/NiSeeGPfyQ81+r7jiCgYNGsSiRYvYsGED77//PvXr1+euu+5i1KhR2eUPOugg6tWrx7Rp0/joo49yzev//u//mD17NgDr16+PO4aKas899wRg/vz5ecZlZGSwePFiKleunKe1wB577MHo0aP5+++/2blzJ0uWLOH+++/Pnk/Hjh0TjmX16tV89NFHOZ2buSxY/hlMHgCfdICFz7Nnt8MBqF69BjU6XZQruQaoX78+ANu2bUt4+SIiFU0iTcTvAaoC+zvnbgSmhY90zjngO6BX8sITESmdivtxTWlpaZhZzFeoRioRiTyuady4cZxxxhl07NiR+vXrU6NGDdq3b89ZZ53F9OnTE162xGft2rUceeSRjBs3jjFjxrBmzRrWrFnDmDFj+Prrr9l///357bff4ppXqAOzww47jBEjRtCmTRvq1q3LySefnH0/7kMPPZRdvlatWjz99NOYGSeffDInnXQS//nPfzj++OM588wz6dq1KwCVKqn7loL0798fgM8//zzPuK+++oqtW7fSu3dvqlWrlmd8NG+88QYAhx56aMKxvPbaa6Snp3PW6aewy/JXYfyeMOUYWD8L9r0XTviTtqd9RNu2bdm2bRt//PFHnnnMnTsXgDZt2iS8fBGRiiaRJuKHAh84537Np8xfwOFFC0lEpPQr7sc1hRxyyCGkpqbmGR7tUT75SfRxTR999BHTpk2jV69eNGvWjKpVq7Jw4UI+/PBD3n33XUaOHMnFF1+cUAwVTWGaCd90001MmTKFjz76KLtTM4AzzjiD6tWrc+KJJ/Liiy9yxRVXFLj8evXqsXr1ak466aQ8444++miqVq3K/Pnz2bhxY3as559/PrvvvjuPPPIIaWlpfPbZZ3Tq1IlRo0axevVqZs2axW677Rb3NqioTj31VG655RbGjBnDNddck33f8/bt27Ob5Ufuw9C9+OFJt3OOBx98kLS0NM4444w8txusWLGCjRs30rRp06gtJZxzvDzyOQAua/8uzBwNDQ+EzvfB7qdAStXssldffTU33nhjdtyVK3tfEZctW8awYcMAcvV4LyIiMTjn4noBO4CHw97fA2RGlBkGbI13nmXx1aNHD1caTZ48OegQJCDa98GYNGmSmz9/vsvKynKTJ092gDvnnHOilt24caNr1KiRq1q1qps2bVr28G3btrkDDzzQAe6dd97JNU1onvfcc0/UeSay3zMzM12fPn1c1apV3ccff5xnfFZWlsvIyMg1bNu2bVHn9fPPP7tq1aq5unXruh07dsQdQ3lU0H4/55xzHODefvvtPOPS09NdzZo1XeXKld327duzh++6664OcJs2bcozzcaNGx3gatSoEVd8ffr0cYAbM2ZM1PENGzZ0gFuxYkVc8zvvvPMc4MaPHx9X+fLmww8/dAMHDnQDBw50Rx55pANc27Zts4fddNNNecqnpKS4WrVquYsuusgNHjzY7bnnng5wp556qsvKyspVfs6cOa5evXrutNNOc4MHD3bXX3+923fffR3gDj74YLdx48Y85/3AgQMd4F577bXcwWZmOPfXR+5/j3V3gOvexpybOtC5tdNjrl96ero74ogjHOD23ntvd+2117oLLrjA1a9f3wHuxhtvLMLWk6LSZ33FVVr3PTDdlYLcrDS+EmnntQpoV0CZvfFqsUVEyrXiflxTMiX6uKZQbNHsu+++dOrUiY0bN+ZpVi65FaaZcKgWM9q2DQ2rUqVKXMs/7LDDgJzmveFWrVrFmjVrqF27dnbnZvnZsGED48ePp1GjRhx+eMVsqDZr1ixGjx7N6NGj+e9//wt4Hb+Fho0dOzZX+RNPPJEpU6bQt29f3n//fYYPH06VKlV48sknGTNmTJ5rR+PGjTn66KOZPn06w4cP55VXXqFmzZo8++yzTJ48mTp16hQc5M718NvjML49fHUCI8d5txNceuOjcOAoaNAj5qSVK1dm/PjxPProo9m3obz33nvstddevP322zzxxBOJbTARkQoqkSbik4CzzGxP51yeLkTNrBdeM/IRyQpORKQ8SORxTZH3ZC5cuJBnn32WTZs20aRJE/r06UP79u0TWn7k45o++eQTVq9eTZMmTTjiiCNo3rx53POaP38+8+bNo2HDhjRt2jShOCqawjQT7tOnDxMmTODee+/ltddey77fOTMzk3vuuQeAbt265ZomdJ92w4YNcyXLF154IY888ggjRozgggsuyO5QKzMzk8GDBwNw2mmnZTcFBvj333/z3H6wdetWBg4cyIYNG3j++efjvm+4vBkyZAhDhgxJaJqDDjqIzz77LK6yjRo14q233kpo/qNGjfI6qtswB364FJa8CZnbYLe+0O1R3j3zRN6tFP9XvapVqzJ48ODs40NERBKXSIL9EHAa8JWZDQGaAZjZ3kBfvCbj/wKPJzlGEZEyrTCPawp566238nzpPuWUUxg4cGDcy0/0cU3h/ve///HNN9+wc+dOFi9ezPjx3lMYX3755QrZ2dW4ceMYN24ckPPc6O+++45BgwYB3qOuHn/c+xisU6cOL730EqeeeiqpqamceeaZNGjQgI8//ph58+Zx6qmncsYZZ+Sa/yOPPMLUqVN5/fXXmTFjRnYt+MSJE/n1119p2LAhl1xySa5pnn32We69917uueeeXAlgixYteO6557jgggvo2rUrJ510Eg0aNCAtLY1Zs2bRoUMHHn300VzzGj16NE888QSpqak0bdqUtWvXMn78eFasWMF1113H5ZdfnqxNKYlY/BbMvoNDtv4J41pClweg1Rmw/GOYNxxWp0FKdWh9LnS4Gup3CTpiEZGKK5H25MBRwHog039lhf1dB/QPus17cb90D7aUNtr3wSvoXtz27ds7wC1YsCDq+N69ezvATZ06NXvY3Llz3cMPP+zmzJnj/v33X/fPP/+4CRMmuG7dujnA7bPPPi4zMzOu+KpXr+4Al5KS4q688kq3aNEit2HDBvf++++73XbbLfo9nL5bbrnFAdmvJk2auM8//zyu5ZZH99xzT67tEflq1apVnmm++eYbN2DAAFevXj1XvXp1t88++7gnn3wyz33vIYsWLXKXXXaZa9u2ratataqrVq2aa9eunbv66qvdsmXL8pzzoZjyu1//yCOPdPXq1XNVqlRxe+yxhxs8eLBbv359nrLfffedGzBggGvSpImrUqWK23XXXd2AAQPcZ599luCWkqRZ9KZzY2o69xY5r7erOPd/Dbz/x7Vy7pdHnNu+JuhIpRjps77iKq37Ht2DHfNl3vaJn5nVAwYCBwC7AhuB74HXnHPrCpPklyU9e/Z0pfERNWlpaVF7GpbyT/s+eGlpafTr149zzjmHN998M8/4Dh06sGDBAhYsWEC7dnm7sjjooIOYOnUqU6dO5cADD8x3WZs2baJr164sXryYcePGccIJJxQYX7Vq1di5cydHHnlknvuBx48fz/HHH0+HDh2ya9qj2bJlC/Pnz+fxxx/nnXfe4f777+eOO+4ocNmSfDrnyyDnICsdXLr3N/Qq6H1WOnx3HuyI0t9BpWpw0BhofhxUSsk7XsoVnfcVV2nd92Y2wznXs+CSFU8iTcQBcM5tAJ72XyIiUoDCPK4pljp16nD22WfzwAMP8NVXX8WVYBfmcU2RatWqRbdu3XjrrbdYt24dd911F0cccQS9evUqcPmSJNGaCbc5J+ioio/Lyj/xTDRZjSeZTfY8Q+9d3mfdF1nWTtj9xOTPV0REiiThBFtERBKz5557Mn36dObPn0+PHrl78c3IyGDx4sVUrlw5uxOqgoSeV71ly5a4l7969eqoCXxKSgp16tRhzZo1bNu2LWaCHe6oo47i888/Z8qUKUqwS8rit+DHSyFzKwawdan3HnIn2VmZpTvRTOS9yyqZbWuVwKpApbBXQe9TakCVOvGXT/R9+LCvT4HtK/PGXbNlyWwfERFJSMIJtpk1BDoBLYCozwpxzr1exLhERMqN/v3789Zbb/H5559z1lln5RoXelxT37594+6d+fvvvweIOyE/7LDD+Prrr5k7d26eTrUSfVwTwPLlywFy9T4tCXAOMrdC+ibYudH7mxH2f/omSN+Y+++yjyFre+75ZG71mg9PuywnMSWx274KzVISTxwr1ypaolkcyWulKl6CXZp1ezz7x5VsKTW9FgwiIlLqxP3tyMyqA08AFwJVYxXD+3RXgi0i4ivM45qmT5+e65nZIW+++SbvvvsuVapU4fTTT881LlmPa9qxYwe///47Xbrk7Yl42rRpvPDCC6SkpER97Fi55hxk7YiRFEdLjvP5P54mwyk1vVrSqnXzJtc5QcEelyaYlFYtYjJbufQnpeVJqIXC7DtwW//EalaA2wNERMqwRKofHgOuAH4D3gWWAxnFEZSISGlX3I9rOvXUU6lcuTI9e/akRYsWbN++nWnTpvHjjz9SuXJlbrzxRlq3bp1rmmQ9rmnbtm107dqVzp07s88++9CiRQu2bt3Kb7/9lv1M78cee4yOHTsmcYsWs8ydhUiEowzLSi94WSnVvcS4sp8cV6kDtdt6f6vUgSp14/i/jpfIhoxr7TULj1SzFfR4MmmbSUqpNudAm3OYUko7OxIRkRyJJNinAz8DvZxzcXzDEBEpv2bNmsXo0aNzDVu0aBGLFi0CoFWrVtkJNsCJJ57IlClTeOCBB3j//ffZvn077dq148knn+Taa6/FzHLN64orruB///sf3377LWvWrME5R/PmzRk0aBDXX38969evTyjegQMH0qpVKx5++GE+/vhjtmzZQsuWLRk8eDC33357rvuza9Wqxf3338+UKVOYMmUKa9aswcxo3rw55557LldddRX7779/gluskLIywpLcBJPinRu9Wub0TZAZqwY4TKUqeRPemrtHJL8xEuKqdb2EukodSInVyKsIujygZsIiIiJlQNyP6TKzzcDzzrnBxRtS6abHdElpo31fMZX6/e6yIP3fxGqHI5PinRtzJ5SxWEoCNcN1cxLiyFrmStUg4oeOUsXvRVzNhCuuUn/eS7HRvq+4Suu+12O6YkukBvsXoGlxBSIiInEo7kc1OQcZWxJPhCPHZ/wbx8Isb+JbbdfYzamjJcVV6no9OpfmxDhZ1ExYRESk1EskwX4cGGVmHZxz84srIBERiSG/RzW1PhsytxUtKU7f6CXG8TweqXLtKM2pW+RuKl21gFrkyrXUWZaIiIiUK3En2M6598ysKfC1mT0HzAQ2xij7VZLiExGRkNl35G0ynbkVvjsfvh8ELo5+J8N7pg4lwjWaRK8ZjpUcV64NlVKKZRVFREREyrJEH2JaH6gF3F1AOX3zEhFJJudg658xRmbBXrfGSIrz6ZlaRERERJIqkedg3wbcA6zFe0zX3+gxXSIixW/dTPjpP0CMTilrtlJv0iIiIiKlQCJVGZcCi4AezrmoTcNFRCSJNi+G2XfC0rehWkNofR789b4e1SQiIiJSSiWSYDfBe0yXkmsRkeK0Yy388iDMf9Z7BNXed8Be//GaeC8+Uo9qEhERESmlEkmwFwH1iikOERHJ2Abzh3vJdca/0PYC2PdeqNk8p4we1SQiIiJSaiWSYD8P3GNmTZxzK4srIBGRCicrE5a8CT/fBVv/gmbHQteHod7eQUcmIiIiIglIJMEeD6QCU83sPmAGsR/TFaurWxERCXEOVnwBs/4DG36GBr3gwNehcWrQkYmIiIhIISSSYC/G68LWgFfyKecSnK+ISMWz7icvsV75P6jdFg56F1qeBmZBRyYiIiIihZRIIvw6MZ8RU3hmdipwCNAV6ALsArzlnDs3n2l6A3cCBwA1gAXAq8Bw51xmjGmOBW4GuuE9p/sX4Dnn3OikrYyISEE2L4Gf74Qlb0G1XaHH09DuckipGnRkIiIiIlJEcSfYzrlBxRTDnXiJ9WZgGdAxv8JmdgLwPrAd73nc64DjgGHAQcBpUaa5GhiO9wzvN4GdwKnAKDPb1zl3c7JWRkQkqh3r/J7Bh4NVgr1ug71ugap1g45MRERERJKkNDTlvgEvsV6IV5M9OVZBM6sDvARkAqnOuen+8LuAScCpZnamc25M2DStgcfxEvGezrkl/vD7gGnATWb2vnPuu+SvmohUeJnbvcdtzX0A0jd6PYN3vhdqtgg6MhERERFJskpBB+Ccm+ycW+Cci6f5+alAI2BMKLn257EdryYc4IqIaS4EqgHPhpJrf5r1wIP+28sLGb6ISHQuCxa/AeP3hJ8GQ6PecPRsOOAVJdciIiIi5VTMGmwzexXvnuvbnXOr/PfxcM65i5ISXV79/b+fRxn3FbAV6G1m1ZxzO+KYZkJEGRGRolvxpdeB2fpZ0KAHHDgKGvcLOioRERERKWYWq+LYzLLwEuxOzrn5/vt4OOdcSqGCMUvFayIetZMzM5sG9MRr6j0jyvi5wN7AXs653/xh/wANgYbOubVRptkM1AJqOee2xojrUuBSgMaNG/cYM2ZMtGKB2rx5M7Vr1w46DAmA9n3pUTt9IW03vUiDHdPZltKUxXUuZnX1VO+e6yTTfq+4tO8rLu37ikv7vuIqrfu+X79+M5xzPYOOozTK7x7sNv7fZRHvgxTqDSjq87fDhtdLcJpafrmoCbZzbiQwEqBnz54uNTU1vmhLUFpaGqUxLil+2velwJalMPsu+PtNqFofug+jRvsr2CulGnsV0yK13ysu7fuKS/u+4tK+r7i078uemAm2c26p3yx8HPCxc25piUUlIlIW7Fzv9Qw+b7j3/Oq9bvF7Bq8XdGQiIiIiEoCCehEfBCwBPi72SOITqoWO9Vyb0PANEdM09MflaSJOwTXcIiK5ZW6H+SPglwdg5wZoOwj2vRdq7R50ZCIiIiISoMB7EU/QPP9vh8gRZlYZrxl7BrAozmma4jUPXxbr/msRkWwuCxa/6fcMfjPsegAMmAUHvKrkWkRERETKXII9yf97VJRxfYGawNSwHsQLmmZARBkRkehW/g8+7wnfnQfVGkL//0G/z6B+56AjExEREZFSoqwl2GOBNcCZZpbda52ZVQeG+m+fj5jmNWAHcLWZtQ6bpj5wu//2heIKWETKuPWzYdKRMOlw757r3m/BUdOgyaFBRyYiIiIipUxB92ADdDWz8xOZqXPu9XjLmtmJwIn+2yb+3wPNbJT//xrn3M3+fDeZ2SV4iXaamY0B1gHHA3v6w9+NiGWxmQ0GngGmm9m7wE7gVKAF8IRz7rtE1k9EKoAtf8LPd8HiN7xOy7o/Ce2vhJRqQUcmIiIiIqVUPAn2Cf4rEXEn2EBXYGDEsLb+C2ApcHNohHNunJkdAtwBnAJUBxYCNwLPuCgP9nbODTezJf58zseruf8VuNM5NzqBWEWkvNu5Hn55COY9473vNBj2vtV7/JaIiIiISD7iSbBn+69i4ZwbAgxJcJpvgaMTnGY8MD6RaUSkAsnc4fcMPtTrGbzN+dD5PqjVMujIRERERKSMiCfBHuecu6/YIxERCYLLgiXvwM93wpYl0PRI6PoI1O8SdGQiIiIiUsbEk2CLiJRPKyfCT4Nh/U9Qvxvs/xI0OSzoqERERESkjFKCLSIVz/qfYdYtsOJzqNUKDnwTWp8FVtYerCAiIiIipYkSbBGpOLb8BXPuhkWjvZ7Buz0BHa6ElOpBRyYiIiIi5UBBCfYUYEkJxCEiUnx2boBfH4Z5T4Nz0Olm2Ps29QwuIiIiIkmVb4LtnOtXUoGIiCRd5g5Y8BzMHeo9fqv1udDlfq9ZuIiIiIhIkqmJuIiUPy4Llr4Ls2/3egZvcgR0ewTqdw06MhEREREpx5Rgi0j5snISzPoPrJsB9bpAv/9C0yOCjkpEREREKgAl2CJSPmyYAz/dAismQM2WcOAb0Pps9QwuIiIiIiVGCbaIlG1bl8HPd8OiUVClLnR7DDpcrZ7BRURERKTEKcEWkbJp50a/Z/CnvHuuO90Ee90G1RoEHZmIiIiIVFBKsEWkbMncAQtegF/uhx1rvZ7BO98PtVsHHZmIiIiIVHBKsEWkbHBZsPT//J7BF0OTw6Dro9CgW9CRiYiIiIgA+STYZpYFuELM0znnlLiLSPKsmgw//QfWTYd6ndUzuIiIiIiUSvklwl9RuARbRCQ5NsyFWbfA359Bzd3hgNHQ+hyolBJ0ZCIiIiIiecRMsJ1zqSUYh4hIjq3LvZ7BF4+CyrtA10egwzVQuUbQkYmIiIiIxKSm3CJSeuzcCL89Cr8PA5cJe14Pe98O1XYNOjIRERERkQIVKsE2s1pAB6C2c+7r5IYkIhVO5k5Y+ALMvc/vGfwc6DxUPYOLiIiISJlSKZHCZtbCzN4H1gPTgclh4w42s1/NLDWpEYpI+eWc1zP4p51gxnVQrwscNR16v6nkWkRERETKnLhrsM2sKfAD0Bj4GNgNODCsyA/+sDOAtOSFKCLl0qop8NNgWDcN6u0LqROg6ZFgFnRkIiIiIiKFkkgN9j14CfThzrmTgS/DRzrn0oGvgYOSF56IlDsbfoG042BiKmxfAQeMgqN+gmZHKbkWERERkTItkXuwjwY+ds5NzqfMn0CfooUkIuXS1uUw5x5Y9JrfM/jD0OFa9QwuIiIiIuVGIgl2Y2BBAWXSgVqFD0dEyp30TfDro/D7k+AyoMN1sM8d6hlcRERERMqdRBLsdcDuBZTpAKwsfDgiUm5k7oSFL/o9g6+BVmdBl6FQu23QkYmIiIiIFItEEuxvgePNrIlzLk8SbWbtgaOAN5MVnIiUQc7BX2Nh1m2w+Q9o3A+6Pgq79gw6MhERERGRYpVIJ2ePAdWBKWY2AKgJ3jOx/ffjgSzgiaRHKSJlw+qv4IsD4JvTIaUGpH4G/ScquRYRERGRCiHuGmzn3A9mdhnwPPBJ2KhN/t8M4ELn3C9JjE9EyoKNv8KsW2H5eKjRHPZ/FdqcD5VSgo5MRERERKTEJNJEHOfcq2b2NXAlcACwK7AR+B541jk3L/khikiptfVvv2fwV6FybejyEOx5LVSuGXRkIiIiIiIlLpEm4gA45xY4525wzh3onOvgnOvlnLtGybVIjlGjRmFm+b5SUnJqd5csWZJv2TPPPDOh5e/YsYMRI0aw33770bBhQ2rXrk2nTp249tprWbp0aVzT77PPPpgZLVq0yFsgfRPMvgvGt4PFo6HDNXDcH7D3rUquRURERKTCirsG28z2cs79WpzBiJQXXbt25Z577ok67uuvv2bSpEkMGDAgz7guXbpw4okn5hm+zz77xL3sjIwMDj30UL799ls6duzIWWedRbVq1Zg2bRrDhw/n9ddfZ+rUqey1114x53H77bdHT8Sz0mHhSJhzL+z4B1qdCV0eUM/gIiIiIiIk1kR8rplNA0YDY5xz64opJpEyr2vXrnTt2jXquAMPPBCASy+9NOp0Q4YMKdKyP/zwQ7799lsOPfRQvvjiCypVymmocs8993Dffffx+OOP8+qrr0adPi0tjWHDhvHcc89xxRVXeAOdg7/eh9m3w78LYLdU6PYo7NqrSLGKiIiIiJQniTQR/y/QHRgO/G1m75nZsWamXoxE4jRnzhy+//57mjdvzjHHHFMsy1i0aBEAxxxzTK7kGuCEE04A4J9//ok67aZNmxg0aBCHHnool19+uTcwayd80Ru+OQ0qVYVDPoVDJym5FhERERGJkEgv4gPMrAlwHjAQOAU4GVhjZm8Bo51zs4snTJHyYeTIkQBcdNFFue7BDvn777958cUXWbt2LbvuuisHHnggnTt3TmgZe++9NwATJkzguuuuy5Vkf/KJ9wCAww47LOq01157LevXr+eVV16Bjb95A7f/A1urwP6vQJuB6hlcRERERCSGRHsRX4n3POzHzKw7MAg4E7geuM7M5gCjnHNPJTdMkbJv27ZtvPnmm6SkpHDxxRdHLfPll1/y5Zdf5hqWmprK6NGjadmyZVzLOeaYYzj55JP54IMP2HfffTnssMOoWrUqM2bM4JtvvuGaa67hqquuyjPdhx9+yOjRo3n5ucdpufIB+PZlb0TVOnDcAnVeJiIiIiJSgIR7EQ9xzs10zl0LNANOAsYBewGPJyc0kfLl//7v/9iwYQNHHXUUu+++e65xNWvW5K677mLGjBmsX7+e9evXM2XKFPr160daWhqHHnooW7ZsiWs5ZsbYsWO55557mDdvHs888wyPP/44kydPpm/fvpx99tlUrpz7t7VVq1Zx6aWXMODgdlzU4G7441Vof7U3svIuSq5FREREROJQ6AQ7TE1gN/9VGbAkzFOk3Ak1D7/sssvyjNttt92477776N69O/Xq1aNevXr07duXL774gv3335+FCxfy8ssvx7Wc7du3c8YZZ/DEE08wYsQIVqxYwcaNG/nss89YunQpffv25aOPPsqZICudS846nIxt63j5zIXQ/Fg49jfo+XRS1ltEREREpKIoVIJtnqPM7B1gBfACcCAwETg/ifGJlAu//PILU6dOpUWLFhx99NFxT1e5cuXs5uRfffVVXNM8/PDDvPfeezzwwANcdtllNGnShDp16jBgwADGjh1Leno61113ndcz+J/v8/pNuzN+8hyevqoDzc74AQ5+F3ZpV6j1FBERERGpyBK6B9vM9sbr4OwcoAlebfUCvEd3ve6cW5b0CEXKgYI6N8tPo0aNAOJuIh7qyKxfv355xnXp0oX69euzdOlS1r6/H7vunM7MRQ0AGPjoPAY+un+eaZYvX46Z1zBl/fr11KtXL6H4RUREREQqirgTbDObAXTFS6o3Ai/jdWj2XfGEJlI+bN++nTfeeIOUlBQuuuiihKf//vvvAWjbtm1c5Xfs2AFEfxTXjtU/8++mDQBU3fkX7PcSB1pNNjeaFHVer7zyCjVr1uSss84CoFq1aomGLyIiIiJSYSRSg90V+BIYBYxzzm0vjoBEypv33nuP9evXc+yxx+bp3Cxk5syZdO3aNc9zqydOnMiwYcMAOPfcc3ON27hxIytWrGDt2rW5hvfp04e5c+fy4IMPctBBB3lJ8baVMGcIQx4YSUamo9c+zdnl9HlQuRZntIMzzjo7alyvvPIK9evXj/v+bxERERGRiiyRBHt359zfxRaJSDkVah5+6aWXxixz4403smDBAnr37k2LFi0A+Pnnn5k0yatZvv/+++ndu3euaT788EMuuOACjjzySE455ZTs4XfccQfjx49n4sSJdOy4J0f1akiNrbP59vcMfvwDatSowdMj34PKtZK9qiIiIiIiFVrcCbaSa5HE/fbbb3zzzTcFdm523nnn8eGHHzJt2jQmTJhAeno6jRs35vTTT+fqq6+mT58+cS+zefPmzJz+A4/cfj6f/jeN18YtJcsZTZs2Y9CgI7jlllvo2LFjMlZPRERERETCJNTJWYiZtQCaA1FvyHTOxdfdsUg516lTJ5xzBZa76KKLEr4/e9CgQQwaNIi0tLScgc7BsnE0mnUrjx86n8fP7APdHoWGByQYeWh2BccuIiIiIiKeRHsRPwIYBhRU/ZVYN8kikrjFb8HsOzhk658wriW0HQQrv4Q1U6FOJ+j7ETQ/DkyPphcRERERKQmJ9CJ+APAJ8A/wLHANMAWYB/QBOgEfAz8lP0wRyWXxW/DjpZC5FQPYuhTm3guV68B+I6HtBVCpUA1URERERESkkCoVXCTbbcB2oJdz7jp/2GTn3OXAPsBQ4DBgbHJDFJE8Zt8BmVvzDq9aF9pdouRaRERERCQAiSTYBwIfR3R2VgnAee4GfgPuTWJ8IhLN1j9jDF9WsnGIiIiIiEi2RBLsukD4t/qdQORzfr4F+hY1KBEpQNV60YfXbFmiYYiIiIiISI5EEuzVQP2I93tElKkC1ChqUCKSj1WTYecGsIi+BFNqQpcHAglJREREREQSS7Dnkzuh/h443Mw6AJhZE+AUYEHywhORXDYvgW9OgzododcLULMVDoOarbzOzdqcE3SEIiIiIiIVViIJ9ufAIWbWwH//NF5t9U9mNg34HWgEPJXUCEXEk7EFvjoRsjKg7zhodzGcuIQpzSbBiUuUXIuIiIiIBCyRBPtFvPur0wGcc98CpwGL8XoRXwFc4Zx7PdlBilR4zsH3F8GGn+GgMVCnQ9ARiYiIiIhIhLif5eOc2wT8EDHsQ+DDZAclIhF+fQT+fBe6PgzNjgo6GhERERERiSLuGmwze9XMbijOYEQkiuWfwezbodWZ0Ok/QUcjIiIiIiIxJNJE/Gxgt+IKRESi2DQfpp4N9bvA/q+AWdARiYiIiIhIDIkk2EtQgi1SctI3wVcnQKUqXqdmlWsGHZGIiIiIiOQjkQT7bWCAmdUvsKSIFI3Lgqnnwr8L4OD3oFaroCMSEREREZECJJJgPwRMByab2bFm1riYYhKRn++B5eOh+1PQODXoaEREREREJA759iJuZucDs5xzPwPbQ4OBj/zx0Sb7//buPN6qulz8+OeRQUAEBRVyCETENCNFHBI1UDSyVBzJn3SdSq1MUUsty+xmpd3Mm6mllnrT7kVTnEltEBHICQecURA1FREUkUFk+P7+WOvY4XgOnMPZ56y9z/68X6/1Wu41fNez9uPm7Gev7/qulFJq9Ojkkup49WZ45nzodxwM+FbR0UiSJElqpDUVwtcCPwKmAQ8AqaUDkqra/KfgwaOh526w8+UOaiZJkiRVkMZcaQ6AlNLQlg1FqnJL58H9B0GHbrDnzdBu3aIjkiRJktQEduWWysHK5TBpFCx5HYZPhC6bFh2RJEmSpCaywJbKweNnwlt/h12vho12LToaSZIkSWuhMQX2BhHxyaY0mlJ6dS3jkarPzD/CCxfDgG/DVscWHY0kSZKktdSYAvvUfGqs1Mh2Jc17BB4+AXoNg0EXFR2NJEmSpGZoTCG8AJjfwnFI1WfJbJh4MHTuDUNuhHU6FB2RJEmSpGZoTIF9cUrpP1s8EqmarPgQJh0GH74D+02BThsVHZEkSZKkZrIrt1SEqd+GtyfDkLGw4Q5FRyNJkiSpBNYpOgCp6rx4Bbx0JWx3NvQZVXQ0kiRJkkrEAltqTXMmwaMnwye+CAPPLzoaSZIkSSVkgS21lkWvwaRDoWs/GPK/sE67oiOSJEmSVEKrvQc7pWQBLpXC8iXwwMHZfJ8J0HGDggOSJEmSVGoOcia1tJSyZ12/8xjsdRt037boiCRJkiS1AAtsqaU9fzHMuh4G/gQ2P6DoaCRJkiS1ELuASy3pzb/CE9+FLQ6BT3+/6GgkSZIktSALbKmlvD8DJo+CbtvBbv8D4cdNkiRJasv8xi+1hGULYeLI7L/3uhU6dC0yGkmSJEmtwHuwpVJLK+HBo2HBszDsHlh/q6IjkiRJktQKLLClUnv6p/DaONjxIug9vOhoJEmSJLUSu4hLpfSv2+Gpc6HvaPjUaUVHI0mSJKkVWWBLpfLeczBlNPQYDLtcCRFFRyRJkiSpFVlgS6Xw4XyYeBC07wx7jsvmkiRJkqqK92BLzbVyBUw+EhbNgr3/AettUXREkiRJkgpggS0117Rz4M27YeffwSZ7FB2NJEmSpILYRVxqjllj4dkLof+JsPWJRUcjSZIkqUAW2NLaeudxeOg42HgP2OmSoqORJEmSVDALbGltfPA2TBwJ6/aEPW6Cdh2LjkiSJElSwbwHW2qqlctg0uGwdA4MfwA69yo6IkmSJEllwAJbaqrHToc598PnroOeg4uORpIkSVKZsIu41BQzrobpl8KnToctRxcdjSRJkqQyYoEtNdbcB+GRb0DvfWGHC4uORpIkSVKZscCWGmPxG/DAIdBlcxgyFtbx7gpJkiRJq7JKkNZkxQdZcb1sAQy7F9btUXREkiRJksqQBba0OinBI9+EeQ/BnjfDBtsXHZEkSZKkMmUXcWl1pl8KM6+B7X8IWxxSdDSSJEmSypgFttSQt+6Dx06DzQ6Ez5xXdDSSJEmSypwFtlSfhbNg0uGw/gDY/ToIPyqSJEmSVs+qQapr+SKYOBJWLoe9boUO3YqOSJIkSVIFcJAzqbaU4MHjYf40GDoeug0oOiJJkiRJFcICW6rt2Qvh1Rtghwtg0xFFRyNJkiSpgthFXKrx+nh48vvQ5yuw7ZlFRyNJkiSpwlRsgR0RsyIiNTDNbmCf3SNifES8ExFLImJaRIyJiHatHb/KzILpMOX/wYafhV3/ABFFRyRJkiSpwlR6F/H3gP+uZ/nCugsi4iDgZuAD4AbgHeAA4GJgCHB4i0Wp8rZsAUw8CNbpkA1q1r5L0RFJkiRJqkCVXmDPTymdt6aNIqIbcBWwAhiaUno0X/5D4B/AYRHxlZTS2JYMVmUorYQpo+H9F2Hvv8F6fYqOSJIkSVKFqtgu4k10GLAxMLamuAZIKX0A/CB/+Y0iAlPBpv0IXr8DBv039BpadDSSJEmSKlilX8FeNyJGA58EFgHTgIkppRV1tts7n99dTxsTgcXA7hGxbkppaYtFq/Ly6s3wzPnQ7zgY8K2io5EkSZJU4SKlVHQMayUiZgH19ed9GTg2pXR/rW0fAQYDg1NKU+tp62ng08B2KaXn6ll/AnACQK9evXYaO7b8epIvXLiQrl27Fh1GxVhv2UwGzf0WC9v344mNLiZFx6JDWmvmvjqZ9+pl7quXua9e5r56lWvuhw0bNjWlNLjoOMpRJV/BvgZ4AHgGeB/oB5xMVgj/JSI+l1J6Mt+2ez5/r4G2apZvUN/KlNKVwJUAgwcPTkOHDm1u7CU3YcIEyjGusrR0Htx9HHTakO5f+Cuf77Jp0RE1i7mvTua9epn76mXuq5e5r17mvvJUbIGdUvpxnUVPAydFxELgDOA84ODWjktlbuVymDQKlrwOwydChRfXkiRJkspHWxzk7Hf5fK9ay2quUHenfjXL57dEQCojj58Jb/0ddv4dbLRr0dFIkiRJakPaYoH9dj5fr9ayF/L5gLobR0R7YEtgOTCzZUNToWb+EV64GAZ8G7Y6tuhoJEmSJLUxbbHA3i2f1y6W/5HPR9Sz/V5AF2CKI4i3YfMegYdPgF7DYNBFRUcjSZIkqQ2qyAI7IraNiPXqWd4XuDR/eX2tVTcBc4GvRMTgWtt3As7PX/62ZaJV4Za8BRMPhs69YciNsE6HoiOSJEmS1AZV6iBno4AzImIi8ArZKOJbAV8COgHjgV/WbJxSWhARXycrtCdExFjgHeBAYJt8+Q2tegZqHSs+hEmHwofvwH5ToNNGRUckSZIkqY2q1AL7PrLCeEdgCNn91vOBScB1wHWpzgO+U0q3RsTngXOAQ8kK8ZeA04FL6m6vNmLqKfD2ZBgyFjbcoehoJEmSJLVhFVlgp5TuB+5fi/0mA/uXPiKVpRevgJeugO3Ohj6jio5GkiRJUhtXkfdgS2s0ZxI8ejJ84osw8Pw1by9JkiRJzWSBrbZn0WvZfddd+8GQ/4V12hUdkSRJkqQqUJFdxKUGLV8CDxyczfeZAB03KDggSZIkSdXCAlttR0rZs67feQz2ug26b1t0RJIkSZKqiAW22o7nL4ZZ18PAn8DmBxQdjSRJkqQq4z3Yahve/Cs88V3Y4lD49DlFRyNJkiSpCllgq/K9PwMmj4Lun4bdroWIoiOSJEmSVIUssFXZli2EiSOBgL1uhQ5dCw5IkiRJUrXyHmxVrrQSHjwaFjwLw+7JHsslSZIkSQWxwFblevqn8No4GPQr6D286GgkSZIkVTm7iKsy/et2eOpc6PtV2GZM0dFIkiRJkgW2KtB7z8GU0dBjMOxyhYOaSZIkSSoLFtiqLB/Oh4kHQfsusNct0L5z0RFJkiRJEuA92KokK1fA5CNh0SzY5z7osnnREUmSJEnSRyywVTmmnQNv3p11C994SNHRSJIkSdIq7CKuyjBrLDx7IfQ/CfqfUHQ0kiRJkvQxFtgqf+88Dg8dBxvvATv9uuhoJEmSJKleFtgqbx+8DRNHwro9YY+boF3HoiOSJEmSpHp5D7bK18plMOlwWDoH9p0EnXsVHZEkSZIkNcgCW+XrsdNhzv3wueuhx05FRyNJkiRJq2UXcZWnGVfD9EvhU2fAlkcVHY0kSZIkrZEFtsrP3AfhkW9A731hhwuKjkaSJEmSGsUCW+Vl8RvwwCHQZXMYMhbW8S4GSZIkSZXB6kXlY8UHWXG9bAEMuxfW7VF0RJIkSZLUaBbYKg8pwSPfhHkPwZ7jYIPti45IkiRJkprELuIqD9MvhZnXwPbnwhYHFx2NJEmSJDWZBbaK99Z98NhpsNmB8JkfFR2NJEmSJK0VC2wVa+EsmHQ4rD8Adr8Owv8lJUmSJFUmqxkVZ/kimDgSVi6HvW6FDt2KjkiSJEmS1pqDnKkYKcGDx8P8aTB0PHQbUHREkiRJktQsFtgqxrMXwqs3wA4XwKYjio5GkiRJkprNLuJqfa+Phye/D32+AtueWXQ0kiRJklQSFthqXQumw5T/Bxt+Fnb9A0QUHZEkSZIklYQFtlrPsgUw8SBYp0M2qFn7LkVHJEmSJEkl4z3Yah1pJUwZDe+/CHv/DdbrU3REkiRJklRSFthqHdN+BK/fATv9BnoNLToaSZIkSSo5u4ir5b16MzxzPvQ7DgZ8q+hoJEmSJKlFWGCrZc1/Ch48GnruBjtf7qBmkiRJktosC2y1nKXz4P6DoEM32PNmaLdu0RFJkiRJUovxHmy1jJXLYdIoWPI6DJ8IXTYtOiJJkiRJalEW2GoZT5wFb/0ddr0aNtq16GgkSZIkqcXZRVyl9/J18PyvYMC3Yatji45GkiRJklqFBbZKa96j8NDXodcwGHRR0dFIkiRJUquxwFbpLHkLHjgYOveGITfCOh2KjkiSJEmSWo33YKs0VnwIkw7NRg7fbwp02qjoiCRJkiSpVVlgqzSmngJvT4YhY2HDHYqORpIkSZJanV3E1XwvXgEvXQHbnQ19RhUdjSRJkiQVwgJbzTNnEjx6MnziizDw/KKjkSRJkqTCWGBr7S16Lbvvums/GPK/sE67oiOSJEmSpMJ4D7bWzvIl2Yjhy5fAPhOg4wYFByRJkiRJxbLAVtOlBA+fAO88BnvdBt23LToiSZIkSSqcBbaa7vmLYdb1MPAnsPkBRUcjSZIkSWXBe7DVNG/+FZ74LmxxKHz6nKKjkSRJkqSyYYGtxnt/BkweBd0/DbtdCxFFRyRJkiRJZcMCW42zbCFMHAkE7HUrdOhacECSJEmSVF68B1trllbCg0fDgmdh2D3ZY7kkSZIkSauwwNaaPf1TeG0cDPoV9B5edDSSJEmSVJbsIq7V+9ft8NS50PersM2YoqORJEmSpLJlga2GvfccTBkNPQbDLlc4qJkkSZIkrYYFtur34XyYeBC07wJ73QLtOxcdkSRJkiSVNe/B1setXAGTj4RFs2Cf+6DL5kVHJEmSJEllzwJbHzftHHjz7qxb+MZDio5GkiRJkiqCXcS1qllj4dkLof9J0P+EoqORJEmSpIphga1/e+dxeOg42HgP2OnXRUcjSZIkSRXFAluZD96GiSNh3Z6wx03QrmPREUmSJElSRfEebMHKZTDpcFg6B/adBJ17FR2RJEmSJFUcC2zBY6fDnPvhc9dDj52KjkaSJEmSKpJdxKvdjKth+qXwqTNgy6OKjkaSJEmSKpYFdjWb+yA88g3ovS/scEHR0UiSJElSRbPALtBdd93Ffvvtx+abb07nzp3p168fhx9+OP/85z8b3cZZZ53FPvvswxFHHEHnzp3p0aMHO+64Iz/+8Y+ZN29ewzsufgMeOAS6bM7XrutFtOtARPDSSy+V4MwkSZIkqfpYYBfkrLPO4stf/jKPPfYYI0aM4NRTT2XQoEHcdtttDBkyhOuvv75R7Vx88cUsWrSInXbaiVNPPZWjjjqK9u3bc9555zFw4EBee+21j++04oOsuF62gDsWncYfrr2erl27lvgMJUmSJKm6OMhZAWbPns0vf/lLevXqxbRp09hkk00+Wnffffex9957c+655zJ69Og1trVgwQI6derEhAkTGDp06EfLzznnHH72s5/x85//nMsvv/zfO6QEj3wT5j3E29tdw9dHnM2oUaOYPXs2999/fylPU5IkSZKqilewC/DKK6+wcuVKdt1111WKa4Bhw4ax/vrr8/bbbzeqrU6dOtW7/IgjjgDgxRdfXHXF9Eth5jWw/bmc8OPbALjsssuaeAaSJEmSpLossAuw9dZb07FjRx5++GHmzp27yrqJEyfy/vvvM3z48GYd44477gBg4MCB/1741n3w2Gmw+UFcO7UPt956K1dccQU9e/Zs1rEkSZIkSXYRL0SPHj248MILOf3009luu+0YOXIkPXv2ZMaMGdx+++3su+++XHHFFU1q84YbbmDChAm89957PProo0yaNImBAwdy9tlnZxssnAWTDof1B/DKpudz6gFDGD16NAcddFDpT1CSJEmSqpAFdkHGjBlD3759Oe6447jqqqs+Wt6/f3+OOeaYj3UdX5MbbriBd99996PXI0aM4Nprr2XjjTeG5Ytg4khYuYKVe9zC0SNPpGvXrlxyySWlOh1JkiRJqnp2ES/IL37xCw477DCOOeYYZsyYwaJFi5g6dSr9+vXjqKOO4swzz2xSe+PGjSOlxOzZsxk3bhwzZ85kxx135LGpU+HB4+G9p2DI/3HxH+7k/vvv56qrrmLDDTdsobOTJEmSpOpjgV2ACRMmcNZZZ3HggQfyq1/9in79+tGlSxcGDRrELbfcwmabbcZFF13EzJkzm9x2r169OPjgg7n33nuZN28e//GVL8GrN8Bnf870hf0455xzOPbYY9l///1b4MwkSZIkqXpZYBfgzjvvBLIRw+vq0qULu+yyCytXruTxxx9f62P06dOH7bbenGdeeou53Q+Bbb/Ls88+y9KlS7nmmmuIiFWmmkd0bb311kQEt95661ofW5IkSZKqkfdgF2Dp0qUADT6Kq2Z5x44d1/4gC6bzxr9eBqDdLpdABH379uX444+vd/O77rqL2bNnc/jhh9OtWzf69u279seWJEmSpCpkgV2APffck0svvZQrr7ySE088kc022+yjdX/5y1+YPHkynTp1Yvfddwdg2bJlzJgxgw4dOrDVVlt9tO306dPp1asX3bt3X6X9lUvn88Ov7c6c9xK777YTG26ctb/DDjvw+9//vt6Yhg4dyuzZs/nZz35G//79S33KkiRJktTmWWAX4LDDDmP48OH87W9/Y9ttt+Xggw+md+/ePPfcc9x5552klLjgggs+ej7166+/zrbbbkufPn2YNWvWR+2MHz+e733ve+yxxx6su+663HPPPbw1ezb333sjM99YTO9NenDVH/5Y0FlKkiRJUnWxwC7AOuusw/jx47nssssYO3Yst9xyC4sXL6ZHjx7sv//+nHLKKey3335rbGf48OG89NJLTJo0iZdffpm7776b9Tq3Z8AmS/nqyV/klB9fT48ePVrhjCRJkiRJFtgF6dChA2PGjGHMmDFr3LZv376klD62fPvtt+fSSy8FspHJh/abB5MOg62Oh12ugohGxzNhwoRGbytJkiRJ+jgL7Er38p/gyXP4/OJX4I2Arv1h8GVNKq4lSZIkSc3nY7oq2ct/godPgMWvkJXTCZa8Dq/eVHBgkiRJklR9LLAr2ZPnwIrFqy5bsSRbLkmSJElqVRbYlWzxq01bLkmSJElqMRbYlazLJ5u2XJIkSZLUYiywK9lnfwrtuqy6rF2XbLkkSZIkqVVZYFeyLY+CXa6ELn1IBHTpk73e8qiiI5MkSZKkqmOBXem2PApGzuL+Tf8BI2dZXEuSJElSQSywJUmSJEkqAQtsSZIkSZJKwAJbkiRJkqQSsMCWJEmSJKkELLAlSZIkSSoBC2xJkiRJkkrAAluSJEmSpBKwwJYkSZIkqQQssCVJkiRJKoGqKrAjYvOIuDoi3oiIpRExKyL+OyI2LDo2SZIkSVJla190AK0lIrYCpgCbALcBzwO7AKcCIyJiSEppXoEhSpIkSZIqWDVdwb6crLg+JaU0MqV0dkppb+BiYBvgp4VGJ0mSJEmqaFVRYOdXr/cDZgGX1Vn9I2AR8NWIWK+VQ5MkSZIktRFVUWADw/L5vSmllbVXpJTeByYDXYDdWjswSZIkSVLbECmlomNocRHxX8B3gO+klC6qZ/2lwLeAb6aUflvP+hOAEwB69eq109ixY1s44qZbuHAhXbt2LToMFcDcVyfzXr3MffUy99XL3Fevcs39sGHDpqaUBhcdRzmqlkHOuufz9xpYX7N8g/pWppSuBK4EGDx4cBo6dGgpYyuJCRMmUI5xqeWZ++pk3quXua9e5r56mfvqZe4rT7V0EZckSZIkqUVVS4Fdc4W6ewPra5bPb/lQJEmSJEltUbUU2C/k8wENrN86n09vhVgkSZIkSW1QtRTY9+Xz/SJilXOOiPWBIcBi4MHWDkySJEmS1DZUxSBnKaUZEXEv2bOwvwX8ptbqHwPrAVeklBatqa2pU6fOjYhXWibSZtkImFt0ECqEua9O5r16mfvqZe6rl7mvXuWa+z5FB1CuquIxXQARsRUwBdgEuA14DtiV7BnZ04HdU0rziouweSLiUYfKr07mvjqZ9+pl7quXua9e5r56mfvKUy1dxEkpzQAGA9eSFdZnAFsBvwZ2q+TiWpIkSZJUvKroIl4jpfQacGzRcUiSJEmS2p6quYJdBa4sOgAVxtxXJ/Nevcx99TL31cvcVy9zX2Gq5h5sSZIkSZJaklewJUmSJEkqAQtsSZIkSZJKwAJbkiRJkqQSsMBuJRHRMyK+FhG3RMRLEbEkIt6LiEkRcXxE1JuLiNg9IsZHxDv5PtMiYkxEtKtn2w0i4rsR8aeIeDYilkdEiojhTYhzo4h4M99vUnPOWZlyzX1EnJdv09A0opTvQ7Up17zX2rd3RFwcES/kx3k3Ih6LiAtKcf7VrFxzv4bPe8301VK+F9WmXHOf77dlRPwuIp6PiMUR8VZE/DMiToiIjqV6D6pVmed+UET8Oc/5hxHxakRcHhG9SnX+1ayVcr9DZN/bJkf2Pf3DiHg9Iv4vIgatJrZ2EXFa3vaS/FjjI2L3Ur4HWpWDnLWSiDgJ+C3wJnAf8CrQCzgE6A7cDByeaiUkIg7Kl38A3AC8AxwAbAPclFI6vM4xdgAez1/+C+iQH2PflNLfGhnnzcB+QFdgckppj7U4XdVSrrmPiPOAHwH/A8yqZ5PrU0ovNfV8lSnXvOf7DQHuBLoA44EXgM5Af+DTKaW+a3/mKtfc55/5+qwPnA4sB7ZIKc1u0gnrI2Wc+53zeDoDdwNPA93y42wG3AuMSH4pXGtlnPsvA+PIHs17BzAd+BTwJeB1YEhK6dXmnX11a6XcPwjsCkwFHgIWAjuQfWdfDoxKKY2rs08ANwKHkf2dvwPoAYwCOgGHppRuK9HboNpSSk6tMAF7k31w1qmzvDfZBzGR/Y9es7wbMAdYCgyutbwTMCXf/it12toQ2Afokb++Nt9ueCNj/I98+2/k80lFv29tYSrX3APn5dsMLfo9aotTGee9NzCX7EeVAfWs71D0e1fpU7nmfjXxnpjvO67o967Sp3LNPXBXvs3RdZavBzyTr9ur6PevkqdyzH3e1ux8m0PqrDsyX3570e9dpU+tlPtvA/3rOfZR+fZzgY4N5Hgy0KnW8p3zY88B1i/6/WuLk13EW0lK6R8ppTtSSivrLJ8N/C5/ObTWqsOAjYGxKaVHa23/AfCD/OU36rT1bkrp7ymld5oaX0R8ErgE+APwl6bur4aVe+7VMso4798HegInpZSm1xP3sia0pXqUce4bckI+v6IEbVW1Ms59v3x+e522FgF/z19u3IT2VEeZ5n53siupj6Y6VzdTSv8HPAl8OSL6NLI91aOVcv+bVE+vwpTSn4AXyf6uf6bO6po2fpC3XbPPI2RXzTfOY1GJWWCXh5ovtMtrLds7n99dz/YTgcXA7hGxbnMPnnchuRZ4j6yboFpPobnP7RER34mIsyJiVERsVKJ21bAi834k8C5wT0RsFxHfznN/WER0bWbbWrNy+Mx/JCJ2AgaR9Wi4t9TtaxVF5v6ZfP6l2gsjoksew2Lgn808hhpWVO575/OZDayfCUStWFR6rZH7jx0jIjqR/cCyGHignn1qLqaZ+xZggV2wiGhP1jUbVv2gbZPP67vCtBx4mex+mn5116+FMWS/rB2fUlpQgvbUCGWSe4CfAP8FXACMBf4VET/Jf3hRiRWZ94jYEtgIeAm4mOxL9yVkuf8zMCsi9l/b9rV6ZfSZr63m6vVVKSXvv20hZZD7H5DdH3ptRNweERdExOXA82T3ZB6WUnqjmcdQPQrO/dx8vmUD62va3qaB9WqG1sh9ROwGbEd2P/3TtVZtBbQDZuZt1vViPh+wpmOo6Sywi3cBsD0wPqV0T63l3fP5ew3sV7N8g+YcPCK2A34G/C41ciA0lUyhuSfrGnYc2T/gnYE+wNeB+WRfxn7azPZVvyLzvkk+H0RWWJ2cL9sUODOP4eaI2LYZx1DDiv7MryLvsXAk2VWPq0vZtj6m0NynlJ4nu+9yCtm9omeRdR/tDVwPPNic9rVaReZ+Mtnf9J3zQbU+EhFHAJ/NX27YjGOoYS2a+4joAfwxf3laSmlFqY+htWOBXaCIOAU4g+wX5FZ/NEpEdACuI/tV+8zWPn41Kzr3ACmlW1JK16SUXk4pfZBSejWl9Htgf7LuRt+xu3hplUHea/7Nbwf8Z0rpspTS2ymlN1NK/0V2NbsTWa8WlVAZ5L4+R5KNIH57cuTwFlMOuY+IHcmK687AnmR53wI4l+zWsIcionvDLWhtFJ37/B77U8kHMcwfI/WLiLidrMfaE/mmKxtoQmuppXMfEesBtwFbA79IKf251MfQ2rPALkhEnAz8GngWGFbPgBU1vyw19AevZvn8ZoTxPWBH4NiU0sJmtKMmKJPcNyil9BjwMNnjPz7XEseoRmWS99r73lLP+ppluzTjGKqjTHJfn5ru4VeWuF3lyiH3eTfVG8kGNDogpTQppbQwpfSvlNIFwG/IvqSftrbH0MeVQ+4BUkp/JBt5/K9ktwOeCnwSOAb4U77ZnOYcQ6tq6dznxfVdwB7Ar1JKZ9WzWaHfJaudBXYBImIM2R+0p8k+ePVdOXghn3/s3oj8j+WWZN36Ghq4ojEGkQ1uMSEiUs1Edu8HwJB82fxmHEO1lFHu1+TtfL5eCx6japRR3mfw70FQ5tez/t183rkZx1AtZZT7uu3uAAwm+/fewc1aQBnl/lNkz7h/roEY7svnOzXjGKqljHIPQErpvpTSiJTShimldVNKO+SF98B8k0eaewxlWjr3EbE+2QBlnye7cn1GA6HMAFYA/fI269o6n3/sPnA1nwV2K4uIs8gGF3qC7IPX0K+G/8jnI+pZtxfQBZiSUlrajHD+SvZYrrrTDfn6t/LXf6x3bzVJmeW+QfmtA4Pyly1ZxFeFcsp7SulD/j2a6Pb1bFKz7OV61qmJyin39Tgxn//ewc1Kr8xyXzMScUO3/NQ8nuvDZhxDuTLLfYMiYgOy+/HfJvs+qGZq6dznt3HcS3abx08buHINfPTIryl5W3vWs8kX68SiUkpl8DDuapmAH5LdB/Mo0GMN23Yj+0ev0Q+hr6eNa/Pthjcxzr75fpOKfs/aylRuuSe7/26bepZ3BC7L930OWKfo966Sp3LLe77Nwfk2k4H1ai3fgOwX9wT8R9HvXaVP5Zj7WtuuR9Z9cBnQu+j3qq1N5ZZ7sgL73Xybr9VZt0H+b30Cvln0e1fpU7nlPt9m/XqWdSG7fzcBJxX9vrWFqaVzTzYQ3SP5unMbGdORtf7ed6q1fOf82HOAbkW/d21xivyNVguLiKPJ/iFcQdZ1pL5R/WallK6ttc9I4CbgA7LBKN4BDiQb3v8m4IhUJ4ER8Uv+/Sv1HmTD9N9LNpAZwK0ppVvXEGtfsitYk1NKezTuDNWQcsx9nuOZZH8Insu32RgYRtY1aS6wb0rpibU7a5Vj3mvtczVwLNnn/C9kg559GdgMuDk/joPerKVyzn2+3/HA74FxKaVD1+IU1YByzX0e1zVkt4X9HXic7Av7gWT/9j8IDE0tdLW0GpRx7k8mG2xrQr5NT7Ir158Afp1SGrM256t/a43cR8R9ZPfQzyAb+b8+t9b+3pY/bvVG4DCywdbuIMv/KLJi/tCU0m1NO1s1StEVfrVMwHlkvyKtbppQz35DgPFkvz4vAZ4iG4ikXQPHmbWGY5zXiFj74hXsNp17sl9PLyH7UjWbrGvgQrJHd10AbFL0+1bpUznmvdY+AXyN7NfwRcBish9bvoW9Ftp07vP9HsrXf6Ho96qtTeWce7Kup+PIiqxl+b/5U4GzqXV1y6lt5R7YLW//TbK/9XPz118s+j1rK1Nr5L4ReU/AMfXs1z5v86n8GO/mx9y96PetLU9ewZYkSZIkqQQc5EySJEmSpBKwwJYkSZIkqQQssCVJkiRJKgELbEmSJEmSSsACW5IkSZKkErDAliRJkiSpBCywJUmSJEkqAQtsSZIkSZJKwAJbkqQWFBHjIiJFxOmr2WbniFgWES9HRLfWjE+SJJVOpJSKjkGSpDYrInoCTwE9gJ1TSk/VWd8FeBzoD3w+pTSp9aOUJEml4BVsSZJaUEppHnAs0BH4U0SsW2eTi4ABwAUW15IkVTYLbEmSWlhK6R7gUuAzwM9rlkfE/sBJwFTgvIhoHxHfjIgHI2JBRCyOiMcj4uSI+Njf7Ig4JiJujoiZEbEk32dyRIyuL46ImJB3V+8YEedGxAsRsTQirm2RE5ckqcrYRVySpFYQEZ3ICultgX2BJ4GngfWBQcBM4A7gC8ALwATgA2AYMBC4PqX01TptLgGeydt5E+gJ7A9sBpyfUvphne0nAJ8H7gR2Bv4CzAHmpJQuKvEpS5JUddoXHYAkSdUgpfRBRBwFPAT8DzAN6AV8M6X0QkScR1ZcXwqMSSmtAIiIdsCVwHERcVNK6bZazW6fUppR+zgR0ZGscD47In6XUnq9nnD65PvOLe1ZSpJU3ewiLklSK0kpPQH8kOwK8xeBu1JKv827f38bmA2cVlNc5/usAM4AEnBUnfZWKa7zZR8Cl5H9iL5PA6H80OJakqTS8wq2JEmt65fAaUBv4Lv5sgFko4y/CPwgIurbbwlZ9/KPRMQngbPICulPAp3r7LNZAzE8vDaBS5Kk1bPAliSpFaWUVkbE0vzlknzeM59vDfxoNbt3rfmPiOhHVihvCDwA3Au8B6wA+gJHA3VHLK8xe21ilyRJq2eBLUlS8d7L57eklA5p5D6nkxXmx6aUrq29IiKOJCuw65Uc4VSSpBbhPdiSJBXveWA+sFtEdGjkPv3z+c31rPt8KYKSJElNY4EtSVLBUkrLgd8AnwAuiYi691ITEZ+IiO1qLZqVz4fW2e4LwNdaJlJJkrQ6dhGXJKk8/AT4LHAScEBE/AN4HdiE7N7sIcA5wLP59pcDxwJ/joibgDeA7YERwI3AqFaNXpIkWWBLklQOUkrLImIkMBo4Bvgy2aBmbwMvkz3e60+1tp8WEcOA84Evkf1NfxI4hKy7uQW2JEmtLBznRJIkSZKk5vMebEmSJEmSSsACW5IkSZKkErDAliRJkiSpBCywJUmSJEkqAQtsSZIkSZJKwAJbkiRJkqQSsMCWJEmSJKkELLAlSZIkSSoBC2xJkiRJkkrg/wP0E9E9gQNn4AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#did the same for latinx and hawaiian\n", + "years = list(travel_time_diff_Latinx_hawaiian.keys())\n", + "differences = list(travel_time_diff_Latinx_hawaiian.values())\n", + "\n", + "# Plotting the line graph\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Difference in Total Travel Time between Latinx and Hawaiian Riders (2011-2021)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Travel Time Difference')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + "\n", + "# make the output bigger\n", + "#plt.rcParams[\"figure.figsize\"] = (10,10)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ltwueds8IxsvrPn50frlMP+Rvm991gWshJkt/L+5A0t8vD/t+Xo/dg0nfd1KAR/G2c1yloBNJv4aomWncOG87s7hE4nSve4w3362kJ9KJES7Pn954H0Uwzv954yzP1L0nYZyfSD8u7gJ+A07xupcBLiR9v/tXiHHzfF0StD3twx0vOnjdDWE+CCCXpN37rZZ4/ReF6F/R+637B//OuBtNt5J+AX55iHFbkp7g/4Yrch44hlbAVUb1cYh4Avvpt946ivP6NSD9XHEg3HUQNO1Eck7arw5aV5fmsC6zbC+Zxn2OjPvKBG8bCNx46Zlp3PycawLLtA93U/Zc0o97rbzPvwets4FA+aBtsKn3Oz6U2/rLNN87vWlOj2S8TNN4Lmi5TKZ+//b6zchh/HZB67xLLvOKDxo22pP2IlsvmcYrQ/r1aFgPSYgwaffGmeaNc29Yw4c50fFElrQ/FLSS4oK657STZ9svxArpGaJfdW9HPYh3gRximFNwFwI7gbIh5m2BD7IZNzZo/udkM0xL70CQDDQI6h68g3ydecPzhvnM6/+fTN1rk35weyqCDeFnb5ybsulfk/TkNpINOaffMLAjJxGUHAb1D9zxOhS8/vPyR/ZJe+ApyC6gfojxAk/mLHBmpn7DCSNpzyWu5t7vf4CsT+Jy3cYjmE9gGQbnsG52EWFJikzbx1Zvf4nP1O92b/pLsxn3Bq//quBtPZ/76NF9qADWXchtJ4fhembqPjho/T8WYrweQf13kunCwhvmO6//sEzdW3vLv5Wg0h2ZhrnSG3dxhMsd2D/TgNNC9D+G9JstAzP1G0kOx0dvmKmEOPkQXtL+uDdM5tJZk73ugeT85Uz9A8e56zN1D5RUeDqb+ZUFFpDpRgA+HefD+O2Cp90zknFzmGZO+3hY+1t2v21BrAtcpUIWd7FWB/duYeC3eaMg1kGm+eV07B4ctDyPhBi3AulP0v+eqd81XvfDhCjZQcbjRWIE8cYFjfdwBOM9GjRemaDuPYksad8M1ArRP1BacWWIfnm+LgmK+S/CKFWQzfR7Bk0n8zbfgvQbGhZ4IA/TD/zWP4ToF5j2MkKU5shmeoFz6Y9kupEaNMyr3jBjI4w1kRDXO7gHVVeSXiJge+b9IaftBXcTZUWoaQf1/yZoPffM1D8/55rAMh0hxA1xb5hAopelFE9e/0i/kfBqHsdvRPoNnyyJMK7md0ummzqZhqkWtE5zvKFJMUnai3q9ZBrvTm+cVKB9mOOsJvKkfYw3zsRwhi/Qd9qD7Ar6P8f3BgvQpbiiS9/abN5TsNbOIL1GwCzvKHiyvBfh6Yl7erPYWhuqZkisqzjhV9wdmp7ZTOcZ6/1SmUz2Pjtk6n4Z7u7uLtxTk1wZY1oCp+Lu3r+VTaw7cQc+iKC5gTAlWGtXhOgeKDJdDvfkqzAE3il701qbpSZHa+3XuCfFAFcU9MyttatwT1Uq4ppM8dM71trsmj3Lkbd9TMedYLtn6v0R7kB2jDHmhBCjB97t/DDTtl5Q+2i0OAK8EKL7L7gbU+AuDHaHGOY77zPz/v533Dr/yFq7jtAScAnAsbm9O5qNn2yIJnystctIr0A087uZg7zP53OY7gfeZ16OJz96n2cEOhhjDC6h2Qe8hDt2BPevgHtyB+5udaB7RVwJhTRC/z5Ya4+QvqzB8fbEn+N8kctlHy9IeV0XDwOLcE/n3sAVt22GS9ruLuAYwz12HyJEc2jWtWYT2F5CncPBvcq3LMS4P5G+/Uci+NpqRwTjBTfPl5/rs9ettaHmO9n7bB78fnoBXpeMtQXTetBs4+pY2GyMOYDbrgJttH9J3pq9+8z7PNkE1VlijKkMXOx9HWa9yrLCEDjuvmTd+/ahvO995vU6bkDQetiKe+jyX1zTb4eAq621kTR72hl3YxNcc1YZeMeCpzJ3D1IQ55qpNnTTZ+BKUIErqVBQAtOKuOlL4+r8eR93bbSWEOsMVzoJQjfvGxD8G1WONI5o4+d6McZ0DJrfWGvtkpyGz6fANhPW9hhpO+3RLHDRcaYxJqdq9wMnqSakJ28BB3FPX3Kafutcpl8taPqhzM6me6CtyxqZup/sff4QwYkqEGtlYL3Jvl6vwAacXax5FXIZrbXJ3kmhHlmXM9+Mq9QocMH0Qw6Dfo97ohsq4Qx3XmcD1+Hen29AessGwRrmdfoFJPP2nYUxpitwM26baUz6QTBYhuWw1m41xnyHK7XwN1z9BYHpNSA9kfmAjApiH40mq0NdfFlr04wx23HrM7sLh8DNlMz7QWAdDTLGXE72ApWWNMEV4YpEYg79puF+06P7hlfpT6AJzynGq1wshEClYnk5nszAe3JtjGltrf0T96pBTeBLb5tbDHQwxtTykoVTvHluzHST8ESvuwUW5XD8C+yzwfH6dZwvNHnZxwtYntaFtfawMWYgrnjwRV7nVOAaa22eW5DJ57F7ibX2QDb9sluewL6UbSVxXr/Tc+gfjXL7XcGVrgqsr4K6Limoc0LtbLo/Djxhs6lkyksmBuES/E64Y1TmChXL47aDwAV5F9z1tsXdEMiVN5+u3tfXjDHjshk0cHMgr9dx5b2/zFYDZ3k3KSMR2N63hLpJ5ZmOe80kQw5SgOeanLaRqcADwEXGVWQ5HpiWzQ2ocAW2pV05DhXay7ib0UeAv1lrC7Jm9uLMl/XiXcNOxp0X5uK2lcIU2GayOx5lUFhJe/BJa2chzSOzwF2Kit5fbkINsyO7A3XQ9Mvhks68TJ8c7rAGnsxlrkEwMK+1YcwzIBBrGfIRaz7kdBc5u+UsCDVJbxFhQw7Drfc+6+RlJsaYMbgi4gHJuO08cCe8Jm75Iq6dv4Bty6mnMeZe4F+4J22QXlnMEe97NdzJPNRyfIBL2gcYY+4LepI2APcbLLbWLso0TkHso9Ekp2Q5NZdhAv0z7weBdVTF+8tNXtZRTvtGoF/wvhF8B7huGNOPOCZrbZIxZjbuwv4M3Lu6gafqid7nNFwi3wN3Ug30z/yUMhCvIfLjn1/H+UKRz328QORnXVhrFxpjXsC9cgfwglcaJ08K4Nidl3NbYF/amMO4Oe2T2Qm+tqoVwXjBF4f5uT4LuS6stYeCEvLgdVFQ1yU5ntci0Nxau9or0dMAV/LuKdy2NgvXpnUG3hPzr8hYMuWgF1Pg2jGwbJVIT9oD3fZEkHwE3wwI5/fNa6s1E6y1g+FoKaVOuNeRegGvG2P6eiWTwpXr9u7dkNsOZG7/uqDONdluI9baacaYYcAwXJ0S/QCMMUtxbXa/5t00jkSgRaJI1hPGmKdwN1RTcSUafslm0MCNr5x+4+B14Xuz2N4NmOxu7F1irZ2ew7i+rBevdvqvca9K/Qmcb609lNM4BSAw/bD238IqHh9oKH59DkV6ClpgWV6y1pow/saHmEZOzRoEpv+/MKc/vCAXLkKBWBeEGetgH2MtLKHuHOebMaYv7qIvFfcOfCugnLW2lrW2vrW2Pq7yMUi/UPZLttuz11TGs7gYxwLH4pajZtByBIoPh1qOT3AHm8ZkfEIUKBqf+Sk7FMw+WtIF1tHdYa6jxCKMCVyN0LnFFJ/H+WQuIh/4nJbpM7v+mePdE+Y67Bli3OJwnM9RAezjvvOSpODXmE4xxuTpuqWYHbtz5V1bBZ6Cdopg1I7e55/W2pSCjSpHBXVdUqDNT1lno7X2Rdw75GWB94wxjUIMPhSXsG/HPW2vZ62taK2t620/wePkdxsK3s6PD2ed5XN+WGuTrLspdh7u1ZQzce+YF5WCOtfkuI1Ya0fgWnd5CHcTZi/QFlcfwxJjzN8jjDtw86t6uCMYYx4hvf6vG621CTkMHrgBklOJqOB+kZa+KwyxuJtVof6ybfbUr/VijKmG2xY64B6SnmXz+HpphAIPucMq6VHgSbtXRLm39/Wngp5+DgIrt2kxnX5u822Wh3EKuth7tNtJ+t3unH6nQPGrvNyxDxRZftNa+7i19i9rs7yvGc5TBL9ditv/v7LW3m6tXWKztpWZ7XJY9/Tsc+/rVXD0ncWueBWKhRjNr32oOCmKdRTOCS543wg+cRVmXJmT8tNxd8bneN+PJvXGmHJAt0zjBQTireqdiCNRkrbRfO3jUWI07v3YdbgL69OA+/M4Lb+O3YF9KdwLy0gEXgPrY7JpYz6Y9yS1j/c1p+L6hSHqr0ustR/grltrEDpZDWxDt1tr37HWbs3UP7vtJ7Ds1SI4JgWaZ4QiPh5Z93TxLu/rXcaYSOogynV79/KEUMWBi+pcg7V2lbX2GWvtubhSDb1w55gywL+NMeE86Q8IlKgI67UnY8zdpG9fd1pr385llMA71e1yuGnZ3vu0wB/hxFGYrLWrI33Y4Nd68eremIJ7jWUzLmGPpHRzfgS2mbDqQyiMJ+03kl6s5f2cBixggSJzPcM5eeVj+h2zuQNbWH71PiNZrkCsNY0x3XIcsgSxrghX4B3iXjkMeqb3OS9T90DCn9Md60DC/1uonsaYZhReJXsFKbflqER6fQrZCTxNv8wYE4ercRZc8xurQwxf2PtoSRBYR+cW4jzOCKPf0X3Dugq6AhdTffMwv3D2K3AV+KUCTYwxF+CKWU4PPA30LpCX4p4qnoMrTbPVWpv5RDwH976kIfL16NdxvjDkZx9PCxrOl6fOxph+uCefabhaue/wej1ujOmch0n6dewO7Es5vbOe0z6Zk9e9z+q4pqpycyvp9TG8lqlfuPtpXhWX65JAhb9/N8a0ydQvx20IOCub7sHHpLCOoV5JisANy7wcd/PFWvs97t3zOFzJlHAFtvd6IdZfQHdCvJ5bAOeaPLHWpnqJ5AW4V2Uq4RK4cAXe3W+e24DGmFtIryD1QWvty2FMP3Bzrhrpla9mFrgZN9NmX+9G1PJrvXjXop/htskduIQ90tcj8iPe+1wazsAFmrQbY84hvfb1GdbaLwpy+rmYiHu/oQbuXZVsGWPyUgnQd7i7/bFkX8N8fqafnQTcO1O5LleAtXYp6cn+v7yEKiRjTAXvqVVJEShKMzhUzdrGmD64CqzANcESLFCraPUcph94H+24bPo/RTEoWknuy/EIub9TPQVXE3At3IExp6LxUPj7aEnwDu6OcDtjzE05DZiPdXSGMSZLbeHGmNak13Q9MVPv8d7nvTkls8apnqlzOPtVoPRG4GI4sH0kZhpsGu689aj3PUut2950Pva+PmGMyXY7NsaU8YpgB/h1nC8M+dnH9wb9X72gAgqX95TrTe/rC9baadbaCbjXcgLFlyM9b/l17A7sS5d4+1gG3r6Yp0rorLWzcesE3Lae7XSMMT1wlayBayJpTqZBAr95pKVTwlJcrkustd/gKiSOwbVgECzbbcg7jjySzTT345qnAnfTKZz6SiD9uDvYGJPjKxCFdDwKHAOv9ErShWM+rsk3CFGJl3cT8MEcxh/vfeblXJMr7yl/do6QXrohku0v8M51jom+MWYQEKhQ8Alr7bPhTNy62ssDlWTfF2K6DUm//irKh6UFwq/14m0Ln+Ae8u0G+lhrf48o+PwL3GzI0qJPKPlO2o0x1Ywx5xhj/ou7iK+Au+jJ3GRQobKu5sdAZTUPGmPeCL7L550EehhjXsHdPYx0+snAbbgL6quMMZOD7/YbY+KMMV2MMf/CNVlVIKy120k/0T5ojBlrjDlabMgY08AYc49XsUawO3DNQp0OfGeMOS1QfMQYE2uMOc4bZyUF2/SF38bi3lupAHxpjOkCR5f5UuBDb7hvvTvJwQI762mhLq4833ifNxljrgucAIwxTY0xE3AHiLzUIFrUAstxvjHmIa/YJMaYOsaY53D7Uo7v2FhrDxN0wYh7ZzaFrDdDAsMX6j5aEngnodHe138bY542xgSe7mCMqWKM6WOMeY+siXW49gKfGGPOCzxF9S7op+IuVH4n62/4DO5YURuYboy5Iri0hLf9D8E9ZemfadzAfnVuqBtpmQSS8CxNuWX6nl3/gAdxr8u08eI9N5AkeBd7rY0x9+Dubh+90PLrOB+hasaY2jn8BZKuPO/j1jVTGHhf8NpCXJbsvIkrsbeI9Bs0ADfhii8eS+gmgHLi17H7I1wxznK4GrFP8+YbY4w5H3cM3ZvD+Lm5AfekrwLwtTHmiUzXCE2MMY/hKliq4A17Y4jp/Il7yljNO1cWhuJyXRJIVq82xgQ/PQ1sQy8YY84IOn6ehLvhl1OFcQ/jKu9rA/xojOkVtOwVjDHnG2MyV373Fu5GR3nge2PMjcaYqoGexpj6xpirjTHTcO1KF7RPgeW4m5gP5TIscLRJt+He1+uMMc8GkmtjTD3gP7jSjtk1I5efc0043jHGvO3lLUdvnhhj4oEJuHV9kMhe7/0Fd85obIzJXLleYPqX4n5PAzxnrX0swrgDN5AuNcb8KxC7MaY97klxFdx6eyOb+R89R5CxGH/1TOePLHmhMaZypvEDN9wqZBo34kpo/VovxjXJ+AGuNN4+oK+1NnPp29xir5ZpvQTWXaVM6yXkDSDjbrQFboaFt73Z8Bp/H4/bII/gTpibcUVYkkhvtN7iild9BNTOZjo9veFWR9IvaJjV3jA9cxjmUS+OQEz7cRdvqUHdVkU676Bhr8WddALTSsJd+KQEr4tM48SH6h7BujG4C/ngdb0bd9c38H18iPH6esMFhjmEe2/iSKZpNQtnOwgjzkSv3+D8/IZhxhGYzvAQ/bp6v3lg+fbiDsKB7wuAuiHGi8PdIQ5sy1u9+awGGnvDlMUV8wtMKwV3oRf4PjS79RDJdhbG8ttQ84hkHeOeRgbvu4E6ASzuonl8dus4aBq9M21LU8OIPS/7aHygXwGsu2y3nXDWIzDY656Y1+08p2ngLpD+nWm97sHty8Hr7YcIlzuwXf4zaDtPwp2wAtPcCrTPZvxWuOQjeNvfTtbzwKBM49XGHSOt9xtv8tZPlv0AuDBoOgeAuEz9G2aa13E5LO9JuFq5A8Me8eI9nGkaZ4QYt8iP87n8dvGZYs7pLzFovDzv47ibxcH76Grv766gYUKOn591gUsorbf+O4YY7/yg5ekVwTrMz7F7cOZ1G2L6w8n+XNwet28F5rWP9P3mT+Ce3Kafy7LVwiXlwdvBfjLu2xaXdNbKYToTgobdHfSbXxY0zGpyOb8ETSM+RL88XZfkNM0I1lPPcKaDK7odWM7Xgrq3wL2zHZjGQW89B44RfXJZ9l6ZtrnAsh89roQYpy7uKVxgnFTcsWg/GdfXYxGui8C2nmV7zWZ/PAI0zW3/Deo/Nii2FDIee+7IaTsi7+eawDINzmF5JgeNn+b9HgcyzeuaPGxb33vjX59N/5VB89icy1/3bKbxaKY4g3OAbUCHMPbJ3P5Cbbfjwxx3eB7Wmy/rBXfjMHg/zmm+s3PZh3L7C7k9AgO9/tPCXV+RPmmPI732v1q4A85K3N24R4AW1toB1j0d9oW1diTuncfXcSfDGNz7KZtwNQPej2syKK/Tfxs4BngR9wQpFaiKO4gmAo95/QuMde7GbWQf4S5EK+AuaubhivU9GWK8qbi7uiO94Q7jijruxT3JfAY40Vq7piDj9Zu1dhbuImk07i5xHG5HnoMrQtPNZq1ABuuesvUG3sWt4xq4CgCb4b1/Zd1782eRfjc4zZv2N0A/62olLS4G4J5I/oF7wmJwd4wHWWtvCHMaP5CxRs7sisYfVdj7aHFn3ft1/8BVuvUesAb3lK48rlbTT3FPg/NammkH7sbWi7ibr2VxT1XfADpb97Q/VFwrgOOBf+B+9124orQpwELc73m+F3PweNtxF6uf4E6gdUjfrzL7ifR3a6fbTK2PWGs3kl70cifpdViEinc2rkbgB3DHu/24418S7lgwBpewZ3la78dxvpDkZx9/ArfuFnrjBX6z6oUVrHFFcAPvNT5qrV2YeRjrXrt73YtpggmziKyfx25vn+qMu1GyCXdO2ow7R51EPpvGtdbusNb2wT01ege3fAZ3bF2JO6f1tdaebXNuj/pmXAmGpbhjTuA3r5zDOJHGGvXXJdbVozHa+zo4UNrJWrsSd+x8D3cTJhZ3A+J94CRr7de5TPcH3HHjWdyxKwV3XP8LV3nrhSHG2Yqr8+BqXGnWbaS/1rIU93tfgVtvheEd3LYaRwRtVltrb8MlJTNxv7HBlYy6wFo7Jpdx83SuCdODuGuML3H7Rlnc7/gX8DZwgrX23TxM9y3v88ps+gfnW9nVqp5j7eretdPZuKbpduH20ZW4c1kHa22258Mo5td6CZ5v+Vzmm6fmocMQ2FbeynGoIMbL9kVERERERCQCxpjywHrcDajG1trN/kYk0cwYUwt3A3c/bnvJ7nWRDAqrnXYREREREZESzbpm8p7GPbW/y99opBi4A1d65V/hJuygJ+0iIiIiIiJ55lU4thzv1Upr7S6fQ5Io5FWWtwb3inlLa+3BcMfN0k6iiIiIiIiIhMdae9gYMxhXB0EzikdLQlL0muHet/8lkoQd9KS9wNWuXdvGx8f7HYbk04EDB6hUqZLfYUgJpm1MCpO2LylM2r6kMGn7Khnmzp273VpbWBW5lTp60l7A4uPjmTNnjt9hSD4lJibSs2dPv8OQEkzbmBQmbV9SmLR9SWHS9lUyGGNKVOtUflNFdCIiIiIiIiJRSkm7iIiIiIiISJRS0i4iIiIiIiISpZS0i4iIiIiIiEQpJe0iIiIiIiIiUUpJu4iI5MmOHTt48803ufjii2nVqhUVKlSgWrVqnHbaabz11lukpaXlOo0bbrgBYwzGGFasWBHR/Lds2cLtt99O8+bNKVeuHHXq1OHiiy9m3rx5IYefNWsWDz30EH379qV+/foYY2jcuHFE8xQREREpamryTURE8mTixInccsstNGjQgF69etG0aVO2bNnCJ598wg033MDUqVOZOHEixpiQ43/22We89dZbVK5cmf3790c079WrV9O9e3c2bdpE165dueSSS9i2bRuffPIJX3zxBZ999hnnnHNOhnE++OADXnrpJeLi4mjfvj1btmzJ87KLiIiIFBU9aRcRkTxp06YNn376KevXr+f999/n6aef5j//+Q9Lly6lSZMmfPzxx3zyySchx922bRs33ngjAwYM4MQTT4x43nfeeSebNm3ijjvu4Ndff+X555/nnXfeYd68eZQvX55rr72WAwcOZBhn8ODBzJs3j/379zN//vy8LLKIiIhIkVPSLiIieXLmmWfSr18/YmIynkrq16/PzTffDEBiYmLIcYcMGQLAuHHjIp7voUOHmDp1KjExMYwcOTLDk/w2bdpw3XXXsWnTJj7++OMM43Xu3Jnjjz+esmXLRjxPEREREb8oaRcRkQIXFxcHQJkyWd/CGj9+PJMnT+a1116jVq1aEU97586dJCcnU7t2bapUqZKlf4sWLQD47rvvIp62iIiISLRR0i4iIgUqJSWFd955B4Bzzz03Q781a9Zw5513MnDgQC666KI8Tb9GjRrExsayffv2kO/Cr1y5EoBly5blafoiIiIi0URJu4iIFKgHH3yQxYsXc95552WoDC4tLY1BgwZRuXJlxowZk+fpV6hQgV69epGWlsawYcMy9FuxYgX/+c9/ANi1a1ee5yEiIiISLVR7vIiIFJgxY8bw/PPP07ZtW959990M/UaPHs20adP44osvqFGjRr7m8+KLL3LqqacyevRoZsyYwamnnsq2bdv4+OOPad26NfPnz8/yrr2IiIhIcaSkXURECsTYsWO58847ad++Pd999x01a9Y82m/58uU88sgjXHvttZx33nn5ntexxx7L3LlzeeKJJ/jmm2+YO3cuDRs25O677+acc86hR48e1K1bN9/zEREREfGbknYREcm3F198kbvvvpsOHTrw3XffZUmYlyxZwuHDh3n77bd5++23Q06jdevWAEyaNIn+/fvnOs+WLVsyYcKELN0DxeNPOumkCJdCREREJPooaRcRkXx59tlnefDBB+ncuTPffPMNtWvXzjJMfHw8119/fcjxv/jiCzZv3szll19O1apViY+Pz1c8gWL5f/vb3/I1HREREZFoUCKTdmNMLeBi4HzgOKARcARYBLwNvG2tTQsaPh5YlcMkP7LWXlloAYuIFFMjRoxg2LBhnHjiiXz99dcZisQH69y5M2+++WbIfj179mTz5s089dRTtGrVKkO/7du3s337dmrXrp3hZsDhw4cBKFeu3NFu1lqeeuopEhMTGTBgACeccEJ+F09ERETEdyUyaQcuB14BNgE/AGuBesAlwJtAX2PM5dZam2m8BcDkENNbXHihiogUTxMmTGDYsGHExsbSo0ePkDXCx8fHM3jw4DzPY+zYsTz++OM89thjDB8+/Gj3P//8kx49enD22WcTHx9PcnIy3333HYsWLeK0007j9ddfzzKtpUuX8swzz2TotmvXrgzxjRo1KmRJARERERG/lNSkfTlwIfBFpifqDwOzgEtxCfzHmcabb60dXlRBiogUZ6tWuQJKqampvPjiiyGHOeOMM/KVtGenXr16nHfeecyYMYPPPvuMuLg42rdvz9ixY7npppsoUybr6W3z5s1Z3oFPSkrK0G348OFK2kVERCSqlMik3Vr7fTbdNxtjXgWeBHqSNWkXEZEwDR8+PMPT77xKTEyMeB516tTh/fffj2g+PXv2JGsBKxEREZHoViKT9lwke58pIfo1NMbcBNQCdgAzrLULiywyERHJaNX7sOARSFoLFZtCpyeh+dV+RyUiIiJSZExpeupgjCkD/AZ0AM611n7ldY8n+4roEoFB1tq1OUx3CDAEoF69eid++OGHBRi1+GH//v1UrlzZ7zCkBNM2lru6Sd9yzJ5RxNrDR7ulmnIsq3YvWyue5WNk0U/blxQmbV9SmLR9lQy9evWaa63t4nccJUVpS9pHAf8Eplhrzw/qXhe4DVcJ3Uqvc0dgONALWAF0ttYeyG0eXbp0sXPmzCnYwKXIJSYm0rNnT7/DkBJM21gYJsdD0pqs3Ss2g/6rizqaYkXblxQmbV9SmLR9lQzGGCXtBSjG7wCKijHmDlzCvhS4JriftXartXaYtXaetXa39/cj0AeYCbQCbijyoEVESrOkbAo4ZdddREREpAQqFUm7MeY24CVgCdDLWrsznPGstSm4JuIATi+k8EREJJSKTSPrLiIiIlIClfik3RhzF/Ayrq31XtbazRFOYpv3Wakg4xIRkVx0eCRrt9gKrjI6ERERkVKiRCftxpgHgNHAfFzCvjUPkznZ+1yZ41AiIlKwYsu7z/L1AeP+r3GCao8XERGRUqXEJu3GmKHAM8BcoLe1dnsOw55gjMmyLowxvYG7va/vFUqgIiIS2tqJULEJXLwR/pYG7R+C7b/A1p/9jkxERESkyJTIdtqNMYOAJ4BU4CfgDmNM5sFWW2vHe/+/ALQ2xkwH1nvdOgJnev8PtdZOL9SgRUQkXfJe2PQVtL4VAsfvDo/A6vdgzq1w7lyIKZGnMBEREZEMSuoVT3PvMxa4K5thpgHjvf/fBS4GTgL6AnHAFuD/gLHW2p8KK1AREQlh/WeQdgSaXpberUwlOOEF+Ply+PMVOOZ2/+ITERERKSIlMmm31g7HtbEe7vBvAW8VVjwiIhKhdQlQoRHUPjlj9yaXQv2zYOFQaHoFVKjnT3wiIiIiRaTEvtMuIiLFVPI+2DjVJeiZqxsxBk58GVKTYMGD/sQnIiIiUoSUtIuISHTZ8AWkHc5YND5YtbbQ9h5YOR62zSjS0ERERESKmpJ2ERGJLusSoEIDqHNq9sMc+yhUbOwqpUtLLbrYRERERIqYknYREYkeKQdg4xRofEnWovHB4irD8c/Drt9gxWtFF5+IiIhIEVPSLiIi0WPjFEg9CE0vz33YppdDvTNhwSNwaFvhxyYiIiLiAyXtIiISPdYmQPm6UOe03Ic1BrqMhZT9sOChwo9NRERExAdK2kVEJDqkJMGGz13R+JjY8Map1g7a3gV/vQXbfy3U8ERERET8oKRdRESiw6YvXVNu4RSND9ZhGFRoCLNVKZ2IiIiUPEraRUQkOqydCOVqQ93TIxsvrgocPwp2zYO/3iic2ERERER8oqRdRET8l3LQFY1vcgnElIl8/GZXQt0zYMHDcGh7wccnIiIi4hMl7SIi4r/NX7sK5ZpclrfxA5XSJe91ibuIiIhICaGkXURE/Ld2IpStCfV65n0a1TtAmzvgrzdhx+wCC01ERETET0raRUTEX6mHYf2n0ORiiInL37Q6Dofy9VyldDatQMITERER8ZOSdhER8demryFlX96LxgeLq+oqpds52zUDJyIiIlLMKWkXERF/rUuAsjWgfu+CmV7831wN9AsegsM7C2aaIiIiIj5R0i4iIv5JPQLr/weNL8p/0fiAQKV0R3bDgkcKZpoiIiIiPlHSLiIi/tn8LSTvKZii8cGqHwdtboMVr8HOuQU7bREREZEipKRdRET8sy4B4qpB/bMKftrHPQ7l66pSOhERESnWlLSLiIg/0pJh/WRodCHEliv46ZetBp3/BTtmwsrxBT99ERERkSKgpF1ERPyx+Xs4sguaXl5482h+DdQ5FeY/4OYlIiIiUswoaRcREX+sS4AyVaDB2YU3D2Ogyzg4shMWPFp48xEREREpJEraRUSk6KUlw/pJ0KgfxJYv3HnV6ASt/wErXoWdvxXuvEREREQKmJJ2EREpelunweEdhVs0PljHEVC2FsxRpXQiIiJSvChpFxGRord2IpSpDA3OKZr5la0OnZ+F7TNg1TtFM08RERGRAqCkXUREilZaCqybBI0ugDIVim6+LQZBrZPht/vhyO6im6+IiIhIPihpFxGRorXtJzi8DZpcVrTzNTFw0jg4vB0WDivaeYuIiIjkkZJ2EREpWmsnQmxFaNi36Odd8wRofQv8OQ52LSj6+YuIiIhESEm7iIgUnbRUWPcJNDofylT0J4ZOI6FsTZhzG1jrTwwiIiIiYVLSLiIiRWfbz3BoS9EXjQ9WtgZ0fsbFsvo9/+IQERERCYOSdhERKTrrEly77A3P8zeOFtdCrW7w231wZI+/sYiIiIjkQEm7iIgUDZsG6z52CXtcZX9jMTHQZSwc2gqLhvsbi4iIiEgOlLSLiEjR2DYdDm7yt2h8sFpdoNUQWP4y7F7kdzQiIiIiISlpFxGRorEuAWLKufbZo0WnJyGumiqlExERkailpF1ERAqfTYO1CdDwXIir4nc06crVgs5Pw9YfYc1//Y5GREREJAsl7SIiUvi2z4SDG6KnaHywFtdDzS4w75+QvNfvaEREREQyUNIuIiKFb10CxJSFRv38jiSrmFjoMs41Rbfocb+jEREREclASbuIiBQua13R+Pp9oGw1v6MJrXZXaHk9LHsJdv/udzQiIiIiRylpFxGRwrVjNiSthaaX+x1Jzjo9DXFVVSmdiIiIRBUl7SIiUrjWTYSYOGgchUXjg5Wv7WqT35oIaz7yOxoRERERQEm7iIgUpkDR+HpnQdkafkeTu5ZDoMYJ8Ns/IXmf39GIiIiIKGkXEZFCtGseHFgd/UXjA2Ji4aRxcHAjLB7hdzQiIiIiStpFRKQQrZ0Ipgw0vsjvSMJX+2RocR0sHQ17/vA7GhERESnllLSLiEjhOFprfG8oV9PvaCLT+RkoUxnm3K5K6URERMRXStpFRKRw7JoP+/+CJpf5HUnkyteBTiNhy3eujXkRERERnyhpFxGRwrEuAUwsNO7vdyR50+pmqNEZ5t0Dyfv9jkZERERKKSXtIiJS8Kx177PX6+WaUiuOYmKhyzhIWg+/P+l3NCIiIlJKKWkXEZGCt3sR7PuzeBaND1anOzQfBEufh73L/I5GRERESiEl7SIiUvDWJYCJgSYX+x1J/nV+FmIrwpw7VCmdiIiIFDkl7SIiUrACRePrngHl6/odTf5VqAcdn4DNX8O6T/yORkREREoZJe0iIlKw9iyBvUuLf9H4YK3/AdU7wry7IeWA39GIiIhIKaKkXURECta6BMBAk0v8jqTgxJSBLmMhaR38/pTf0YiIiEgpoqRdREQK1tqJULcHVKjvdyQFq24PiL8G/hgFe//0OxoREREpJZS0i4hIwdnzB+z5HZpc7nckheP4f0FseZirSulERESkaChpFxGRgrM2wX2WpKLxwSrUh+Meh01fwvr/+R2NiIiIlAJK2kVEpOCsS4A6p0LFhn5HUnja3AbVOsC8uyAlye9oREREpIRT0i4iIgVj73LYvbDkFo0PiCkDJ42DA2tgyTN+RyMiIiIlnJJ2EREpGOu8ovFNL/U3jqJQ93Ro9jdY8i/Y95ff0YiIiEgJpqRdREQKxtoEqH0KVGzsdyRF4/jnICYO5t7pdyQiIiJSgilpFxGR/Nv3F+z6DZpc5nckRadiQzhuOGz8AtZ/5nc0IiIiUkIpaRcRkfwrTUXjgx1zB1Rr7562pxz0OxoREREpgZS0i4hI/q1NgFpdoVIzvyMpWjFx0GUsHFgFf/zL72hERESkBFLSLiIi+bN/FeycU7qKxger1wuaDoDfn4b9K/2ORkqpHTt28Oabb3LxxRfTqlUrKlSoQLVq1TjttNN46623SEtLCzne9OnTOe+886hZsyYVKlSgY8eOvPjii6SmpuYpjoSEBM455xxq165N+fLladq0KRdddBG//vprhuF2797Nc889x9VXX0379u0pU6YMxhi+/fbbPM1XRKQkK+N3ACIiUsyt+9h9Ni2lSTvACaNg4+cw9y4441O/o5FSaOLEidxyyy00aNCAXr160bRpU7Zs2cInn3zCDTfcwNSpU5k4cSLGmKPj/O9//+PSSy+lfPnyDBgwgJo1a/LZZ59x991388svvzBx4sSw55+SksKgQYP44IMPaN26NQMGDKBatWps3ryZGTNmMHfuXE4++eSjw69evZr7778fgMaNG1O7dm22bNlScCtERKQEUdIuIiL5s3Yi1DwRKjf3OxL/VGwMHYbB/AdgwxfQ6Hy/I5JSpk2bNnz66aecf/75xMSkF6R86qmn6Nq1Kx9//DGffPIJl17q6p3Yu3cvN954I7GxsSQmJtKlSxcARowYwZlnnklCQgIffvghV155ZVjzf+yxx/jggw945JFHeOKJJzLEAJCcnJzhe7Nmzfj22285/vjjqVmzJoMHD2bChAn5WQUiIiWWiseLiEjeHVgDO2aV3qLxwY65C6q2hbl3QOohv6ORUubMM8+kX79+WZLl+vXrc/PNNwOQmJh4tHtCQgLbtm3jyiuvPJqwA5QvX56RI0cC8Morr4Q1782bNzNq1ChOPvlkRo4cmSUGgLi4uAzfa9SoQe/evalZs2ZY8xARKc30pF1ERPJu3SfuszQXjQ+ILesqpfv+LFjyHBw31O+IRID0hLlMmfTLvu+//x6Ac889N8vwp59+OhUrVmT69OkcPnyYcuXK5Tj9hIQEjhw5wpVXXsnBgwf54osvWLFiBVWqVOG0006jU6dOBbg0IiKlj5J2ERHJu7UToUZnqNLK70iiQ/3e0PRyWPIUNL8GKsf7HZGUcikpKbzzzjtAxgR92bJlgCtWn1mZMmVo3rw5v//+OytXrqRdu3Y5zmP27NkAJCUl0bZtW9auXZuh/6WXXso777xDxYoV87UsIiKllYrHi4hI3iSth+0zXJIq6Y5/HoiBeXf7HYkIDz74IIsXL+a8887jnHPOOdp9z549AFSrVi3keIHuu3fvznUeW7duBWDo0KHEx8czb9489u/fz6+//kqXLl34+OOP+cc//pHPJRERKb2UtIuISN6s9WqN1/vsGVVqAh2GwvrJsPFLv6ORUmzMmDE8//zztG3blnfffbfQ5hNoTi5Q+/zxxx9PpUqV6NatG59++imVK1fm3XffZcOGDYUWg4hISaakXURE8mZdAlQ/DqpmLV5b6rW9B6q0gTm3Q+phv6ORUmjs2LHceeedtG/fnh9++CFLhW+BJ+mBJ+6ZBbpXr14913kFhunduzdVq1bN0K9BgwZ069aNtLQ05syZE+FSiIgIKGkXEZG8SNoI236BJioaH1JsWejyMuxfAUuf9zsaKWVefPFFbr/9djp06MAPP/xA/fr1swxzzDHHALB8+fIs/VJSUli1ahVlypShRYsWuc4vMK3sEvwaNWoAcPDgwXAXQUREgihpFxGRyK37BLCqNT4nDfpAk0tg8Ug4sDb34UUKwLPPPsvdd99N586d+eGHH6hbt27I4c4880wAvvwy6yscP/74I0lJSXTv3j3XmuMBzjrrLAAWL14csv/vv/8OQPPmzcNaBhERyUhJu4iIRG5dAlQ7FqrlXKt0qXfCaPc57x5/45BSYcSIETz44IOceOKJfPfdd9SuXTvbYS+77DJq167Nhx9+mKHY+qFDh3j00UcBuOWWWzKMk5SUxNKlS9myZUuG7j169KBz5878/PPPTJo0KUO/N954gz/++INWrVplaA9eRETCpybfREQkMgc3w9YfocMwvyOJfpWawrGPwMJHYdM30OBsvyOSEmrChAkMGzaM2NhYevTowZgxY7IMEx8fz+DBgwGoWrUqb7zxBpdddhk9e/bkyiuvpGbNmnz66acsW7aMyy67jAEDBmQYf9asWfTq1YtOnTpl6GeMYcKECZxxxhlceuml9OvXjzZt2vD7778zdepUKlWqxIQJE4iNjc0wvXvvvZft27cD8PPPPwPw3HPP8d577wHQv39/+vfvX1CrSESk2FLSLiIikVk/CRWNj0C7e2HleJhzG5y3EGJzL24sEqlVq1YBkJqayosvvhhymDPOOONo0g4uKZ42bRpPPvkkH3/8MYcOHaJVq1a88MIL3HHHHRhjwp5/x44dmTdvHo8//jhff/01U6ZMoXbt2lx99dUMHTr06HvvwRISElizZk2Gbl9//fXR/+Pj45W0i4igpF1ERCK1NgGqtnXF4yV3seWgyxhIPA+WjoZjH/Q7IimBhg8fzvDhwyMe79RTT2XKlClhDduzZ0+stSQmJobs37x5c8aPHx/2vFevXh32sCIipZneaRcRkfAd2gpbE13b7BE8hSv1GvaFxhfB4hFwYJ3f0Yjkzar3YXI8Z2w8EybHu+8iIlLolLSLiEj41k8GmwZN1dRbxE54EUiD3/7pdyQikVv1PswaAklrMFhIWuO+K3EXESl0StpFRCR8aydCldZQ/Ti/Iyl+KsdD+4fdOtz8rd/RiERmwSOQmpSxW2qS6y4iIoVKSbuIiITn0HbY8oOKxudH+/ugckuYczukHvE7GpHwJa2NrLuIiBQYJe0iIhKeDf8Dm6qi8fkRWx5OfAn2LoVlL/kdjUj4KjaNrLuIiBQYJe0iIhKetROhcguo0dnvSIq3RudDo36w+HFI2uB3NCLhaXNr1m4x5aDTk0Ufi4hIKaOkXUREcnd4J2z+zj1lV9H4/DvxRUhLgd/u9TsSkfDsmgemLFRojMWAiYVytVXyRkSkCChpFxGR3K3/H9gU9z675F/lFtD+QVjzoasnQCSa7V4Eaz6Cdv+Ei9cxreH30GMSHNwAS0f5HZ2ISImnpF1ERHK3LgEqxUPNE/2OpORo/wBUag5zboO0ZL+jEcnewscgrgq0CyoZ0rifu4m36AnY+6d/sYmIlAJK2kVEJGdHdsPmb6Cpao0vUGUquGLye5bAspf9jkYktJ3zYP0kOOZuKFczY78uY1zlirNvAmv9iU9EpBQokUm7MaaWMeYGY8wkY8wKY8xBY8weY8zPxpjrjTEhl9sY090YM8UYs9MbZ6Ex5i5jTGxRL4OISNRY/6l7Eqyi8QWvUT9oeB4sGg4HN/kdjUhWC4dB2RrQ9u6s/So0gM7Pulc8Vr1T9LGJiJQSJTJpBy4H3gC6ATOBF4GPgQ7Am8D/GZPxcZEx5iLgR+B0YBIwFigLjAY+LKrARUSizroEqNgEanX1O5KSxxjXBFzaYfjtPr+jEclo+6+w8Qtodx+UrRZ6mFY3Qp1TYd49cGhb0cYnIlJKlNSkfTlwIdDYWnu1tfYha+11QFtgHXApcElgYGNMVVySnwr0tNZeb629D+gMzAAuM8ZcWcTLICLivyN7YNNX7im7isYXjiqtoN39sPp92Pqj39GIpFs4DMrVgTa3Zz+MiYGTXoOUfTDvn0UXm4hIKVIik3Zr7ffW2s+stWmZum8GXvW+9gzqdRlQB/jQWjsnaPhDwKPe11sKL2IRkSi14XNIO+LeZ5fCc+xDUKkZzL5VldJJdNj6o6vLov0DEFc552GrHwvtHoDV78Kmb4omPhGRUqREJu25CFwNpQR1O9P7/DLE8D8CSUB3Y0y5wgxMRCTqrEuACo2g9sl+R1KylakIJ7wIexbD8nF+RyOlnbWwcKh7Z711mM8sOjwCVVrD7JshJalw4xMRKWVKVdJujCkD/N37GpygH+N9Ls88jrU2BVgFlAFaFGqAIiLRJHkfbJwKTS51RWClcDW+CBqcC4seg4Ob/Y5GSrMt37kn7e0fdjeUwhFbHrq+DvtXwuIRhRufiEgpU8bvAIrYM7jK6KZYa78K6h6oXWVPNuMFulcP1dMYMwQYAlCvXj0SExPzHaj4a//+/fodpVAVh22s7sHvaZ92mN92tGJPlMdaUlRIvZqTkr9j65d/Z2mNh/M8neKwfUmUspbjt99Judi6zNzYBrspMcsgOW1fx1Q4l/pL/sWcba04ENeycGOVEknHL5GsSk3Sboy5A/gnsBS4piCnba19HXgdoEuXLrZnz54FOXnxQWJiIvodpTAVi23sp5fhUAOO73OrnrQXpQV/UP/3p6h/6jCoe1qeJlEsti+JThumwKYl0PU1zmjVJ+QgOW5fh4+Dz9txUtobcPovEKNWcyUyOn6JZFUqrsKMMbcBLwFLgF7W2p2ZBgk8Sc+mPZOj3XcXfHQiIlEoeT9snAKNL1HCXtSOfdg1sTfnVkhLyX14kYISeJe9UnNocW3eplGuFpwwGnbMhD9fKdj4RERKqRJ/JWaMuQt4GViMS9hDvSi4zPtsE2L8MkBzXMV1KwspTBGR6LJpKqQegqaX+x1J6VOmkkt6di9U0iNFa/1k2DUPjnsMYuLyPp34v0H9PrDgIUhaX2DhiYiUViU6aTfGPACMBubjEvat2Qz6vfd5boh+pwMVgenW2sMFHqSISDRaOxHK14M6eSueLfnU5BKof7Z76nlwi9/RSGlg01y77FXaQPzV+ZuWMdD1FbCpMCeHNt5FRCQsJTZpN8YMxVU8Nxfoba3dnsPgCcB24EpjTJegaZQHRnpf9bhDREqHlCTY8IVLHPU+qj+MgS4vQ2oSLHjQ72ikNFg70TU5eNxwiCmAKo8qt3DTWj8Z1k3K//REREqxElkRnTFmEPAEkAr8BNxhjMk82Gpr7XgAa+1eY8yNuOQ90RjzIbATuBDXHFwC8FHRRC8i4rONU12y2OQyvyMp3aoeA23vgSXPQsshUOcUvyOSkiotxTU1WO1YaDag4Kbb9m5Y/QHMuQ3q94a4qgU3bRGRUqREJu24d9ABYoG7shlmGjA+8MVaO9kYcwbwCHApUB5YAdwDjLHW2sIKVkQkqqxLgHK1oe7pfkcixz4Kq993ldKdM1slH6RwrP4A9i6D0xIKtuLJmDjXdvvXJ8P8h+GksQU3bRGRUqREFo+31g631ppc/nqGGO8Xa+151toa1toK1trjrLWjrbWpPiyGiEjRSzkIGz73isaX1Pu6xUhcZTj+edj1G6x4ze9opCRKS4bFj0ON46HJxQU//dpdoc3t8Oe/YfuvBT99EZFSoEQm7SIikkebvoKU/SoaXwgSEhK4/fbb6dGjB1WrVsUYw8CBA7Md/vDhw4wbN46ul4+i9i1xVO54K+3atuGOO+5gzZo1Ec8/NTWVN998k9NPP50aNWpQoUIFWrRowYABA1i+fHmO4x4+fJgOHTpgjKFx48YRz1ui2MoJsH8ldHyi8Jp37DQSKjaCmTe6mwQiIhIRJe0iIpJuXYJrZ7leT78jKXFGjhzJ2LFjmT9/Po0aNcpx2JSUFHr37s1tt93Gvn37uGrA5dx8lqFuhX28/PLLdOrUiSVLloQ97/3799OnTx9uvPFG9u3bx6BBg7jzzjs59dRTmTlzZq5J+8MPP5ynGwUS5VIPw+IRUKsbNDy/8OYTVwW6jHMV3f0xqvDmIyJSQqnso4iIOKmHYP2n0OyK/LXRLCGNHj2axo0b06pVK6ZNm0avXr2yHXbSpEn88ssv9O7dm6+//pqYmBj4rRH88RyP/XYdT4z6D6NGjeI///lPWPO+6aab+P7773n11Ve56aabsvRPTs7+6WdiYiKjR4/m3//+N7fccktY85Ni4q83IWktdHvDtVhQmBpfCE0uhcVPQNPLoUqrwp2fiEgJoiftIiLibPoGUvapaHwh6dWrF61btyZEayZZrFy5EoDzzz/fJewAHYZChYZc1HQ6ANu2bQtrvvPmzeODDz5gwIABIRN2gLi40Ddp9u7dy+DBg+nduzc333xzWPOTYiLlIPz+JNTpAfXPLpp5njgGYsrCrJtB9fuKiIRNSbuIiDjrEqBsDdc0k/jq2GOPBWDq1KmkpaW5jnFV4Pjn+XzaUgDOOuussKb1wQcfAHDVVVexZ88e3nvvPZ5++mlef/11VqxYkeO4d9xxB7t27eKtt97K45JI1FrxKhzcBB1HFP5T9oCKDaHzM7DlO1j9XtHMU0SkBFDxeBERce+2rv+fqz1aReN9d/7553PJJZfwySefcNxxx3HWWWdRtmxZ5s6dy88/GW7vW5Zbrw+vPe3Zs2cDsGbNGlq2bMmOHTuO9jPGcMsttzBmzBhiYzM2Jzdp0iQmTJjAm2++SdOmTQtu4cR/KQdgyTNQrzfUO6No593qJlj1Lsy7Gxr0hfK1i3b+IiLFkJ60i4gIbP4OkvdAk8v9jkRwyXRCQgKPPfYYy5YtY8yYMYwaNYoffviB00/txt9OTqHM78PCmtbWrVsBuOeee+jZsyd//PEH+/bt49tvv6Vly5b8+9//ZsSIERnG2bJlC0OGDKFv375cf/31Bb584rPlY+HQVveUvaiZGNd2+5E98Ns/i37+IiLFkJJ2ERGBdRMhrpqKxkeJQ4cOMWDAAJ5//nnGjRvHpk2b2LNnD1OmTGHNhu2cPsLyv4/egB2zc51WoHh927Zt+eijj2jbti2VK1emd+/eJCQkEBMTwwsvvMCRI0eOjnPjjTeSkpLCm2++WWjLKD5J3gtL/uWectc5xZ8YqneA9g/AqnfcDUMREcmRknYRkdIu9QismwyNLoTYcn5HI8AzzzzDxIkTefLJJ7npppuoX78+VatWpW/fviQkJJCcksad78XA7FvBpuU4rerVqwPQr1+/LEXgO3XqRPPmzdm3bx9//PEHAO+88w6fffYZL730Eg0bNiyU5RMfLX0RjuyETj48ZQ927CNQuRXMuslViiciItlS0i4iUtpt+QGSd7tmmCQqfP755wAhm4Xr1KkTNWrUYM22NHasmQ1/5VxJ3DHHHAOkJ++Z1ahRA4CDB13iNG/ePAAGDRqEMSbDH8CGDRuOft+9e3fEyyY+OrILlr4AjftDzRP9jaVMBej6Guz/C34f6W8sIiJRThXRiYiUdusmQpkq0KCImn2SXB0+fBgI3azb4cOH2bdvHwBlG3SHBQ+59q/L1Qw5rbPOOot3332XxYsXh5zWn3/+CUB8fDwAp5xyCvv37w85rbfeeouKFSty1VVXAVCunEpmFCt/PO/qrjjucb8jceqfCc0HueL6za6E6sf5HZGISFRS0i4iUpqlJcO6SdD4Qogt73c04unRoweLFy/mqaee4tRTT82QHA8fPpyUlBROOukkqpz+Kkw9HhY8wp5jnmHTpk1Uq1Ytw7QuvfRSHnroIT766CNuv/12unbterTfiBEj2LNnD7169aJ+/foADBgwgAEDQtdM/9Zbb1GjRg29614cHdoGy16CpldAjY5+R5Pu+FGw8QuYOQT6/OIqqhMRkQyUtIuIlGZbEt37rU0u8zuSEm/y5MlMnjwZgM2bNwMwY8YMBg8eDEDt2rUZNWoUAI888gifffYZ3333HW3btuXcc8+lQoUK/PLLL8yaNYsKFSrw0ksvuSeTbW6DZWOYNKse197+OIMGDTo6TYBKlSoxfvx4LrjgAnr06MEll1xCo0aNmDlzJj///DN169bltddeK8pVIX7441+QmgTHDfc7kozK14YTRsOMa+DPV6HNP/yOSEQk6ihpFxEpzdYlQJnK0OAcvyMp8ebPn8+ECRMydFu5ciUrV64EoFmzZkeT9kaNGjFv3jyeffZZvvjiC95++23S0tJo0KABgwcP5oEHHqBt27ZuIsc9Dms+dG1fZ+Pss89m1qxZjBgxgm+//ZY9e/ZQv359br75ZoYOHaoK50q6g5th+ThodjVUa+d3NFnFX+1qkp//IDS+CCo28jsiEZGooqRdRKS0SktxReMbXeAqhZJCNXz4cIYPHx728HXq1GHUqFFHE/lsla0Gxz/H4EN/Z/CKt6DldSQmJmYZrFOnTiQkJEQWdCbW2nyNLz75/WlIOwLHDfM7ktCMgZNegSkdYO4d0ONjvyMSEYkqenFIRKS02vojHN6movElQfxAqHMazLkTJjXhjI1nwuR4WPW+35GJ3w6sgxWvQovBUKWV39Fkr0pL6PAYrPvENUEpIiJHKWkXESmt1iVAbEVo2NfvSCS/jIEG50Hqfji4HoOFpDUwa4gS99Lu9ycBCx2G+h1J7tr909XTMOc2SN7rdzQiIlFDSbuISGmUluqeaDU6H8pU9DsaKQgrQlQml5oECx4p+lgkOuxfBX+9BS1vhErN/I4mdzFx0PUNOLgRFjzqdzQiIlFDSbuISGm07Wc4tEVF40uSpLWRdZeSb/EIMLFw7MN+RxK+2t2gza2wfCxsn+l3NCIiUUFJu4hIabQuAWIrQMPz/I5ECkrFppF1l5Jt75+uRvbWtxS/2tg7PQkVGrrXO9KS/Y5GRMR3StpFREobmwbrPnbvssdV9jsaKSidnnR1FASLKee6S+mz+HH3+7d/0O9IIhdXFbqMhd0LYekLfkcjIuI7Je0iIqXNtulwcBM0udzvSKQgNb8aur4OFZthMWDKeKUpVNFgqbP7d1j9AbS5DSrU8zuavGnSHxpfDIseh31/+R2NiIivlLSLiJQ2aye6J3CNzvc7Eiloza+G/quZ1vB7OGcmpOyH2beA2lcvXRYNhzKVof39fkeSP11edjeftA2LSCmnpF1EpDQ5WjT+XIir4nc0UphqngAdn4C1/wer1exbqbFrvquzou1dUK6W39HkT8VG0Plp2PyNtmERKdWUtIuIlCbbZ8LBDSoaX1q0ux/qnAZzboUDa/yORorCwscgrjq0vcfvSApGq5uh1skw7244vMPvaEREfKGkXUSkNFk7EWLKQqML/I5EikJMLJzyritaPOPvkJbqd0RSmHbMhg2fQrt/QtnqfkdTMGJiodvrcGQ3/Hav39GIiPhCSbuISGlhrSs22+AcKFvN72ikqFSOdzVxb/0Rlo7yOxopTAuHuiLxx9zpdyQFq/px0O4+WDkeNn/vdzQiIkVOSbuISGmxYxYkrYMml/kdiRS15te4333hUNj5m9/RSGHY9gts+graPVAy66voMBQqt4RZN0HKQb+jEREpUlGTtBtj6hhjbjbGvGSMeTNT967GmAp+xiciUuytS4CYOGh8od+RSFEzBrq+CuXqwPSrlfSURAuHQvl60OZWvyMpHGUqQNfXYP8K+P1Jv6MRESlSUZG0G2OuB1YD44DbgWuDetcDZgB/K/rIRERKCGthbQLUP7vkvOsqkSlXC04eD3v/gPkP+h2NFKTN38OWH6D9Q1Cmot/RFJ76vaH532HJs7B7sd/RiIgUGd+TdmPM2cDrwHLgYuCV4P7W2sXA70D/Ig9ORKSk2DkXDqxW0fjSrsHZ7n3n5WNg09d+RyMFwVr3lL1CI2h9k9/RFL7jn3d1csy6yTVhKSJSCvietAMPAJuAM6y1nwJbQwyzEGhfpFGJiJQk6xLAlIHGF/kdifit09NQrT38OlhNaJUEm76C7dOhw6MQW97vaApf+dpw/AtumVe87nc0IiJFIhqS9i7A59bavTkMsx6oX0TxiIiULNa6pt7q94ZyNf2ORvxWpgJ0fx8Ob/eeVlq/I5K8CjxlrxQPLa7zO5qi0/waqNcb5j8ASRv9jkZEpNBFQ9JeFjiQyzDVATUuKyKSF7vmw/6VKhov6Wp0ho4jYd3HsOodv6ORvNrwGeyc42pWjy3rdzRFJ1CxYtoRmHuH39GIiBS6aEjaVwMn5jJMN2BZ4YciIlICrUsAEwuN+/sdiUSTtv+EumfAnNth/yq/o5FI2TRYOAwqt3KVs5U2VVpBh2HuxtP6T/2ORkSkUEVD0v4/oIcx5vJQPY0x1wIdgY+LNCoRkZIgUDS+Xi/3LqhIQEwsnDLBPbWccQ2kqUBbsbLuY9i9AI57DGLK+B2NP9rdC9U6wJxbIXmf39GIiBSaaEja/wWsBf5rjPkIOAXAGHOb9/114E/gZf9CFBEppnYvgn1/QtOQ90WltKvUDLr8G7b9An8863c0Eq60VFj4GFRtB82u8jsa/8TEQdfXIWmDe7dfRKSE8j1pt9buAs4AfgYuB/oABhjjfZ8O9LbW5vbeu4iIZLZ2IpgYFY2X7MX/DZoOcEngjjl+RyPhWPMh7P0DOj7uSkyUZnVOgdb/gGVjYPssv6MRESkUviftANbatdbankBn4BbgUeB24CRr7RnW2g0+hiciUjxZC+smuveWy9f1OxqJVsZA11egQn2YMRBSkvyOSHKSlgKLH4fqHaHJpX5HEx06PwUVGsCsIZCW7Hc0IiIFLiqS9gBr7UJr7WvW2qesteOstXP9jklEpNjaswT2LlPReMld2Rpw8ni3vfx2n9/RSE5Wveteeen4hCtFIxBXFbqMde/4L33R72hERAqc70d7Y0wFY0xTY0zItkqMMeW8/uWLOjYRkWJt7UTAQOOL/Y5EioP6vaHtPfDnv2HDFL+jkVBSj7in7DW7QKML/Y4mujS5GBpfBIseU2sIIlLi+J60A8NwzblVzqZ/JWAp8HCRRSQiUhKsS4C6p7tizyLh6PQkVD8OZl4Hh7b5HY1ktvI/cGANdBzhXmuQjLqMdc1bzr7FvR4kIlJCREPS3hf41lq7M1RPr/u3wAVFGpWISHG25w/Y8zs0uczvSKQ4iS0Pp7wHR3a594OV+ESP1EOweCTU7g4NzvE7muhUsTF0eho2fQVr/ut3NCIiBSYakvZ4YHkuwyz3hhMRkXCsTQAMNLnE70ikuKnR0SU+6ye7J7sSHVa8Dgc36Cl7blrfArW6wdy74HDI50EiIsVONCTtcUBaLsNYQO+0i4iEa10C1DkVKjb0OxIpjtreBfXOhLl3wr6//I5GUpLg96egbk+of6bf0US3mFjXdvuRXapUUURKjGhI2lfi2mnPSU9gTeGHIiJSAuxdDrsXqmi85J2JcbXJmziYPtA1Myb+WT4ODm1xT9kldzU6Qrt/upIiWxL9jkZEJN+iIWn/FDjRGHN/qJ7GmAeBE4DJRRmUiEixtS7BfTZVG86SD5WawEmvwI5f4fen/Y6m9EreB388695jr3ua39EUHx2GQeUWMOsmVx+AiEgxFg1J+yhgHfC0MWaOMeYpY8yt3udc4ElgLfAvX6MUESku1k6E2qe4SplE8iP+Soi/2jUztn2W39GUTsvGwOEdcNwTfkdSvJSpCCe9CvuWu1cLRESKMd+TdmvtLlzx95m4J+oPAmO8z+OBGUAvbzgREcnJvhWwa76KxkvB6TIWKjSEGQMh5YDf0ZQuR3bDH6OgUT+o3dXvaIqfBmdD/EBY8gzsWeJ3NCIieeZ70g5grV1tre0OdAFuA4Z6n12stadZa1f7GZ+ISLGx7mP32VRJuxSQstXhlHfcDaF5//Q7mtJl6QuQvBs66il7np3wApSp4jVhmFu9xyIi0amM3wEEs9bOA+b5HYeISLG1diLU6gqVmvodiZQk9XpCu3vhj+eg4fnQuJ/fEZV8h3fA0hddqZkanf2OpvgqXwdOeB5+vRZWvAGtb/I7IhGRiEXFk3YRESkA+1fBzrkqGi+Fo+MIqN4JZl4PB7f4HU3J98dzkLIfjhvudyTFX/NBUK8XzH8ADm7yOxoRkYhFxZN2Y0wccBHQFagBxIYYzFprry/SwEREihMVjZfCFFsOur8PX54IM2+AMz4FY/yOqmQ6uAWWvQzNroLqx/odTfFnDJz0Gkw5DubeCaf9n98RiYhExPek3RjTEPgGaAvkdPa3gJJ2EZHsrJ0INU+Eys39jkRKqurHQudnYd5d8Ncb0GqI3xGVTEuehbRDcNxjfkdSclRtDR2GwsJHYcPn0OgCvyMSEQlbNBSPfx5oB3wInAm0BpqH+GvhV4AiIlHvwBrYMQuaXu53JFLSHXM71D8L5t4Ne5f7HU3Jk7QB/vy3K9JdtY3f0ZQs7e6DasfC7H9A8n6/oxERCVs0JO19gB+ttVdbaxOttX9Za9eE+vM7UBGRqLXWKxrf5FJ/45CSz8TAyeNdcfkZ10Bast8RlSy/PwU21T0VloIVWxa6vg5J62Ch1q+IFB/RkLSXx7XRLiIiebUuwdUwXaWV35FIaVCxEXR9zZXuWDzS72hKjgNr3GsHLa/Xay6FpU53aH0LLB8DO+b4HY2ISFiiIWlfDDTzOwgRkWIraT1sn6Gi8VK0ml4Ozf8Ovz8J22b4HU3JsHgkYODYR/yOpGTr9DSUrwezboS0FL+jERHJVTQk7c8BFxpj2vsdiIhIsXS0aLxqjZciduIYqNjYFZPXO8L5s28FrHwbWt0ElZr4HU3JVrYanPgy7JoPy17yOxopoRISErj99tvp0aMHVatWxRjDwIEDQw67evVqjDHZ/l155ZVhz3fw4ME5TssYQ+/evbOMt3XrVu6//346dOhAlSpVqFWrFieeeCLPPfcc+/bty/N6kILhe+3xwFbgM2C6MeYlYC6wO9SA1tofizAuEZHiYV0CVO+oSquk6JWtBqe8C9+eAfPuhm5v+B1R8bXoCYgpC8c+5HckpUOTS6DRhbBwmPtfryNIARs5ciQLFiygcuXKNG7cmKVLl+Y6TqdOnejfv3+W7h06dAh7vv379yc+Pj5kv3fffZeVK1fSt2/fDN1Xr15Nt27d2Lp1Kz179qRv374cOnSIr7/+mvvvv5/33nuPX3/9lQoVKoQdhxSsaEjaE3HNuRlgqPd/dkK13y4iUnolbYRtv8Bxj/sdiZRWdXtA+wdhydPQ8Hxo0t/viIqfPUthzfvQ9h6o0MDvaEoHY6DLWPiivatNvucU102kgIwePZrGjRvTqlUrpk2bRq9evXIdp3PnzgwfPjxf8+3fv3/IxH/37t3861//omzZsgwePDhDv+eee46tW7cyfPhwHnssvanJ1NRU+vTpw/fff8/EiRP5+9//nq/YJO+iIWl/gpwTdRERyc66TwALTVU0Xnx03HDY9JV7R7j2yVChvt8RFS+LhkNsBWh3v9+RlC6VmkCnJ2HunbDmI4gPvwiySG7CSdKL0rvvvsvBgwe58sorqV27doZ+K1euBODCCy/M0D02Npbzzz+f77//nm3bthVZrJKV70m7tXa43zGIiBRb6xJcu8PV2vkdiZRmsWWh+3vw5Qnw63XQ8ws9tQzXroWw9iM49mEoX8fvaEqf1rfCqvdg3p3QoA+Uq+l3RFKKbdy4kddee40dO3ZQq1YtTjnlFDp27Fgg037jDff60pAhQ7L0O/bYY/nyyy/54osvOP744492T0tLY+rUqcTExHDmmWcWSBySN74n7SIikkcHN8PWH6HDML8jEXE3jjo/B3NvhxWvuma1JHeLHoO4qtD2n35HUjrFxEK31+HLLjD/AdXLIL765ptv+OabbzJ069mzJxMmTKBp06Z5nu6MGTNYtGgRbdq0CVkC4P777+fzzz9n6NCh/PDDD5xwwgkcOXKEr7/+ms2bN/Pmm29mSOal6EVD7fEAGGPijDHnGmPuNsYMDepe3hhT1xgTNbGKiESF9ZNwRePV1JtEiTa3QoNzYd4/3XvakrOdc2H9ZJew6wmvf2p0dr/BX2/Clml+RyOlUMWKFRk6dChz585l165d7Nq16+h78ImJifTu3ZsDBw7kefqvv/46ADfeeGPI/nXr1uXXX3/l4osv5vvvv2fUqFGMGTOGZcuWccUVV3DWWWfled5SMKIiETbGnAusBr4AngeGB/XuDGwCBhR1XCIiUW3tRKjaFqqpxUyJEsbAyf+BMhVhxkBIPeJ3RNFt4TAoWxPa3uV3JHLcY1CpOcy+CVIP+x2NlDJ169bliSee4IQTTqB69epUr16d008/na+//ppu3bqxYsUK3nzzzTxNe8+ePfzf//1fyAroAlavXs3pp5/OokWLmDJlCnv27GHTpk288sorvP/++5x00kmsWrUqH0so+eV70m6M6QJMxlVGdzfwQXB/a+2vwCrg4iIPTkQkWh3aClunubbZ9e6wRJMKDaDr6+4p8uIn/I4mem2bARunQLv7XPF48VeZinDSK7B3Gfz+tN/RiABQpkwZbrjhBgB+/DFvLV+/9957JCUlcckll2SpgC5g8ODBLFq0iI8//pi+fftStWpV6tevz0033cSTTz7Jli1bePxxtVLjJ9+Tdlwzb0lAF2vtGODPEMPMBjoVaVQiItFs/WSwaSoaL9GpySXQ4lrXDNy2X/yOJjotGgbl6kCb2/yORAIangPN/gZLnoI9f/gdjQgAdeq4CirzWjw+UAHdTTfdFLL/vn37mDZtGjVr1gxZ6V3gHfi5c+fmaf5SMKIhaT8VmGyt3ZzDMOsANVwqIhKwdiJUaQ3Vj/M7EpHQTnwJKsXD9Gsgea/f0USXLdNg87dw7EMQV9nvaCTYiaOhTGWYdZO7MSris19//RWAFi1aRDzuzJkzWbBgAW3atKFnz54hhzlyxL3GtHfv3qP/Bws09Va2bNmI5y8FJxqS9srA9lyGqUh0xCoi4r9D22HLD+4pu4rGS7SKqwKnvAtJa1w72OJYCwuHQoWG0Opmv6ORzMrXheOfh20/wV9v+R2NlBLz5s0jLS3rTaLvvvuO0aNHAzBw4MAM/fbs2cPSpUvZtGlTttMNVEAXqpm3gFq1atGuXTtSUlIYMWJEhn6HDh1i5MiRAPTu3Tu8hZFCEQ1Nvm0Ajs1lmM7AysIPRUSkGFg/GWyqe59dJJrV6Q7tH4bfR0LDC6DppX5H5L/N37qEsMtYKFPB72gklBaDYdU78Nv90KgfVKjvd0RSDE2ePJnJkycDsHmzK1A8Y8aMo5XB1a5dm1GjRgFwzz338Oeff9K9e3caN24MwMKFC/n+++8BGDFiBN27d88w/UmTJnHttdcyaNAgxo8fn2X+e/fu5aOPPqJcuXIMGjQox1jHjBnD+eefz8iRI/nmm2/o3r07Bw8eZOrUqaxZs4ZWrVrxwAMP5HVVSAGIhqR9KnCzMeY0a+3PmXsaY/oC3YFnijwyEZFotC4BKrdwzRSJRLvjhsGmL2HWEKh9ClRs6HdE/gk8Za/YBFre4Hc0kh1joOtrMKUjzL0LTvvQ74ikGJo/fz4TJkzI0G3lypWsXOmeQzZr1uxo0n7NNdcwadIkZs+ezdSpU0lOTqZevXpcccUV3HbbbfTo0SPi+b///vscOHCAK6+8MtsK6ALOOussZs+ezXPPPce0adMYO3YssbGxtGjRgoceeoj777+f6tWrRxyDFBxjrfU3AGMaAQtwReBfBuKBy4ALgdOBW4E9QCdrbW7F6H3XpUsXO2fOHL/DkHxKTEzM9t0fkYKQ523s8E74pB60+yd01r1MCS3qjmF7l8HU46FOD+g1FUwpfeNtw+cwrZ+rXb9V6PaSi4Oo274Ky6IRrsLAM76ARuf5HU2pUWq2rxLOGDPXWtvF7zhKCt/PmtbaDUAfYCNwH3A5YIBPve+bgHOLQ8IuIlLo1v8PbIqKxkvxUvUYOOF52Pw1LB/ndzT+sNa1y165hSt+LdGv/QNQrT3M+Qck7/c7GpGMVr0Pk+Phgxj3uep9vyOSQuR70g5grZ0HHAP0B54F3gRewCXw7ay1i/yLTkQkiqxLcDVy1zzR70hEItPqZmh4Hsy/H/Ys8Tuaord+Euz6DTo8BjFxfkcj4Ygt60pFHFgDix7zOxqRdKved68cJa0BrPucNUSJewnme9JujPmPMeZua22qtfZTa+1D1toh1tr7rLUfW2tT/I5RRCQqHNkNm7+Bppep1ngpfoyBbm+55rSmXw2pWZsWKrFsGix8zJU4iL/a72gkEnVOhVY3wbIXYec8v6MRcRY8AqlJGbulJrnuUiL5nrQDfwPq+h2EiEjUW/8ppCWraLwUXxXqQ7c3Ydf80vXkcs3/wZ7F0GE4xMT6HY1EqvMzUK4uzLwR0vQsSaJA0trIukuxFw1J+2qUtIuI5G5dgqt1ulZXvyMRybvGF7ma05c8C1t/9DuawpeW4m5QVOsAza7wOxrJi7LVocsY2DUPlo3xOxoRdy0QsnvToo1Dikw0JO0fAH2NMTX8DkREJGod2QObvnJP2VU0Xoq7E0a7Ctlm/N1t2yXZ6vdh33Lo+ETprTW/JGhyGTS8wDXZt3+139FIaVe1bdZusRWh05NFH4sUiWg4ezwNzAF+MMZcYIyp53dAIiJRZ8PnkHYEml7udyQi+RdXGbq/B0nrYc7tfkdTeNKSYfETUOMEaNzf72gkP4yBk8a5zzm3utYARPyw+kPXEkf9c6FiM8C4z66vQ3PVmVFSlfE7AOCQ92mA/wGY0E+RrLU2GuIVESl66yZChUZQu5vfkYgUjNonw7GPwuLHodEFJbPo+MrxsH8lnPG5SsiUBJWaQseRMO9uWDuxZG6zEt32/AGzbnAVJPb8VC1RlCLR8KT9J+BHYJr3md3fT34FKCLiq+R9sPFLaHKpitdKydLhEajVDWbf7J66lySph2HxCLd8Dc/zOxoJISEhgdtvv50ePXpQtWpVjDEMHDgw55Ha3A41u8DcO7jhumswxmCMYcWKFRHP//Dhwzz//POcdNJJVK1alUqVKtGmTRsGDRrEtm3bMgwbmE9Of++++27EMUgxkrwffroUylSCUz9Swl7K+P7k2lrb0+8YRESi2oYvIO2wisZLyRMTB6e8C1M7w4zBcObXJefG1Io3IGkdnPwfPWWPUiNHjmTBggVUrlyZxo0bs3Tp0txHiomFrq/z2VNdeOvt96hcuTL79++PeN6bN2+mT58+LFq0iFNPPZUbb7yR2NhY1q5dy1dffcV9991HnTp1jg7/2GOhW1vYt28fL7zwAmXKlOHss8+OOA4pJqyFWTfCvmXQ6xuo2MjviKSI+Z60i4hILtZNhAoNoE53vyMRKXhVW8OJL8KsIa5m7rZ3+R1R/qUchCVPQd3ToV5vv6ORbIwePZrGjRvTqlUrpk2bRq9evcIab1tqY24cX54BJyexOaYV06bPj2i+aWlpXHHFFSxbtoxPP/2Ufv36ZehvrSUtLS1Dt+HDh4ec1muvvQZAv379qF+/fkRxSDGyfBys+dBVNFf/TL+jER9E1e1sY0wlY8zxxpgefsciIhIVkvfDxikqGi8lW8sboNGFMP9B2L3Y72jy789X4OAm6DhCT9mjWK9evWjdunV2dSlla8iQIVCmMuP+0QT2Lot4vpMnT+ann37i7rvvzpKwgysKHxsbG9a0Xn/9dQBuuummiOOQYmL7r/DbPa71gvYP+h2N+CQqnrQbYxoDLwH9gFjA4sVmjDkNeB34h7U20a8YRUR8sXEKpB5yzQ2JlFTGQLc3YMpxMP1qOGcWxJbzO6q8Sd4PS56B+me5J+1SoowfP57JkyczefJkap1UDp7uG/E0PvjgAwCuuuoqtmzZwueff87WrVupX78+ffr0oVGj8Io+z507l3nz5hEfH0+fPn0ijkOKgUPb4OfLoUJj6P6Obt6XYr4n7caYBsBMoB7wKVAXOCVokJletwFAYlHHJyLiq3UJUL4e1DnN70hEClf5utDtPzDtAlj4KBz/nN8R5c3ysXB4m3vKLiXKmjVruPPOOxk4cCAXXXSR61iuLrAV9v0FtAprOrNnzwZg1qxZ3HXXXSQlJR3tFxcXx7Bhw3j00UdznU7gKfuNN94YcWkBKQbSUt1NzEPboM90KFvD74jER9Fwu+YxXFJ+trX2EuCb4J7W2mRczfGnRjJRY8xlxpiXjTE/GWP2GmOsMea9bIaN9/pn9/dhHpdNRCTvUpJcJXRNLnGVH4mUdI3Oh1Y3wx/Pw5Yf/I4mckf2wB//gobnuybtpMRIS0tj0KBBVK5cmTFjxqT3qNLSfS4cCjYt9MiZbN26FYBbbrmFwYMHs3LlSnbv3s3HH39MjRo1GDp0KOPHj89xGvv37+e///0vZcqU4brrrsvLIkm0W/wEbP4GurwMNU/wOxrxme9P2oHzgE+ttTmdndcCkb7n/ijQCdgPrAfahjHOAmByiO4l4AU7ESl2Nk6F1CQVjZfS5YRRsOU7mDEIzlsIZav7HVH4lr0IR3ZBxyf8jkQK2OjRo5k2bRpffPEFNWoEPfGMKes+d8yGlW9Dy+tznVagkrmzzjqLcePGHe1+ySWXEBcXx4UXXsjTTz/N4MGDs53Gf//7X/bt28cll1yiCuhKoo1TXZORLQa7Oj+k1IuGpL0e8GcuwyQDlSKc7t24ZH0FcAYQzi37+dba4RHOR0SkcKxLgHJ19F6slC5lKkH39+HrU2D2rXDq+35HFJ7DO2HpC9D4Yj0VK2GWL1/OI488wrXXXst5550XeqBaXWHeva6ysAr1cpxe9erV2bp1KxdffHGWfueddx5ly5Zl+fLl7Nmzh2rVqoWcRqBo/JAhQyJbGIl+B9bA9IFQ/TjoMk6VWQoQHcXjdwJNchmmDbA5kolaa3+w1v5prbV5jkxExC8pB2HDZ9DkYoiJhvurIkWo1knQ4TFY8wGs/q/f0YRn6fOQvA86Pu53JFLAlixZwuHDh3n77bcxxmT4mzZtGgCtB8/CXLGbyS9enuv0jjnmGMAl75nFxsZStWpVAA4ePBhy/Pnz5zNnzhyaN2+uCuhKmtTD8NNlYFOgx8dQpqLfEUmUiIYrwV+AC40x9a21WRJzY0xr4Fwg5PvoBayhMeYmoBawA5hhrV1YBPMVEclo01eQckBF46X0OvYh2DQVZt8CdU6FSk39jih7h7bBspeg6RXu6ZiUKPHx8Vx/fehi71988QWbN2/m8ssvp2rKX8Tbn1zR5obZ1yp/1lln8dNPP7F48WIGDBiQod+WLVvYvn07lStXpnbt2iHHD7TNfsMNN6gCupJm3t2wcw70mARVwqvYUEqHaEjanwMuAqYZY+4CKoJrsx04HRgNpAHPF0EsZ3t/RxljEoFB1tq12Y1kjBkCDAGoV68eiYmJhRiiFIX9+/frd5RClds21m7XOGrGVGX6UoNdlv1wIqGUlGNY+Zhb6ZJyI/umXsSCWs9HbXNHLfe8QuOUg8w+eB5JJWC956akbF/B5s+fD7ikOdSyDRw4MOR4c+bMYfPmzVx00UU0bliH1ttu5NBP1zKrztvs2nfkaBH34GLu7dq1o3z58rz00ku0a9eOhg0bApCamsqzzz4LwGmnncbPP/+cZX4HDx7k3XffJTY2lrZt25a43wFK5vYVjnpJ39Bu9yusrTSAlX9Vh78S/Q5JoojvSbu1dqb3dPsV4POgXnu9zxTgOmvt74UYRhIwAlcJ3UqvW0dgONAL+M4Y09laeyDUyNba13FtydOlSxfbs2fPQgxVikJiYiL6HaUw5biNpR6Cj2dC8ys4o9tZRRqXlAwl6hj212FqzLyenvXnQbt7/Y4mq4Ob4NNPoflAup7yd7+jKRIlZfsKtLcOsHmzK+y5cuXKozW3165dm1GjRuU4jUAR927dutGqVSvY+h58ezqn1/ie4T9W4vHHH+exxx5j+PDhGcZLSkri2muv5eabb+biiy+mZs2aJCYmMn/+fNq0acO7774b8kn7W2+9xYEDB7jkkku45JJL8rX80aqkbF8R2b0YvhoNdU+n6Znv0VSvxUkmUbFFWGv/Y4z5CfgHcDKuePoe4FdgrLV2WSHPfyswLFPnH40xfYCfgW7ADcBLhRmHiAgAm76BlH3QJPd3I0VKvBbXuvodFjwC9c+GGp38jiij35+GtGTokPkyQqLd/PnzmTBhQoZuK1euZOVK9/ymWbNmuSbtWdTtAS1vhKWj4WD2NckPGjSIZs2a8cwzz/Dpp59y4MABmjZtyn333cfDDz8c8n13UAV0JVLyXvjpUoirCqd+qHpsJKQi3yqMMRcCS621y4O7W2v/xNX4HjWstSnGmDdxSfvpKGkXkaKwdiKUrQH1z/Q7EhH/GQNd34Apx8H0q+HcORBb3u+onAPrYMVr7sZCoL1uKTaGDx+e5Ql4pEIW4z7+WdjwKcN7zWX40ykQExty3J49e0b8RHnmzJmRBynRy1r49XrY/xf0/h4qNPA7IolSfrwcNgm4MvDFGLPSGHOHD3GEa5v3GWmTcyIikUs9DBs+hcYXQUyc39GIRIfyteHkt2HP7zD/Yb+jSff7SPfZYai/cUh0KVsDThwDO+fCx3XggxiYHA+riknzhVJ0lr3omnft9LSad5Uc+ZG0JwPBV6LxQHUf4gjXyd7nyhyHEhEpCJu/g+Q9KhovklnDc6H1rbBsNGz+1u9oYP9K+Os/rih0NNdsL/5IS3YVJybvAiwkrYFZQ5S4S7ptv8Bv90Pj/tFZX4dEFT+S9rXAacaY4LJCvralbow5wZisVdIaY3qTXmS/KJqcE5HSbt1EiKsG9VUBnUgWx/8LqraFGYPh8E5/Y1k8wr17emwUPfmX6LHgEbBpGbulJrnuIoe2ws9XQKVmrhSRmu6TXPhR08F/gaHATmPMDq/b3caYa3MZz1prw35hzBjTH+jvfa3vfZ5ijBnv/b/dWhu4rfUC0NoYMx1Y73XrCAReKB1qrZ0e7rxFRPIk9Qism+yKxseW9TsakehTpiJ0fx++6ubabz/1Q38udvcuh1XvQJs7oWLDop+/RL+kbFoKzq67lB5pKfDLlXBkJ/T5FcpW9zsiKQb8SNpHAAeB84GGuKfsxvvLSaRn5c7AoEzdWnh/AGuAQNL+LnAxcBLQF1d8fwvwf7ja63+KcN4iIpHb8j0k74Yml/kdiUj0qnkCdHwCFjwMjfpB89DtZxeqRY9DTHk49sGin7cUDxWbuiLxWbo3LvpYJLosHAZbfnBP2KOtNQyJWkWetFtrU4BnvD+MMWnAaGvtEwU8n+G4dtbDGfYt4K2CnL+ISMTWJUCZKtDgbL8jEYlu7e6HjVNgzq2uia1KzYpu3rsXw5r/QvsHoHzdopuvFC+dnnTvsKcmZexepjKkJLlSI1L6rP8MljwNLW+AFoP9jkaKkSJ/p90Y09EYE3yWmwDML+o4RESiSloyrJsEjS+MnuasRKJVTCyc8o5rLmnG3yEttejmvWi4S7xUcZTkpPnV0PV1qNgMMO6z1c2wbxlM6+cSdyld9q90x6saJ0CXl/2ORooZPyqi+w24Oeh7M6K79ngRkcK3JdG936ai8SLhqdzcXfhu/RGWjiqaee6aD+s+hrZ3Q7laRTNPKb6aXw39V8Pf0txn11fg5AmuaLQS99Il9RD85J3feyTo5rxEzI+kPQ0Irjm+J67ZNxERXyUkJHD77bfTo0cPqlatijGGgQNDvy+7bt06/vGPf9CtWzfq169PuXLlaNiwIT169ODtt98mOTk57PmuXr0a07AP5mowTS/GGJPh78orr8x1GocPH6ZDhw4YY2jcWO9MSinR/O/uRtfCobDzt8Kf38JhEFfdJe0iedF8oCslosS9dJlzO+z6Dbq/6244ikTIj4ro1uMqiRMRiSojR45kwYIFVK5cmcaNG7N06dJsh/3rr794//336datG/3796dmzZrs2LGDqVOnct111/Huu+/y9ddfU6ZMGIfZtBQAOrWqTv+r78zSu0OHDrlO4uGHH2bNmhCVHomUZMZA11dhyi8w/Wo4dy6UqVA489o+EzZ8Bh1HqrZnyZ9A5Ykz/g7TLoQzPtU77iXZyvHw15vQ/iFodIHf0Ugx5UfS/hlwmzHmD2CT122wMaZnLuNZa23vwgxMREq30aNH07hxY1q1asW0adPo1atXtsN2796dXbt2EROTscBScnIyffr04YcffuCTTz7hiiuuyH3GO2YB0LlzZ4YPHx5x3ImJiYwePZp///vf3HLLLRGPL1KslasFJ4+HH86B+Q9Cl5cKZz4Lh0G52nDMHYUzfSldmg8ELMwYpMS9JNu1wDVPWe9M1+qFSB75kbQ/ApTFNfl2Bq7Jt3hyLyJvCzUqESn1ckrSMytbNnQ76nFxcfTv35/ExET+/PPP8Ca2cYr7rNAo7PkH7N27l8GDB9O7d29uvvlmJe1SOjXoA23ugOVjoOF50PCcgp3+1p9h89dw/HMQV6Vgpy2lV/Nr3KcS95LpyG746VIoWxNO/S/E+JF2SUnhR5Nv+wiqiM5r8m14QTf5JiLih9TUVKZMcUl4x44dcx8hLRU2fQXAxs1bee2119ixYwe1atXilFNOyXUad9xxB7t27eKtt9RqpZRynZ+BLd/CzGvhvEUFW1HcwqFQvj60/kfBTVMEMibuP14Ep/9PiXtJYC38ei0cWANnJap5SMm3aLjlMw1Y7XcQIiJ5sX37dsaOHYu1lm3btvHNN9+wYsUK/va3v9GvX7/cJ7DtZzi8HYBvvvmGb775JkPvnj17MmHCBJo2bZpl1EmTJjFhwgTefPPNkP1FirOEhASmTZvG/PnzWbBgAfv27ePqq6/mvffeyzLsunXrePrpp5k7M4Y1f21iV1JdatWuR8uWLbnuuusYOHAgcXFxYc336LTmzmXNmjXs2rWTWhWTadmyBdft+SjHaU2YMIFx48axZMkSYmNjOf7447n33nu54AK9xyo5aH6Nl+QNVuJeUvwxCtZPhhNegDqn+h2NlAB+1B6fgbW2l7X2Hb/jEBHJi+3bt/P444/zxBNP8Morr/DXX39x7733Mn78+PAmsHYiFSuUZ+jDDzB37lx27drFrl27jr5Tn5iYSO/evTlw4ECG0bZs2cKQIUPo27cv119/fcEvmIjPRo4cydixY5k/fz6NGuX86kigYshqtRrQ/5xu/LNvGv3OaMOaNWu47rrrOOecc0hJSQlrvkenVa0a/S+6iH9eXJd+XSuxZuuRHKd17733MnjwYDZt2sSNN97IwIEDWbRoEf369WPs2LF5WgdSirT4u6ubYfN3LnFPOeh3RJJXW6bBgodcyxbH3OV3NFJSWGv1V4B/J554opXi74cffvA7BPHZDz/8YAF79dVXhzV8SkqKXbNmjX3xxRdt1apV7cknn2x37NiR4/RtWqq1H9e39sdLQg6TnJxsu3XrZgH74osvZujXr18/W716dbthw4YM3QHbqFGjsGKWkqskHMO+//57u3z5cpuWlpbr/nj48GGbmprqvqSmWPvN6dZ+VNke2bnU9uzZ0wL2o48+Cmu+Gaa1YYq172Pt8lftkSNHsp3WL7/8YgHbsmVLu3PnzqPdV61aZWvWrGnLlStnV61aFfE6iFYlYfuKWn9NsPZ9Y+13Z1mbnOR3NL4o1ttX0kZrP65n7adtrD2yx+9ofAXMsVGQm5WUvyJ/0m6MWWmM+csY0zzoezh/fxV1rCIi4YqNjaVp06bceeedvPbaa/z6668MGzYs55G2TYdDm6HJ5SF7lylThhtuuAGAH3/88Wj3d955h88++4yXXnqJhg0bFtgyiESTXr160bp1a4wxuQ5btmzZ9JYcYmJdO9gmhrg519P/ogsBwq4Y8ui0rHU1xleKhxbXHq1kMtS0Xn31VQAeeeQRatSocbR7fHw8t956K4cPH+btt98Oa/5SyrX4O5z8tvfE/UI9cS9O0lLglysheR/0+BjiqvodkZQgfhSPj8k03xjAhPHne1F+EZFw9O3bF3BNseVo7USIKQeNzs92kDp16gBkKB4/b948AAYNGoQxJsMfwIYNG45+3717d94XRKS4qtQMuvyb1C2/MGXiv4EwK4YMtuFT2DkHOgyD2LI5VjL5/fffA3DuuedmmUzgeBAYRiRXLQYpcS+OFjwMW3+Erq9D9Q5+RyMljB+1x8fn9F1EpLjbsGED4J6UZ8umwbqPoWHfHJuQ+vXXXwFo0aLF0W6nnHIK+/fvDzn8W2+9RcWKFbnqqqsAKFeuXKThixRbGSqG3LqVbz6vzIr1K/jbpeeEVzFkgE1j+08PM/azmtilK9m2/R/ZVjJ54MABNmzYQOXKlWnQoEGWSbVu3RqA5cuX53v5pBRpMQiw8Ot1QZXTVfA7KsnOuknwx3PQ+hZofrXf0UgJFA21x4uIFDvz5s2jU6dOxMbGZui+f/9+7rzzTgDOPz/jE/Q9e/awadMmqlWrRtXkJXBwAzR5lnnz5tG5c+f04r2e7777jtGjRwMwcODAo90HDBjAgAEDQsb11ltvUaNGDd588818L6NIcROoGDLAGMO9F1XmqYGrICUp/Bq51yawfcMSHv8QYGT6tO69l6eeeirDoHv27AGgWrVqIScV6K5SLxKxFoPdpxL36Lb3T1fzf82T4ITRfkcjJVTUJO3GmBigJmCBXdbaNJ9DEpFSZvLkyUyePBmAzZs3AzBjxgwGDx4MQO3atRk1ahQATzzxBL/88gvdu3enadOmVKxYkXXr1jF16lR2795N9+7deeihhzJMf9KkSVx77bUMGjSIERcfgJiy0OgC7rn2Iv7880+6d+9O48aNAVi4cOHR4rQjRoyge/fuRbAGRIq3tm3bYq0lNTWVDRs2MGnSJIYNfZiff1/OF5Vvp+ZZb+U+kbRUWDSctu3aY1MXkmpJn9awYfz888988cUX1KxZs/AXSESJe3RLSYKfLwNTBnpMhFiVbpPC4WvSbowpC9wMXA0cDwQeWaUaY+YC7wGvW2uTfQpRREqR+fPnM2HChAzdVq5cycqVKwFo1qzZ0aT9xhtvpHLlysyaNYvExESSkpKoUaMGJ554IldccQXXXXddjsXj6xz6ERqcA2Wrcc011zBp0iRmz57N1KlTSU5Opl69elxxxRXcdttt9OjRo/AWWqQECq4Ysl69elx11VUM+9d/GNvuUmh0Xs4jr/kv7P0DTpsIMbHEQtZpDRt2tBm3wJP0wBP3zALdq1evXlCLJ6VNi8GuYsSZ18OP/eH0yUrco4G1MOdW2L0Iek5xdWmIFBLfknZjTF1gKtAZV9FcsDJAN6ArMNgY09dau71oIxSR0mb48OEMHz48rGHPP//8LMXfczN48GD31H77TPh6gmvDFbj++usLrK1118qKiAQcrRhyeTmYeR2ctwjK1wk9cFoyLHocqneCJpdkP62gSiYrVapEo0aN2LBhA5s2bcryXnugpvk2bdoUwNJIqdXyWvepxD16/PUWrBzvKqtsmLUSSpGC5GeN7O/gnq4vA24AWgEVgIre/0OA5cCJwHh/QhQRKWCr3ofv+2ABFj7ivotIoTlaMWSVeDiyC2YNcU/IQln1DuxfAR2fAJP1Eim7SibPPPNMAL788sss40ydOjXDMCJ51vJa6PYWbP7GJe6qVd4/O+fBnNugfh+XtIsUMl+SdmPMaUAf4AfgRGvtf6y1K621h621h7z/3wROAKYBfY0xeqFTRIq3Ve+7hCFlrytelLTefVfiLpIv8+bNIzU1NUv3DBVDXngpdHoK1k+Glf9hz549LF26lE2bNrmBU4/A4hHM29me1PpZi9DnVMnkzTffDMCTTz7Jrl27jnZfvXo148aNo1y5clx77bUFsahS2rW8Frq96RL3ny6G1EN+R1T6HNkFP13qSux0fx9iYnMfRySf/CoePwBIBa631mZ7m9Bae9AYcx3wJ3AFML2I4hMRKXgLHoHUpIzdUpNcdzURI5JBoVQMWakibPwC5t7JpJ07uPaWBxg0aBDjx4+HlW/BgTU88fkp/PJ4g4gqmezevTv33HMPL7zwAh07duSyyy7jyJEjfPTRR+zcuZOXX36Z+Pj4IlpzUuK1vM59zrwhvah8bHk/Iyo9bBpM/7tr/eWsH6F8bb8jklLCr6S9C/CrtXZ1bgNaa1cZY2bg3m8XESm+ktZG1l2kFCu0iiFPngBTOsKfr6VPOPUQLH4S6pzKjbc9SOUPP4y4ksnnn3+e4447jnHjxvH6668TExPDCSecwH333ccFF1xQOCtJSi8l7v5Y8ixs/BxOfBlqn+x3NFKK+JW0twAmRjD8AuCyQopFRKRoVGwKSWtCdxeRDAqtYshKTeCkVxicfBWDFz4Oxw2DpS+5J2fd3+X8er04P49J9tHKJkWKghL3orX5O1j4KDS7Etrc6nc0Usr4VRFdFWBnBMPvAqoWUiwiIkWj/YNZu8VWhE5PFn0sIqVZ/JXQ7G+waDh8Ug/m3QUx5SBpo9+RiUSm5XXuHfdNX7vEXe+4F46kDfDLVVDlGOj6BpjMDV+JFC6/kvbyQEoEw6cA5QopFhGRohFX2X2Wr4/FQMVm0PV1vc8u4od6Pd3noa3uM+2wKoaU4ulo4v6VEvfCkJYMP1/h6qDp8XH6uVykCPnZ5JsaExaR0mXjFChfFy7ewLSG30P/1UrYRfyy+EmyXIoEKoYUKW4yJO6qVb5A/XY/bJ/umtur1s7vaKSU8uuddoC7jTHhtn9SvTADEREpdGmpsOlLaHRhyPafRaSIqWJIKWlaXu8+Z97gEvfTJ+kd9/xaOxGWvQhtbodmA/yORkoxP5P26kSWjOvJvIgUXztmurZdG2Zt/1lEfKCKIaUkUuJecPYug1+vg1onw/Gj/I5GSjm/kvbmPs1XRMQfG6eCiYUGZ/sdiYiAqwBy1hBXJD5AFUNKSdDyerAWZt0IP14Cp3+ixD1SKQfgp0vdeusxEWLL+h2RlHK+JO3W2hC3tkVESrCNU6B2dyhbw+9IRATS65NY8IgrEl+xqUvYVc+ElAStbnCfStwjZy3Mugn2LIFeX0HFxn5HJOJr8XgRkdLh4CbYNQ86Pe13JCISrPnVStKl5FLinjcrXoXV78NxT6h0nEQN1YYkIlLYNn7pPvU+u4iIFKVWN7imRTdNdcW9Vat8znbMhrl3QYO+0EEtSUj0UNIuIlLYNk6BCo2g+nF+RyIiIqVNqxtd4r5xihL3nBzeAT9dBhUaQPf31NKLRBVtjSIihSktGTZ/7Z6yG+N3NCIiUhplSdwP+x1RdLFpMH0gHNoMpyVAuZp+RySSgZJ2EZHCtG06JO9V0XgREfFXqxuh62te4n6JEvdgi0fCpi/hxDFQq4vf0YhkoaRdRKQwbZwCMXFQv7ffkYiISGnXaogS98w2fQ2LhkP8NW79iEQhJe0iIoVp4xSo0wPiqvgdiYiIiBL3YAfWwvS/QbVjoeureo1NolZUJe3GmLbGmIuNMdf4HYuISL4dWAt7FqtovIiIRJdWQ+CkV0v3O+6pR+DnK9xnj4+hTEW/IxLJVlQk7caYzsaYOcDvQAIwPqjfGcaYJGNMP7/iExHJk41T3aeSdhERiTatb/IS9y9KZ+L+2z9hx0w4+W2o2sbvaERy5HvSboxpAyQCxwAvAVMzDfIjsBO4rGgjExHJp41ToFI8VG3rdyQiIiJZldbEffV/YflYaHsPNL3U72hEcuV70g48BpQFullr7wFmB/e01lpgBnCSD7GJiORN6mHY/K2aehMRkejW+iY46RUvcb+s5Cfue5bAzBugzmnQ+Rm/oxEJSzQk7f/f3n2GWVXdfxu/FzP03mukCmpULKjYEOwYjSaaoFFjiX9bgopJNMYkklhiorGbGE3RGPNoxIgSB9QgIAKiotgpSlE8ICi9l1nPi31AygAzTNlnZu7PdZ1re3b9ncMS5jt77bWOAf4TY/xgB/t8CrSroHokqfTmvwwbVto1XpKU+3a/NBvc/1u1g/u6ZUmPgpoN4PAnktldpEogF0J7U2DOTvYJJHfjJalyyBRAjdrQul/alUiStHNVPbjHCBP/D5ZNg8Mfh3reD1TlkQuh/XOg2072+TrJ3XZJqhwyBUlgdzRaSVJlsfulcNAfk+D+yneqVnCfdh988gTse7O/UFelkwuh/SXglBBCj6I2hhAOIulC/3yFViVJu2rZR8lv8u0aL0mqbHa/LAnunw2rOsF9wQR482pofwrsdU3a1Ugllguh/bfAeuDlEMJlZJ9dDyF8Pft+GLAMuD29EiWpBDZN9dY/3TokSdoVVSm4r14A474L9XeDQx+BkAvxRyqZ/LQLiDFODSGcDvw/4L7s6gC8k10uBr4dY/wknQolqYQyBdCwOzTc2ZM/kiTlqN0vS54Df+OHSXA/YgjkVbIhpgo3wPjvJcH9+AlQq2naFUm7JPXQDhBjHBFC6AycB/QGmgNLgFeBv8cYF6ZZnyQV2/qV8Pmo5IcdSZIqs+6XJ8s3fgivnFH5gvt7v06mXz3kL9Bs/7SrkXZZToR2gBjjYuDu7EuSKqfPR0HhGp9nlyRVDZU1uH9WAO/dCF0ugK4/SLsaqVR8qEOSylKmAPLqQas+aVciSVLZ6H459Lpvs2fc16Zd0Y4tnwUTzoEmPaHX/WlXI5VaztxpDyG0APYEOgA1i9onxviPCi1KkkoixiS0tzkW8mqnXY0kSWWn+w+T5Rs/yj7j/mRu3nHfsCapLxbCkU9Bft20K5JKLfXQHkKoA/wBuBDY3v/5AYiAoV1S7lo6BVbMgr1+lnYlkiSVvcoQ3CddBQvfgD5DoWHXtKuRykTqoR24DbgM+BB4AviMZAo4SapcMgXJ0qneJElV1ebBfdx34fB/505wn/kofPQA7HkNdDg17WqkMpMLof27JNO7HRRjXJd2MZK0yzIF0HjvZC5YSZKqqlwM7ovfhdcugVZHQc+b061FKmO5MBBdfeBFA7ukSm3dMlgw1lHjJUnVQ/cfwoH3wpxnkuCe5uB065bC2NOhVhM4/HGokQv3JaWykwuh/X2gbdpFSFKpzBsJhesM7ZKk6qPHjzYL7gPSCe4xwqsXwvIZcPgTULdNxdcglbNcCO23A98KIXRPuxBJ2mWZAqjZCFoelnYlkiSVuSFDhjBw4ECOPPJIGjVqRAiBc845Z7PgPnSb4D5+/HhOOukkmjVrRt26ddl3332566672LBhQ4muvXbtWn7/+9/Ts2dP6tWrR6NGjTjiiCP497//DVPuhE+fgv1uhVZHAjBu3DiuueYaDjroIFq2bEnt2rXp3LkzF110ER999FFZfi1ShUi970iM8ckQQltgbAjhj8CbwJLt7PtyhRYnScWxaaq346FGkTNWSpJUqd100028/fbbNGjQgA4dOjBlypSvNvb4ERBh0hVJcD/8CZ7573BOP/106tSpw4ABA2jWrBnDhg1j0KBBjBs3jieffLJY1127di0nnHACo0ePplOnTlxwwQUUFhZSUFDAgAEDeO9bgd8M+jbs8eNNx5x++uksWLCAww47jLPPPpv8/HwmTJjAX//6Vx5//HFefPFFDj300DL+hqTyk3poz2pK8mz7r3ayX14F1CJJJbP4XVj1mV3jJUlV1p133kmHDh3o1q0bY8aMoV+/flvu0GNgspx0BUtfOJ3/+7+J5OXlMXr0aHr16gXAjTfeyNFHH82QIUN4/PHHOfPMM3d63fvvv5/Ro0dz6KGH8uKLL1K/fn0Ali+YQd9D9+Cmoev45k8H0iuETccMGjSIc889l3bt2m1xrltuuYXrr7+eiy++mHfffbcU34ZUsVLvHh9CuA64AVgFPAzcAvxmOy9Jyj2bpno7Md06JEkqJ/369WP33XcnbBaOt9FjIBx4D0Oe/i8LFizgzAHf3RTYAerUqcNNN90EwJ/+9KdiXffpp58G4Prrr98U2ClcT4O3f8AvTgvECH986B9bHHPttdduE9g3rq9bty7vvfceX375ZbGuL+WCXLjTfjEwAzgwxlhkt3hJymmZAmi6P9R1TE1JUjXXYyAvzXsEmMSJnaYkg7Ru9uhYnz59qFevHuPHj2fNmjXUrl17h6ebN28eAF26dPlq5Tu/hPmj6XL0jfCHXzJy5MhilRZCID8/iT95eXbgVeWR+p12oA3wrIFdUqW0dhF8Md6u8ZIkZU2dm9yN7177DXhlQBLcs/Lz8+ncuTPr169nxowZOz1XixYtAJg5c2ayYs6z8MGt0O1iZqzdG4BPPvmEVatW7fRcTz75JMuWLaN37940adKkhJ9KSk8uhPYZQJO0i5CkXTL3RYgbDO2SJGUtWZLci2t80C9gztPbBPfGjRsDsHjx4p2e6xvf+AYAN998M6sWfAATvg9ND2DFHrdwyy23bNpvZ+eaOXMmAwcOJD8/nzvuuKOEn0hKVy6E9j8Bp4QQnFRRUuWTKYBazaD5IWlXIklSbulyHhxwV5HBvbiuvPJKevbsyfjx4/n6vvvzo7+u5odP78XX9z2QBQsWbPoFQI0a24818+fPp3///ixYsIC7777bkeNV6eRCaB8GjAHGhxDODyHsE0LYrahX2oVK0hZiIcwdDm1PgBo+GydJEnx1J33JkiWwx5VfBfdxZ0Lhuk134ovTRb1Bgwa88sorXHdeT/JZy0OjNvDEf4bTp08fXnnlFTZs2EB+fj7NmjUr8vj58+dz9NFHM3XqVO6++24uv/zysvqYUoXJhYHoZgIRCMBfd7BfJDfqlaTEwjdh9Xy7xkuStJkePXrwxhtvMG3aNA488MAkuAO8eRXrx3yXmTNnkp+fv+XgcjvQ4PMnueX4t7ll0PXQ86ZN62fMmMHy5cs58MADqVmz5jbHzZ07l2OOOYYpU6Zw//33G9hVaeVCCP4HSSCXpMolUwCE5E67JEkC4Oijj+axxx5jxIgRnHXWWcnKPa4EIi8/OoiVK6FPnyN3OnI8AIsmwxuXQ+tjYJ9fb7HpH/9Ipnr73ve+t81hc+bM4eijj+ajjz7igQce4OKLLy7lp5LSk3pojzGen3YNkrRLMgXQ/GCo0zLtSiRJyhlnnHEG1157LY8//jgDBw7cNFf76k6X8ouCu4FZXNZvzRbTwa1cuZJPPvmEzz///KsTrV0MY09n6fqmNDr8X1s8ivbiiy/yu9/9jq5du3LJJZdscf3Zs2fTr18/Zs+ezd/+9jfOP//8cv7EUvlKPbRLUqW0egF8+RrsMzjtSiRJKndDhw5l6NChwFdzp0+YMGFTIG7RogW33347AI0aNeKhhx7ijDPOoG/fvpx55pk0a9aMZ599lqlTZ3HGCT0ZsPtrMO4sOPz/QY2avPbaa/Tr14+ePXsyYMAAiBFePR9WfMIeP27Mvo9+nz322IM6derw5ptv8r///Y82bdrwzDPPUL9+/S1q7du3L7NmzeLAAw9k1qxZDB48eJvPc/7559OpU6dy+raksmVol6RdMfd5IPo8uySpWpg8eTKPPPLIFutmzJixaa71jh07bgrtAKeddhpjxozh5ptv5qmnnmL16tV069aNO+64gyuuuIIw/R548+qvgvvWPrwN5jwDB9zF2efOYcSIEYwfP55169bRsWNHrrnmGq655poiB6CbNWsWAJMmTWLSpElFfp6+ffsa2lVpVHhoDyH8jeQZ9p/HGD/Pvi+OGGP8QTmWJknFlymAOq2g2QFpVyJJUrkbPHhwkXesd+Twww+noKCg6I17DEqWb14N475H3z7/IsbI6NGj4fPR8PZ1sNt3occV3HZb4Lbbbiv2dWN0uCxVLWncaT+fJLT/Dvg8+744ImBol5S+wg0wdwS0/yaEXJg5U5KkSmjz4P7CJ7B6Hket/BQyIfnF+CF/gRDSrVHKAWmE9s7Z5Zyt3ktS5fDlRFi7yK7xkiSV1h6D4Ms3YPa/gGQOaIjJIHRznoXOZ6dYnJQbKjy0xxhnZ7vEDwWejTHOrugaJKlUMgUQ8qDtcWlXIklS5bdg3LbrClfD29cb2iUgrX6d5wP7pXRtSSqdTAG0OAxqNU27EkmSKr+Vn5RsvVTN+DCmJJXEygwsesuu8ZIklZV6u5VsvVTNGNolqSTmjkiWhnZJkspGz5shr96W6/LqJeslGdolqUQyBVC3PTTZJ+1KJEmqGjqfDQc/CPU6EglQr2Py3ufZJSCd0eM32i+E8P2SHBBj/Ed5FSNJO1W4Dua+AB3PdAoaSZLKUuezofPZjBk9mr59+6ZdjZRT0gztp2ZfJWFol5SeBeNg/TK7xkuSJKnCpBna386+JKlyyAyHGjWhzTFpVyJJkqRqIs3QPjTG+JsUry9JJZMpgJZ9oGbDtCuRJElSNeFAdJJUHCs+gSXv2TVekiRJFcrQLknFkRmeLA3tkiRJqkCGdkkqjkwB1O8EjXqkXYkkSZKqkbRC+xhgVkrXlqSS2bAG5v0vucvuVG+SJEmqQKkMRBdj7JfGdSVpl8x/GTastGu8JEmSKlyV7R4fQjgjhHBvCGFsCGFpCCGGEP65k2MOCyEUhBAWhhBWhRDeCSFcFULIq6i6JeWgTAHUqA2t/X2jJEmSKlaaU76Vt18APYHlwBxgjx3tHEI4FXgKWA08ASwETgHuBA4HvlOexUrKYZmCJLDn10u7EkmSJFUzVfZOOzAI6A40Ai7b0Y4hhEbAQ8AGoG+M8Qcxxp8C+wETgDNCCGeWb7mSctKyj2DZNLvGS5IkKRVVNrTHGEfFGKfHGGMxdj8DaAk8HmN8Y7NzrCa5Yw87Cf6SqqhNU731T7cOSZIkVUtVNrSX0NHZ5Ygitr0MrAQOCyHUrriSJOWETAE07A4Nu6VdiSRJkqohQ3ti48TL07beEGNcD8wkef6/S0UWJSll61fC56PsGi9JkqTUVOWB6EqicXa5ZDvbN65vUtTGEMLFwMUArVu3ZvTo0WVZm1KwfPly/xxFs9UT2LdwDW8vaM+iMm4PtjGVJ9uXypPtS+XJ9iVtq8JDewihECjOc+ZbizHGnPwlQ4zxQeBBgF69esW+ffumW5BKbfTo0fjnKF5/EpbUo+dxAyGvbJ+OsY2pPNm+VJ5sXypPti9pW2mE4JfZtdBenjbeSW+8ne0b1y8u/1Ik5YQYk+fZ2xxb5oFdkiRJKq4KD+0xxr4Vfc1imAr0IpkibtLmG0II+UBnYD0wo+JLk5SKpVNgxSzY62dpVyJJkqRqzIHoEi9llycWsa0PUA8YH2NcU3ElSUpVpiBZOtWbJEmSUpRToT2EUD+EsH8I4cgKvvQQ4AvgzBBCr83qqQPclH37pwquSVKaMgXQeG+ov1valUiSJKkay4mB3UIIHYC7gVOAPJJn3vOz244gGeTt8hjj6BKc8zTgtOzbNtnloSGEh7P//UWM8ScAMcalIYT/Iwnvo0MIjwMLgW+STAc3BHhi1z6dpEpn3VJYMBZ6DEq7EkmSJFVzqYf2EEJbYCLQGngWaAUcutkuE7PrBgCjS3Dq/YDztlrXha/mWp8N/GTjhhjj0BDCUcD1wOlAHeAj4Grgnhhjrg2eJ6m8zBsJheucn12SJEmpSz20AzeQhPLjYoyjQgg3sFlojzGuCyGMBQ4vyUljjIOBwSU8ZhzgT+lSdZcpgJqNoOVhaVciSZKkai4Xnmk/CXg2xjhqB/t8ArSroHokVWebpno7HmrUTLsaSZIkVXO5ENpbA9N3ss86oH4F1CKpulv8DqzK2DVekiRJOSEXQvtC4Gs72ac7MK8CapFU3W2a6q2oGSAlSZKkipULoX0c8M0QQpuiNoYQdieZP31H3eclqWxkCqDpAVC3bdqVSJIkSTkR2m8jGal9TAihP1APNs3Z3h8YBhQCf0ivREnVwtpF8MUEu8ZLkiQpZ6Q+enyMcWII4RLgT8B/N9u0NLtcD1wYY3y/wouTVL3MfRHiBkO7JEmSckbqoR0gxvi37LRulwO9gebAEuBV4L4Y49Q065NUTWQKoFYzaH5w2pVIkiRJQI6EdoAY43RgUNp1SKqmYiHMHQ5tT4AaeWlXI0mSJAE58Ex7CGGvtGuQJBa+Cavn2zVekiRJOSX10A68F0KYGEK4PITQLO1iJFVTmQIgJHfaJUmSpByRC6H9eeAA4F4gE0J4MoRwcgjB/qmSKk6mIHmWvU7LtCuRJEmSNkk9tMcY+wNfA34GfAScDjxDEuDvCCH0TLM+SdXA6gXw5Wt2jZckSVLOST20A8QY58UYb4sx7g30Au4HAnAV8GYIYXII4aoUS5RUlc19HoiGdkmSJOWcnAjtm4sxvhljvAJoB3wLGArsBdyeZl2SqrBMAdRpBc0OSLsSSZIkaQs5F9o3Uw9olX3lk9x5l6SyVbgB5o6Atv0h5PJfiZIkSaqOcmaedoAQQgBOAM4DvgnUASIwEng4vcokVVlfToS1i+waL0mSpJyUE6E9hPB1kqB+NtCG5K76dOAR4B8xxjkpliepKssUQMiDtselXYkkSZK0jdRDewhhErAfSVBfAvwFeDjGOCHNuiRVE5kCaHEY1GqadiWSJEnSNlIP7SSB/UWS7u9DY4yrU61GUvWxMgOL3oKev027EkmSJKlIuRDavxZjzKRdhKRqaO6IZOnz7JIkScpRqQ+VbGCXlJpMAdRtD032SbsSSZIkqUi5cKd9kxBCB6A9ULuo7THGlyu2IklVVuE6mPsCdDwTgjNKSpIkKTflRGgPIRwP3AnssZNd8yqgHEnVwYJxsH6ZXeMlSZKU01LvHh9C6A38F2gC3EcyivzLwEPAlOz7YcBvUipRUlWUKYAaNaHNMWlXIkmSJG1X6qEduA5YDRwUY7wyu25UjPFSYG/gJuBYYEhK9UmqijIF0LIP1GyYdiWSJEnSduVCaD8UeHarAelqAMTEr4APgV+nUZykKmjFbFjyvl3jJUmSlPNyIbQ3Bj7Z7P1aoP5W+4wD+lRYRZKqtszwZGlolyRJUo7LhdA+H2i61fuuW+1TE6hbYRVJqtoyBVC/MzTqkXYlkiRJ0g7lQmifxpYh/VXguBBCd4AQQhvgdGB6CrVJqmo2rIZ5I5O77E71JkmSpByXC6F9BHBUCKFZ9v3dJHfV3wohvE4ygnxL4K50ypNUpcx/GTastGu8JEmSKoVcCO1/JnlefR1AjHEc8B1gJsno8XOBy2KM/0itQklVR2Y41KgNrfumXYkkSZK0U/lpFxBjXApM3Grd08DT6VQkqUrLFEDrfpBfL+1KJEmSpJ1K/U57COFvIYRBadchqRpY9hEsm2bXeEmSJFUaqYd24HtAq7SLkFQNbJrqrX+6dUiSJEnFlAuhfRaGdkkVIVMADbtDw25pVyJJkiQVSy6E9n8B/UMITXe6pyTtqvUr4fNRdo2XJElSpZILof23wBvAqBDCySGE1mkXJKkK+nwUFK4xtEuSJKlSSWX0+BDC94HJMcZ3gNUbVwPPZLcXdViMMaY+2r2kSipTAHn1oFWftCuRJEmSii2tEPwwcAPwDjAWiCnVIak6iDEJ7W2OhbzaaVcjSZIkFVuad64DQIyxb4o1SKoOlk6BFbNgr5+lXYkkSZJUIrnwTLskla9MQbJ0qjdJkiRVMoZ2SVVfpgAa7w31d0u7EkmSJKlE0uwe3ySEUKKfoGOMn5RXMZKqqHVLYcFY6DEo7UokSZKkEksztF+ZfRVXJN16JVVG80ZC4TqnepMkSVKllGYIXgosTvH6kqqDTAHUbAQtD0u7EkmSJKnE0gztd8YYf5Pi9SVVdZumejseatRMuxpJkiSpxByITlLVtfgdWJWxa7wkSZIqLUO7pKpr01RvJ6ZbhyRJkrSLDO2Sqq5MATQ9AOq2TbsSSZIkaZcY2iVVTWsXwRfj7RovSZKkSi2VgehijP6yQFL5mvsCxEJDuyRJkio1w7OqlJEjR/Ktb32LNm3aULt2bdq1a8cJJ5xAQUFBsY4fMmQIAwcO5IorrqBRo0aEEDjnnHN2etyGDRv4y1/+Qp8+fWjatCl169alS5cuDBgwgGnTppX2Y2lXZAqgVjNofnDalUiSJEm7LM0p36Qydc0113DbbbfRoUMHvvnNb9KiRQsWLFjApEmTGD16NCedtPM7rjfddBNvv/02devWpWPHjkyZMmWnxyxfvpxTTz2Vl156if3224/zzjuPOnXq8NlnnzF27FimTZtG9+7dy+IjqrhiIWSGQ9sToUZe2tVIkiRJu8zQrirhoYce4rbbbuO8887jwQcfpFatWltsX7duXbHOc+edd9KhQwfmzJlDCIF+/frt9JhLLrmEl156iQceeIBLLrlkm+3FvbbK0MJJsGaBXeMlSZJU6RnaVemtWbOG66+/nt12263IwA5Qs2bNYp1rY0j/7LPPirX/m2++yb/+9S8GDBhQZGAvybVVhjIFQIC2J6RdiSRJklQqhnZVei+++CILFizgqquuokaNGjz33HO899571KlTh4MPPphDDz203K79r3/9C4CzzjqLJUuWMGzYMD799FOaN2/O0UcfTbdu3crt2tqBzPDkWfY6LdKuRJIkSSoVQ7sqvddffx2AOnXqsP/++/Pee+9tsb1Pnz4MGTKEli1bltu1Z8+eTdeuXfnyyy83bQshcNlll3HPPfeQl+dz1RVm9QL48jXYZ3DalUiSJEml5ujxqvTmz58PwG233UYIgbFjx7Js2TLeeecdjj/+eF5++WW+853vlOu1r776avr27cuHH37IsmXL+N///kfXrl354x//yI033lgu19Z2zH0eiD7PLkmSpCrB0K5Kr7CwEID8/HyeffZZjjjiCBo0aMA+++zD008/TYcOHRgzZgwTJkwot2vvsccePPHEE+yxxx40aNCAY445hiFDhlCjRg3uuOMO1q5dW+bX1nZkCqBOK2h2QNqVSJIkSaVmaFel16RJEwD2339/OnXqtMW2evXqccIJyWBkr732Wrld+5RTTtmmC3zPnj3p3Lkzy5Yt48MPPyzza6sIhRtg7gho2x+Cf71JkiSp8vOnWlV6PXr0AL4K0Ftr2rQpAKtWrapS11YRvpwIaxfZNV6SJElVhqFdld4xxxxDCIEPPvhgU3f1zW0cmK5z585lfu1jjz12i2tsbs2aNUyfPh1gmx4AKieZAgh50Pa4tCuRJEmSyoShXZVex44dOeWUU/jkk0+4++67t9j2wgsv8Pzzz9OkSRNOPPFEANatW8eUKVP4+OOPS33t008/nXbt2vHEE09s0/3+xhtvZMmSJfTr1482bdqU+loqhkwBtDgMajVNuxJJkiSpTDjlm6qE+++/n7feeourr76a5557jv3335+ZM2cydOhQ8vLy+Mtf/kLjxo0B+Oyzz9hzzz3p2LEjs2bN2uI8Q4cOZejQocybN2/TugkTJnD++ecD0KJFC26//fZN2+rXr8/DDz/MySefzJFHHsm3v/1t2rdvz8SJE3nllVdo1aoVf/7zn8v98wtYmYFFb0HP36ZdiSRJklRmDO2qEjp06MCkSZP4zW9+w7PPPsvLL79Mo0aNOOWUU7juuus4+OCDi3WeyZMn88gjj2yxbsaMGcyYMQNI7upvHtoBjjvuOF577TVuvPFG/ve//7FkyRLatGnDpZdeyi9/+UvatWtXNh9SOzZ3RLL0eXZJkiRVIYZ2VRktW7bk3nvv5d57793hfp06dSLGWOS2wYMHM3jwYEaPHk3fvn2Lfe2ePXsyZMiQkpSrspYpgLrtock+aVciSZIklRmfaZc2N/MxGNqJozJHw9BOyXvlvsJ1MPeF5C57CGlXI0mSJJUZ77RLG818DF67GDasJACsnJ28B+h8dpqVaWcWjIP1y+waL0mSpCrHO+3SRm9fDxtWbrluw8pkvXJbpgBq1IQ2x6RdiSRJklSmDO3SRis/Kdl65Y5MAbTsAzUbpl2JJEmSVKYM7dJGtZoVvb7ebhVbh0pmxWxY8r5d4yVJklQlGdolgMxwWLuQIv+X6HZxhZejEsgMT5aGdkmSJFVBhnbpi9dg7BnQtCcc/Geo15FIgLodIL8RfPJv2LA27Sq1PZkCqN8ZGvVIuxJJkiSpzBnaVb0tnQZjvgF1WkPf4dDtIjhtFmPavQTf+hQOexQWvw3v35x2pSrKhtUwb6RTvUmSJKnKMrSr+lo1F0adAATo9zzUbbPtPh2+CZ3OTUL7wjcrvETtxPyXkxH+7RovSZKkKsrQrupp7RIY1R/WLIC+z0Gj3be/b6+7oU4rePV82LCmwkpUMWQKIK8OtO6bdiWSJElSuTC0q/rZsAbGfjsZcfyIp6D5QTvev1ZTOPghWPwuvHdjxdSo4skUQKt+kF8v7UokSZKkcmFoV/USC2HC9+Hzl6D336DdCcU7rv03oMv58MGt8OUb5VqiimnpdFg23a7xkiRJqtIM7ao+YoRJVyWjwe/3e+h8bsmOP+BOqNMGXj3PbvK5YG52qrf2hnZJkiRVXYZ2VR8f/A6m3Qs9BsGePyn58bWawCEPwZIP4N3BZV2dSipTAA27Q4MuaVciSZIklRtDu6qHGQ/D29dBx7PggNt3fXqwdv2h6w/gw9/DFxPLtESVwPqV8Plou8ZLkiSpyjO0q+r7rAAmXgRtjoXeD0MoZbPf/w9Qt312NPnVZVGhSurzUVC4xtAuSZKkKs/Qrqrti4nwynegSU848j+QV6v056zVGA75KyydAu/8qvTnU8llCiCvHrTqk3YlkiRJUrkytKvqWjoVxnwD6raBvgVQs2HZnbvtcdDtYvjwdlgwvuzOq52LMQntbY6FvNppVyNJkiSVK0O7qqaVGRh1AoQ86Pc81G1d9tfY/3aovxu8egGsX1X251fRlk6BFbPsGi9JkqRqwdCuqmftEhjdH9Z8kdxhb9itfK5Ts2HSTX7ZNHjnF+VzDW0rU5As2/VPtw5JkiSpAhjaVbVsWA0vn5ZMy3bkf6DZgeV7vTbHwO6XwZQ7Yf4r5XstJTIF0HjvpJeDJEmSVMUZ2jcTQpgVQojbec1Luz7tROEGGH8uzB+djBLf9viKue5+v4f6HbPd5FdWzDWrq3VLYcFYu8ZLkiSp2shPu4ActAS4q4j1yyu4DpVEjPDmVfDpkORZ885nV9y1azaA3n+Hkf3g7Z/DgXdV3LWrm3kjoXCdoV2SJEnVhqF9W4tjjIPTLkIl9MFvYdp9sMePYc8fV/z1W/eF7j+CqXdDh29B66MqvobqIFMANRtBy8PSrkSSJEmqEHaPV+X38d/h7euh09mw/+/Tq2O/W6FBF5h4IaxfkV4dVdWmqd6Ohxo1065GkiRJqhCG9m3VDiGcE0L4eQjhyhBCvxBCXtpFaTs++y+89n9JkDvkbxBSbNL59ZNn6ZfPhMk/S6+OqmrxO7AqY9d4SZIkVSshxph2DTkjhDAL6FjEppnABTHGMds57mLgYoDWrVsf+Pjjj5dbjfpKo7Uf0PPLq1mZ35HJze9kQ416ZXbu5cuX06BBg106ttuS++iw4ikmN7+DxbX3L7Oaqrvdlj1Gl2V/YXzrIazNa552OaVWmjYm7YztS+XJ9qXyZPuqGvr16zcpxtgr7TqqCkP7ZkIINwBjgfeBZUAX4EckgXw1cGiM8e0dnaNXr17xjTfeKO9StWQKvHg41GoKx4+HOq3K9PSjR4+mb9++u3bw+pVQ0BPiejjp3WSgOpXei0cm323/SWlXUiZK1caknbB9qTzZvlSebF9VQwjB0F6G7B6/mRjjr2OML8UYP48xrowxvhdjvBS4A6gLDE63QgGw8jMYdQLUyId+z5d5YC+1/Hpw6MOwYjZMvibtaqqGtYvgi/F2jZckSVK1Y2gvngeyyz6pViFYuxhG94e1C6HvcGjYNe2KitbycNhjEEz/E8z7X9rVVH5zX4BYaGiXJElStWNoL54F2WX9VKuo7jashpdPhaVToM/T0OyAtCvasX1vgobd4dUfwLqlaVdTuWUKoFYzaH5w2pVIkiRJFcrQXjy9s8sZqVZRnRVugPHnwPyXofcj0ObYtCvaufy6cOgjsGoOvPXTtKupvGIhZIZD2xOhhhM5SJIkqXoxtGeFEPYMIWxzJz2E0Am4L/v2nxValBIxwqQr4NOn4IA7oNNZaVdUfC16wx4/gY8eTLp4q+QWToI1C+waL0mSpGrJ0P6VAcC8EMJzIYQ/hhB+F0IYAnwIdAMKgNtTrbC6ev9mmP5H2POnyXPilc2+v4ZGe8LEH8DaJWlXU/lkCoAAbU9IuxJJkiSpwhnavzIK+C/QFfgecDVwFPAKcB5wcoxxbXrlVVMf/QXe+SV0Ohf2uzXtanZNXh3o/TCsysBbP067msonUwDND4E6LdKuRJIkSapw+WkXkCtijGOAMWnXoc3MGQavX5LcYe39VwiV+HdMLQ6GPa+FD34LXzsd2vVPu6LKYfV8+PJ12OfXaVciSZIkpaISpyBVaQvGw7jvQtMD4IghUKNm2hWV3j43QOOvw8SLkqnrtHNznwcitPd5dkmSJFVPhnblniUfwphToG4H6Psc1GyQdkVlI6920k1+9efwZiV8Nj8NmQKo0wqa7p92JZIkSVIqDO3KLSs/g1EnJHfWj34+CWxVSfNesNd1MONh+Oy/aVeT2wrXJ3fa2/av3I9GSJIkSaXgT8LKHWsXwagTk67jfYdDgy5pV1Q+9v4lNNkHXrs4+cwq2pcTk+/Hqd4kSZJUjRnalRvWr4Ixp8KyqdBnKDSrwt2h82plu8kvgDeuTLua3JUpgJAHbY9LuxJJkiQpNYZ2pa9wA4w/GxaMhUMfhTZHp11R+Wt2AHz9epj1KMx5Ju1qclNmOLQ4DGo1TbsSSZIkKTWGdqUrRnjjRzDnaTjgLug4IO2KKs7Xfw5NesJrl8CaL9OuJreszMCit+waL0mSpGrP0K50vXcTfPQA7HUt7FHNuorn1YJDH0kC+xsD064mt8wdkSwN7ZIkSarmDO1Kz0cPwbu/gs7fh56/TbuadDTtCXv/Cmb/P/j0P2lXkzsyBVC3fTJgnyRJklSNGdqVjjnPwOuXJtN5HfIXCCHtitLz9Z9B0wPgtUuTwemqu8J1MPeF5C57dW4XkiRJEoZ2pWHBOBh3JjQ9EI58MpmTvTqrURMOfRjWLU6e76/uFoyD9cvsGi9JkiRhaFdFW/w+jDkF6n0N+j4H+fXTrig3NNkH9hkMn/wbPnky7WrSlSlIfpHR5pi0K5EkSZJSZ2hXxVk5B0afCDVqQ7/noU7LtCvKLXteA816weuXw+r5aVeTnkwBtOwDNRumXYkkSZKUOkO7KsbaRTDqRFi7BPoNhwad064o99TIh94Pw7qlSXCPMe2KKt6K2bDkfbvGS5IkSVmGdpW/9atgzDdh2XQ46hloul/aFeWuJl+HfX8Dnz6VdJWvbjLDk6WhXZIkSQIM7Spvheth/PeSwcUOfRRa90u7oty3x4+h+SHJ3fZV89KupmJlCqB+Z2jUI+1KJEmSpJxgaFf5iRHe+CHMGQoH3g0dv5t2RZXDxm7y61fA65dVn27yG1bDvJFO9SZJkiRtxtCu8vPeb+CjB2Gv66DHwLSrqVwa7wE9b0p+4TH7/6VdTcWY/zJsWGnXeEmSJGkzhnaVj+l/hncHQ5fzoefNaVdTOfUYBC0OTeZuXzU37WrKX6YA8upA675pVyJJkiTlDEO7yt6nQ+GNy5M7pgc/aFfnXVUjL+kmv2EVvHZJ1e8mnymAVv0gv17alUiSJEk5w9CusjX/FRh/FjQ7CI74N9SomXZFlVuj7tDzFvhsGMz6Z9rVlJ+l05PZBewaL0mSJG3B0K6ys/g9GHMK1O8IR/0X8uunXVHV0P0KaHkEvHEFrPws7WrKx9zsVG/tDe2SJEnS5gztKhsrPoVRJ0J+Xeg7Auq0SLuiqqNGHvT+OxSugdcurprd5DMFyTRvDbqkXYkkSZKUUwztKr01C2HUCbB+WRLYG3RKu6Kqp2E32O/WJNzOfCTtasrW+hXw+Who6112SZIkaWuGdpXO+lVJl/jlH0OfZ6DpvmlXVHV1/xG06gOTroSVc9Kupux8PirpRWDXeEmSJGkbhnbtusL1MO5M+GICHPaYU3WVt1Aj201+PUy8qOp0k88UJOMftDwy7UokSZKknGNo166JEV6/DD57FnrdC7udkXZF1UODLrD/72Hu8zDjb2lXU3oxJqG99TGQVzvtaiRJkqScY2jXrnl3MHz8F/j69dD9h2lXU73sfhm07geTBsGKT9KupnSWfggrZjvVmyRJkrQdhnaV3PQH4L3fQJcLYd8b066m+gk14JC/AoWVv5t8piBZtuufbh2SJElSjjK0q2Q+/Q+8fjm0OxkO/jOEkHZF1VODzrD/7TDvRfj4obSr2XWZAmi8N9TfLe1KJEmSpJxkaFfxzX8Zxn0Pmh8CRzwBNfLTrqh663YJtDkW3vwxLJ+VdjUlt24pzB9r13hJkiRpBwztKp7F78KYbyZ3ePv+F/LrpV2RQoBD/gIEmPgDiIVpV1Qy80ZCXG9olyRJknbA0K6dW/EJjOqfTMvVbwTUbp52Rdqofkc44A/w+UvJWAOVSaYAajaCloelXYkkSZKUswztOWrIkCEMHDiQI488kkaNGhFC4JxzzinxeWKMPPTQQxxyyCE0aNCA+vXr06tXLx544AEKC4u+M/v5558zcOBAOnfuTO3atWnZrgvf+u3nvNnsjiQkKrd0vQjaHA+Tr4HlM9Kupng2TvXW5nioUTPtaiRJkqScZWjPUTfddBP33XcfkydPpn379rt8nnPOOYeLL76YWbNmcdZZZ3HRRRexcuVKLrvsMs4///xt9p81axb7778/9913H61atuBHJzenf8/Ii+/XpPdx5/L888+X4lOpXGzsJh/y4NULK0c3+cXvwKqMXeMlSZKknXAksRx155130qFDB7p168aYMWPo169fic/x9NNP869//YvOnTvz2muv0aJFCwDWrl3L6aefzqOPPsppp53Gt7/97U3HXHnllcydO5crBv6Iu741kzB3Ehw5hF+s2ptevXpxwQUXMH36dOrXr19mn1VloP7X4IA7k2fbp90PPQamXdGObZrq7cR065AkSZJynHfac1S/fv3YfffdCaWYUu3pp58G4Mc//vGmwA5Qq1YtbrwxmV/9vvvu27R+9erVDB8+nBo1anDTaUsIc5+Dg+6Hr32b7t27c+GFFzJ37lyeeuqpXa5J5ajLBdC2P0z+GSz7KO1qdixTAE0PgLpt065EkiRJymmG9ips3rx5AHTp0mWbbRvXjR07lrVr1wKwcOFC1q1bR4smdWk471HY+5ew+2XbHDNy5MjyLl27IgQ45KHkGfFXL8jdbvJrF8EX4+0aL0mSJBWDob0K23h3febMmdtsmzEjGbBs/fr1m/67adOm5OXV4ItFK1je9jzY59dFHjN16tTyLFulUa89HHg3LHgFpt6TdjVFm/tC8gsFQ7skSZK0U4b2Kuwb3/gGAHfccQcLFy7ctH7dunXccMMNm94vWrQIgLoLnqPfnoUURvjVs02SO7dZH330EX/729+22F85qvP3od3J8PbPYem0tKvZVqYAajWD5genXYkkSZKU8wztVdiZZ57JCSecwMcff8xee+3FJZdcwpVXXsl+++3H2LFj2W233QCoUaMGfD4Gxp/NXQP3o3Hjxtx5190ceuih/OQnP+G8885jv/32o2vXrl/tr9wVAhz8Z8irk3STL9yQdkVfiYWQGQ5tT4QaeWlXI0mSJOU801cVlpeXx7Bhw7j11ltp2bIljzzyCI888gi7774748ePp2HDhgC0qv0lvPxNaNCVr583kkmTJvH973+f2bNnc8899zBmzBgGDRrEvffem+zfqlWaH0vFUa8dHHhP8uz41LvSruYrCyfBmgV2jZckSZKKySnfqriaNWty7bXXcu21126xfvXq1UyfPp0WzZvReeb/QX5D6DcCajeja9dmPPLII9uca2P3+IMOOqhCalcpdTobPh0C7/wC2n0DGu+RdkXZqd4CtD0h7UokSZKkSsE77dXU448/ztq1aznr0EJYvzIJ7PV32+Exjz76KADf+973KqJElVYIcNADkFcPXj0/N7rJZwqg+SFQp8XO95UkSZJkaK8K1q1bx5QpU/j444+32bZ06dJt1k2ePJmf/vQnNG2Qx8/6r4SjnoUmewOwZs0a1qxZs8X+MUZuvvlmRo8ezYABAzjggAPK54Oo7NVtA73ugy8nwpQ/pFvL6vnw5et2jZckSZJKwO7xOWro0KEMHToU+Gq+9QkTJnD++ecDyXRut99+OwCfffYZe+65Jx07dmTWrFlbnOe4446jbt267L333jRs2JAPP/yQ5557jrq1YNiPC2l3ypPQ6shN+0+fPp0jjzyS4447jk6dOrFu3TpGjhzJu+++yxFHHMGDDz5Y7p9dZazjmdlu8r+C9idD473SqWPu80CE9oZ2SZIkqbgM7Tlq8uTJ2zxXPmPGjE1zpXfs2HFTaN+RM844g8cff5x//vOfrFq1ivbt23PxN7txXd8pdDjxAfjat7bYv3Xr1px00klMmDCBYcOGUbNmTfbaay/uu+8+LrnkEvLzbTKVTghw0J9g/tdhwvlw/HiokcKfY6YA6rSGpvtX/LUlSZKkSsoElqMGDx7M4MGDi7Vvp06diDEWue2nP/0pP/3pT79a8fb18P4tsPcNsPsl2+zfsmVLHnvssV0pWbmsTivodT+MGwAf3gZfv65ir1+4PrnT3uFUCD6VI0mSJBWXob2qm/lYEtRXfgK1msDaRdDtYtjnhrQrU0Xr+N2km/y7g6H9KZvGMagQX05M2p7Ps0uSJEkl4i2vqmzmY/DaxbByNhCT0BTyoMURSZdpVT+97oeajbOjya+ruOtmCpK21+a4irumJEmSVAUY2quyt6+HDSu3XBc3wDu/TKcepa9Oy+T59oWT4IPfVdx1MwXQ4rCkt4ckSZKkYjO0V2UrPynZelUPu52ejCj/3m9g0Tvlf72Vn8GiyXaNlyRJknaBob0qq7dbydar+uh1H9RqBq+eV/7d5OeOSJaGdkmSJKnEDO1VWc+bIa/eluvy6iXrVb3Vbg4H/zm5A/7+LeV7rUwB1G0PTfYp3+tIkiRJVZChvSrrfDYc/CDU6wiEZHnwg8l6qcOp0OlseO+mJLyXhw1rYe6LyV12Bz+UJEmSSswp36q6zmcb0rV9B94D80bChPPghNchr1bZnv+LcbB+mV3jJUmSpF3knXapOqvdLOl9sfgdeP+msj9/pgBq1IQ2x5T9uSVJkqRqwNAuVXcdToHO30+ebV84qWzPnRkOLftAzYZle15JkiSpmjC0S4ID74I6rWHC+bBhTdmcc8VsWPK+XeMlSZKkUjC0S4JaTeHgh2DJe8n87WUhMzxZGtolSZKkXWZol5RofxJ0uQA+uBW+fL3058sUQP3O0KhH6c8lSZIkVVOGdklfOeAOqNsOXj0fNqze9fNsWJ2MSu9Ub5IkSVKpGNqlamrOnDlceOGFtGvXjtq1a9OpUyeuumYwi7rfCUs+gHcH7/Qcffv2JYSw7Su/LuHMlaxuduwW+w8ePLjo/Td7de3atZw+sSRJklT5OE+7VA19/PHHHHbYYcyfP59TTz2VPfbYg9dee427776bESNGMO6ec2j+4W3Q4TRo0Xun57vhhhu2XJEZAQvfIL/90Vus7tu373bPMWzYMN5880369++/C59IkiRJqpoM7VI1dPnllzN//nzuueceBg4cuGn91VdfzZ133sn1Qw7jgZPaw6sXwIlvQn7dHZ5v8ODBW64Y9i9ocDzUabTF6r59+xYZ3Dds2MBf//pXAC6++OJd+kySJElSVWT3eKma+fjjj3nhhRfo1KkTP/zhD7fY9utf/5r69evz6GNPsGLf+2HpFHj3VyW7wNLpsGx6iUaNLygoYM6cOfTu3Zt99923ZNeTJEmSqjDvtEvVzKhRowA4/vjjqVFjy9/bNWzYkMMPP5wXXniBV2fW45hul8CHf4AO34KWh233nE888QQzZ86kVq1a7NlwGkfXgtrtix/aH3zwQcC77JIkSdLWDO1SNTN16lQAunfvXuT23XffnRdeeIFp06ZxzEW3wdwRyWjy/SdDfr0ijznzzDO3eN+qSR7313+TM87ostN65syZw/Dhw2ncuDEDBgwo0WeRJEmSqjq7x0vVzJIlSwBo3Lhxkds3rl+8eDHUbAiH/C3p7v72L7bZ99RTT2XYsGHMmTOHVatWMeW9N7nu1DwWr4ABAwYwYsSIndbz17/+lQ0bNnDOOedQr17RvxSQJEmSqitDu6Qda3M07H45TL0L5o/dYtOgQYM4+eSTad++PXXq1KFHk8+45bsb+MPgSyksLOS6667b4akLCws3DUB3ySWXlNcnkCRJkiotQ7tUzWy8k77xjvvWNq5v0qTJVyv3+x3U7wSvXgjrV2z/5JkCyK/PRVfeTH5+PpMnT2bZsmXb3X348OF8+umn9O7dm3322aekH0WSJEmq8gztUjXTo0cPAKZNm1bk9unTpwNbPfNeswH0/jss/wgm/7zoE8eYhPY2x1KnfmMaNmwIwIoV2w/5Gweg8y67JEmSVDRDu1TN9OvXD4AXXniBwsLCLbYtW7aMcePGUa9ePXr37r3lga2Pgu5XwLR74PMx25546YewYja0O4mpU6eyaNEiGjZsSIsWLYqsI5PJ8NxzzzkAnSRJkrQDhnapmunatSvHH388s2bN4v77799i2w033MCKFSs499xzqV+//qb1U6ZMYcqUKbDfLdCgK0y8kJnT3mPhwoVfHZwpAGBB/sFccMEFQDKqfH5+0ZNUbByA7txzz6Vu3bpl/CklSZKkqsEp36Rq6I9//COHHXYYV1xxBSNHjmTPPfdk4sSJjBo1iu7du3PzzTdvsf+ee+4JQIwx6Sb/v6MY89iVXPq7cRxxxBF06dKFZkuf55N5jSm4vC9LliyhV69e/P73vy/y+psPQOfc7JIkSdL2Gdqlaqhr16688cYb/OpXv2LEiBEUFBTQtm1brrzySm644QaaNm26/YNbHQk9ruLAT+7kzFOPZ9IHGd56602WLllEw/p12GffXnz3u9/lkksuoVatWkWe4vnnn2f27NkOQCdJkiTthKFdqqa+9rWv8fe//71Y+8YYt1zR8yb2yfyXh/ecBie9A/NehLGnwzEjkmffd6J///7bnlOSJEnSNgztkkouvx70fhhePByebgfrlwMhGYhOkiRJUplxIDpJu2b5TAj52cAOEOH1y2DmY6mWJUmSJFUlhnZJu+bt6yGu33LdhpXJekmSJEllwtAuades/KRk6yVJkiSVmKFd0q6pt1vJ1kuSJEkqMUO7pF3T82bIq7flurx6yXpJkiRJZcLQLmnXdD4bDn4Q6nUEQrI8+MFkvSRJkqQy4ZRvknZd57MN6ZIkSVI58k67JEmSJEk5ytC+lRBChxDC30IImRDCmhDCrBDCXSGEpmnXJkmSJEmqXuwev5kQQldgPNAKeAaYAhwMXAmcGEI4PMb4ZYolSpIkSZKqEe+0b+mPJIH9ihjjaTHGn8UYjwbuBHoADostSZIkSaowhvas7F3244FZwP1bbb4BWAGcG0KoX8GlSZIkSZKqKUP7V/plly/EGAs33xBjXAaMA+oBvSu6MEmSJElS9WRo/0qP7HLadrZPzy67V0AtkiRJkiQ5EN1mGmeXS7azfeP6JltvCCFcDFwM0Lp1a0aPHl3WtamCLV++3D9HlSvbmMqT7Uvlyfal8mT7krZlaC8DMcYHgQcBevXqFfv27ZtuQSq10aNH45+jypNtTOXJ9qXyZPtSebJ9Sduye/xXNt5Jb7yd7RvXLy7/UiRJkiRJMrRvbmp2ub1n1nfPLrf3zLskSZIkSWXK0P6VUdnl8SGELb6XEEJD4HBgJfBqRRcmSZIkSaqeDO1ZMcaPgReATsAPt9r8a6A+8GiMcUUFlyZJkiRJqqYciG5LlwPjgXtCCMcAHwKHkMzhPg24PsXaJEmSJEnVjHfaN5O9294LeJgkrP8Y6ArcDfSOMX6ZXnWSJEmSpOrGO+1biTF+ClyQdh2SJEmSJHmnXZIkSZKkHGVolyRJkiQpRxnaJUmSJEnKUYZ2SZIkSZJyVIgxpl1DlRJCWADMTrsOlVoL4Iu0i1CVZhtTebJ9qTzZvlSebF9VQ8cYY8u0i6gqDO1SEUIIb8QYe6Vdh6ou25jKk+1L5cn2pfJk+5K2Zfd4SZIkSZJylKFdkiRJkqQcZWiXivZg2gWoyrONqTzZvlSebF8qT7YvaSs+0y5JkiRJUo7yTrskSZIkSTnK0C5JkiRJUo4ytEuSJEmSlKMM7aqUQgjNQwgXhRCeDiF8FEJYFUJYEkJ4JYTwgxBCkW07hHBYCKEghLAwe8w7IYSrQgh5RezbJITw0xDCYyGED0II60MIMYRw7HbOHUIIJ4YQ7g0hTA4hLAohrA4hTA0h3BVCaF3W34PKTy62se1cr0UIYW72uFdK85lVcXK9fYUQ2oQQ7sz+/bUq+/fZmyGEW8vi86t85XL7CiF0DiE8EEKYEkJYGUL4PIQwIYRwcQihVll9Byo/FdS+9gshDA4hjMv+G7c2hPBZCOH/hRAO2EFteSGEQdlzr8peqyCEcFhZfgdSRXMgOlVKIYRLgT8Bc4FRwCdAa+DbQGPgKeA7cbMGHkI4Nbt+NfAEsBA4BegBDIkxfmera+wHvJV9Oweomb3GcTHG/xVRUx1gFbAWeBl4G8gDjgb2BT4HjowxTi/1F6Byl4ttbDt1PgUcDzQAxsUYj9iFj6sKlsvtK4RwOPBfoB5QAEwF6gLdgK/HGDvt+idXRcjV9hVCOChbT11gBPAe0Ch7nfbAC8CJ0R9Oc1oFta9XgUOAScBEYDmwH8m/d+uBATHG/2x1TAD+DZxB8vfWMKAZMACoA5weY3ymjL4GqWLFGH35qnQvkiB8ClBjq/VtSP7xiCR/OW9c3wiYD6wBem22vg4wPrv/mVudqylwDNAs+/7h7H7HbqemmsD1QNOt1tcAHsgeOyzt785X5W1jRdT4/ez+l2WXr6T9vfmq3O0re/0vgFlA9yK210z7u/NVqdvXc9l9zttqfX3g/ey2Pml/f75yon0NBLoVce2zs/t/AdTaattZ2W3jgDqbrT8oe+35QMO0vz9fvnblZfd4VUoxxpdijMNijIVbrZ9HEpAB+m626QygJfB4jPGNzfZfDfwi+/ayrc61KMY4Msa4sJg1rYsx3hxjXLTV+kLgN0XUpByWi21scyGE3YB7gL8Cw0t6vNKVw+3r50Bz4NIY47Qi6l5XgnMpJTncvrpkl89uda4VwMjs25YlOJ9SUEHt694Y40dFXPsxYDrJ31P7bLV54zl+kT33xmNeJ7m73zJbi1TpGNpVFW38oXL9ZuuOzi5HFLH/y8BK4LAQQu0KrEmVV6ptLNsF8GFgCXB1ac+nnJNm+zoLWAQ8H0LYK4QwMIRwbQjhjBBCg1KeW7khzfb1fnb5jc1XhhDqZWtYCUwo5TWUropoX9tcI/uI4mHZc40t4piNv9w+uohtUs4ztKtKCSHkk3QZhi3/ceiRXRZ152g9MBPI56u7AGXtwiJqUiWUI23sKpK7GD+IMS4tg/MpR6TZvkIInYEWwEfAnSQB6x7gVuBJYFYI4aRdPb/SlwN/f/2C5Dnoh0MIz4YQbg0h/BGYQvLs8Rkxxkwpr6GUVET7CiH0BvYCPiMZE2GjriTjCM3InnNrG8cT6r6za0i5yNCuquZWYG+gIMb4/GbrG2eXS7Zz3Mb1Tcq6oOzAOzcAy/iqG5gqr1TbWAhhL+AW4IFYzMHqVKmk2b5aZZcHABcDP8quawdck63hqRDCnqW4htKV6t9fMcYpJM8Xjyd5Jvpaki7NbYB/Aq+W5vxKXbm2rxBCM+Af2beDYowbyvoaUq4ytKvKCCFcAfyY5Df256ZcDgAhhO4ko5fWBM6JMX6cckkqhbTbWAihJvAoyZ2qayr6+ipfabcvvvqZIA/4TYzx/hjjghjj3BjjbSR33euQ9PRQJZMD7YsQwv4kgb0ucCTQEPga8CuSR30mhhAab/8MylXl3b5CCPWBZ4Ddgd/HGJ8s62tIuczQriohhPAj4G7gA6BfEQPjbPwN6/Z+GNi4fnEZ1tSdZCqUZiSjoj67k0OUw3KkjV0H7A9cEGNcXorzKMfkSPva/Nini9i+cd3BpbiGUpAL7SvbdfrfJIOBnRJjfCXGuDzGOCfGeCtwL0kgG7Sr11A6yrt9ZQP7c8ARwB0xxmuL2K3Cf86TKpKhXZVeCOEqkn/s3yP5x2JeEbtNzS63eZYp+4NEZ5IBTWaUUU17AqNJng/9TozxqbI4r9KRQ23sACAAo0MIceOL5HlAgMOz6xaX4hqqYDnUvj7mq4GdFhexfePMGHVLcQ1VsBxqX3sA3YAPt1PDqOzywFJcQxWsvNtXCKEhySByR5HcYf/xdkr5GNgAdMmec2u7Z5fbPFcvVQaGdlVqIYRrSQZMmkzyj8X87ez6UnZ5YhHb+gD1gPExxjVlUNM+JIG9GfDtGOMzpT2n0pNjbexFkinetn49kd3+efb9P4o8Wjknl9pXjHEtX426vHcRu2xcN7OIbcpBudS+gI0jg7fYzvaNU72tLcU1VIHKu31lH5V4geRRipu3c4cd2DR93PjsuY4sYpf+W9UiVS67OsG7L19pv4BfAhF4A2i2k30bAQuANUCvzdbXIflLPpJ0Yd/ROR7O7nfsDvbZD/iCZMqRE9L+jnyV7pWLbWw7x3XKHvdK2t+ZrxL9ueVc+wK+ld1nHFB/s/VNSO6kReD7aX93vipf+yIJ7Yuy+1y01bYmwIfZbZen/d35Sr99AU2B17PbflXMms7a7O+vOputPyh77flAo7S/O1++duUVYoxIlU0I4TySHxA2kHTLKmq00Fkxxoc3O+Y0YAiwGngcWAh8k2QqkiHAd+NW/0OEEG7nq7sCR5BMKfICyUBgAENjjEOz+zYlmSqpGTASeGU75d8VY1xczI+qlORiG9tBrZ1I7n6OizEeUbxPqDTlcvsKIfwNuICkTQ0nGZjuZKA98FT2OoUl/tCqMLnavrJ1/Z3kMZ+RwFsk4eybJHfaXwX6xjLo9abyUxHtK4QwimRq049JZhYoytAY4+TNjgkk4yacQTIg3jCgOTCA5BcEp0d7P6qySvu3Br587coLGEzy29QdvUYXcdzhQAHJb/tXAe+SDHqTt53rzNrJNQZvtm+nYtQUgU5pf3++Kmcb20GtG9ued9orySuX2xdJoLqI5C7XCpKeQ28APwRqpP3d+ar07asP8B+SYL8OWA5MAn7GZndHfeXuqyLaVzHaVgTOL+K4/Ow5381eY1H2moel/b358lWal3faJUmSJEnKUQ5EJ0mSJElSjjK0S5IkSZKUowztkiRJkiTlKEO7JEmSJEk5ytAuSZIkSVKOMrRLkiRJkpSjDO2SJEmSJOUoQ7skSZIkSTnK0C5JUopCCP8JIcQQwtU72OegEMK6EMLMEEKjiqxPkiSlK8QY065BkqRqK4TQHHgXaAYcFGN8d6vt9YC3gG7AUTHGVyq+SkmSlBbvtEuSlKIY45fABUAt4LEQQu2tdvkD0B241cAuSVL1Y2iXJCllMcbngfuAfYDfblwfQjgJuBSYBAwOIeSHEC4PIbwaQlgaQlgZQngrhPCjEMI2/6aHEM4PITwVQpgRQliVPWZcCOGcouoIIYzOdtWvFUL4VQhhaghhTQjh4XL54JIkaafsHi9JUg4IIdQhCed7AscBbwPvAQ2BA4AZwDDgBGAqMBpYDfQD9gX+GWM8d6tzrgLez55nLtAcOAloD9wUY/zlVvuPBo4C/gscBAwH5gPzY4x/KOOPLEmSiiE/7QIkSRLEGFeHEM4GJgKPAO8ArYHLY4xTQwiDSQL7fcBVMcYNACGEPOBB4MIQwpAY4zObnXbvGOPHm18nhFCLJIz/LITwQIzxsyLK6Zg99ouy/ZSSJKmk7B4vSVKOiDFOBn5Jcie8P/BcjPFP2a7vA4F5wKCNgT17zAbgx0AEzt7qfFsE9uy6tcD9JL+4P2Y7pfzSwC5JUm7wTrskSbnldmAQ0Ab4aXZdd5LR5acDvwghFHXcKpKu9ZuEEHYDriUJ57sBdbc6pv12anhtVwqXJEllz9AuSVIOiTEWhhDWZN+uyi6bZ5e7Azfs4PAGG/8jhNCFJHw3BcYCLwBLgA1AJ+A8YOuR6jeatyu1S5KksmdolyQp9y3JLp+OMX67mMdcTRL2L4gxPrz5hhDCWSShvUjRUWolScoZPtMuSVLumwIsBnqHEGoW85hu2eVTRWw7qiyKkiRJ5c/QLklSjosxrgfuBdoC94QQtn42nRBC2xDCXputmpVd9t1qvxOAi8qnUkmSVNbsHi9JUuVwI9ATuBQ4JYTwEvAZ0IrkWffDgeuBD7L7/xG4AHgyhDAEyAB7AycC/wYGVGj1kiRplxjaJUmqBGKM60IIpwHnAOcDJ5MMPLcAmEkyVdxjm+3/TgihH3AT8A2Sf/PfBr5N0tXe0C5JUiUQHGtGkiRJkqTc5DPtkiRJkiTlKEO7JEmSJEk5ytAuSZIkSVKOMrRLkiRJkpSjDO2SJEmSJOUoQ7skSZIkSTnK0C5JkiRJUo4ytEuSJEmSlKMM7ZIkSZIk5aj/D25VuDTarejAAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# did the same for latinx and other\n", + "years = list(travel_time_diff_Latinx_other.keys())\n", + "differences = list(travel_time_diff_Latinx_other.values())\n", + "\n", + "# Plotting the line graph\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Difference in Total Travel Time between Latinx and Other Race Riders (2011-2021)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Travel Time Difference')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + "\n", + "# make the output bigger\n", + "#plt.rcParams[\"figure.figsize\"] = (10,10)\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# did the same for latinx and two or more\n", + "years = list(travel_time_diff_Latinx_two_or_more.keys())\n", + "differences = list(travel_time_diff_Latinx_two_or_more.values())\n", + "\n", + "# Plotting the line graph\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Difference in Total Travel Time between Latinx and Riders in two or more races (2011-2021)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Travel Time Difference')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + "\n", + "# make the output bigger\n", + "#plt.rcParams[\"figure.figsize\"] = (10,10)\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bus Commute Disparity between Latinx and White Riders(2011-2021): 13 hours\n", + "Bus Commute Disparity between Latinx and Black Riders(2011-2021): -35 hours\n", + "Bus Commute Disparity between Latinx and American Indian Riders(2011-2021): 11 hours\n", + "Bus Commute Disparity between Latinx and Alaska Native Riders(2011-2021): 0 hours\n", + "Bus Commute Disparity between Latinx and Asian Riders(2011-2021): 5 hours\n", + "Bus Commute Disparity between Latinx and Hawaiian Riders(2011-2021): 109 hours\n", + "Bus Commute Disparity between Latinx and Other Riders(2011-2021): 13 hours\n", + "Bus Commute Disparity between Latinx and Riders with two or more race(2011-2021): 4 hours\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "# Calculating the average of the yearly differences from 2017 to 2021\n", + "#average_difference_2017_2021 = round(sum(travel_time_difference[year] for year in range(2017, 2022)) / 5)\n", + "#print(f'Bus Commute Disparity between Black and White Riders(2017-2021):', average_difference_2017_2021, f'hours')\n", + "\n", + "average_difference_2011_2021_latinx_white = round(sum(tavel_time_diff_Latinx_White[year] for year in range(2011, 2022)) / 11)\n", + "print(f'Bus Commute Disparity between Latinx and White Riders(2011-2021):', average_difference_2011_2021_latinx_white, f'hours')\n", + "\n", + "average_difference_2011_2021_latinx_black = round(sum(travel_time_diff_Latinx_black[year] for year in range(2011, 2022)) / 11)\n", + "print(f'Bus Commute Disparity between Latinx and Black Riders(2011-2021):', average_difference_2011_2021_latinx_black, f'hours')\n", + "\n", + "average_difference_2011_2021_latinx_american_indian = round(sum(travel_time_diff_Latinx_american_indian[year] for year in range(2011, 2022)) / 11)\n", + "print(f'Bus Commute Disparity between Latinx and American Indian Riders(2011-2021):', average_difference_2011_2021_latinx_american_indian, f'hours')\n", + "\n", + "average_difference_2011_2021_latinx_alaska_native = round(\n", + " sum(travel_time_diff_Latinx_alaska_native[year] for year in range(2011, 2022) if not np.isnan(travel_time_diff_Latinx_alaska_native[year])) / 11\n", + ")\n", + "print(f'Bus Commute Disparity between Latinx and Alaska Native Riders(2011-2021):', average_difference_2011_2021_latinx_alaska_native, f'hours')\n", + "\n", + "average_difference_2011_2021_latinx_asian = round(sum(travel_time_diff_Latinx_asian[year] for year in range(2011, 2022)) / 11)\n", + "print(f'Bus Commute Disparity between Latinx and Asian Riders(2011-2021):', average_difference_2011_2021_latinx_asian, f'hours')\n", + "\n", + "average_difference_2011_2021_latinx_hawaiian = round(\n", + " np.nanmean([travel_time_diff_Latinx_hawaiian[year] for year in range(2011, 2022)])\n", + ")\n", + "print(f'Bus Commute Disparity between Latinx and Hawaiian Riders(2011-2021):', average_difference_2011_2021_latinx_hawaiian, f'hours')\n", + "\n", + "average_difference_2011_2021_latinx_other = round(sum(travel_time_diff_Latinx_other[year] for year in range(2011, 2022)) / 11)\n", + "print(f'Bus Commute Disparity between Latinx and Other Riders(2011-2021):', average_difference_2011_2021_latinx_other, f'hours')\n", + "\n", + "average_difference_2011_2021_latinx_two_or_more = round(sum(travel_time_diff_Latinx_two_or_more[year] for year in range(2011, 2022)) / 11)\n", + "print(f'Bus Commute Disparity between Latinx and Riders with two or more race(2011-2021):', average_difference_2011_2021_latinx_two_or_more, f'hours')\n", + "\n", + "# make a dataframe to represent the difference\n", + "\n", + "# graph the difference, Y axis is the group, X axis is the difference\n", + "\n", + "difference_data=[average_difference_2011_2021_latinx_white, average_difference_2011_2021_latinx_black, average_difference_2011_2021_latinx_american_indian, average_difference_2011_2021_latinx_alaska_native, average_difference_2011_2021_latinx_asian, average_difference_2011_2021_latinx_hawaiian, average_difference_2011_2021_latinx_other, average_difference_2011_2021_latinx_two_or_more]\n", + "group=[\"Latinx and White\", \"Latinx and Black\", \"Latinx and American Indian\", \"Latinx and Alaska Native\", \"Latinx and Asian\", \"Latinx and Hawaiian\", \"Latinx and Some Other\", \"Latinx and Two or More\"]\n", + "plt.figure(figsize=(10, 8))\n", + "plt.barh(group, difference_data, color='navy')\n", + "plt.xlabel('Difference in Total Travel Time in Hours')\n", + "plt.title(\"Average gap of each race group's commute time with Latinx\")\n", + "plt.gca().invert_yaxis() # Invert y-axis to match the provided graph's order\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ds", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ea858e2a9bcf37a71c2bee6dd0cf00a0d2396954 Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 21:44:56 -0500 Subject: [PATCH 05/39] Update q4_analisys.ipynb --- fa23-team/q4_analisys.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/fa23-team/q4_analisys.ipynb b/fa23-team/q4_analisys.ipynb index 85570da..db0134e 100644 --- a/fa23-team/q4_analisys.ipynb +++ b/fa23-team/q4_analisys.ipynb @@ -33,6 +33,7 @@ } ], "source": [ + #data from: https://www2.census.gov/programs-surveys/acs/data/pums/2021/5-Year/ "df_2011= pd.read_csv('data/psam_p25_2011.csv')\n", "df_2012= pd.read_csv('data/psam_p25_2012.csv')\n", "df_2013= pd.read_csv('data/psam_p25_2013.csv')\n", From b34ef6391f827e71790f28919f1035ed96fb9e3c Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 21:48:24 -0500 Subject: [PATCH 06/39] Add files via upload --- fa23-team/q4_analisys.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fa23-team/q4_analisys.ipynb b/fa23-team/q4_analisys.ipynb index db0134e..cda3480 100644 --- a/fa23-team/q4_analisys.ipynb +++ b/fa23-team/q4_analisys.ipynb @@ -33,7 +33,7 @@ } ], "source": [ - #data from: https://www2.census.gov/programs-surveys/acs/data/pums/2021/5-Year/ + "# data from https://www.census.gov/programs-surveys/acs/microdata/access.2020.html#list-tab-735824205\n", "df_2011= pd.read_csv('data/psam_p25_2011.csv')\n", "df_2012= pd.read_csv('data/psam_p25_2012.csv')\n", "df_2013= pd.read_csv('data/psam_p25_2013.csv')\n", From 753300273cfa5802f69fed62187a94eca75c23c3 Mon Sep 17 00:00:00 2001 From: laowangbushilaowang <53445096+laowangbushilaowang@users.noreply.github.com> Date: Tue, 28 Nov 2023 22:33:09 -0500 Subject: [PATCH 07/39] Delete fa23-team/q2_vis.ipynb --- fa23-team/q2_vis.ipynb | 683 ----------------------------------------- 1 file changed, 683 deletions(-) delete mode 100644 fa23-team/q2_vis.ipynb diff --git a/fa23-team/q2_vis.ipynb b/fa23-team/q2_vis.ipynb deleted file mode 100644 index 116f27d..0000000 --- a/fa23-team/q2_vis.ipynb +++ /dev/null @@ -1,683 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "year=[\"2021\",\"2016\",\"2011\"]\n", - "area=['Roxbury','Mattapan','Dorchester']\n", - "x=[\"Less 10 mins\",\"10-14 mins\",\"14-19 mins\",\"20-24 mins\",\"25-29 mins\",\"30-34 mins\", \"35-44 mins\",\"45-59 mins\",\"60 and more\"]\n", - "combined_df = pd.DataFrame()\n", - "c=[]\n", - "for a in area:\n", - " for y in year:\n", - " df=pd.read_csv(\"./data/q2/{}_data_{}.csv\".format(a,y),index_col=\"Label (Grouping)\")\n", - " df=df.astype(\"str\")\n", - " df = df.apply(lambda x: x.str.replace(',', ''))\n", - " # boston=df[['Label (Grouping)',\"Boston city, Massachusetts!!Estimate\"]]\n", - " # cambridge=df[['Label (Grouping)',\"Cambridge city, Massachusetts!!Estimate\"]]\n", - " df = df.iloc[:, 0::2].apply(pd.to_numeric, errors='coerce')\n", - " roxbury= df.iloc[:, 0::2].sum(axis=1)\n", - " roxbury.reset_index(drop=True, inplace=True)\n", - " combined_df = pd.concat([combined_df, roxbury], axis=1)\n", - " c.append(\"{}_{}\".format(a,y))\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "combined_df.columns=c\n", - "combined_df.index=df.index\n", - "df=combined_df\n", - "df_total=df.iloc[1:10,:]\n", - "df_bus=df.iloc[71:80,:]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Roxbury_2021Roxbury_2016Roxbury_2011Mattapan_2021Mattapan_2016Mattapan_2011Dorchester_2021Dorchester_2016Dorchester_2011
Label (Grouping)
Total:11474106408719692567786129277532732123383
Less than 10 minutes7991088816217105240146311971320
10 to 14 minutes8201096718278379271201820682059
15 to 19 minutes13211388963564569530273428762265
20 to 24 minutes111713751461805805669346635543050
..............................
25 to 29 minutes510510450463123
30 to 34 minutes433028111506663164
35 to 44 minutes178212903861015
45 to 59 minutes15046478158380
60 or more minutes132348390022911156
\n", - "

120 rows × 9 columns

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" - ], - "text/plain": [ - " Roxbury_2021 Roxbury_2016 Roxbury_2011 \\\n", - "Label (Grouping) \n", - "Total: 11474 10640 8719 \n", - "    Less than 10 minutes 799 1088 816 \n", - "    10 to 14 minutes 820 1096 718 \n", - "    15 to 19 minutes 1321 1388 963 \n", - "    20 to 24 minutes 1117 1375 1461 \n", - "... ... ... ... \n", - "        25 to 29 minutes 51 0 51 \n", - "        30 to 34 minutes 43 30 28 \n", - "        35 to 44 minutes 17 8 21 \n", - "        45 to 59 minutes 15 0 4 \n", - "        60 or more minutes 132 34 8 \n", - "\n", - " Mattapan_2021 Mattapan_2016 Mattapan_2011 \\\n", - "Label (Grouping) \n", - "Total: 6925 6778 6129 \n", - "    Less than 10 minutes 217 105 240 \n", - "    10 to 14 minutes 278 379 271 \n", - "    15 to 19 minutes 564 569 530 \n", - "    20 to 24 minutes 805 805 669 \n", - "... ... ... ... \n", - "        25 to 29 minutes 0 45 0 \n", - "        30 to 34 minutes 11 15 0 \n", - "        35 to 44 minutes 29 0 38 \n", - "        45 to 59 minutes 64 7 8 \n", - "        60 or more minutes 39 0 0 \n", - "\n", - " Dorchester_2021 Dorchester_2016 Dorchester_2011 \n", - "Label (Grouping) \n", - "Total: 27753 27321 23383 \n", - "    Less than 10 minutes 1463 1197 1320 \n", - "    10 to 14 minutes 2018 2068 2059 \n", - "    15 to 19 minutes 2734 2876 2265 \n", - "    20 to 24 minutes 3466 3554 3050 \n", - "... ... ... ... \n", - "        25 to 29 minutes 46 31 23 \n", - "        30 to 34 minutes 66 63 164 \n", - "        35 to 44 minutes 6 10 15 \n", - "        45 to 59 minutes 158 38 0 \n", - "        60 or more minutes 229 111 56 \n", - "\n", - "[120 rows x 9 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "df_total_per=df_total/df.iloc[0,:]\n", - "df_bus_per=df_bus/df.iloc[70,:]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "label = x\n", - "index = np.arange(len(label))\n", - "ti = index - 0.4\n", - "\n", - "for a in area:\n", - " fig, axe = plt.subplots()\n", - " values1 = df_total_per[\"{}_2021\".format(a)]\n", - " values2 = df_bus_per[\"{}_2021\".format(a)]\n", - " axe.bar(index, values1, width=0.4, label=\"Total\", alpha=0.8)\n", - " axe.bar(index + 0.4, values2, width=0.4, label=\"Bus\", alpha=0.8)\n", - " axe.set_xticks(index + 0.15)\n", - " axe.set_xticklabels(label)\n", - "\n", - " axe.set_xticks(index + 0.15)\n", - " axe.set_xticklabels(label)\n", - "\n", - " plt.xticks(fontsize=5.5)\n", - " plt.ylabel(\"Percentage\")\n", - " plt.xlabel(\"Travel time to work (mins)\")\n", - " plt.title(\"2021 {} travel time to work\".format(a))\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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8AADY2dmhWbNm+OmnnyAiSv1Nmzbhww8/VI4BWf3M3ofs7hvZOV5lRnJyMvbs2YPAwEAUL15cKS9YsCA6d+6MQ4cO4eXLl+kurz0Gao/JBw8eRJUqVdCoUSMcPHgQAPD8+XNcuHBBqZud9+zbty9MTEzSjOHChQuoW7cuPD09sW/fPjg6Omb5c8gJHFCcgyIiItCiRQvY29vj559/1tn42oNCfHx8quXi4uJ06qQUHR2Nli1b4tWrVzh06JDOIOOc4unpidWrV0OlUsHCwgIlSpSAi4uL8vq1a9dw7tw5nYQlJe1ARq1ixYrpPNceEMqWLZtuDJGRkXj+/DlWrVqFVatWZep93kyUtH9Ez549S/d9Ulq/fj3mzZuHy5cvIzExMc34b9++jYIFC6Ya7Ont7a3z/Pr16xARfPHFF/jiiy/Sjd/d3T3N127fvo1ChQrB1tZWp7x06dLK6znp9u3bOgfvtN4vo+0FAOHh4Zg4cSJ++eWXVJ+59osuqwoXLpxqThxHR0ecO3cuW+tLb5329vYoUqRIqjIg7f2nRIkSOs+9vLygVquVS6mvXbsGEUlVT+vNQfnu7u6ZGmSv3e4lS5ZM9Vrp0qWxe/fu9zKg882/Ya3Dhw9j0qRJOHr0KGJiYnRee/HihfIZduzYEdu2bcPRo0dRo0YN3LhxAydPntSZliGrn9n7kN19IzvHq8yIjIxETExMuttbo9Hgzp07KFOmTJrLu7q6okSJEjh48CA++eQTHDx4EPXr10edOnUwePBg3Lx5E5cuXYJGo1GSm+y8Z3r7BwAEBATA1dUVu3fvfi/fV5nF5CaHvHjxAs2aNcPz589x8OBBFCpUSOf1ggULAnj9S+xNDx48QP78+VP16iQkJKBNmzY4d+4cdu/e/dYvm+yytrbWuQLrTRqNBo0aNUq31+iDDz7QeZ6dqyq0c+p07dpVp8crpfLly+s8T++XQ8pfi+n5/vvv0aNHDwQGBmLUqFFwcXGBiYkJZs6ciRs3bmQx+n/jHzlyZJq/eIHUCVFelpycjEaNGuHp06cYM2YMSpUqBWtra9y7dw89evTI9hxJmdmm6U0ImJycnKV1vsv+82YMGo0GKpUKu3btSnO9bx7kDf3Ko7Tiu3HjBho2bIhSpUph/vz5KFKkCMzMzLBz504sWLBAZ5sHBATAysoKP/30E2rUqIGffvoJarUa7du3V+pk9TN7H7K7b2TneJVbatWqhdDQUMTGxuLkyZOYOHEiypYtCwcHBxw8eBCXLl2CjY0NKlasmO33yGj/bdu2LdavX48ffvgBn3zySbbf410xuckBcXFxCAgIwNWrV7Fv3z74+PikquPu7g5nZ+c0J+I7duwYfH19dco0Gg26deuG0NBQ/PTTT6hbt+77Cv+tvLy8EBUVlWEClBFtV+eFCxfSrePs7AxbW1skJydn+33Skt4X4c8//4zixYsjODhYp86kSZN06nl4eGD//v2pLtW9fv26Tj1tG01NTbMVv4eHB/bt24dXr17p9N5cvnxZeT070mu/h4cHrly5kqr8zfdLb/nz58/j6tWrWL9+Pbp166aU7927N1txZoW2h+758+c65Tndu5XStWvXdH6tXr9+HRqNRpk118vLCyKCYsWKpUr234V2O6S3rZycnLLVa5OdGaN//fVXxMfH45dfftHpNU3r9JG1tTVatmyJzZs3Y/78+di0aRNq166t86PvfXxmuTUTdk4cr9KK1dnZGVZWVulub7VanapX6U21a9fG2rVrsXHjRiQnJ6NGjRpQq9WoVauWktzUqFFDSeBy4j1Tmjt3LvLly4cBAwbA1tYWnTt3zvSyOYljbt5RcnIyOnbsiKNHj2Lz5s2oXr16unXbtm2LHTt24M6dO0pZaGgorl69qvOLBnh9Tn/Tpk1YtmwZ2rRp897iz4wOHTrg6NGj2L17d6rXnj9/jqSkpAyXd3Z2Rp06dRAUFITw8HCd17S/hExMTNC2bVts2bIlzSTozVmcM8va2jrNUyTaP+yUv9L//vtvHD16VKdekyZNkJiYiNWrVytlGo0GS5cu1ann4uKCevXqYeXKlWn2zr0t/ubNmyM5ORlLlizRKV+wYAFUKhWaNWuW4fLpsba2TpUEaN/v2LFjOu2Njo7GqlWr4OnpqSTo2i/ON9eR1ucnIvj666+zFWdWeHl5AYDOWK/k5OR0Tw/khDe39+LFiwFA2S5t2rSBiYkJvvzyy1Q9PyKCJ0+eZOt9CxYsCF9fX6xfv15nG1y4cAF79uxB8+bNs7Xe9LZrRtLa5i9evMDatWvTrN+xY0fcv38f33zzDc6ePYuOHTvqvP4+PrP09veclhPHq7RiNTExQePGjbF9+3ad2aMfPnyIDRs2oFatWrCzs8twvdrTTbNnz0b58uWVU2q1a9dGaGgoTpw4odTJqfdMSaVSYdWqVWjXrh26d++OX375JdPL5iT23Lyjzz77DL/88gsCAgLw9OlTnUn7AOgMjhw/fjw2b96M+vXrY+jQoYiKisLcuXNRrlw5ncGFCxcuxLJly1C9enVYWVmlWmfr1q2Vg9OLFy+UA+3hw4cBAEuWLIGDgwMcHBwwaNCgd27jqFGj8Msvv6Bly5bo0aMH/Pz8EB0djfPnz+Pnn39GWFgYnJycMlzHokWLUKtWLVSqVAn9+vVDsWLFEBYWht9++02ZeHDWrFnYv38/qlWrhr59+8LHxwdPnz7FqVOnsG/fPjx9+jTLsfv5+WHTpk0YMWIEqlSpAhsbGwQEBKBly5YIDg5G69at0aJFC9y6dQsrVqyAj48PoqKilOUDAwNRtWpVfPbZZ7h+/TpKlSqFX375RYkl5a+vpUuXolatWihXrhz69u2L4sWL4+HDhzh69Cju3r2Ls2fPphtnQEAA6tevj88//xxhYWGoUKEC9uzZg+3bt2PYsGHKF3p22r98+XJMmzYN3t7ecHFxQYMGDTB27Fj8+OOPaNasGYYMGYL8+fNj/fr1uHXrFrZs2QK1+vXvHi8vLzg4OGDFihWwtbWFtbU1qlWrhlKlSsHLywsjR47EvXv3YGdnhy1btmR6vNO7KFOmDD788EOMGzcOT58+Rf78+bFx48a3Jtnv4tatW2jVqhWaNm2Ko0eP4vvvv0fnzp1RoUIFAK8/p2nTpmHcuHEICwtDYGAgbG1tcevWLWzduhX9+vXDyJEjs/Xec+fORbNmzVC9enX07t0bsbGxWLx4Mezt7TF58uRsrdPX1xcmJiaYPXs2Xrx4AXNzczRo0EBnrN2bGjduDDMzMwQEBOCTTz5BVFQUVq9eDRcXlzQT+ubNm8PW1hYjR45UkoGU3sdnlt7+/j686/HKz88P+/btw/z581GoUCEUK1YM1apVw7Rp07B3717UqlULAwYMQL58+bBy5UrEx8djzpw5b43L29sbbm5uuHLlijLwHQDq1KmDMWPGAIBOcgPgnd/zTWq1Gt9//z0CAwPRoUMH7Ny5871th3Tl2nVZRiqtieJSPt504cIFady4sVhZWYmDg4N06dIl1TwlaU3IlfKR8hLY9CZ2A5CpSycze1nxq1evZNy4ceLt7S1mZmbi5OQkNWrUkK+++koSEhJ0Ykk5j8WbbW/durU4ODiIhYWFlCxZMtWl7Q8fPpSBAwdKkSJFxNTUVNzc3KRhw4ayatUqpU56l7xq3z/l5ZpRUVHSuXNncXBw0PlMNBqNzJgxQzw8PMTc3FwqVqwoO3bsSPOS08jISOncubMyiV+PHj3k8OHDAkA2btyoU/fGjRvSrVs3cXNzE1NTU3F3d5eWLVvKzz//nKnPePjw4VKoUCExNTWVEiVK6Ezip5WVS8EjIiKkRYsWYmtrqzOpmTbWdu3aKdujatWqOpP4aW3fvl18fHwkX758Op/vxYsXxd/fX2xsbMTJyUn69u0rZ8+eTfdy6rdJr11pbZMbN26Iv7+/mJubi6urq4wfP1727t2b5uW+aa1TO1HdmwDIwIEDU8V+8eJFadeundja2oqjo6MMGjQozUn8tmzZIrVq1RJra2uxtraWUqVKycCBA+XKlStvjSkj+/btk5o1a4qlpaXY2dlJQECAzgRsIlm7FFxEZPXq1VK8eHExMTHR+dzS+2xEXl9aXL58ebGwsBBPT0+ZPXu2BAUFpTsFRZcuXQSA+Pv7pxtHZj6zzF4Knt7+ntEkfm/K7L4hkrnjVXouX76sTB6INCbxa9KkidjY2IiVlZXUr19fmQcnM9q3by/A64lOtRISEsTKykrMzMzS3Hcz857aS8GPHz+eavm0JvGLiYmRunXrio2NzVvn6MlpKpFMjJ4jIh3btm1D69atcejQIdSsWVPf4RARUQpMbojeIjY2VufqgOTkZDRu3BgnTpxARESEwV/5QkT0X8MxN0RvMXjwYMTGxqJ69eqIj49HcHAwjhw5ghkzZjCxISIyQOy5IXqLDRs2YN68ebh+/Tri4uLg7e2N/v3758hgbSIiynlMboiIiMiocJ4bIiIiMipMboiIiMio/OcGFGs0Gty/fx+2tra5NlU3ERERvRsRwatXr1CoUCFlotH0/OeSm/v372fpPhlERERkOO7cuYPChQtnWOc/l9xob0p4586dLN0vg4iIiPTn5cuXKFKkiM7NhdPzn0tutKei7OzsmNwQERHlMZkZUsIBxURERGRUmNwQERGRUWFyQ0REREblPzfmhoiI8g4RQVJSEpKTk/UdCuUCU1NTmJiYvPN6mNwQEZFBSkhIwIMHDxATE6PvUCiXqFQqFC5cGDY2Nu+0HiY3RERkcDQaDW7dugUTExMUKlQIZmZmnHjVyIkIIiMjcffuXZQoUeKdenD0ntwsXboUc+fORUREBCpUqIDFixejatWq6dZ//vw5Pv/8cwQHB+Pp06fw8PDAwoUL0bx581yMmoiI3qeEhARoNBoUKVIEVlZW+g6HcomzszPCwsKQmJiYd5ObTZs2YcSIEVixYgWqVauGhQsXokmTJrhy5QpcXFxS1U9ISECjRo3g4uKCn3/+Ge7u7rh9+zYcHBxyP3giInrv3jbNPhmXnOqd02tyM3/+fPTt2xc9e/YEAKxYsQK//fYbgoKCMHbs2FT1g4KC8PTpUxw5cgSmpqYAAE9PzwzfIz4+HvHx8crzly9f5lwDiIiIyODoLSVOSEjAyZMn4e/v/28wajX8/f1x9OjRNJf55ZdfUL16dQwcOBCurq4oW7YsZsyYkeEo+pkzZ8Le3l558L5SRERExk1vPTePHz9GcnIyXF1ddcpdXV1x+fLlNJe5efMmfv/9d3Tp0gU7d+7E9evXMWDAACQmJmLSpElpLjNu3DiMGDFCea69NwUREeU9AYsP5er7/Tq4Vq6+X2asW7cOw4YNw/Pnz/UdisHKUyczNRoNXFxcsGrVKvj5+aFjx474/PPPsWLFinSXMTc3V+4jxftJERHR+9ajRw+oVCqoVCqYmpqiWLFiGD16NOLi4vQd2n+G3npunJycYGJigocPH+qUP3z4EG5ubmkuU7BgwVQT/JQuXRoRERFISEiAmZnZe42ZiIgoM5o2bYq1a9ciMTERJ0+eRPfu3aFSqTB79mx9h5YuY/oe1VvPjZmZGfz8/BAaGqqUaTQahIaGonr16mkuU7NmTVy/fh0ajUYpu3r1KgoWLGg0G4SIiPI+c3NzuLm5oUiRIggMDIS/vz/27t0L4PWFLkOGDIGLiwssLCxQq1YtHD9+HAAQFxeHMmXKoF+/fsq6bty4AVtbWwQFBem8x7Zt21CiRAlYWFigSZMmuHPnjvJajx49EBgYqFN/2LBhqFevnvK8Xr16GDRoEIYNGwYnJyc0adIEvXr1QsuWLXWWS0xMhIuLC9asWZMTH02u0OvVUiNGjED37t1RuXJlVK1aFQsXLkR0dLRy9VS3bt3g7u6OmTNnAgD69++PJUuWYOjQoRg8eDCuXbuGGTNmYMiQIfpsBhERAKDjjo76DuGtNrXcpO8Q/nMuXLiAI0eOwMPDAwAwevRobNmyBevXr4eHhwfmzJmDJk2a4Pr168ifPz9++OEHVKtWDS1atEDLli3RtWtXNGrUCL169VLWGRMTg+nTp+Pbb7+FmZkZBgwYgE6dOuHw4cNZim39+vXo37+/styTJ09Qp04dPHjwAAULFgQA7NixAzExMejY0fD3by29JjcdO3ZEZGQkJk6ciIiICPj6+iIkJEQZZBweHq4zx0GRIkWwe/duDB8+HOXLl4e7uzuGDh2KMWPG6KsJREREqezYsQM2NjZISkpCfHw81Go1lixZgujoaCxfvhzr1q1Ds2bNAACrV6/G3r17sWbNGowaNQq+vr6YNm0a+vTpg06dOuH27dvYsWOHzvoTExOxZMkSVKtWDcDrJKV06dI4duxYhhPhvqlEiRKYM2eOTlnJkiXx3XffYfTo0QCAtWvXon379u98S4TcpPcZigcNGoRBgwal+dqBAwdSlVWvXh1//fXXe46KiIgo++rXr4/ly5cjOjoaCxYsQL58+dC2bVucO3cOiYmJqFmzplLX1NQUVatWxaVLl5Syzz77DNu2bcOSJUuwa9cuFChQQGf9+fLlQ5UqVZTnpUqVgoODAy5dupSl5MbPzy9VWZ8+fbBq1SqMHj0aDx8+xK5du/D7779npfl6p/fkhoiIyNhYW1vD29sbwOsJaCtUqIA1a9boJCQZefToEa5evQoTExNcu3YNTZs2/fe1mEcAgBvPb+ic3dCIBo9iHuHG8xuITopGVEIUbjy/obz+OOoxYpNilbLYpFgk5UvSqQMAtT+qjTFjx2Dzns04dewUCnsURqFyhVLVy4iXg1em674PeepScCIiorxGrVZj/PjxmDBhAry8vGBmZqYzNiYxMRHHjx+Hj4+PUtarVy+UK1cO69evx5gxY3R6dQAgKSkJ50+fV57fvHYTL1+8hNcHr5OK/AXyI/JhpM4yl87rriM9jvkd0ahFI2zZsAXBG4LRtnPbLLdZ35jcEBERvWft27eHiYkJli9fjv79+2PUqFEICQnBxYsX0bdvX8TExKB3794AXt9Q+ujRo1i/fj26dOmCwMBAdOnSBQkJCcr6TE1NMWXMFJw5cQYXzlzA6IGj4VvFFxX8KgAAPqzzIc6fPo+tG7ci7EYYFs5ciKuXrmY63g7/64DgH4Nx4+oNtPm4Tc5+GLmAp6WIiCjPMMQZgzMjX758GDRoEObMmYNbt25Bo9Hgf//7H169eoXKlStj9+7dcHR0xOXLlzFq1CisWbNGmU1/2bJlKF++PL744gtlnhwLSwv0G9oPw/sOx8MHD1GlehXMXDRTeb86Detg4KiBmD1pNuLj4tGuazu07tQaVy5eyVS8NevVhIurC0qUKgHXgq5vX8DAqERE9B1Ebnr58iXs7e3x4sULzlZMRDmKl4LnnLi4ONy6dQvFihWDhYWFvsMxKFkZ+5Jd0VHRqOlTE7OXzkaTgCZZXj67Y24y2u5Z+f5mzw0REREBeD2Z7rMnz7BmyRrY2duhYbOG+g4pW5jcEBEREQDg/t37qFehHtwKuWHOsjnIly9vpgl5M2oiIiLKcYWLFsb1Z9f1HcY749VSREREZFSY3BAREZFRYXJDRERERoXJDRERERkVJjdERERkVJjcEBERkVHhpeBERJR3rKybu+/3yR+5+36UI9hzQ0RElIN69OgBlUqFTz/9NNVrAwcOhEqlQo8ePTK1rrCwMKhUKpw5c0anfPSA0fi0S+r102tMboiIiHJYkSJFsHHjRsTGxiplcXFx2LBhA4oWLarHyP4bmNwQERHlsEqVKqFIkSIIDg5WyoKDg1G0aFFUrFhRKQsJCUGtWrXg4OCAAgUKoGXLlrhx498bYxYrVgwAULFiRahUKtSrVw9fz/oawT8GY9/OffB29Ia3ozf+OvQXAGDOpDnwr+yPsoXKor5vfSyYvgCJiYnK+r6e9TUCagfgx7U/olaZWihbqCwG9xyMVy9eKXXOnTqH7q27o4pXFfgW9cXHLT7GhbMXdNrn7eiNTd9uQv+u/VG2UFk09GuIfTv35eyH+A6Y3BAREb0HvXr1wtq1a5XnQUFB6Nmzp06d6OhojBgxAidOnEBoaCjUajVat24NjUYDADh27BgAYN++fXjw4AGCg4PRZ1AfNG/dHHUa1sHRy0dx9PJRVKpaCQBgbWuNOUvnIOSvEEyYOQGbvt2EtcvW6rzn7Vu3sXPbTqzauApBm4Nw8dxFTBo56d+YoqLRulNrbNy1ET/v/RmeXp7o06EPol5F6axn8ezFaB7YHDsO7UC9RvXw2Sef4fmz5zn2+b0LDigmIiJ6D7p27Ypx48bh9u3bAIDDhw9j48aNOHDggFKnbdu2OssEBQXB2dkZFy9eRNmyZeHs7AwAKFCgANzc3AAAz9TPYGFhgYT4BDi7OussP3DkQOX/hYsWxq3rt7AjeAf6De2nlMfHxWPu8rlwK/R6fRNnT0Tfjn0xbto4OLs6o3qd6jrrnL5wOip6VsSxw8fQoGmDf2Pv3BYB7QIAAJ998RnWr1yPsyfPoq5/Lg/6TgOTGyIiovfA2dkZLVq0wLp16yAiaNGiBZycnHTqXLt2DRMnTsTff/+Nx48fKz024eHhKFu2bJbf87fg37B+5XqEh4UjJjoGSUlJsLG10alTqHAhJbEBgEpVKkGj0eDm9ZtwdnXG40ePMX/6fPx96G88iXwCjUaD2JhY3L97X2c9JcuUVP5vZW0FG1sbPH38NMsxvw9MboiIiN6TXr16YdCgQQCApUuXpno9ICAAHh4eWL16NQoVKgSNRoOyZcsiISEhy+916tgpjOg3AkPHDkXthrVha2eLHcE7sGbJmiytZ9SAUXj+9Dm+mPkFChUpBDNzM7Rv3F5n7A4AmJqa6jxXqVRKcqZvTG6IiIjek6ZNmyIhIQEqlQpNmjTRee3Jkye4cuUKVq9ejdq1awMADh06pFPHzMwMAJCcnKxTbmpmmqrs1LFTKFSkEAaMHKCU3btzL1VM9+/ex8MHD+Fa0BUAcPrEaajVahT3Lv56PX+fwuS5k1GvcT2l/rMnz7LadL1ickNERPSemJiY4NKlS8r/U3J0dESBAgWwatUqFCxYEOHh4Rg7dqxOHRcXF1haWiIkJASFCxeGhYUFAMC9iDsOhh7EzWs34ZDfAbZ2tvAs7okHdx9gx5YdKFepHA7sOYC9O/amisncwhyjB4zG2CljEfUqClPHTkXzwObK+B2P4h7Y9tM2lKtYDlGvojB74mxYWFq8j4/nvWFyQ0REeUcenDHYzs4uzXK1Wo2NGzdiyJAhKFu2LEqWLIlFixahXr16Sp18+fJh0aJFmDJlCiZOnIjatWtjzbY16Ni9I/4+/DdaN2iN6KhofP/r9/Bv7o+e/Xviy9FfIiEhAfUa1cPAUQOxaNYinff1KOaBxi0bo0/HPnj+7DnqN6mPL+d9qbw+c/FMTBg2AR/V+wgF3Qvisy8+w6wvZr2Xz+Z9UYmI6DuI3PTy5UvY29vjxYsX6e5wRETZ0XFHR32H8FabWm7SdwiZEhcXh1u3bqFYsWJKbwW9duP5jbdXSsfXs77Gvt/24deDv+ZgRKl5OXhla7mMtntWvr85zw0REREZFSY3REREZFSY3BAREf1HDB079L2fkjIETG6IiIjIqDC5ISIiIqPC5IaIiIiMCpMbIiIiMipMboiIiMioMLkhIiIio8LbLxARUZ6R27NA55UZnbW2bNiCaeOm4fTt0/oORa/Yc0NERJSDevToAZVKBZVKBVNTU7i6uqJRo0YICgqCRqPRd3jvzNvRG3t/S31DTkPC5IaIiCiHNW3aFA8ePEBYWBh27dqF+vXrY+jQoWjZsiWSkpKytc6EhIQcjlK/EhMT39u6mdwQERHlMHNzc7i5ucHd3R2VKlXC+PHjsX37duzatQvr1q0DAISHh+Ojjz6CjY0N7Ozs0KFDBzx8+FBZx+TJk+Hr64tvvvlG50aSL1+8xIRhE1Dtg2rwcfNBs+rN8HvI7zrv/2fon2hSrQnKFy6Pnu164lHEI53XN327CU2qNYGPmw8aV22M77/5XnktISEBk0dNRvVS1eHj5oM65epg+fzlAIC65esCAPp37Q9vR2/lOQDs3bkXreq2go+bD4oXL44vv/xSJ5FTqVRYvnw5WrVqBWtra0yfPj0HPum0ccwNERFRLmjQoAEqVKiA4OBg9OrVS0ls/vjjDyQlJWHgwIHo2LEjDhw4oCxz/fp1bNmyBcHBwTAxMYFGo0Gv9r0Q/Soa81bOQ9FiRXH98nWYmJgoy8TFxmHNkjX4asVXUKlV+OyTzzDri1mYv3o+AGD7T9vx9cyvMWnOJPiU98HFcxfx+dDPYWVthTYft8G3K79F6K5QLApahEKFC+HBvQd4cO8BACD492BUK1ENs5fORp2GdaA2ed1HcvzIcYz6dBQmzp6IytUrIykyCf369QMATJo0SYlt8uTJmDVrFhYuXIh8+d5fCsLkhoiIKJeUKlUK586dQ2hoKM6fP49bt26hSJEiAIBvv/0WZcqUwfHjx1GlShUAr3tRvv32Wzg7OwMA9uzZg3Mnz2H337tRzLsYAKCoZ1Gd90hMTMSU+VPgUcwDAPC/Pv/DkrlLlNe/nvU1xk0dhyYBTQAARTyK4PqV6/hx7Y9o83Eb3L97H55enqhcvTJUKhXci7oryxZwKgAAsLO3g7Ors1K+eM5ifDLsE7T5uA0AwMvXC1OnTsXo0aN1kpvOnTujZ8+eOfBJZozJDRERUS4REahUKly6dAlFihRREhsA8PHxgYODAy5duqQkNx4eHkpiAwBnzpyBWyE3JbFJi6WVpZLYAICLmwueRD4BAMRExyD8VjjGDRmHz4d9rtRJSkqCrZ0tAKBN5zbo0boHGlVphDoN66B+k/qo3aB2hu26dOESTv59Ujl9pYIKycnJiIuLQ0xMDKysrAAAlStXztTn9K6Y3BAREeWSS5cuoVix9BOTN1lbW+s8t7S0fOsyb57uUalUEBEAr5MbAJi+cDoqVK6gU097aqtshbLYf2Y//tz3Jw7/cRhDeg5BjXo1sHT90nTfMyY6BkPHDkXjgMYAAA+7f5Mr7VihtNrzvjC5ISIiygW///47zp8/j+HDh6Nw4cK4c+cO7ty5o/TeXLx4Ec+fP4ePj0+66yhfvjwi7kfg1vVbGfbepMfJxQmuBV1x5/YdfNTho3Tr2drZokWbFmjRpgWatmqKXu164fmz53BwdICpqSmSk5N16pcpXwY3r9+EZ3FPAICXg1eWY8tJTG6IiIhyWHx8PCIiIpCcnIyHDx8iJCQEM2fORMuWLdGtWzeo1WqUK1cOXbp0wcKFC5GUlIQBAwagbt26GZ66qVu3LqrUqIKB3QZi/PTx8CjugZtXbwIqoK5/3XSXS2nI2CGYOnYqbO1sUadhHSTEJ+D8mfN48fwFeg/sjTVL18DF1QU+5X2gVquxa/suOLs6w87eDgDgXtQdR/44Ar9qfjAzN4O9gz0GjR6Efp36oVDhQmjaqikSHBJw9uxZXLhwAdOmTcuRzzQrmNwQEVGekVdmDA4JCUHBggWRL18+ODo6okKFCli0aBG6d+8Otfr1FUbbt2/H4MGDUadOHajVajRt2hSLFy9+67qXfrsUs76YheF9hiMmJgYexTwwatKoTMfWsVtHWFpaYvXi1Zg1cRasrKzwgc8H6Nn/9UBfaxtrrFq0Crdv3oZarUb5SuXxzU/fKHGPmzoOMybMwE/f/gTXgq7449wfqNOwDlZtXIUlc5Zg1derYGZqhlKlSqFPnz7Z+PTenUq0J+L+I16+fAl7e3u8ePECdnZ2+g6HiIxIbt8aIDvySnIQFxeHW7du6czvQq/deH5D3yG8VXZPS2W03bPy/c1J/IiIiMioGERys3TpUnh6esLCwgLVqlXDsWPH0q27bt065Z4d2gezeiIiItLSe3KzadMmjBgxApMmTcKpU6dQoUIFNGnSBI8ePUp3GTs7Ozx48EB53L59OxcjJiIiIkOm9+Rm/vz56Nu3L3r27AkfHx+sWLECVlZWCAoKSncZlUoFNzc35eHq6pqLERMREZEh02tyk5CQgJMnT8Lf318pU6vV8Pf3x9GjR9NdLioqCh4eHihSpAg++ugj/PPPP+nWjY+Px8uXL3UeRESUN/zHrnn5z8up7a3X5Obx48dITk5O1fPi6uqKiIiINJcpWbIkgoKCsH37dnz//ffQaDSoUaMG7t69m2b9mTNnwt7eXnmknOqaiIgMk6mpKQAgJiZGz5FQbkpISAAAnRuBZkeem+emevXqqF69uvK8Ro0aKF26NFauXImpU6emqj9u3DiMGDFCef7y5UsmOEREBs7ExAQODg7K+EsrKyuoVCo9R2UYkhOS315Jz+Li4rK8jEajQWRkJKysrN75juF6TW6cnJxgYmKChw8f6pQ/fPgQbm5umVqHqakpKlasiOvXr6f5urm5OczNzd85ViIiyl3a74GMLjD5L4qMjdR3CG+VbJm9BEytVqNo0aLvnMjqNbkxMzODn58fQkNDERgYCOB15hYaGopBgwZlah3Jyck4f/48mjdv/h4jJSKi3KZSqVCwYEG4uLggMTFR3+EYjEX7F+k7hLdaUH9BtpYzMzNTZkJ+F3o/LTVixAh0794dlStXRtWqVbFw4UJER0ejZ8/X00B369YN7u7umDlzJgBgypQp+PDDD+Ht7Y3nz59j7ty5uH37tt6meCYiovfLxMTkncdgGJOnyU/1HcJb6Xv+Ob0nNx07dkRkZCQmTpyIiIgI+Pr6IiQkRBlkHB4erpPFPXv2DH379kVERAQcHR3h5+eHI0eOZHgXVSIiIvrv4L2liIhyCO8tRbnhv7qf8d5SRERE9J/F5IaIiIiMCpMbIiIiMipMboiIiMioMLkhIiIio8LkhoiIiIwKkxsiIiIyKkxuiIiIyKgwuSEiIiKjwuSGiIiIjAqTGyIiIjIqTG6IiIjIqDC5ISIiIqPC5IaIiIiMCpMbIiIiMipMboiIiMioMLkhIiIio8LkhoiIiIwKkxsiIiIyKkxuiIiIyKgwuSEiIiKjwuSGiIiIjAqTGyIiIjIqTG6IiIjIqDC5ISIiIqPC5IaIiIiMCpMbIiIiMipMboiIiMioMLkhIiIio8LkhoiIiIwKkxsiIiIyKkxuiIiIyKgwuSEiIiKjwuSGiIiIjAqTGyIiIjIqTG6IiIjIqDC5ISIiIqPC5IaIiIiMSj59B0BERESZt+lehL5DMHhMboiIyOh13NFR3yFkaFPLTfoOwajwtBQREREZFSY3REREZFSY3BAREZFRYXJDRERERoXJDRERERkVJjdERERkVJjcEBERkVFhckNERERGhckNERERGRWDSG6WLl0KT09PWFhYoFq1ajh27Fimltu4cSNUKhUCAwPfb4BERESUZ+g9udm0aRNGjBiBSZMm4dSpU6hQoQKaNGmCR48eZbhcWFgYRo4cidq1a+dSpERERJQX6D25mT9/Pvr27YuePXvCx8cHK1asgJWVFYKCgtJdJjk5GV26dMGXX36J4sWLZ7j++Ph4vHz5UudBRERExkuvyU1CQgJOnjwJf39/pUytVsPf3x9Hjx5Nd7kpU6bAxcUFvXv3fut7zJw5E/b29sqjSJEiORI7ERERGSa9JjePHz9GcnIyXF1ddcpdXV0REZH2Ld0PHTqENWvWYPXq1Zl6j3HjxuHFixfK486dO+8cNxERERmufPoOICtevXqF//3vf1i9ejWcnJwytYy5uTnMzc3fc2RERERkKPSa3Dg5OcHExAQPHz7UKX/48CHc3NxS1b9x4wbCwsIQEBCglGk0GgBAvnz5cOXKFXh5eb3foImIiMig6fW0lJmZGfz8/BAaGqqUaTQahIaGonr16qnqlypVCufPn8eZM2eUR6tWrVC/fn2cOXOG42mIiIhI/6elRowYge7du6Ny5cqoWrUqFi5ciOjoaPTs2RMA0K1bN7i7u2PmzJmwsLBA2bJldZZ3cHAAgFTlRERE9N+k9+SmY8eOiIyMxMSJExEREQFfX1+EhIQog4zDw8OhVuv9inUiIiLKI/Se3ADAoEGDMGjQoDRfO3DgQIbLrlu3LucDIiIiojyLXSJERERkVJjcEBERkVFhckNERERGhckNERERGRUmN0RERGRUmNwQERGRUWFyQ0REREaFyQ0REREZFSY3REREZFSY3BAREZFRyXZy8/z5c3zzzTcYN24cnj59CgA4deoU7t27l2PBEREREWVVtu4tde7cOfj7+8Pe3h5hYWHo27cv8ufPj+DgYISHh+Pbb7/N6TiJiIiIMiVbPTcjRoxAjx49cO3aNVhYWCjlzZs3x59//pljwRERERFlVbaSm+PHj+OTTz5JVe7u7o6IiIh3DoqIiIgou7KV3Jibm+Ply5epyq9evQpnZ+d3DoqIiIgou7KV3LRq1QpTpkxBYmIiAEClUiE8PBxjxoxB27ZtczRAIiIioqzI1oDiefPmoV27dnBxcUFsbCzq1q2LiIgIVK9eHdOnT8/pGImI8oRN93hansgQZCu5sbe3x969e3Ho0CGcO3cOUVFRqFSpEvz9/XM6PiIiIqIsyVZyo1WrVi3UqlUrp2IhIiIiemfZSm4WLVqUZrlKpYKFhQW8vb1Rp04dmJiYvFNwRERERFmVreRmwYIFiIyMRExMDBwdHQEAz549g5WVFWxsbPDo0SMUL14c+/fvR5EiRXI0YCIiIqKMZOtqqRkzZqBKlSq4du0anjx5gidPnuDq1auoVq0avv76a4SHh8PNzQ3Dhw/P6XiJiIiIMpStnpsJEyZgy5Yt8PLyUsq8vb3x1VdfoW3btrh58ybmzJnDy8KJiIgo12Wr5+bBgwdISkpKVZ6UlKTMUFyoUCG8evXq3aIjIiIiyqJsJTf169fHJ598gtOnTytlp0+fRv/+/dGgQQMAwPnz51GsWLGciZKIiIgok7KV3KxZswb58+eHn58fzM3NYW5ujsqVKyN//vxYs2YNAMDGxgbz5s3L0WCJiIiI3iZbY27c3Nywd+9eXL58GVevXgUAlCxZEiVLllTq1K9fP2ciJCIiIsqCd5rEr1SpUihVqlROxUJERET0zrKd3Ny9exe//PILwsPDkZCQoPPa/Pnz3zkwIiIiouzIVnITGhqKVq1aoXjx4rh8+TLKli2LsLAwiAgqVaqU0zES0Rs67uio7xDealPLTfoOgYj+o7I1oHjcuHEYOXIkzp8/DwsLC2zZsgV37txB3bp10b59+5yOkYiIiCjTspXcXLp0Cd26dQMA5MuXD7GxsbCxscGUKVMwe/bsHA2QiIiIKCuyldxYW1sr42wKFiyIGzduKK89fvw4ZyIjIiIiyoZsjbn58MMPcejQIZQuXRrNmzfHZ599hvPnzyM4OBgffvhhTsdIRERElGnZSm7mz5+PqKgoAMCXX36JqKgobNq0CSVKlOCVUkRERKRX2Upuihcvrvzf2toaK1asyLGAiIiIiN5FtsbcFC9eHE+ePElV/vz5c53Eh4iIiCi3ZSu5CQsLQ3Jycqry+Ph43Lt3752DIiIiIsquLJ2W+uWXX5T/7969G/b29srz5ORkhIaGwtPTM8eCIyIiIsqqLCU3gYGBAACVSoXu3bvrvGZqagpPT0/eCZyIiIj0KkvJjUajAQAUK1YMx48fh5OT03sJioiIiCi7snW11K1bt3I6DiIiIqIcke27goeGhiI0NBSPHj1SenS0goKC3jkwIiIiouzIVnLz5ZdfYsqUKahcuTIKFiwIlUqV03ERERERZUu2kpsVK1Zg3bp1+N///pfT8RARERG9k2zNc5OQkIAaNWrkdCxERERE7yxbyU2fPn2wYcOGnI6FiIiI6J1l67RUXFwcVq1ahX379qF8+fIwNTXVeZ03zyQiIiJ9yVbPzblz5+Dr6wu1Wo0LFy7g9OnTyuPMmTNZXt/SpUvh6ekJCwsLVKtWDceOHUu3bnBwMCpXrgwHBwdYW1vD19cX3333XXaaQUREREYoWz03+/fvz7EANm3ahBEjRmDFihWoVq0aFi5ciCZNmuDKlStwcXFJVT9//vz4/PPPUapUKZiZmWHHjh3o2bMnXFxc0KRJkxyLi4iIiPKmbM9zAwDXr1/HjRs3UKdOHVhaWkJEsnxZ+Pz589G3b1/07NkTwOsrsX777TcEBQVh7NixqerXq1dP5/nQoUOxfv16HDp0iMkNERGladO9CH2HQLkoW6elnjx5goYNG+KDDz5A8+bN8eDBAwBA79698dlnn2V6PQkJCTh58iT8/f3/DUithr+/P44ePfrW5UUEoaGhuHLlCurUqZNmnfj4eLx8+VLnQURERMYrW8nN8OHDYWpqivDwcFhZWSnlHTt2REhISKbX8/jxYyQnJ8PV1VWn3NXVFRER6WfZL168gI2NDczMzNCiRQssXrwYjRo1SrPuzJkzYW9vrzyKFCmS6fiIiIgo78nWaak9e/Zg9+7dKFy4sE55iRIlcPv27RwJLCO2trY4c+YMoqKiEBoaihEjRqB48eKpTlkBwLhx4zBixAjl+cuXL5ngEBERGbFsJTfR0dE6PTZaT58+hbm5eabX4+TkBBMTEzx8+FCn/OHDh3Bzc0t3ObVaDW9vbwCAr68vLl26hJkzZ6aZ3Jibm2cpJiIiIsrbspXc1K5dG99++y2mTp0KAFCpVNBoNJgzZw7q16+f6fWYmZnBz88PoaGhCAwMBABoNBqEhoZi0KBBmV6PRqNBfHx8ltpA/y0dd3TUdwhvtanlJn2HQERkFLKV3MyZMwcNGzbEiRMnkJCQgNGjR+Off/7B06dPcfjw4Syta8SIEejevTsqV66MqlWrYuHChYiOjlaunurWrRvc3d0xc+ZMAK/H0FSuXBleXl6Ij4/Hzp078d1332H58uXZaQoREREZmWwlN2XLlsXVq1exZMkS2NraIioqCm3atMHAgQNRsGDBLK2rY8eOiIyMxMSJExEREQFfX1+EhIQog4zDw8OhVv877jk6OhoDBgzA3bt3YWlpiVKlSuH7779Hx46G/8uciIiI3r9sz3Njb2+Pzz//PEeCGDRoULqnoQ4cOKDzfNq0aZg2bVqOvC8REREZn2xdCr527Vps3rw5VfnmzZuxfv36dw6KiIiIKLuyldzMnDkTTk5OqcpdXFwwY8aMdw6KiIiIKLuyldyEh4ejWLFiqco9PDwQHh7+zkERERERZVe2khsXFxecO3cuVfnZs2dRoECBdw6KiIiIKLuyldx8/PHHGDJkCPbv34/k5GQkJyfj999/x9ChQ9GpU6ecjpGIiIgo07J1tdTUqVMRFhaGhg0bIl++16vQaDTo1q0bx9wQERGRXmU5uRERREREYN26dZg2bRrOnDkDS0tLlCtXDh4eHu8jRiIiIqJMy1Zy4+3tjX/++QclSpRAiRIl3kdcRERERNmS5TE3arUaJUqUwJMnT95HPERERETvJFsDimfNmoVRo0bhwoULOR0PERER0TvJ1oDibt26ISYmBhUqVICZmRksLS11Xn/69GmOBEdERESUVdlKbhYuXJjDYRARERHljGwlN927d8/pOIiIiIhyRLbvCn7jxg2sXbsWN27cwNdffw0XFxfs2rULRYsWRZkyZXIyRiIiymUdd3TUdwhvtanlJn2HQAYqWwOK//jjD5QrVw5///03goODERUVBeD17RcmTZqUowESERERZUW2kpuxY8di2rRp2Lt3L8zMzJTyBg0a4K+//sqx4IiIiIiyKlvJzfnz59G6detU5S4uLnj8+PE7B0VERESUXdlKbhwcHPDgwYNU5adPn4a7u/s7B0VERESUXdlKbjp16oQxY8YgIiICKpUKGo0Ghw8fxsiRI9GtW7ecjpGIiIgo07KV3MyYMQOlS5dG0aJFERUVBR8fH9SpUwc1atTAhAkTcjpGIiIiokzL0qXgGo0Gc+fOxS+//IKEhAT873//Q9u2bREVFYWKFSvyJppERESkd1lKbqZPn47JkyfD398flpaW2LBhA0QEQUFB7ys+IiIioizJ0mmpb7/9FsuWLcPu3buxbds2/Prrr/jhhx+g0WjeV3xEREREWZKl5CY8PBzNmzdXnvv7+0OlUuH+/fs5HhgRERFRdmQpuUlKSoKFhYVOmampKRITE3M0KCIiIqLsytKYGxFBjx49YG5urpTFxcXh008/hbW1tVIWHByccxESERERZUGWkpu07gbetWvXHAuGiIiI6F1lKblZu3bt+4qDiIiIKEdkaxI/IiIiIkPF5IaIiIiMCpMbIiIiMipMboiIiMioMLkhIiIio8LkhoiIiIwKkxsiIiIyKkxuiIiIyKgwuSEiIiKjwuSGiIiIjAqTGyIiIjIqTG6IiIjIqDC5ISIiIqPC5IaIiIiMCpMbIiIiMipMboiIiMio5NN3AESUdZvuReg7BCIig8WeGyIiIjIqTG6IiIjIqDC5ISIiIqNiEMnN0qVL4enpCQsLC1SrVg3Hjh1Lt+7q1atRu3ZtODo6wtHREf7+/hnWJyIiov8WvSc3mzZtwogRIzBp0iScOnUKFSpUQJMmTfDo0aM06x84cAAff/wx9u/fj6NHj6JIkSJo3Lgx7t27l8uRExERkSHSe3Izf/589O3bFz179oSPjw9WrFgBKysrBAUFpVn/hx9+wIABA+Dr64tSpUrhm2++gUajQWhoaC5HTkRERIZIr5eCJyQk4OTJkxg3bpxSplar4e/vj6NHj2ZqHTExMUhMTET+/PnTfD0+Ph7x8fHK85cvX75b0ERE6QhImK7vEN7qV30HQJQL9Npz8/jxYyQnJ8PV1VWn3NXVFRERmZvHY8yYMShUqBD8/f3TfH3mzJmwt7dXHkWKFHnnuImIiMhw6f201LuYNWsWNm7ciK1bt8LCwiLNOuPGjcOLFy+Ux507d3I5SiIiIspNej0t5eTkBBMTEzx8+FCn/OHDh3Bzc8tw2a+++gqzZs3Cvn37UL58+XTrmZubw9zcPEfipbyLM/oSEf136LXnxszMDH5+fjqDgbWDg6tXr57ucnPmzMHUqVMREhKCypUr50aoRERElEfo/d5SI0aMQPfu3VG5cmVUrVoVCxcuRHR0NHr27AkA6NatG9zd3TFz5kwAwOzZszFx4kRs2LABnp6eytgcGxsb2NjY6K0dREREZBj0ntx07NgRkZGRmDhxIiIiIuDr64uQkBBlkHF4eDjU6n87mJYvX46EhAS0a9dOZz2TJk3C5MmTczN0IiIiMkB6T24AYNCgQRg0aFCarx04cEDneVhY2PsPiIiIiPKsPH21FBEREdGbmNwQERGRUWFyQ0REREbFIMbckGHquKOjvkN4q00tN+k7BCIiMjDsuSEiIiKjwuSGiIiIjApPSxERUSq8ZQnlZey5ISIiIqPC5IaIiIiMCpMbIiIiMipMboiIiMioMLkhIiIio8LkhoiIiIwKkxsiIiIyKkxuiIiIyKgwuSEiIiKjwhmKiUiveINWIspp7LkhIiIio8LkhoiIiIwKkxsiIiIyKkxuiIiIyKhwQHEO4+BIIiIi/WLPDRERERkVJjdERERkVJjcEBERkVFhckNERERGhckNERERGRVeLUVERJSHBCRM13cIb/Wrnt+fyQ0RERk9Q08I9J0MGBueliIiIiKjwuSGiIiIjAqTGyIiIjIqTG6IiIjIqDC5ISIiIqPC5IaIiIiMCpMbIiIiMipMboiIiMioMLkhIiIio8LkhoiIiIwKkxsiIiIyKkxuiIiIyKgwuSEiIiKjwuSGiIiIjAqTGyIiIjIq+fQdAFFuCEiYru8Q3upXfQdARGQk2HNDRERERoU9N5SuTfci9B0CERFRljG5ISK9YhJNRDmNp6WIiIjIqOi952bp0qWYO3cuIiIiUKFCBSxevBhVq1ZNs+4///yDiRMn4uTJk7h9+zYWLFiAYcOG5W7ARET/ARyET3mZXntuNm3ahBEjRmDSpEk4deoUKlSogCZNmuDRo0dp1o+JiUHx4sUxa9YsuLm55XK0RERElBfoNbmZP38++vbti549e8LHxwcrVqyAlZUVgoKC0qxfpUoVzJ07F506dYK5uXkuR0tERER5gd5OSyUkJODkyZMYN26cUqZWq+Hv74+jR4/m2PvEx8cjPj5eef7y5cscW3daODiSiIhIv/TWc/P48WMkJyfD1dVVp9zV1RURETmXIMycORP29vbKo0iRIjm2biIiIjI8eh9Q/L6NGzcOI0aMUJ6/fPmSCQ7leRzsSUSUPr0lN05OTjAxMcHDhw91yh8+fJijg4XNzc05PoeIiOg/RG+npczMzODn54fQ0FClTKPRIDQ0FNWrV9dXWERERJTH6fW01IgRI9C9e3dUrlwZVatWxcKFCxEdHY2ePXsCALp16wZ3d3fMnDkTwOtByBcvXlT+f+/ePZw5cwY2Njbw9vbWWzuIiIjIcOg1uenYsSMiIyMxceJEREREwNfXFyEhIcog4/DwcKjV/3Yu3b9/HxUrVlSef/XVV/jqq69Qt25dHDhwILfDJyIiIgOk9wHFgwYNwqBBg9J87c2ExdPTEyKSC1ERERFRXsV7SxEREZFRYXJDRERERoXJDRERERkVJjdERERkVJjcEBERkVFhckNERERGhckNERERGRUmN0RERGRUmNwQERGRUWFyQ0REREZF77dfIMMVkDBd3yG81a/6DoCIiAwOk5scxoSAiIhIv3haioiIiIwKkxsiIiIyKkxuiIiIyKgwuSEiIiKjwuSGiIiIjAqTGyIiIjIqTG6IiIjIqDC5ISIiIqPC5IaIiIiMCpMbIiIiMiq8/QIR6RVvWUJEOY09N0RERGRUmNwQERGRUWFyQ0REREaFyQ0REREZFSY3REREZFSY3BAREZFRYXJDRERERoXJDRERERkVJjdERERkVJjcEBERkVFhckNERERGhckNERERGRUmN0RERGRUmNwQERGRUWFyQ0REREaFyQ0REREZFSY3REREZFSY3BAREZFRYXJDRERERoXJDRERERkVJjdERERkVJjcEBERkVFhckNERERGhckNERERGRUmN0RERGRUDCK5Wbp0KTw9PWFhYYFq1arh2LFjGdbfvHkzSpUqBQsLC5QrVw47d+7MpUiJiIjI0Ok9udm0aRNGjBiBSZMm4dSpU6hQoQKaNGmCR48epVn/yJEj+Pjjj9G7d2+cPn0agYGBCAwMxIULF3I5ciIiIjJEek9u5s+fj759+6Jnz57w8fHBihUrYGVlhaCgoDTrf/3112jatClGjRqF0qVLY+rUqahUqRKWLFmSy5ETERGRIcqnzzdPSEjAyZMnMW7cOKVMrVbD398fR48eTXOZo0ePYsSIETplTZo0wbZt29KsHx8fj/j4eOX5ixcvAAAvX758x+jTlhgb/V7Wm5My23a2JXdlZZ80pvawLbnrv9gWwPDbY0xtAd7Pd6x2nSLy9sqiR/fu3RMAcuTIEZ3yUaNGSdWqVdNcxtTUVDZs2KBTtnTpUnFxcUmz/qRJkwQAH3zwwQcffPBhBI87d+68Nb/Qa89Nbhg3bpxOT49Go8HTp09RoEABqFQqPUb2di9fvkSRIkVw584d2NnZ6Tucd2ZM7WFbDJMxtQUwrvawLYYpL7VFRPDq1SsUKlTorXX1mtw4OTnBxMQEDx8+1Cl/+PAh3Nzc0lzGzc0tS/XNzc1hbm6uU+bg4JD9oPXAzs7O4He6rDCm9rAthsmY2gIYV3vYFsOUV9pib2+fqXp6HVBsZmYGPz8/hIaGKmUajQahoaGoXr16mstUr15dpz4A7N27N936RERE9N+i99NSI0aMQPfu3VG5cmVUrVoVCxcuRHR0NHr27AkA6NatG9zd3TFz5kwAwNChQ1G3bl3MmzcPLVq0wMaNG3HixAmsWrVKn80gIiIiA6H35KZjx46IjIzExIkTERERAV9fX4SEhMDV1RUAEB4eDrX63w6mGjVqYMOGDZgwYQLGjx+PEiVKYNu2bShbtqy+mvDemJubY9KkSalOq+VVxtQetsUwGVNbAONqD9timIypLSmpRDJzTRURERFR3qD3SfyIiIiIchKTGyIiIjIqTG6IiIjIqDC5ISIiIqPC5IaIiIiMCpMbIiIiMipMbki5w2pUVJSeI8k6bexXrlzB5cuX9RzNu9G25ezZszhx4oSeo3k32rY8ePAAz54903M0707bnrCwMNy9e1fP0bwbY9o2xtgWY9jHDAGTm/84EYFKpcKePXswaNAgnDp1St8hZZo29q1bt6JNmzbYvn07IiMj9R1WtmjbEhwcjDZt2uDnn39GRESEvsPKFm1btm/fjo4dO2Lfvn149eqVvsPKNm17tm3bhnbt2uG3337D06dP9R1WthjTtjHGthjDPpYWbeJ2584dnDt3DteuXUNcXNx7f1P6j/v555/F2tpapk6dKsePHxcREY1Go+eoMmfHjh1iaWkpixcvlqdPn+o7nHeyc+dOsbCwkBUrVsjLly/1Hc472bZtm1hbW8usWbMkPDxc3+G8s19++UUsLS1l/vz5EhERker1vPL3ImJc28aY2mJM+1hK2riDg4OlbNmy4unpKbVq1ZImTZrIkydP3tv7Mrn5jzt//rwUKlRIVq1apVMeFhamp4gyR6PRyLNnz6Rhw4Yyc+ZMERGJioqSW7duyfLly2Xz5s16jjBroqOjpV27djJx4kQREXn58qVcvHhRpk6dKsuWLZOoqCg9R5h5d+/elTJlysiiRYtERCQhIUFevXoloaGhcubMGT1Hl3UPHz6UqlWryrx580REJDY2Vh49eiSbN2+W33//Xc/RZY0xbRtjaosx7WNp+f3338Xa2lqWLl0qr169ktWrV4tKpZJly5a9t/fU+72lSL9u3ryJ/Pnzo2/fvoiNjcXGjRuxYcMGnDp1Cp06dcLSpUv1HWKaVCoVHBwcAAARERF4/Pgxpk+fjlOnTiE8PBx3797FtWvXMG7cOP0GmklWVlaIi4vD+fPn8fDhQ0yaNAlXrlzB48ePcfPmTVy6dAmLFi3Sd5iZYmJiAhsbGxQpUgRPnjzB8uXLsXfvXvzzzz8oUKAAZs2ahdatW+s7zEyztraGmZkZ1Go1njx5gnnz5uHQoUO4evUqoqKisHDhQvTp00ffYWaKMW0bY2qLMe1jadm7dy/69OmDAQMG4MGDB5gyZQoGDhyI/v37AwASEhJgZmaWo+/JMTf/cUWKFMGzZ8/w8ccfo27duti2bRtKlSqFVatWYfny5di5c6e+Q0xXXFwcqlWrhoMHD6JgwYK4ffs2evbsifPnz2PAgAE4evQokpOT9R1mmiSNW7q1bdsW169fh7u7Ox4/foxPPvkE58+fx5QpU3D69GnExsbqIdLsSUpKwqJFi1C8eHGcOnUKbdq0QUhICNzc3HD+/Hl9h5cliYmJKFSoEH766Se4u7vj8uXL6NKlC/7++280b948zw3+NqZtYyxtMbZ9THt8u3fvHkQET58+hY2NDe7evYsqVaqgWbNmyo+17du34/vvv4dGo8nRGNhz8x8i/z9o7fHjxzA1NYW9vT3KlCmDGTNm4KeffkK9evXQvXt3+Pj4ICkpCbVq1YKFhYW+wwbwb+ynT5/GxYsX4eHhgVq1amH8+PFo06YN7t69i48++kip/+zZM7i5uUGlUukx6rRp2/LXX3/h0KFD8PT0RJ06ddCjRw80atQIly9fRsOGDZX6V65cQeHChZEvn+H9uWrbcv36dURHRyN//vwoUqQINm7ciD179qBDhw74+OOPYW9vDwCwtbWFWm24v6m07bl48SIePHiAggULwsfHB19//TVOnDiB58+fo3379sodlEUENjY2eo46bca0bYyxLcawj6VHpVLh559/xty5c7F27VoUKlQIf/zxB77//ns0b94cK1euhIggLi4OO3bsgIuLC5KSknK29+a9nfAigxQcHCzVqlUTDw8PGTlypJw+fTrNel988YV4eHjInTt3cjfADGzdulUsLCykTJkyolKpZMCAAXL+/HmdOnfu3JHRo0dL/vz55Z9//tFTpG+3fft2MTc3l6pVq4qlpaW0a9dOdu/erVPn8uXLMmrUKHFwcJBz587pKdK327Jli+TPn1+KFy8uNjY2sn79+lR1YmJiZOzYseLs7CxXrlzRQ5SZFxwcLNbW1uLt7S0qlUqmTJkijx8/1qnz5MkTGTt2rDg5OcmlS5f0FOnbGdO2Maa2GNM+lpJ28PDz58+lXr16ynioR48eSYkSJcTJyUlu3LghIiLx8fEyfvx4cXd3fy/bismNEXtzdP3p06elQIECMmPGDJkwYYJUrlxZAgICdL5Ud+3aJT169BBnZ2c5depUboesIzk5WWnDnTt3pFmzZrJq1SqJjY2Vn376SUqWLCk9evSQkydPiojIb7/9Jt26dZMPPvgg3aRNX5KSknTa0r17d2UQ9969e8Xf31+aNWsmu3btEhGRgwcPSp8+fcTHx8fgBkdqNBqlLeHh4fLBBx/IihUr5OjRozJhwgRRq9Xy9ddfS2xsrIiIrF+/Xtq0aSNFixbV+z6Vljf3s6pVq8rKlSvl3r17smzZMrG2tpYxY8Yoif5PP/0knTp1kuLFixtce4xp2xhTW4xpH3ubPXv2SKdOnaR169Y6F6ZcuHBBXFxcpFKlSlKlShVp1arVe/2eYXLzH3H16lWZOnWqfPnll0rZ77//Lo0aNZIWLVrInj17REQkKChI+vbtKxcvXtRXqMrl6Fp//PGHjBgxQtq0aSORkZFK+datW6V06dLSo0cPuXTpkkRHR8vWrVvl9u3buR1yurSfq9bff/8t3bp1k4YNGyq/YEREDhw4II0bN5ZmzZrJgQMHRETkzz//lLt37+ZqvBl587LNffv2yYoVK2Tw4ME65bNnzxa1Wq38artz545MnTpVrl27lmuxZsb169d1nu/Zs0emTZsmvXr1kpiYGKU8KChIbG1tZcyYMfLs2TN5/PixrFy5Um7evJnbIafLmLaNMbXFmPaxzNqzZ4+YmZmJmZmZkrhoE7tHjx7JvHnzZMSIEbJixYpUn09OYnJjhObPny/z588Xkde/GO7duydVqlSRAgUKyNChQ3XqhoaGir+/v7Rq1Ur2798vIqLzR5fbVq9eLc2aNdOZs2bx4sViYmIiTk5OqbL8bdu2Sfny5aV169Zy+fLl3A43Q8HBwVKvXj15+PChUrZhwwYpXry42Nrapkp8/vjjD2nevLl8+OGH8scff+R2uBkaPXq0dO7cWRISEpSy/v37i0qlkkqVKsmLFy906s+ePVvMzc1lxowZIvJ6PzQks2bNktatW+tcYv/ll1+KSqUSDw+PVHOmBAUFSf78+aV///7y/Pnz3A43Q8a0bYypLca0j2XVgQMHxNbWVjp37iyvXr0Skde917mJyY2RefHihXz22WepzmH+8MMPUrZsWalSpYqcOHFC57X9+/dLlSpVpEOHDhIdHZ2b4aYSHh6u9Gik7LX47rvvxNnZWQYPHpzq18ymTZukWrVqcu/evVyN9W0ePXqktCHlL5QdO3ZI+fLlpU2bNvL333/rLLNv3z5p06aNQfU+ibyeD0l7eixl8jthwgQxMTGRjRs3plpm0qRJkj9/foOcXPHatWvKOIaUPQWLFi0SlUols2bNSjWR4rJly6Rw4cI6yaohMKZtY0xtMaZ9LD3aHpkHDx7IlStXJCoqSuLi4kREJCQkRCwtLaVPnz4621K7zPuelJDJjRFKTEwUEZEjR47ItGnTlPKffvpJKlWqJN27d1fGqWj9+eefev9CTfmr68SJE1K7dm2dyQWXL18u7u7uMmrUqFQJjvbXgaFI+Yd78eJFqVy5snz++edK2U8//SRVqlSRLl26yLFjx3SW1WfPWVpS/uLavXu3fPTRRzrJ2sCBA8XS0lKCg4NTLfvmIElDkHI/+/PPP6Vx48Y6vWjTpk0TlUolCxYsSLVfGdovamPaNsbUFmPax9KTcubhkiVLiru7u3h6esonn3yiDGvQJjiffPJJrv9wZnJjhDQajcTHx8uAAQOkTJkyMmvWLOW1H374QSpXrizdunUz6IFqYWFh0qBBA/H395d169Yp5cuWLZNChQrJ2LFjdQ58hjw1eUREhPTr109q1qwpU6ZMUco3bdokVapUke7du8uRI0f0GGHmnT17VlQqlbRv314nwdR+8Wzbtk2P0WXd5cuXxcPDQ1q2bCmhoaFK+dSpU0WlUsnXX3+tcyrEkPczY9o2xtQWY9rH3rR//36xtLSUefPmybFjx2T27NnSoEEDadiwodJrtXfvXlGpVDJkyJBcjY3JjRELDw+XIUOGSLVq1ZRz0iKvE5wPP/xQWrduLWfPntVjhP9K6w86LCxMAgICpG7durJ27VqlfOXKlWJhYSFffPGF0ktlSNJqy4MHD5RtkXJQ988//yze3t7St29f5aoPQ3fu3DmxsbGRwMBAnS+eoUOHikqlkl9//VWP0WXdlStXpGzZstK0aVOdL5+ZM2cqU8TnlS8cY9o2xtQWY9rHRP69ku2zzz6TTp066bz2yy+/SN26dWXYsGFKb9z+/ftz/XJ2JjdGIuW5z5cvXypdm+Hh4TJw4MBUCc6aNWukfv36BjFORRv7kSNHZOnSpbJjxw5lrMrNmzfTTHCCgoLk6tWr+gg3Q9q2HDhwQKZOnSorVqxQrt64f/9+mgnOtm3bDPKqCG1b/vnnH9m9e7fcv39fOWV25syZNL94Ro0aZbBzcmjbc/LkSdmwYYOcOnVKGQtx6dKlNL98vvrqK71eOZgeY9o2xtgWY9jHMmPw4MFSp04dnQHgIiITJ04ULy8viY+P11NkTG6MytatW8Xb21sqVKggrVu3Vv5gUiY4KU9RvXnlgT5t375dbGxspGTJkuLl5SUtWrSQCxcuiMi/CU7Dhg1l+fLleo707Xbs2CHW1tZSuXJlKVWqlJQpU0YZxK1NcGrWrCljxozRc6Rvp504zc3NTYoVKybz5s2T+/fvi8i/Xzzt2rUzqMtvMxIcHCx2dnZSpEgR8fT0lEGDBikD2LVfPi1btpSQkBA9R/p2xrRtjKktxrSPpUebxH311VdSrFgxOXXqlE7P0549e+SDDz5QtqE+MLkxElevXhV3d3dZuHChTJ8+XVq2bClFixZVTjtpT1GVLFlSuUzcELpBtd2bffv2lfXr10tSUpJs3LhRmjZtKh9++KGS4Ny6dUvq1q0rLVu2NPgBd+PGjZM1a9aIiMjhw4elXbt24urqqszfc//+fenVq5f4+/vrzNtjSJKTk+XJkydSr149Wb16tdy7d0+GDBkiFStWlPHjxys9ftqxEV27djXIU4RaycnJEhMTI23btpX169fLq1evZO7cuVK7dm35+OOPlfFbly9fFnd3d2nXrp3erxxMjzFtG2Nri7HsY2/SfldcuXJFLl26pNNr7ufnJ+XKlZPjx48r7RkyZIhUrlw51dVguYnJTR6WMjm5cuWKfPbZZ8rz06dPS6tWrcTd3V1JcG7duiWjRo2SW7du5XaoqWhjj4iIkOfPn0ubNm3k6NGjyus7d+5MleCEhYWlmhvCEGjbcvv2bblz5460a9dOdu7cqbx+/vx5adu2rbi6uio9OA8ePJCIiAi9xJsRbVtiY2MlMTFRevfuLQ8ePFBenzBhQqovnvPnzxvkKQKRf9vz7NkziYuLk06dOunMh7Ry5UqpVauWfPzxx8qv66tXr+pMsGgojGnbGGNbjGEfy8iWLVvExcVFChcuLJUqVZIlS5aIyOurOytXrizFihWTChUqSNOmTcXe3l7vs8QzucmjtH9Q+/btky+++EJ69eolH330kU6dM2fOSKtWrcTDw0O59NuQfvUEBwdL0aJFpVKlSlKwYEGd5EbkdYLTsmVLKVWqlMGfk96yZYs4OzuLr6+v2Nvby48//qjz+vnz56VDhw6SL18+g75KTeT1KcI6deqIn5+fVKhQQR49eqTz+oQJE6Rq1aoybNgwnS8kQ7V161YpW7as+Pn5iZeXV6ovlZUrV0r9+vWlZcuWBpH4Z8SYto0xtcWY9rG0PHr0SCpWrCjr1q2T3377TcaOHSuOjo4yd+5cpc7KlStl0qRJMm3aNIMYD8nkJg/77bffxNLSUqpWrSrly5cXa2tr+euvv3TqnD17VurVqyelS5eW+Ph4vc/gqU3Krl+/Lk5OTjJv3jyZOnWqfPjhh+Lm5pbqnPq2bdukXbt2BnlA0Lbl5s2b4unpKUuWLJGgoCBp06ZNmtvi9OnT0q1bN4O8oZ+2LadPnxYzMzMZPXq0BAYGSsGCBaVTp06pvlyGDx8udevWTfWFZCi07bl8+bLY2NjIlClTpH///lKiRAmpVq1aqjmdFi5cKM2aNTOo211oGdO2Mca2GMM+lhZt+5KSkuTRo0fSq1cvZbbliIgImTp1qtjZ2cns2bP1GWa6mNzkUc+ePZMJEybIN998IyKvT0u1bdtWHB0dU/UMnD9/3qBO5+zfv182bdokEyZMUMpOnjwpTZs2FQ8Pj1QJTsrpyw1NaGiorFy5UoYNG6aURURESOfOncXGxiZVgqPPqwfe5tSpU7JixQqdq+oWLlwotWrVkp49e6aaNdUQv3BSOnTokGzcuFGmTp2qlG3dulUaNGgg9erVS/U38ezZs1yOMPOMadsYU1uMaR9LSZvY7NixQ1q3bi2dO3eWihUr6hyLtQlOgQIFdNpvCGM5RZjc5Elnz54Va2trKVu2rGzfvl0pv3//vrRt21YcHBz0fr4zPXFxcRIQECAqlUpatmyp85o2wfH29ja4+0Slp3fv3qJSqaRq1ao6g+cePnwoH3/8sTg6OsqhQ4f0GGHm3L9/X+rVqyfW1tY6SaeIyIIFC6RGjRrSt29fgz89oPX8+XOpX7++qFQq6dOnj85rwcHBUr9+ffH39zfIHsE3GdO2Maa2GNM+lpbff/9drK2tpUOHDhIQECBqtVpnCguR18e58ePHS9GiReXJkycGk9iIMLnJkzQajfTp00eZ/CmlBw8eSKdOnUSlUhnMBH1vunXrlvTo0UPs7OxSJWGnTp2S6tWrS/ny5SUhIcGg/ljSkpycLKNHjxZTU1PZunWrzmsPHz6Uli1bSuHChQ1+gr7k5GRZv369VK5cWXx8fFL9wly0aJH4+PjIoEGD9H5qM7O0NyJ1dXVNNXh727ZtUqlSJQkICDCocWhpMaZtY0xtETGefexNYWFhEhQUpFxZ+/TpU1myZImYmJjo9LiJvO5ZM8SrPpnc5FFJSUnSu3fvNO8ufe/ePenevbtB937cvXtXAgICxMnJSf755x+d186cOWNQp9Eyo2/fvmJjYyO//fabTnnKm2caqpQ3svvpp5+katWq0qxZs1T361m+fHme+BWaMiH+66+/pEaNGlKyZMlUvQE7duyQsLCw3A4vS4xp2xhjW0Ty/j72pvv374tKpRIrKyudedFiY2NlyZIlolarDXacTUpMbgxUZnosNBqNdO/eXezs7FIlOLl9e3mtlL+2tHPYpOfu3bvSsmVLcXJyMsirodKKPaNfk3369BEbGxvZtWvX+wwrW972K1jb1uTkZNmwYYPUrFlTmjVrpnM347zq6NGjUqdOHSlVqpRBXn5vTNvGmNqSFYa+j2XVxo0bxdbWVv73v//p3Mg3Li5Oli1bJiqVSunVMVRMbgxMegeH9Mq1CU6BAgVS9RrkNu2B6+rVq3L48GGdsvTcvXtXAgMDRaVSGVRPk/bzfvXqlTx79kyn+zyjNn366aeiUqlSJZv6pG3L3bt3M7xEM+UXz48//ih169aVGjVqGNwXj7Y9SUlJqaZ9T5nUp9xOR44ckQYNGoiLi0uqAav6ZEzbxhjbYgz7WEYyOl32448/Sr58+WT8+PE6n0FsbKysXr3aIH+QpsTkxoBo/6CuX78uM2bMkGnTpklQUNBbl9NoNNKmTRspWrSo3me8jIyMFJVKJSqVSunBeFsPTnh4uHTq1MlgLpHWbodz585JgwYNpHz58lK3bl355JNPUh3o0jJ06FCDm2zs7t27YmNjI3Xr1s1wsHnKL561a9dK06ZNDeoUoXbbXLhwQbp16ya1atWSQYMGKTNCi6T/5fPnn39KixYtdO4mbwiMZduIGEdbjHEfe9OKFSuUyREz6uX/4YcfxMTERMaPH5/nxg0xuTEwFy5cEHt7e2nUqJFUqFBBnJ2dpW7dunLx4sUMEwSNRmMQN8GMj4+XqlWrSocOHcTBwSHTd+7V12m09Fy5ckWcnJxk9OjREhwcLCtXrhRXV1dp1KhRqjFCecGJEyfEwcFBAgMDpVWrVpn+4jGk+49pXbx4URwcHKR///4ya9Ys6dSpkxQpUkQGDx6s1ElvfzLEgd3GtG2MpS3Gto+ldO/ePSlfvrx4e3srp9DeluBYWlrK0KFD81SCw+TGgMTGxkrjxo2lb9++IiLy8uVL+eeff6RcuXLi4+Oj3JvIUGk0GklMTBQ/Pz9ZvXq1DBw4UOzt7ZVTNMeOHZO4uDg9R5k5kydPlm7duinPk5OT5ZNPPhGVSiU1atRQrg7IC1d0iLy+cqtatWoyf/58qV+/vgQEBCi9S2nd/8VQr1KLi4uTbt26yZAhQ5SyyMhIqVy5sqhUKunSpYtSbqhteJOxbBsR42iLMe5jKSUnJ8vhw4elbt26OoOgM0pwgoKCxMnJyaDnHHqTGmQwzM3NERMTg/LlywMAbGxs4OPjg2PHjsHc3Bz9+vXDs2fPAAAajUafoaYpOTkZ+fLlQ6VKleDh4YHJkyejc+fO6NChA2rUqIFJkyYhJiZG32FmysWLFxEZGak8V6vVqFixIoYMGYK7d++ib9++SrmhS0pKgqmpKUxMTNChQwcMHz4ccXFx+OKLL1C3bl0MHz4cSUlJOsuoVCo9RZsxc3NzhIeHw8bGBgCQmJgIJycnNG7cGF26dMGff/6J6dOnAzDcNqRkTNvGWNpibPtYSsnJyVCr1ahRowamTJmCQoUKoXnz5nj8+DFMTEyQnJyc5nI9e/bEjRs34OzsnMsRZ5/hH5n/Q1QqFWJjY/H3338rzxMSEmBhYYHQ0FA8e/YMw4YNA2CYX6r58uUDADg5OWHHjh1wcnLC1KlT4erqimPHjqFRo0ZwdHSEiOg50rerWbMmoqOjsWfPHgDA9evXMWrUKJQrVw5LlizBhQsXcPnyZT1HmTn58uWDo6MjPD09cezYMQQEBGDYsGE4evQojh8/jnr16inbzpBpNBpERUXBxsYGjx49wuPHj2Fqaorbt29j7dq1qFevHgICAnDgwAEkJibqO9w0afd97b95dduk/BvW/tAyhrYAyPP7WEa03xu7d+/G0qVLkZCQgDNnzqBhw4Z4+PBhhgmOnZ1dbob6zgzvG/I/JK0DxLhx4/DHH39g2bJlAAAzMzMkJCTA0dERkyZNwokTJ3Dv3j29xKuVstcorR6kQoUK4enTpwCAUaNG4dmzZwgMDMTMmTOxdetWg/q1k15b6tWrBysrKwwaNAjVq1dH+fLl0bVrV/Tu3RsVK1bEvXv3cPfuXX2EnK70evO0v5bVajVOnjwJAPj555+VXsJt27Yp5Ybkzfao1WrY2NigX79+2LhxIzp06ID//e9/8PHxQUBAAHr37o3u3bvj0KFDuH37tp6iTpu2LW9+kWq/IPPSttFoNFCpVHj+/DmA17FrNBqlbXmxLeHh4cpx1cbGBv37989z+1hmqFQq7N+/H82bN0edOnUwe/ZsLFq0CCYmJqhfv/5bE5y8hMmNHmgPdNodSNtVCAA1atRAy5YtsX79egQFBQF4neAAQIECBRAbGwsTExM9RP2aRqOBWq3GnTt3EB4erhzYUmrcuDFEBAEBAdi1axf27duHBQsWoFGjRhg+fDiioqIMovcmrbYkJydDRFC+fHksWrQIkydPRmBgINavX68knK9evUKZMmVQqFAhPbfgX9q2hIWF4ZtvvsHkyZMREhKCuLg45dfyRx99BHNzc/Tu3RshISE4fPgwxowZg7CwMMyfPx/x8fF6bsW/0mrPzp07ERMTg4CAAGzduhWlS5eGubk55s2bh5UrVwIAHj9+jBIlShhU97m2LdeuXcPo0aPRo0cPTJw4EVFRUTA1NQWQt7aNWq3GpUuX4OLigokTJypl2iQ6r7RFu13OnDkDT09PHD16FMDrBLR58+bYvn17ntnHsuLAgQNo1aoVBg4ciJo1a2LQoEGYP38+TE1N0bhxYzx58sQ4Ehz9DPX570p5mWHlypWVuQJSjkLXXoLo6+ur3JDs+fPnMn78ePHz85OnT5/mfuDyb+yXL1+WAgUKSLly5ZT5LFIOrL148aJYWFiIh4eHnDx5Uim/deuW3L9/P3eDTkdGbUlMTMxwoOCoUaPEx8fHYAbXpbx0vWDBgtK8eXMpXLiwVK1aVaZOnaq8vmvXLlGpVFKwYEE5ceKEsvzWrVsNahbVjNrz5ZdfZnjFxogRI6Ru3boGc/VNyrY4OTlJly5dpEWLFlKtWjWZOHGiUm/nzp15YttorV+/Xuzs7MTZ2VnGjh2r85p2P3NzczPYtmi3y5kzZ8TGxkZGjhyZZr20Btka2j6WVSNHjhRvb+9U5XPnzhWVSiVFixbNM/P0ZITJjR7cunVLSpQoIWZmZlKsWLE0E5wrV67Il19+KQ4ODlK4cGHx9fUVZ2fnVHf8zm3379+X+vXrS61ataRx48ZSo0YNnQRHmxQcOXLEYO9tpZVRW9I6qP3111/KzTAN7cakt27dEm9vb2U+itjYWBk+fLjUrFlT507k69evV2I35Cs9MmqP9oq7lPGfPXtW+vXrJ3Z2dnLmzBl9hZ2ma9euiaenp3z++eciIpKQkCBdu3aVcePGici/+9qaNWvyxLYREdm0aZN8+OGHsmLFCsmfP7/SFpHXE19u3LjR4Nty4cIFsbKyUmJPTk6WP/74Q7799lv5448/lDnDtMdlQ97HsmLnzp3i6+sr3377rc7VqyEhIVK/fn1p27atXLt2TY8R5gwmN7ksNjZWRo8eLW3btpXt27crN1ZMmeBoDwbx8fFy584dWbFihWzZskVu3rypz9BFRGTfvn3SqFEj+fPPP+W3335LlRTkhZtdar2tLW8mOPfu3ZPBgwfL+fPn9RFuupKSkmTevHnSpk0biYyMVOK+du2a5M+f3+CTzDe9rT3nzp3TqR8bGyshISHSvn17g2trcnKyzJs3T7p06SIvX75U/jY+/fRTadCggbRq1Uo6deqk/FLOK387d+7ckbZt20pERITMmTNHHB0dZcaMGTJy5EhZtWqVwc/1IvJ6sk2VSiW3b98WEZGGDRtK5cqVxdLSUkqXLi116tSR58+fi4hIVFSUwe5j6dHuSxcuXJA///xT+WEcFRUlrVu3ltq1a8vatWuViUnHjh0r3bp1k1evXukt5pzE5EYPNm/eLN9//72IvO6uTivBETHcA92ff/6p/P/XX39VkgLtDMN5Ze4Xkcy1JeV2MMS2JScnS1BQkKxcuVKn/N69e+Lg4CB///13qmUMbdLElLLTnujoaL3Pzp2eu3fv6nwhzpo1S0xMTOSLL76QL774QmrWrCnFihVLcx4YQ3Xnzh0pVqyYXLp0SaKjo+Wbb74RS0tLUalUmZr51hDExMRIYGCguLq6SpUqVeSjjz6SM2fOyMOHD2Xbtm1StWpVad26tfLlHx8fb7D7WHq2bNkitra24u3tLWq1WqZMmSIiIi9evJB27dpJhQoVpGDBglKvXj2xsrJK9cMhL2Nyk4vSS1ZOnz6dKsGJiYmRCxcu6JxS0Lf0vth37NghjRs3lpo1ayq9HpMmTTLoPxRjaouI7qyo2rYlJiZK6dKldU5lbt++PU8coI2tPVqPHj2SRo0aSUhIiFL2999/i5OTk+zevVuPkWWe9jjWsmVL5XjVvn17sbe3F3t7e/nyyy/1GV6WxMXFSbt27aR06dKpTsXMmzdPSpQoIXfv3tVTdNmj3T4PHz4UX19fWbNmjVy6dEnWrFkjarVahg0bJiKvv2MOHTokU6ZMkfnz5xvM7W9yCpMbPUr5BZsywTl79qwMGTJESpcubdCD1lLGr00KateuLV26dBGVSpWnblNgLG1JmUDHxMSIp6en/PXXXyIi8vnnn4uTk5PSDZ8XGFN7tG2JiooSkX/3uWPHjkmZMmUM7nTn2/To0UNWrVol3bt3l4IFC8r+/ftl6dKlolKpZNq0afoOL9OioqLk999/V8afaLfLpk2bpGTJksps5HnJ7t275csvv5S+ffvq3NV78+bNolarZfjw4Xni1OG7YHJjQM6cOSOtWrUSlUoltra2aXa/69ubXc0pB0Fv375dHBwcDHLAbVqMuS0ajUYiIyPFwcFBjh8/LjNnzhRzc3Odq1cMmTG15822vPl83LhxUqNGDXn8+HFuhpUtKWOfMWOGqFQq8fb2VnrTnjx5IitWrJDLly/rK8RMe9tps8GDB0vz5s3zVM+g1rx580SlUkmxYsWU+0dpk+vNmzeLhYWFfPrppwb94/ldMbkxIAkJCdK6dWtxdHTUW0/Bm6fOUj7XHgxu374t8+bNS1Vn8ODBYmVlJRcuXMiFSN9O+wssrdOBea0tKdvw5l3WtW25e/eurF+/XimPiYkRPz8/8ff3FwsLC4O6N5l226T1BZPX2pOdbSMiEhYWJqNGjRJHR0eDGaSame1y584dCQ4OlmfPnkm7du1SJZiGMi4tO/uYyOsr9UaNGpXm4PW8ZNWqVaJSqWTu3LmpjoHff/+9FChQwCgu+U4Pk5v3SLtDZWZgcFJSkixYsEAsLS31drm39mDw/PlzefLkiZLxpxQWFibu7u4yYMAAnfLDhw9LyZIldea10SftZ379+nX59ttvlaseRP5tZ15pizbeV69eSVxcnM6vLW07tW1JeUnu8+fPxc3NTWxtbQ3q0tWUcz2NHj1a5+qMN7eNobcnu9vm77//lmHDhomPj4/BtSUz20U7L4yhDhrO7j529OhR+fTTT8XLyytP9NiK/LufJSQkKIOftebNmydqtVoWLlyYqn5eGsCeHUxu3hPtDrRv3z6ZP3++PHny5K3LbNu2TW/duSknG6tQoYKULVtWrK2tZeTIkXLkyBEREXn27JkUK1ZMPvnkkzQTtsy0MTc9ffpUPDw8xNPTU1avXq3zxfPo0SPx9vY2+Lak3C41a9aUSpUqiYeHhyxcuFCZGuDRo0fi7Owsn376qU5bEhIS5Msvv1QGRhuClJOnvTk2Qxv7/fv3xcXFxeDb8y7bJjY2Vg4ePKhcWaRv77JdDM27tCU2Nlb27dsnd+7cyd2gs0kb++7du6Vdu3ZSv359GThwoDx+/Fh57auvvhK1Wi2LFy/WZ6i5jsnNe6DdqX7++WdxdHSUYcOGyfXr1/Uc1duFh4eLq6urDB8+XPbs2SOrV6+WMmXKSJMmTWTz5s0SGxsra9asSfVrzVC6od/07Nkz8fPzk3LlykmJEiVk1apVyuzO9+/flzVr1qT6pWOIbbl165YUKFBAhg4dKuvWrZMpU6aIo6OjdO7cWQ4dOiTPnj2TOXPm6GyXjE7J6Ys2prNnz4qVlZWMGTNG53XtmKc7d+7IrFmzdLaFIbZHJHvbxtDa8C7bxdC8S1sMbbtk1rZt28TOzk769+8vq1evFmdnZ2nTpo2cPn1aadP8+fNFpVLJihUr9Bxt7mFy854cOXJE7O3tZd26dTrl8fHxWTpdlZs2bdokFStW1Jm18siRIxIYGCg1a9aUvXv36jG6rEk5Wdo///wjgwcPFi8vL1m7dq2IiBw8eFCP0WXN8uXLpVq1ajplISEh4ufnJ4GBgcrluHlBWFiYFChQQNq1a6eUffnll9K5c2dp2rSpbN68WY/RZZ2xbBtj2i7G1Ja3uXjxopQqVUqWLl0qIq9PjxYsWFDMzMzkww8/lDNnzijHwsWLF+eZ/TEn8MaZ78k///yD2rVro3v37nj16hW2bduGNm3aIDAwEIsWLUJiYqJB3R0bAMzNzfHo0SPcuXMHwOsbyFWvXh0TJkyAra0tVq1ahfv37+s5yszRfrbx8fHYu3cvFi1apNwFt3r16ujQoQNevHhhEDfwfBuVSoWXL1/i2bNnEBFoNBo0adIEs2fPxuXLl7Fs2TIkJibmibbcvn0bDg4OcHR0xMGDB1GvXj388ccfSEpKgoODAzp06IBp06YBSH0HbUNkLNvGmLaLMbXlbV69eoXOnTvj008/xb1791C+fHm0b98e169fx/Xr1zFp0iScOHECIoJBgwahdOnS+g459+grqzI2b/bCLFy4UFQqlfzwww/SoEEDad68uXz88cfy8ccfi4+Pj0Heu+P48eNSoEAB+eabb0RE9xTN77//Lubm5rJ161Y9RZc12thnzJghPXv2VMq9vb3FzMxMJkyYkGfmeQgJCRFTU1Ol5yzlqTTtvBWHDh3SV3hZtnPnTqlataq4urpKixYt5OHDh8r2WrlypZiYmChz2Ri6Xbt2Gc22+e233/LkdknrNJkx7WMZiYuLk3/++Uc0Go107txZunbtqsxr06BBA1GpVNK4cWOd3vj/CiY3OUCb2ISGhsqCBQuU8m7duomPj4/07t1bOcDdvXtXSpcurddLP988GKRMzCZOnCgWFhYSGhoqIrpXQ9SpU0eGDh2aKzFm1tvO///xxx/Spk0bEXm9PQoVKiQfffSRlClTRhYtWmRQVwxk1JbevXuLo6Oj0q2ccubq8uXLy8yZM997fFmVUXt27twpbdu21bn9hcjry70LFy6s83dkCLRtiY+PVybh08pr2yajtuTV7XL//v1U02fs2rUrT7XlXcTHx0vt2rV12jR8+HA5evRonhjv+T4wuXlHKQcPFyhQQBnjofXgwQOd+uPGjZPy5cvrbX4B7cHg7t27cuLEiVRzQcTFxUmPHj3EyspKfv31V53Ep379+gZ1oE6vLSmdPHlSypYtKy1atBA3Nze5dOmSiLyeLt7Pz0+ePXuWmyGnSxv7tWvXZOTIkdKuXTuZOHGicjn+tWvXpHnz5uLs7Kxz6XBSUpJUrVpVli9frpe405Nee1L+Pdy4cUPpPdPuZ3fu3JFKlSrJjh07cj/odGjbcvHiRenUqZNUqVJFPv74Yzl69KiIvJ5uIK9sm7e1RSTvbBetu3fvSoECBaR169ap5j66efNmnmpLShnNOfam2NhYKV26tAQGBsqePXtk5MiR4uLiIo8ePXrfYRosJjc54PDhw2Jra6sMVk3Lli1bZPDgweLo6Ki3eWy0fxyXLl0SCwsLKVeunJw4cSLVH83Tp09lwIABki9fPhkyZIhMmTJFhg4dKnZ2dgYz8+jb2pJy7ofGjRtL6dKlU81bc//+/dwNOh3aL5zz58+Lm5ubtG7dWrp27Sq2trby8ccfK/XOnz8vrVu3FjMzM5k9e7asWrVKmQTOkE5zZtSezp07Z7js559/Lj4+PgZzP5+UbSlQoID06tVL5s+fL15eXjoDVs+fPy8fffSRQW+bjNrSvn37DJc1tO2S0v79+yVfvnzSoEED6datm87feVoJgSG3Rettc46ldTPfs2fPiouLi3h5eUmxYsX09j1jKJjc5IBFixZJ27ZtReT15ce//vqrtG/fXpo1ayY//PCDPH78WGbPni3+/v56v39MZGSkNGzYUDp16iSlS5eW8uXLy/Hjx9M8CKxZs0YCAgLEz89PWrZsaTCTjWllti2HDx9W5h0RMcyJx+7evSvlypWTzz77TCk7e/asWFtby++//66URUVFycyZM8XX11fKlSsntWvXNsjJxjJqz4EDB1LV37lzp/Tr188gb3cRHh4uH3zwgc5lxVu3bpV27dqlumXC1KlTDXrbZNQW7TQJWhqNxqC3i9aTJ0+kVatWsnLlSqlUqZJ06dJFmVk8ZW9uXmiLSMZzjh0+fFip9+bM2CKv7/Z99erVPHk/rJzG5CabUu5Yc+fOFZVKJb/++qs0adJEmjVrJh07dpQWLVpIiRIlJCIiQhITEw3iFMiZM2ekX79+8tdff0l8fLyUKVMmVVKQsm2vXr2S5ORknZuvGYrMtCWvWLdunTRs2FD5NZmYmCjPnz+X0qVL69xBWuvRo0cSExNjUGOGUspKe6Kjo+Wbb76ROnXq6D35f5NGo5FNmzbJ8OHDdX49jxgxQooXLy5eXl7StGlTndO1hrptMtOW5s2by4wZM0Tkda+noW4XraSkJHn06JF88MEHcvfuXQkODpYqVapI3759pUaNGsqPzlevXsmaNWsMui0ppTfnWOPGjWXTpk1KvbR6cOg1JjdZpN2ZUs5XIyLStWtXKVSokPTo0UP++OMPEXn967VUqVIGdUfpmJgYOXv2rNJ7ERsbKz4+PkpSoPXm5HaGKLNtMcSemjfdvHlTvvjiC+W5dt/y8/NLde+bvCA77THUm/g9f/5c5wKA6dOni4mJiXz99dfyyy+/yCeffCKVKlVSLhow5C+ZzLZFOwYnMTHRYLeLyL/7VZcuXZSk+bfffhMnJ6dUQwUMvS0pZTTnWJ06dWT79u16jC5vYHKTBdo/pJCQEGnbtq34+/tLu3btlFMeb54XHTNmjFSsWNFg7/arvaojPj5eJymIjY2V6dOnKxND5QXG1JaUSbOfn58sW7ZMeb5x40Y5duyYPsLKtre15++//9ZHWNmSmJgoS5cu1el9evz4sVhaWuq0Ky/IqC156e9F5PWVkGPHjhWRf69e8/HxkV69euXJS763bdsm7u7uyngt7d/QiRMnpFmzZtK+fXuDuXWHoWJyk0Xbt28Xa2trGT16tPz8889SunRpKVGihM7ldiEhITJw4ECDP7cr8u905NqkoGLFihIYGCimpqYG1eOUGcbYllq1askPP/wgIq8HQqpUKrlx44Y+Q8sWY2rPm6cC7t+/L3Xq1JE9e/boMarsyett0ca/bt06mTRpkvTv318KFiwoN2/elODgYPHy8pJPP/3UoOe00vb0pezxM6Y5x/SFyU0WPHv2TGrWrCmzZs0SkdcDWj09PeXTTz9V6kRGRsr06dOlQYMGeeLcrsi/XzwvX74UtVot+fPnN/ikLD3G0hbtqbQaNWrIzz//LNOnTxcrK6tUl7rmFcbUnjfHc02YMEHKli1r0FffpMdY2vLHH3+ISqUSNzc3OXHihFK+detWnYsJDI02abl8+bIsWLBApzcmr805ZmiY3GRBZGSk+Pj4yIMHDyQiIkIKFSok/fr1U17XZtJRUVGprjwwdDExMTJw4ECxsrLKc70cbzKmtjRs2FAKFSok5ubmeTIReJMxteevv/6SsWPHip2dncFdSZhVeb0tCQkJsmbNGmU8UV64oCDlVVH58+eXvn37yu3bt5XY4+LipFu3bnlizjFDlE/ft3/IS5ycnGBnZ4dVq1Zh3bp1aNWqFRYtWgQAePDgAb7++muoVCp89NFHsLa21nO0WfP48WNcu3YN+/fvh4+Pj77DeSfG0BYRQXx8PJ4+fYoHDx7g/PnzKFOmjL7DyjZja8+zZ8+wYsUKXL58GQcPHkT58uX1HVK2GUNbTE1N0aNHD6jVr2+XaGj37UuLWq1GREQEOnTogF69emHu3LkAXt8PD3h9r79vvvkG5ubmaN26NQYMGAAnJyc8efIEJ0+exPLly/UZvsFTieTxO4flII1Go/xxvPlco9FApVLhiy++wIoVK1CxYkXs3btXqTt+/Hj89ttv2LFjB4oUKZLrsb8rEUFcXBwsLS31Hco7M6a2XLp0CSKSZ5O0NxlTeyIjIyEicHFx0Xco78yY2pKXnDp1CsOHD8euXbuQL18+DBs2DBcvXgQAVKtWDbNnzwYArFq1Cjt27MD9+/dRsGBBTJs2DRUqVNBn6AaPyc0bwsPDsW3bNgwZMgRA6oTnxo0bGDp0KCIjI9GgQQN4e3vjr7/+wubNm/HHH39whyMiokz57rvvMGnSJNy8eRMfffQRYmNjERAQgBs3buD3339HkSJF8NtvvwEAoqOjYWlpifj4eKP44fa+8bRUCsnJyVi2bBm2bt2K+Ph4jBo1Cmq1WklwRAReXl6YO3cuvv/+e2zfvh2Wlpbw8PDAoUOHULZsWX03gYiI8oiaNWsif/78mDt3LmJiYrB8+XJ4e3tDo9Fg+/btmDJlCn7++We0a9cOFhYWUKvVTGwyiT03b7h37x7mzp2Lv/76C61bt8aYMWMA/HtaSnsuV6PRQKPRIF++fIiPj4e5ubk+wyYiojzmyZMn6NixI8LCwmBlZYVjx47BwsICABAVFQU/Pz90794d48eP13OkeY/67VX+W9zd3TF27FhUqVIFW7duVc55antuACAhIQHTp0/HmjVrAABmZmZ6i5eIiPKmAgUKYNGiRYiKisKFCxfw448/Kq/Z2NigTJkycHJy0mOEeRd7btIRERGB6dOn4/jx4wgMDMTYsWMBALGxsRg5ciRWr16Ns2fPonTp0nqOlIiI8rJ//vkHzZs3h729PQIDA9GgQQP8+uuv+Pbbb/HXX3/By8tL3yHmOUxuMpAywWndujVGjRqFkSNHYtWqVfjzzz9RqVIlfYdIRERG4Nq1a5gzZw4OHDgAU1NTWFtbY/Xq1fD19dV3aHkSk5u30CY4p06dwsuXL3Hjxg0cOnSIiQ0REeWoxMRExMfHIyoqCtbW1rC1tdV3SHkWk5tMiIiIwPjx43Ho0CFs3ryZl3sTEREZMCY3mRQZGQmNRgNXV1d9h0JEREQZYHJDRERERoWXghMREZFRYXJDRERERoXJDRERERkVJjdERERkVJjcEBERkVFhckNERERGhckNERERGRUmN0T/YWFhYVCpVDhz5kyWllu3bh0cHBzeS0zGJLufb0JCAry9vXHkyJF3ev8ePXogMDDwndaR0ocffogtW7bk2PqI3hcmN0S5QKVSZfiYPHmyvkNMl6enJxYuXKhT1rFjR1y9ejXXY8luspDXrFixAsWKFUONGjXeaT1ff/011q1blzNBAZgwYQLGjh0LjUaTY+skeh+Y3BDlggcPHiiPhQsXws7OTqds5MiRSl0RQVJSkh6jfTtLS0u4uLjoOwyDlpCQkK3lRARLlixB79693zkGe3v7HO1ha9asGV69eoVdu3bl2DqJ3gcmN0S5wM3NTXnY29tDpVIpzy9fvgxbW1vs2rULfn5+MDc3x6FDh3Djxg189NFHcHV1hY2NDapUqYJ9+/Yp6xw/fjyqVauW6r0qVKiAKVOmKM+/+eYblC5dGhYWFihVqhSWLVuW6bjr1auH27dvY/jw4UovE5D6tNTkyZPh6+uLoKAgFC1aFDY2NhgwYACSk5MxZ84cuLm5wcXFBdOnT9dZ//Pnz9GnTx84OzvDzs4ODRo0wNmzZ9ONp1ixYgCAihUrQqVSoV69egAAjUaDKVOmoHDhwjA3N4evry9CQkLSXc+OHTvg4OCA5ORkAMCZM2egUqkwduxYpU6fPn3QtWtX5fmWLVtQpkwZmJubw9PTE/PmzdNZp6enJ6ZOnYpu3brBzs4O/fr1S/W+ycnJ6NWrF0qVKoXw8PA0Yzt58iRu3LiBFi1aKGXaHquffvoJtWvXhqWlJapUqYKrV6/i+PHjqFy5MmxsbNCsWTNERkYqy715WqpevXoYMmQIRo8ejfz588PNzU2n11BEMHnyZBQtWhTm5uYoVKgQhgwZorxuYmKC5s2bY+PGjel+tkQGQYgoV61du1bs7e2V5/v37xcAUr58edmzZ49cv35dnjx5ImfOnJEVK1bI+fPn5erVqzJhwgSxsLCQ27dvi4jIhQsXBIBcv35dWZe27Nq1ayIi8v3330vBggVly5YtcvPmTdmyZYvkz59f1q1bJyIit27dEgBy+vTpNGN98uSJFC5cWKZMmSIPHjyQBw8epNmGSZMmiY2NjbRr107++ecf+eWXX8TMzEyaNGkigwcPlsuXL0tQUJAAkL/++ktZzt/fXwICAuT48eNy9epV+eyzz6RAgQLy5MmTNOM5duyYAJB9+/bJgwcPlHrz588XOzs7+fHHH+Xy5csyevRoMTU1latXr6a5nufPn4tarZbjx4+LiMjChQvFyclJqlWrptTx9vaW1atXi4jIiRMnRK1Wy5QpU+TKlSuydu1asbS0lLVr1yr1PTw8xM7OTr766iu5fv26XL9+XefzjYuLk9atW0vFihXl0aNHacalbUupUqV0yrTrKVWqlISEhMjFixflww8/FD8/P6lXr54cOnRITp06Jd7e3vLpp58qy3Xv3l0++ugj5XndunXFzs5OJk+eLFevXpX169eLSqWSPXv2iIjI5s2bxc7OTnbu3Cm3b9+Wv//+W1atWqUTy/Lly8XDwyPd+IkMAZMbolyWXnKzbdu2ty5bpkwZWbx4sfK8QoUKMmXKFOX5uHHjdL6gvby8ZMOGDTrrmDp1qlSvXl1E3p7ciLz+0l6wYEGGbZg0aZJYWVnJy5cvlbImTZqIp6enJCcnK2UlS5aUmTNniojIwYMHxc7OTuLi4nTW7eXlJStXrkwzlvTiLVSokEyfPl2nrEqVKjJgwIB021WpUiWZO3euiIgEBgbK9OnTxczMTF69eiV3794VAEpy1LlzZ2nUqJHO8qNGjRIfHx/luYeHhwQGBqYZ78GDB6Vhw4ZSq1Ytef78eboxiYgMHTpUGjRokOZ6vvnmG6Xsxx9/FAASGhqqlM2cOVNKliypPE8rualVq5bOuqtUqSJjxowREZF58+bJBx98IAkJCenGt337dlGr1TrblcjQ8LQUkYGoXLmyzvOoqCiMHDkSpUuXhoODA2xsbHDp0iWd0xldunTBhg0bAPxfO/cX0lQbB3D8u9LypLVs5nIYjUxTcSMHFbVyK7JCWrWwVLxRqouKJLTwqkTton8khBBlpBeOoouCwpL0YmbLm4jMQiKWRiQR1KBmw8L2XoTnbfnn1ff11Ri/Dwx2nvM8z/mdc25+5znPc36+Urh27RqFhYUA9Pf34/V62bt3LzExMerv5MmTeL3eSY/faDQyd+5cdVuv15Oens6MGTNCyj58+ABAZ2cnfr8fnU4XEl9PT8+E4vv8+TN9fX1YrdaQcqvVSnd396jtbDYbbrebYDBIe3s7u3btIi0tjYcPH9LW1obBYCA5ORmA7u7uEft/9eqV+moLht/DIQUFBfT393P//n20Wu2Y5xMIBIiKihpxn9lsVv/r9XoATCZTSNnQ9R3Nr30AJCQkqG12795NIBBg6dKl7N+/n1u3bg2b/6UoCj9+/GBgYGDM4wgxnSKmOwAhxE/R0dEh20ePHqWlpYVz586xbNkyFEUhNzc3ZKJqQUEB5eXlPHnyhEAgwNu3b8nLywN+JkcAdXV1w+bmzJw5c9Ljj4yMDNnWaDQjlg2ttPH7/SQkJOB2u4f1NRXLzO12O1evXqWzs5PIyEhSU1Ox2+243W58Ph82m23Cff5+D4fk5OTQ2NhIR0cHGzduHLOPuLg4urq6Rtz36/Ucmv/0e9k/rWQa654sXryYly9f0traSktLCwcPHuTs2bO0tbWp7T59+kR0dDSKoox5HCGmkyQ3QvyhPB4PRUVFOJ1O4Gcy0NvbG1InMTERm82Gy+UiEAiQnZ2trmLS6/UYDAZev36tjub8G7NmzQoZnZgsFouF9+/fExERgdFoHHcsQEg88+bNw2Aw4PF4QhISj8fDqlWrRu1r/fr1fPnyhZqaGrWd3W7n1KlT+Hw+ysrK1LppaWl4PJ6Q9h6Ph5SUlHEligcOHCAjI4Pt27fT1NQ0ZuKUmZnJxYsXCQaDagIzlRRFweFw4HA4OHToEKmpqXR1dWGxWAB4/vw5mZmZUx6XEBMhyY0Qf6jk5GRu3ryJw+FAo9Fw/PjxEZ/KCwsLqaio4Nu3b9TU1ITsq6yspKSkBK1Wy9atWxkYGODx48f4fD5KS0vHFYfRaOTBgwfk5+cze/Zs4uLiJuX8Nm3axJo1a9i5cydnzpwhJSWFvr4+mpqacDqdI77iiY+PR1EUmpubSUxMJCoqCq1Wy7Fjx6ioqCApKYkVK1ZQX1/P06dPcblcox4/NjYWs9mMy+WitrYWgKysLPbs2cP3799DEpCysjJWrlxJdXU1eXl5dHR0UFtbO6GVZ4cPH2ZwcJBt27Zx79491q1bN2K9DRs24Pf7efHiBRkZGePufzI0NDQwODjI6tWrmTNnDo2NjSiKwpIlS9Q67e3tbN68eUrjEmKiZM6NEH+o8+fPExsby9q1a3E4HGzZskV9ev5Vbm4uHz9+5OvXr8O+Rrtv3z6uXLlCfX09JpMJm81GQ0ODuqR6PKqqqujt7SUpKYmFCxf+19NSaTQa7t69S1ZWFsXFxaSkpJCfn8+bN2/U+SS/i4iI4MKFC1y6dAmDwcCOHTsAKCkpobS0lLKyMkwmE83Nzdy+fVudMzMam83G4OCguqR8wYIFpKens2jRIpYvX67Ws1gs3Lhxg+vXr5ORkcGJEyeoqqqiqKhoQud85MgRKisrycnJGfXrwzqdDqfTOWZi9n+ZP38+dXV1WK1WzGYzra2t3LlzB51OB8C7d+949OgRxcXFUx6bEBOhCQaDwekOQgghxN+ePXtGdnY2Xq+XmJiY6Q5HVV5ejs/n4/Lly9MdihBjkpEbIYT4w5jNZk6fPk1PT890hxIiPj6e6urq6Q5DiH8kIzdCCCGECCsyciOEEEKIsCLJjRBCCCHCiiQ3QgghhAgrktwIIYQQIqxIciOEEEKIsCLJjRBCCCHCiiQ3QgghhAgrktwIIYQQIqxIciOEEEKIsPIXsxqhaVPfzrsAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data_by_area = []\n", - "\n", - "for a in area:\n", - " data = df_total_per['{}_2021'.format(a)]\n", - " data_by_area.append(data)\n", - "\n", - "# Convert data to numpy arrays for plotting\n", - "data_by_area = np.array(data_by_area)\n", - "\n", - "# Create a stacked bar chart\n", - "plt.bar(x, data_by_area[0], label='Roxbury', alpha=0.8)\n", - "for i in range(1, len(area)):\n", - " plt.bar(x, data_by_area[i], label=area[i], bottom=np.sum(data_by_area[:i], axis=0), alpha=0.8)\n", - "\n", - "plt.xticks(rotation=45)\n", - "plt.ylabel(\"Percentage\")\n", - "plt.xlabel(\"Travel time to work (mins)\")\n", - "plt.title(\"2021 Percentage of total number of travel time to work\")\n", - "plt.legend(loc=\"upper right\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data_by_area = []\n", - "\n", - "for a in area:\n", - " data = df_bus_per['{}_2021'.format(a)]\n", - " data_by_area.append(data)\n", - "\n", - "# Convert data to numpy arrays for plotting\n", - "data_by_area = np.array(data_by_area)\n", - "\n", - "# Create a stacked bar chart\n", - "plt.bar(x, data_by_area[0], label='Roxbury', alpha=0.8)\n", - "for i in range(1, len(area)):\n", - " plt.bar(x, data_by_area[i], label=area[i], bottom=np.sum(data_by_area[:i], axis=0), alpha=0.8)\n", - "\n", - "plt.xticks(rotation=45)\n", - "plt.ylabel(\"Percentage\")\n", - "plt.xlabel(\"Travel time to work (mins)\")\n", - "plt.title(\"2021 Percentage of total number of travel time to work (Bus)\")\n", - "plt.legend(loc=\"upper right\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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HhYUF1q9fj6ZNm2L//v343//+p9CnWrVqOH/+PFq3bv3OwxqlWcWKFVGuXDnk5OQofBfShyho/hUrVoSenh6uX7+eb9m1a9egpqb2ztBoamoqhR59fX3Y2dnByMhIWt6kSRMcPXoU//77L9TV1aWwZGVlBQBv3XaFChWgr6//3nNbs2YNOnbsiK5du2LXrl1wc3N7r3Vzc3Nx584dhe9FyvvSyNe/QNTY2Bienp5Ys2YN/Pz8cPTo0XeezA68+r6qY8eOYejQofl+33Jzc9GrVy+sXbsW48aNK3ScatWqYe/evXB1dX1rMAReXeH45MkTbN68Gc2bN1eog8ouHjKjMisnJwe+vr6Ijo7Gxo0bpQ+Rgvj4+CA8PFzh0Ma+fftw48YNdO3aVaHvsGHDsH79eixYsACdO3f+ZPUXVd6ekrw9IwBw4sQJREdH5+urpqaGLl26YPv27Vi1ahWys7PzXTnWrVs3/Pfff1i6dGm+9V++fJnvkExppa6uDh8fH2zatAmXLl3Kt/zNbyUviL6+PpKSkvKN26ZNG2zdulXh8FBCQgLWrl2Lpk2bQi6Xv3Pspk2bIiYmBnv27JHOH8rTpEkTREdH4/Dhw6hfv770dQ4WFhZo2LAhwsLCFOq6dOkS9uzZg/bt279zu6/T0tLC5s2b0bhxY3To0AEnT55873XnzZsn/VsIgXnz5kFTUxOtW7dW6NerVy9cuXIFo0ePhrq6usL5YG+Tt0cnMDAQXbp0UXh069YNLVq0eOten9d169YNOTk5mDRpUr5l2dnZ0mtY0O9QZmYmFixY8M5t0OeLe4iozPrhhx+wbds2dOjQAU+fPsXq1asVlr9+me/PP/+MjRs3omXLlvj++++RmpqKP//8E/Xq1VP4rp5Zs2ZhwYIFcHFxgZ6eXr4xO3XqJP1FnpycjLlz5wL4v0ul582bByMjIxgZGSmchFqcvLy8sHnzZnTq1Amenp6IjY3FokWLYGdnV+D3yfj6+mLu3LkICgpCvXr1YGtrq7C8V69e2LBhA7777jscOHAArq6uyMnJwbVr17Bhwwbs3r27wK8sKI2mTJmCAwcOwMnJCQMHDoSdnR2ePn2Ks2fPYu/evfkuCX+Tg4MD9u7dixkzZsDS0hI2NjZwcnLCr7/+isjISDRt2hRDhgyBhoYGFi9ejIyMDEydOvW9amvatClCQkJw6tQpBAQEKCxr0qQJkpOTkZycjGHDhiks+/PPP9GuXTu4uLigf//+ePnyJebOnQtDQ0MEBwd/0OsDvDqkFh4ejlatWqFdu3Y4ePDgOw8J6+joICIiAv7+/nBycsKuXbuwY8cO/Pzzz/lO+vf09ISJiQk2btyIdu3awdTU9J01rVmzBg0bNnzrnravv/4aw4YNw9mzZ2Fvb//WcVq0aIFBgwZh8uTJiImJQZs2baCpqYmbN29i48aNmD17Nrp06YImTZqgfPny8Pf3x/DhwyGTybBq1SqFgERlkBKvcCP6pPIu0X7b402XLl0Sbdq0EXp6esLIyEj4+fmJ+Ph4hT55l/O+7fH6ZfV5l8YX9LCysvqgubztsvs///wzX9/c3Fzx+++/CysrK6GtrS0aNWokwsPDhb+/f4Hbzc3NFZUrVxYAxK+//lrg9jMzM8Uff/wh6tSpI7S1tUX58uWFg4ODmDBhgkhOTpb6lcRl93Xq1MnX18rKSnh6euZrByACAgIU2hISEkRAQICoXLmy0NTUFObm5qJ169ZiyZIl76z32rVronnz5kJXV1cAUJjr2bNnhYeHhzAwMBB6enqiZcuW4tixY+8cM0/eZekAxI0bNxSW5ebmCiMjIwFArF+/Pt+6e/fuFa6urkJXV1fI5XLRoUOHfN98nXfZfd5l8K8r6NumHz9+LOzs7IS5ubm4efPmW+vOW/f27dvS74+ZmZkICgoSOTk5Ba4zZMiQfF+f8DZnzpwRAAr9Wou7d+8KAGLkyJFvnc/rlixZIhwcHISurq4oV66cqFevnggMDBQPHz6U+hw9elQ4OzsLXV1dYWlpKQIDA6Wvcnj9Z5LKDpkQjLxERFRyRo4cieXLlyM+Pl46iZxI2XgOERERlZj09HSsXr0aPj4+DENUqvAcIiIi+uQSExOxd+9e/P3333jy5Am+//57ZZdEpICBiIiIPrkrV67Az88PpqammDNnTr6vsyBSNp5DRERERCqP5xARERGRymMgIiIiIpXHc4jeQ25uLh4+fIhy5cp91rcuICIiUiVCCDx//hyWlpb57m33Jgai9/Dw4cMSv4ElERERFY8HDx6gUqVKhfZhIHoPefcNevDgwXvdk4iIiIiULyUlBZUrV5Y+xwvDQPQe8g6TyeVyBiIiIqLPzPuc7sKTqomIiEjlMRARERGRymMgIiIiIpXHc4iIiKjMycnJQVZWlrLLoBKgpaX1zkvq3wcDERERlRlCCMTHxyMpKUnZpVAJUVNTg42NDbS0tD5qHAYiIiIqM/LCkKmpKfT09PhlumVc3hcnx8XFoUqVKh/1fjMQERFRmZCTkyOFIRMTE2WXQyWkYsWKePjwIbKzs6GpqVnkcXhSNRERlQl55wzp6ekpuRIqSXmHynJycj5qHAYiIiIqU3iYTLUU1/vNQEREREQqj4GIiIiojAsNDYWRkZGyyyjVeFI1ERGVeR3mHimxbW0f1vSD1+nTpw/CwsIAABoaGqhUqRK6du2KiRMnQkdHp7hLpAIwEBEREZUCbdu2RUhICLKysnDmzBn4+/tDJpPhjz/+UHZpb5WZmfnR3/9TWvCQGRERUSmgra0Nc3NzVK5cGd7e3nB3d0dkZCQAICMjA8OHD4epqSl0dHTQtGlTnDp1CgCQnp6OOnXq4Ntvv5XGun37NsqVK4cVK1YobGPLli2oUaMGdHR04OHhgQcPHkjL+vTpA29vb4X+I0aMgJubm/Tczc0NQ4cOxYgRI1ChQgV4eHigX79+8PLyUlgvKysLpqamWL58eXG8NCWCgYiIiKiUuXTpEo4dOybtfQkMDMSmTZsQFhaGs2fPonr16vDw8MDTp0+ho6ODNWvWICwsDFu3bkVOTg569uyJr776Cv369ZPGfPHiBX777TesXLkSR48eRVJSErp37/7BtYWFhUFLSwtHjx7FokWLMGDAAERERCAuLk7qEx4ejhcvXsDX1/fjX4wSwkNmREQqxDe86B9Q673WF2Ml9Kbw8HAYGBggOzsbGRkZUFNTw7x585CWloaFCxciNDQU7dq1AwAsXboUkZGRWL58OUaPHo2GDRvi119/xYABA9C9e3fcu3cP4eHhCuNnZWVh3rx5cHJyAvAq2Nja2uLkyZP48ssv37vOGjVqYOrUqQpttWrVwqpVqxAYGAgACAkJQdeuXWFgYPAxL0mJ4h4iIiKiUqBly5aIiYnBiRMn4O/vj759+8LHxwe3b99GVlYWXF1dpb6ampr48ssvcfXqVanthx9+QM2aNTFv3jysWLEi37d1a2hooHHjxtLz2rVrw8jISGGM9+Hg4JCvbcCAAQgJCQEAJCQkYNeuXQp7pz4HDERERESlgL6+PqpXr44GDRpgxYoVOHHixAedg5OYmIgbN25AXV0dN2/e/ODtq6mpQQih0Jb37d9v1vmm3r17486dO4iOjsbq1athY2ODZs2afXANysRAREREVMqoqanh559/xrhx41CtWjXpnJ08WVlZOHXqFOzs7KS2fv36oV69eggLC8OYMWPy7fnJzs7G6dOnpefXr19HUlISbG1tAby6J9jr5wEBQExMzHvVa2JiAm9vb4SEhCA0NBR9+/b90CkrHQMRERFRKdS1a1eoq6tj4cKFGDx4MEaPHo2IiAhcuXIFAwcOxIsXL9C/f38AwPz58xEdHY2wsDD4+fnB29sbfn5+yMzMlMbT1NTEsGHDcOLECZw5cwZ9+vSBs7OzdP5Qq1atcPr0aaxcuRI3b95EUFAQLl269N71DhgwAGFhYbh69Sr8/f2L98UoAQxEREREpZCGhgaGDh2KqVOn4rfffoOPjw969eoFe3t73Lp1C7t370b58uVx7do1jB49GgsWLEDlypUBAAsWLMDjx4/xyy+/SOPp6elhzJgx+Oabb+Dq6goDAwOsX/9/J8p7eHjgl19+QWBgIBo3boznz5+jd+/e712vu7s7LCws4OHhAUtLy+J7IUqITLx5wJDySUlJgaGhIZKTkyGXy5VdDhFRkZXlq8zS09MRGxsLGxsbfruzEqSmpuKLL75ASEgIOnfuXGLbLex9/5DPb152T0REREWWm5uLx48fY/r06TAyMsLXX3+t7JKKhIGIiIiIiuz+/fuwsbFBpUqVEBoaCg2NzzNafJ5VExERUalgbW2d73L9zxFPqiYiIiKVx0BEREREKo+BiIiIiFQeAxERERGpPAYiIiIiUnkMRERERKTyGIiIiIhI5fF7iIiIqOxb3KLktjXo4Aev0qdPH4SFhWHQoEFYtGiRwrKAgAAsWLAA/v7+CA0NfedYd+/ehY2NDc6dO4eGDRsqbCMpKQlbtmz54PpUAfcQERERlQKVK1fGunXr8PLlS6ktPT0da9euRZUqVZRYmWpQeiD677//0LNnT5iYmEBXVxf16tXD6dOnpeVCCIwfPx4WFhbQ1dWFu7s7bt68qTDG06dP4efnB7lcDiMjI/Tv3x+pqakKfS5cuIBmzZpBR0cHlStXxtSpU0tkfkRERO/D3t4elStXxubNm6W2zZs3o0qVKmjUqJHUFhERgaZNm8LIyAgmJibw8vLC7du3peU2NjYAgEaNGkEmk8HNzQ3BwcEICwvD1q1bIZPJIJPJEBUVBQAYM2YMatasCT09PVStWhW//PILsrKypPGCg4PRsGFDLF68GJUrV4aenh66deuG5ORkqc+pU6fw1VdfoUKFCjA0NESLFi1w9uxZhfnJZDIsW7YMnTp1gp6eHmrUqIFt27YV62v4MZQaiJ49ewZXV1doampi165duHLlCqZPn47y5ctLfaZOnYo5c+Zg0aJFOHHiBPT19eHh4YH09HSpj5+fHy5fvozIyEiEh4fj0KFD+Pbbb6XlKSkpaNOmDaysrHDmzBn8+eefCA4OxpIlS0p0vkRERIXp168fQkJCpOcrVqxA3759FfqkpaVh1KhROH36NPbt2wc1NTV06tQJubm5AICTJ08CAPbu3Yu4uDhs3rwZP/74I7p164a2bdsiLi4OcXFxaNKkCQCgXLlyCA0NxZUrVzB79mwsXboUM2fOVNjmrVu3sGHDBmzfvh0RERE4d+4chgwZIi1//vw5/P39ceTIERw/fhw1atRA+/bt8fz5c4VxJkyYgG7duuHChQto3749/Pz88PTp0+J7AT+CUs8h+uOPP1C5cmWFNz8v2QKv9g7NmjUL48aNQ8eOHQEAK1euhJmZGbZs2YLu3bvj6tWriIiIwKlTp+Do6AgAmDt3Ltq3b49p06bB0tISa9asQWZmJlasWAEtLS3UqVMHMTExmDFjhkJwIiIiUqaePXti7NixuHfvHgDg6NGjWLdunbQ3BwB8fHwU1lmxYgUqVqyIK1euoG7duqhYsSIAwMTEBObm5lI/XV1dZGRkKLQBwLhx46R/W1tb48cff8S6desQGBgotaenp2PlypX44osvALz6nPX09MT06dNhbm6OVq1aKYy5ZMkSGBkZ4eDBg/Dy8pLa+/Tpgx49egAAfv/9d8yZMwcnT55E27ZtP/i1Km5K3UO0bds2ODo6omvXrjA1NUWjRo2wdOlSaXlsbCzi4+Ph7u4utRkaGsLJyQnR0dEAgOjoaBgZGUlhCADc3d2hpqaGEydOSH2aN28OLS0tqY+HhweuX7+OZ8+e5asrIyMDKSkpCg8iIqJPrWLFivD09ERoaChCQkLg6emJChUqKPS5efMmevTogapVq0Iul8Pa2hrAq7vOF8X69evh6uoKc3NzGBgYYNy4cfnGqlKlihSGAMDFxQW5ubm4fv06ACAhIQEDBw5EjRo1YGhoCLlcjtTU1Hzj1K9fX/q3vr4+5HI5EhMTi1R3cVNqILpz5w4WLlyIGjVqYPfu3Rg8eDCGDx+OsLAwAEB8fDwAwMzMTGE9MzMzaVl8fDxMTU0VlmtoaMDY2FihT0FjvL6N102ePBmGhobSo3LlysUwWyIionfr168fQkNDERYWhn79+uVb3qFDBzx9+hRLly7FiRMnpD/+MzMzP3hb0dHR8PPzQ/v27REeHo5z587hf//73weP5e/vj5iYGMyePRvHjh1DTEwMTExM8o2jqamp8Fwmk0mH+pRNqYfMcnNz4ejoiN9//x3AqxPALl26hEWLFsHf319pdY0dOxajRo2SnqekpDAUERFRiWjbti0yMzMhk8ng4eGhsOzJkye4fv06li5dimbNmgEAjhw5otAn72hITk5OvvY3244dOwYrKyv873//k9ryDte97v79+3j48CEsLS0BAMePH4eamhpq1aoF4NWhvQULFqB9+/YAgAcPHuDx48cfPHdlUuoeIgsLC9jZ2Sm02draSrvY8o5zJiQkKPRJSEiQlpmbm+fb3ZadnY2nT58q9ClojNe38TptbW3I5XKFBxERUUlQV1fH1atXceXKFairqyssK1++PExMTLBkyRLcunUL+/fvV/gDHgBMTU2hq6uLiIgIJCQkSFeDWVtb48KFC7h+/ToeP36MrKws1KhRA/fv38e6detw+/ZtzJkzB//880++mnR0dODv74/z58/j8OHDGD58OLp16yZ9htaoUQOrVq3C1atXceLECfj5+UFXV/cTvUKfhlIDkaurq3T8Mc+NGzdgZWUF4NUJ1ubm5ti3b5+0PCUlBSdOnICLiwuAV8cxk5KScObMGanP/v37kZubCycnJ6nPoUOHFC4jjIyMRK1atRSuaCMiIioN3vbHuJqaGtatW4czZ86gbt26GDlyJP7880+FPhoaGpgzZw4WL14MS0tL6aKkgQMHolatWnB0dETFihVx9OhRfP311xg5ciSGDh2Khg0b4tixY/jll1/ybbd69ero3Lkz2rdvjzZt2qB+/fpYsGCBtHz58uV49uwZ7O3t0atXLwwfPjzf6SylnUwIIZS18VOnTqFJkybSZXgnT57EwIEDsWTJEvj5+QF4dSXalClTEBYWBhsbG/zyyy+4cOECrly5Ah0dHQBAu3btkJCQgEWLFiErKwt9+/aFo6Mj1q5dCwBITk5GrVq10KZNG4wZMwaXLl1Cv379MHPmzPe6yiwlJQWGhoZITk7m3iIi+qz5hvsWed31XuuLsZLil56ejtjYWNjY2EifD/TxgoODsWXLFsTExCi7lAIV9r5/yOe3Us8haty4Mf755x+MHTsWEydOhI2NDWbNmiWFIQAIDAxEWloavv32WyQlJaFp06aIiIhQmPSaNWswdOhQtG7dGmpqavDx8cGcOXOk5YaGhtizZw8CAgLg4OCAChUqYPz48bzknoiIiACUgnuZeXl5KXxHwZtkMhkmTpyIiRMnvrWPsbGxtDfoberXr4/Dhw8XuU4iIiIqu5R+6w4iIiIqvYKDg0vt4bLixEBEREREKo+BiIiIiFQeAxERERGpPAYiIiIiUnkMRERERKTyGIiIiIhI5TEQERERqbDQ0FAYGRkpuwylU/oXMxIREX1qH3PLkg9VlFuc9OnTB2FhYQBe3YvM2NgY9evXR48ePdCnTx+oqX3e+y9kMhn++ecfeHt7K7uUt/q8X2EiIqIyom3btoiLi8Pdu3exa9cutGzZEt9//z28vLyQnZ1dpDEzMzOLuUrlev0m7cWNgYiIiKgU0NbWhrm5Ob744gvY29vj559/xtatW7Fr1y6EhoYCAO7fv4+OHTvCwMAAcrkc3bp1Q0JCgjRGcHAwGjZsiGXLlinc7DQpKQmDBg2CmZkZdHR0ULduXYSHhytsf/fu3bC1tYWBgYEUzl63bNky2NraQkdHB7Vr11a4231mZiaGDh0KCwsL6OjowMrKCpMnTwYAWFtbAwA6deoEmUwmPQeArVu3wt7eHjo6OqhatSomTJigEP5kMhkWLlyIr7/+Gvr6+vjtt98++nV+Gx4yIyIiKqVatWqFBg0aYPPmzejXr58Uhg4ePIjs7GwEBATA19cXUVFR0jq3bt3Cpk2bsHnzZqirqyM3Nxft2rXD8+fPsXr1alSrVg1XrlyBurq6tM6LFy8wbdo0rFq1CmpqaujZsyd+/PFHrFmzBsCrm6iPHz8e8+bNQ6NGjXDu3DkMHDgQ+vr68Pf3x5w5c7Bt2zZs2LABVapUwYMHD/DgwQMAwKlTp2BqaoqQkBC0bdtW2u7hw4fRu3dvzJkzB82aNcPt27elm64HBQVJtQUHB2PKlCmYNWsWNDQ+XWxhICIiIirFateujQsXLmDfvn24ePEiYmNjUblyZQDAypUrUadOHZw6dQqNGzcG8GpvzcqVK1GxYkUAwJ49e3Dy5ElcvXoVNWvWBABUrVpVYRtZWVlYtGgRqlWrBgAYOnSowk3Vg4KCMH36dHTu3BkAYGNjgytXrmDx4sXw9/fH/fv3UaNGDTRt2hQymQxWVlbSunl1GBkZwdzcXGqfMGECfvrpJ/j7+0s1TZo0CYGBgQqB6JtvvkHfvn2L4ZUsHAMRERFRKSaEgEwmw9WrV1G5cmUpDAGAnZ0djIyMcPXqVSkQWVlZSSEEAGJiYlCpUiUpDBVET09PCkMAYGFhgcTERABAWloabt++jf79+2PgwIFSn+zsbBgaGgJ4dVL4V199hVq1aqFt27bw8vJCmzZtCp3X+fPncfToUYXDYDk5OUhPT8eLFy+gp6cHAHB0dHzna1QcGIiIiIhKsatXr8LGxua9++vr6ys819XVfec6mpqaCs9lMhmEEACA1NRUAMDSpUvh5OSk0C/v8Je9vT1iY2Oxa9cu7N27F926dYO7uzv+/vvvt24zNTUVEyZMkPY6vS7v3KeC5vOpMBARERGVUvv378fFixcxcuRIVKpUSTo3J28v0ZUrV5CUlAQ7O7u3jlG/fn38+++/uHHjRqF7id7GzMwMlpaWuHPnDvz8/N7aTy6Xw9fXF76+vujSpQvatm2Lp0+fwtjYGJqamsjJyVHob29vj+vXr6N69eofXNOnwEBERERUCmRkZCA+Ph45OTlISEhAREQEJk+eDC8vL/Tu3RtqamqoV68e/Pz8MGvWLGRnZ2PIkCFo0aJFoYeVWrRogebNm8PHxwczZsxA9erVce3aNchkMrRt2/a9apswYQKGDx8OQ0NDtG3bFhkZGTh9+jSePXuGUaNGYcaMGbCwsECjRo2gpqaGjRs3wtzcXPrCR2tra+zbtw+urq7Q1tZG+fLlMX78eHh5eaFKlSro0qUL1NTUcP78eVy6dAm//vprcbykH4SX3RMREZUCERERsLCwgLW1Ndq2bYsDBw5gzpw52Lp1K9TV1SGTybB161aUL18ezZs3h7u7O6pWrYr169/9RZCbNm1C48aN0aNHD9jZ2SEwMDDfHpvCDBgwAMuWLUNISAjq1auHFi1aIDQ0VDqUV65cOUydOhWOjo5o3Lgx7t69i507d0pfKDl9+nRERkaicuXKaNSoEQDAw8MD4eHh2LNnDxo3bgxnZ2fMnDlT4YTskiQTeQcJ6a1SUlJgaGiI5ORkyOVyZZdDRFRkH/ONzUX5BuaSlJ6ejtjYWIXv36Gyr7D3/UM+v7mHiIiIiFQeAxERERGpPAYiIiIiUnkMRERERKTyGIiIiKhM4bVCqqW43m8GIiIiKhPyvm35xYsXSq6ESlJmZiYAKNystij4xYxERFQmqKurw8jISLoHl56eHmQymZKrok8pNzcXjx49gp6eHjQ0Pi7SMBAREVGZkXc39bxQRGWfmpoaqlSp8tHhl4GIiIjKDJlMBgsLC5iamiIrK0vZ5VAJ0NLSkr4R+2MwEBERUZmjrq7+0eeUkGrhSdVERESk8hiIiIiISOUxEBEREZHKYyAiIiIilcdARERERCqPgYiIiIhUHgMRERERqTwGIiIiIlJ5DERERESk8hiIiIiISOUxEBEREZHKYyAiIiIilcdARERERCqPgYiIiIhUnlIDUXBwMGQymcKjdu3a0vL09HQEBATAxMQEBgYG8PHxQUJCgsIY9+/fh6enJ/T09GBqaorRo0cjOztboU9UVBTs7e2hra2N6tWrIzQ0tCSmR0RERJ8Jpe8hqlOnDuLi4qTHkSNHpGUjR47E9u3bsXHjRhw8eBAPHz5E586dpeU5OTnw9PREZmYmjh07hrCwMISGhmL8+PFSn9jYWHh6eqJly5aIiYnBiBEjMGDAAOzevbtE50lERESll4bSC9DQgLm5eb725ORkLF++HGvXrkWrVq0AACEhIbC1tcXx48fh7OyMPXv24MqVK9i7dy/MzMzQsGFDTJo0CWPGjEFwcDC0tLSwaNEi2NjYYPr06QAAW1tbHDlyBDNnzoSHh0eJzpWIiIhKJ6XvIbp58yYsLS1RtWpV+Pn54f79+wCAM2fOICsrC+7u7lLf2rVro0qVKoiOjgYAREdHo169ejAzM5P6eHh4ICUlBZcvX5b6vD5GXp+8MYiIiIiUuofIyckJoaGhqFWrFuLi4jBhwgQ0a9YMly5dQnx8PLS0tGBkZKSwjpmZGeLj4wEA8fHxCmEob3nessL6pKSk4OXLl9DV1c1XV0ZGBjIyMqTnKSkpHz1XIiIiKr2UGojatWsn/bt+/fpwcnKClZUVNmzYUGBQKSmTJ0/GhAkTlLZ9IiIiKllKP2T2OiMjI9SsWRO3bt2Cubk5MjMzkZSUpNAnISFBOufI3Nw831Vnec/f1Ucul781dI0dOxbJycnS48GDB8UxPSIiIiqlSlUgSk1Nxe3bt2FhYQEHBwdoampi37590vLr16/j/v37cHFxAQC4uLjg4sWLSExMlPpERkZCLpfDzs5O6vP6GHl98sYoiLa2NuRyucKDiIiIyi6lBqIff/wRBw8exN27d3Hs2DF06tQJ6urq6NGjBwwNDdG/f3+MGjUKBw4cwJkzZ9C3b1+4uLjA2dkZANCmTRvY2dmhV69eOH/+PHbv3o1x48YhICAA2traAIDvvvsOd+7cQWBgIK5du4YFCxZgw4YNGDlypDKnTkRERKWIUs8h+vfff9GjRw88efIEFStWRNOmTXH8+HFUrFgRADBz5kyoqanBx8cHGRkZ8PDwwIIFC6T11dXVER4ejsGDB8PFxQX6+vrw9/fHxIkTpT42NjbYsWMHRo4cidmzZ6NSpUpYtmwZL7knIiIiiUwIIZRdRGmXkpICQ0NDJCcn8/AZEX3WfMN9i7zueq/1xVgJ0af3IZ/fpeocIiIiIiJlYCAiIiIilcdARERERCqPgYiIiIhUHgMRERERqTwGIiIiIlJ5DERERESk8hiIiIiISOUxEBEREZHKYyAiIiIilcdARERERCqPgYiIiIhUHgMRERERqTwGIiIiIlJ5DERERESk8hiIiIiISOUxEBEREZHKYyAiIiIilcdARERERCqPgYiIiIhUHgMRERERqTwNZRdAREREZYNvuG+R113vtb4YK/lw3ENEREREKo+BiIiIiFQeAxERERGpPAYiIiIiUnkMRERERKTyGIiIiIhI5TEQERERkcpjICIiIiKVx0BEREREKo+BiIiIiFQeAxERERGpPAYiIiIiUnkMRERERKTyGIiIiIhI5TEQERERkcpjICIiIiKVx0BEREREKo+BiIiIiFQeAxERERGpPAYiIiIiUnkMRERERKTyGIiIiIhI5ZWaQDRlyhTIZDKMGDFCaktPT0dAQABMTExgYGAAHx8fJCQkKKx3//59eHp6Qk9PD6amphg9ejSys7MV+kRFRcHe3h7a2tqoXr06QkNDS2BGRERE9LkoFYHo1KlTWLx4MerXr6/QPnLkSGzfvh0bN27EwYMH8fDhQ3Tu3FlanpOTA09PT2RmZuLYsWMICwtDaGgoxo8fL/WJjY2Fp6cnWrZsiZiYGIwYMQIDBgzA7t27S2x+REREVLopPRClpqbCz88PS5cuRfny5aX25ORkLF++HDNmzECrVq3g4OCAkJAQHDt2DMePHwcA7NmzB1euXMHq1avRsGFDtGvXDpMmTcL8+fORmZkJAFi0aBFsbGwwffp02NraYujQoejSpQtmzpyplPkSERFR6aP0QBQQEABPT0+4u7srtJ85cwZZWVkK7bVr10aVKlUQHR0NAIiOjka9evVgZmYm9fHw8EBKSgouX74s9XlzbA8PD2mMgmRkZCAlJUXhQURERGWXhjI3vm7dOpw9exanTp3Ktyw+Ph5aWlowMjJSaDczM0N8fLzU5/UwlLc8b1lhfVJSUvDy5Uvo6urm2/bkyZMxYcKEIs+LiIiIPi9K20P04MEDfP/991izZg10dHSUVUaBxo4di+TkZOnx4MEDZZdEREREn5DSAtGZM2eQmJgIe3t7aGhoQENDAwcPHsScOXOgoaEBMzMzZGZmIikpSWG9hIQEmJubAwDMzc3zXXWW9/xdfeRyeYF7hwBAW1sbcrlc4UFERERll9ICUevWrXHx4kXExMRID0dHR/j5+Un/1tTUxL59+6R1rl+/jvv378PFxQUA4OLigosXLyIxMVHqExkZCblcDjs7O6nP62Pk9ckbg4iIiEhp5xCVK1cOdevWVWjT19eHiYmJ1N6/f3+MGjUKxsbGkMvlGDZsGFxcXODs7AwAaNOmDezs7NCrVy9MnToV8fHxGDduHAICAqCtrQ0A+O677zBv3jwEBgaiX79+2L9/PzZs2IAdO3aU7ISJiIio1FLqSdXvMnPmTKipqcHHxwcZGRnw8PDAggULpOXq6uoIDw/H4MGD4eLiAn19ffj7+2PixIlSHxsbG+zYsQMjR47E7NmzUalSJSxbtgweHh7KmBIRERGVQjIhhFB2EaVdSkoKDA0NkZyczPOJiOiz5hvuW+R113utL8ZKqCwqbT9fH/L5rfTvISIiIiJSNgYiIiIiUnkMRERERKTyPjgQZWVloXXr1rh58+anqIeIiIioxH1wINLU1MSFCxc+RS1ERERESlGkQ2Y9e/bE8uXLi7sWIiIiIqUo0vcQZWdnY8WKFdi7dy8cHBygr6+vsHzGjBnFUhwRERFRSShSILp06RLs7e0BADdu3FBYJpPJPr4qIiIiohJUpEB04MCB4q6DiIiISGk+6rL7W7duYffu3Xj58iUAgF96TURERJ+jIgWiJ0+eoHXr1qhZsybat2+PuLg4AK9uxvrDDz8Ua4FEREREn1qRAtHIkSOhqamJ+/fvQ09PT2r39fVFREREsRVHREREVBKKdA7Rnj17sHv3blSqVEmhvUaNGrh3716xFEZERERUUoq0hygtLU1hz1Cep0+fQltb+6OLIiIiIipJRQpEzZo1w8qVK6XnMpkMubm5mDp1Klq2bFlsxRERERGVhCIdMps6dSpat26N06dPIzMzE4GBgbh8+TKePn2Ko0ePFneNRERERJ9UkfYQ1a1bFzdu3EDTpk3RsWNHpKWloXPnzjh37hyqVatW3DUSERERfVJF2kMEAIaGhvjf//5XnLUQERERKUWRA9GzZ8+wfPlyXL16FQBgZ2eHvn37wtjYuNiKIyIiIioJRTpkdujQIVhbW2POnDl49uwZnj17hjlz5sDGxgaHDh0q7hqJiIiIPqki7SEKCAiAr68vFi5cCHV1dQBATk4OhgwZgoCAAFy8eLFYiyQiIiL6lIq0h+jWrVv44YcfpDAEAOrq6hg1ahRu3bpVbMURERERlYQiBSJ7e3vp3KHXXb16FQ0aNPjoooiIiIhK0nsfMrtw4YL07+HDh+P777/HrVu34OzsDAA4fvw45s+fjylTphR/lUT02fEN9/2o9dd7rS+mSoiI3u29A1HDhg0hk8kghJDaAgMD8/X75ptv4Ov7cf8REhEREZWk9w5EsbGxn7IOIiIiIqV570BkZWX1KesgIiIiUpoifzHjw4cPceTIESQmJiI3N1dh2fDhwz+6MCIiIqKSUqRAFBoaikGDBkFLSwsmJiaQyWTSMplMxkBEREREn5UiBaJffvkF48ePx9ixY6GmVqQr94mIiIhKjSKlmRcvXqB79+4MQ0RERFQmFCnR9O/fHxs3bizuWoiIiIiUokiHzCZPngwvLy9ERESgXr160NTUVFg+Y8aMYimOiIiorPuYLzHlF5gWnyIHot27d6NWrVoAkO+kaiIiIqLPSZEC0fTp07FixQr06dOnmMshIiIiKnlFOodIW1sbrq6uxV0LERERkVIUKRB9//33mDt3bnHXQkRERKQURTpkdvLkSezfvx/h4eGoU6dOvpOqN2/eXCzFEREREZWEIgUiIyMjdO7cubhrISIiIlKKIgWikJCQ4q6DiIiISGn4VdNERESk8oq0h8jGxqbQ7xu6c+dOkQsiIiIiKmlFCkQjRoxQeJ6VlYVz584hIiICo0ePLo66iIiIiEpMkQLR999/X2D7/Pnzcfr06Y8qiIiIiKikFes5RO3atcOmTZveu//ChQtRv359yOVyyOVyuLi4YNeuXdLy9PR0BAQEwMTEBAYGBvDx8UFCQoLCGPfv34enpyf09PRgamqK0aNHIzs7W6FPVFQU7O3toa2tjerVqyM0NPSj5klERERlS7EGor///hvGxsbv3b9SpUqYMmUKzpw5g9OnT6NVq1bo2LEjLl++DAAYOXIktm/fjo0bN+LgwYN4+PChwuX+OTk58PT0RGZmJo4dO4awsDCEhoZi/PjxUp/Y2Fh4enqiZcuWiImJwYgRIzBgwADs3r27+CZOREREn7UiHTJr1KiRwknVQgjEx8fj0aNHWLBgwXuP06FDB4Xnv/32GxYuXIjjx4+jUqVKWL58OdauXYtWrVoBeHW5v62tLY4fPw5nZ2fs2bMHV65cwd69e2FmZoaGDRti0qRJGDNmDIKDg6GlpYVFixbBxsYG06dPBwDY2triyJEjmDlzJjw8PIoyfSIiIipjihSIOnbsqBCI1NTUULFiRbi5uaF27dpFKiQnJwcbN25EWloaXFxccObMGWRlZcHd3V3qU7t2bVSpUgXR0dFwdnZGdHQ06tWrBzMzM6mPh4cHBg8ejMuXL6NRo0aIjo5WGCOvz5snhr8uIyMDGRkZ0vOUlJQizYmIiIg+D0UKRMHBwcVWwMWLF+Hi4oL09HQYGBjgn3/+gZ2dHWJiYqClpQUjIyOF/mZmZoiPjwcAxMfHK4ShvOV5ywrrk5KSgpcvX0JXVzdfTZMnT8aECROKa4pERERUyn3QOURqampQV1cv9KGh8WEZq1atWoiJicGJEycwePBg+Pv748qVKx80RnEbO3YskpOTpceDBw+UWg8RERF9Wh+UXv7555+3LouOjsacOXOQm5v7QQVoaWmhevXqAAAHBwecOnUKs2fPhq+vLzIzM5GUlKSwlyghIQHm5uYAAHNzc5w8eVJhvLyr0F7v8+aVaQkJCZDL5QXuHQIAbW1taGtrf9A8iIiI6PP1QYGoY8eO+dquX7+On376Cdu3b4efnx8mTpz4UQXl5uYiIyMDDg4O0NTUxL59++Dj4yNt6/79+3BxcQEAuLi44LfffkNiYiJMTU0BAJGRkZDL5bCzs5P67Ny5U2EbkZGR0hhERERERTqHCAAePnyIoKAghIWFwcPDAzExMahbt+4HjTF27Fi0a9cOVapUwfPnz7F27VpERUVh9+7dMDQ0RP/+/TFq1CgYGxtDLpdj2LBhcHFxgbOzMwCgTZs2sLOzQ69evTB16lTEx8dj3LhxCAgIkPbwfPfdd5g3bx4CAwPRr18/7N+/Hxs2bMCOHTuKOnUiIiIqYz44ECUnJ+P333/H3Llz0bBhQ+zbtw/NmjUr0sYTExPRu3dvxMXFwdDQEPXr18fu3bvx1VdfAQBmzpwJNTU1+Pj4ICMjAx4eHgqX9aurqyM8PByDBw+Gi4sL9PX14e/vr7CXysbGBjt27MDIkSMxe/ZsVKpUCcuWLeMl90RERCT5oEA0depU/PHHHzA3N8dff/1V4CG0D7F8+fJCl+vo6GD+/PmYP3/+W/tYWVnlOyT2Jjc3N5w7d65INRIREVHZ90GB6KeffoKuri6qV6+OsLAwhIWFFdhv8+bNxVIcERERUUn4oEDUu3dvhS9kJCIiIioLPigQ8aaoREREVBYV681diYiIiD5HDERERESk8hiIiIiISOUxEBEREZHKYyAiIiIilVfkW3cQ0afhG+5b5HXXe60vxkqIiFQH9xARERGRymMgIiIiIpXHQEREREQqj4GIiIiIVB4DEREREak8BiIiIiJSeQxEREREpPIYiIiIiEjlMRARERGRymMgIiIiIpXHQEREREQqj4GIiIiIVB4DEREREak8BiIiIiJSeQxEREREpPIYiIiIiEjlMRARERGRytNQdgFEREQfyjfc96PWX++1vpgqobKCe4iIiIhI5TEQERERkcpjICIiIiKVx3OIiIhKWIe5R4q87vZhTYuxEiLKwz1EREREpPIYiIiIiEjlMRARERGRymMgIiIiIpXHQEREREQqj4GIiIiIVB4DEREREak8BiIiIiJSeQxEREREpPIYiIiIiEjlMRARERGRymMgIiIiIpXHQEREREQqT6mBaPLkyWjcuDHKlSsHU1NTeHt74/r16wp90tPTERAQABMTExgYGMDHxwcJCQkKfe7fvw9PT0/o6enB1NQUo0ePRnZ2tkKfqKgo2NvbQ1tbG9WrV0doaOinnh4RERF9JpQaiA4ePIiAgAAcP34ckZGRyMrKQps2bZCWlib1GTlyJLZv346NGzfi4MGDePjwITp37iwtz8nJgaenJzIzM3Hs2DGEhYUhNDQU48ePl/rExsbC09MTLVu2RExMDEaMGIEBAwZg9+7dJTpfIiIiKp00lLnxiIgIheehoaEwNTXFmTNn0Lx5cyQnJ2P58uVYu3YtWrVqBQAICQmBra0tjh8/DmdnZ+zZswdXrlzB3r17YWZmhoYNG2LSpEkYM2YMgoODoaWlhUWLFsHGxgbTp08HANja2uLIkSOYOXMmPDw8SnzeREREVLooNRC9KTk5GQBgbGwMADhz5gyysrLg7u4u9alduzaqVKmC6OhoODs7Izo6GvXq1YOZmZnUx8PDA4MHD8bly5fRqFEjREdHK4yR12fEiBEF1pGRkYGMjAzpeUpKSnFNkT4R33DfIq+73mt9MVZCRESfo1JzUnVubi5GjBgBV1dX1K1bFwAQHx8PLS0tGBkZKfQ1MzNDfHy81Of1MJS3PG9ZYX1SUlLw8uXLfLVMnjwZhoaG0qNy5crFMkciIiIqnUpNIAoICMClS5ewbt06ZZeCsWPHIjk5WXo8ePBA2SURERHRJ1QqDpkNHToU4eHhOHToECpVqiS1m5ubIzMzE0lJSQp7iRISEmBubi71OXnypMJ4eVehvd7nzSvTEhISIJfLoaurm68ebW1taGtrF8vciIiIqPRTaiASQmDYsGH4559/EBUVBRsbG4XlDg4O0NTUxL59++Dj4wMAuH79Ou7fvw8XFxcAgIuLC3777TckJibC1NQUABAZGQm5XA47Ozupz86dOxXGjoyMlMYgIiIiAItbfNz6X5gXTx1KoNRAFBAQgLVr12Lr1q0oV66cdM6PoaEhdHV1YWhoiP79+2PUqFEwNjaGXC7HsGHD4OLiAmdnZwBAmzZtYGdnh169emHq1KmIj4/HuHHjEBAQIO3l+e677zBv3jwEBgaiX79+2L9/PzZs2IAdO3Yobe5ERERUeij1HKKFCxciOTkZbm5usLCwkB7r1//fVT8zZ86El5cXfHx80Lx5c5ibm2Pz5s3ScnV1dYSHh0NdXR0uLi7o2bMnevfujYkTJ0p9bGxssGPHDkRGRqJBgwaYPn06li1bxkvuiYiICEApOGT2Ljo6Opg/fz7mz5//1j5WVlb5Dom9yc3NDefOnfvgGomIiKjsKzVXmREREREpCwMRERERqTwGIiIiIlJ5DERERESk8hiIiIiISOUxEBEREZHKYyAiIiIilcdARERERCqPgYiIiIhUHgMRERERqTwGIiIiIlJ5DERERESk8pR6c1ciIiIqXh3mHinyutu1irGQzwwDERERET4ySAxrWoyVkDLwkBkRERGpPAYiIiIiUnkMRERERKTyGIiIiIhI5TEQERERkcpjICIiIiKVx0BEREREKo+BiIiIiFQeAxERERGpPH5TtQrzDff9qPXXe60vpkqISreP+V3h7wnR54F7iIiIiEjlMRARERGRymMgIiIiIpXHQEREREQqj4GIiIiIVB4DEREREak8XnZPVIAOc48Ued3tw5oWYyVERFQSuIeIiIiIVB4DEREREak8BiIiIiJSeQxEREREpPIYiIiIiEjl8SozIiIqMl6RSWUF9xARERGRymMgIiIiIpXHQEREREQqj4GIiIiIVB5PqiYq43jSKxHRuzEQERERfazFLYq+7hfmxVcHFRkDERHR5+RjPngBfvgSvYVSzyE6dOgQOnToAEtLS8hkMmzZskVhuRAC48ePh4WFBXR1deHu7o6bN28q9Hn69Cn8/Pwgl8thZGSE/v37IzU1VaHPhQsX0KxZM+jo6KBy5cqYOnXqp54aERERfUaUGojS0tLQoEEDzJ8/v8DlU6dOxZw5c7Bo0SKcOHEC+vr68PDwQHp6utTHz88Ply9fRmRkJMLDw3Ho0CF8++230vKUlBS0adMGVlZWOHPmDP78808EBwdjyZIln3x+RERE9HlQ6iGzdu3aoV27dgUuE0Jg1qxZGDduHDp27AgAWLlyJczMzLBlyxZ0794dV69eRUREBE6dOgVHR0cAwNy5c9G+fXtMmzYNlpaWWLNmDTIzM7FixQpoaWmhTp06iImJwYwZMxSCExEREamuUnvZfWxsLOLj4+Hu7i61GRoawsnJCdHR0QCA6OhoGBkZSWEIANzd3aGmpoYTJ05IfZo3bw4tLS2pj4eHB65fv45nz54VuO2MjAykpKQoPIiIiKjsKrWBKD4+HgBgZmam0G5mZiYti4+Ph6mpqcJyDQ0NGBsbK/QpaIzXt/GmyZMnw9DQUHpUrlz54ydEREREpVapDUTKNHbsWCQnJ0uPBw8eKLskIiIi+oRKbSAyN391aWhCQoJCe0JCgrTM3NwciYmJCsuzs7Px9OlThT4FjfH6Nt6kra0NuVyu8CAiIqKyq9QGIhsbG5ibm2Pfvn1SW0pKCk6cOAEXFxcAgIuLC5KSknDmzBmpz/79+5GbmwsnJyepz6FDh5CVlSX1iYyMRK1atVC+fPkSmg0RERGVZkoNRKmpqYiJiUFMTAyAVydSx8TE4P79+5DJZBgxYgR+/fVXbNu2DRcvXkTv3r1haWkJb29vAICtrS3atm2LgQMH4uTJkzh69CiGDh2K7t27w9LSEgDwzTffQEtLC/3798fly5exfv16zJ49G6NGjVLSrImIiKi0Uepl96dPn0bLli2l53khxd/fH6GhoQgMDERaWhq+/fZbJCUloWnTpoiIiICOjo60zpo1azB06FC0bt0aampq8PHxwZw5c6TlhoaG2LNnDwICAuDg4IAKFSpg/PjxvOSeiIiIJEoNRG5ubhBCvHW5TCbDxIkTMXHixLf2MTY2xtq1awvdTv369XH48OEi10lERERlW6k9h4iIiIiopDAQERERkcpjICIiIiKVp9RziKjs6TD3SJHX3T6saTFWQkRE9P4YiEoBhggqtRa3KPq6XxT8xadERKURD5kRERGRymMgIiIiIpXHQEREREQqj4GIiIiIVB4DEREREak8BiIiIiJSeQxEREREpPIYiIiIiEjlMRARERGRymMgIiIiIpXHQEREREQqj/cyI6LPBu/7R0SfCgMRUXH7mBuiArwpKhGREvCQGREREak8BiIiIiJSeTxkRkREyvExh5d5aJmKGfcQERERkcpjICIiIiKVx0BEREREKo+BiIiIiFQeT6omItXAE3iJqBDcQ0REREQqj4GIiIiIVB4DEREREak8BiIiIiJSeQxEREREpPIYiIiIiEjl8bJ7Kj14WTQRESkJ9xARERGRymMgIiIiIpXHQEREREQqj+cQfe543g0REdFH4x4iIiIiUnkMRERERKTyGIiIiIhI5TEQERERkcpjICIiIiKVx0BEREREKk+lAtH8+fNhbW0NHR0dODk54eTJk8ouiYiIiEoBlQlE69evx6hRoxAUFISzZ8+iQYMG8PDwQGJiorJLIyIiIiVTmUA0Y8YMDBw4EH379oWdnR0WLVoEPT09rFixQtmlERERkZKpRCDKzMzEmTNn4O7uLrWpqanB3d0d0dHRSqyMiIiISgOVuHXH48ePkZOTAzMzM4V2MzMzXLt2LV//jIwMZGRkSM+Tk5MBACkpKZ+kvqyXaUVeNyUnu+jbfZFV5HWBgl+Pz3EupWkeQNmZC3++/v92S9E8gLIzF/58/f/tlqJ5AMU/l4+VN6YQ4t2dhQr477//BABx7NgxhfbRo0eLL7/8Ml//oKAgAYAPPvjggw8++CgDjwcPHrwzK6jEHqIKFSpAXV0dCQkJCu0JCQkwN89/g9OxY8di1KhR0vPc3Fw8ffoUJiYmkMlkn7ze4pKSkoLKlSvjwYMHkMvlyi6nyMrKPADOpTQqK/MAOJfSqKzMA/g85yKEwPPnz2FpafnOvioRiLS0tODg4IB9+/bB29sbwKuQs2/fPgwdOjRff21tbWhrayu0GRkZlUCln4ZcLv9sfngLU1bmAXAupVFZmQfAuZRGZWUewOc3F0NDw/fqpxKBCABGjRoFf39/ODo64ssvv8SsWbOQlpaGvn37Krs0IiIiUjKVCUS+vr549OgRxo8fj/j4eDRs2BARERH5TrQmIiIi1aMygQgAhg4dWuAhsrJKW1sbQUFB+Q7/fW7KyjwAzqU0KivzADiX0qiszAMoW3MpiEyI97kWjYiIiKjsUokvZiQiIiIqDAMRERERqTwGIiIiIlJ5DERKFBUVhSlTphTLWM+fP4ejo6PC9yVlZmbC19cXrq6uWLNmzQeNFxgYWORabt++jQYNGqBu3bpSW1JSEtq1a4cmTZrgwIED+dbx9/dH+fLlcfz4cYX2OXPmoG3bth+0/dDQUFy5cqVoxb+hoLnksbe3x7p16/K1FzSXwYMHo2XLlgpf+Pk+imsuO3bsgLOzM5ydnbF+/XoAhb8naWlpaN26NZo3b46ePXsiNzdXWqbs96SguYSGhsLOzg5ubm6YPXu2Qv+3zUXZ7wkAXLp0Ca6urmjSpAmCgoIAvPv39vvvv0eLFi3QtGlT3Lp1S2o/f/48dHV1P2j7MTExWLt27cdNAgXPIyoqClWrVoWbmxvGjh1b4HoJCQnQ1dVFfHy81KbMebxu1KhR+O677wC8ey59+vRBkyZN4ObmhoMHDwIAVqxYAVdXV3h7e+P58+fvvd3imMvixYvh7u4ONzc3AB/3WVCQD/39/2wUz80xqCgOHDggJk+eXCxjZWVlicePHwsPDw+pbf369WLevHkiJydHtG7dWmRnZxfLtt4lLS1NpKSkKNTyxx9/iPDwcJGamiratm2bb524uDgRFBQkoqOjpbasrCzRp08fhXFKWkFzEUKIPXv2iJYtW4q//vor3zpvzuX48ePihx9+EEIIERgYKM6ePfvpC3/DgwcPRHZ2tsjKyhLNmzcXQhT+nqSnp4u4uDghhBDjxo0T+/btE0KUjvekoLmEhIQU+F4IUfBcSsN7IsSr1zNP69atRWpq6jt/bzMzM4UQQhw6dEiMHj1aah8yZIho0qRJyRT+hoLm8T7/v40fP140b95cen+EUO488jx9+lR06NBBDBo0SAjx7v+r/f39FeaQlZUlWrVqJXJzc8XOnTvFjBkzPnnNee7duye+//57hbbi/iwozt//nJycYhvrY3EPUSmSk5ODPn36oFWrVvj2228BALNnz0aTJk3QsmVLJCQk5HueR0NDAyYmJgrjnTp1Cq1atYKamhpsbW1x7949adndu3fRsmVLfP3112jdujXmzJkDJycnzJkzB8D//QUQHByMfv36oUWLFtJffqNHj0bz5s3RsmXLAuehp6eHcuXKFViLvr4+tLS0kJ2teAPBgm6hsmHDBnTp0qXAbbi5uaFPnz5o0KABQkND4e7ujh49ekg1Hz9+HFFRUejQoQM8PT3Rvn17CCGwceNGODk5oWXLljh//nyBY79rLgCwZs0aaXtvenMusbGxqFevHgCgfv36iI6OLvG5VKpUCerq6tIDKPw90dbWluahqakp3bKmNLwnBc0FAH7//Xe0bt0aFy5cUOhf0FxKw3sCvPq9BV797n/xxRfQ0dEp9Pc2bw4AkJqaKu25vH//PuRyeYE/q3369EFAQAAcHBywePFidOzYEU5OTkhKSlLYS+3o6AhfX184ODjg33//xfXr1+Hi4oKWLVsiNDT0g+cBACEhIWjevDn279+fb520tDTEx8fDxsZGalP2PPIsWrRI+j84T2Fzkclk6NKlC7p3745nz57h8ePHqFKlCmQyWYE/X59yLnv27EFKSgpatWqF33//HUDhnwUZGRlo1aoVXFxc8L///Q/Aq72gvr6+8PDwQP/+/QEAx48fh6OjI/z8/JCenq6wzaioKOn3oEePHggKCoK9vT02b94MAPj111/RtGlTeHl54fnz54iKikKnTp3g6emJc+fO5fvcUxYGolJk27ZtcHR0xP79+/HFF1/g+PHj2LdvHw4ePIgDBw7A1NQ03/PCJCUlSV+vbmhoiKSkJIXlOTk52LZtG+rXrw8NDQ1ER0djy5Yt+cZxcXGRtgkAly9fxsGDBwv8j+Ft0tLSpN3g5cqVe6+7GoeHh6N9+/YFLnv8+DHmzJmDiRMnYufOndi7dy9yc3Pz3a9OLpdjx44dqFatGi5cuIBt27Zhx44dOHDgAOrXr//e9b8uJiYGtra20gfTu9SuXVvajX7w4EEkJycrbS6hoaFo06YNgPd7Tx49eoR9+/ahefPmAErXe/L6XDp16oTz589j+fLlGD16dIH9X59LaXpP/v77b9jZ2UEul0NdXf2dv7cA0L59ewwdOhSNGjUCAMydOxdDhgx56zY6d+6Mffv2YdKkSdi4cSP69OmD3bt355vz6tWr8fPPP2PTpk04fPgwfvzxRxw4cAD+/v4fPA9HR0dcunQJW7ZskT5oX7ds2bJ8dwooDfPIyMjA9evXFQ6Tv2suM2bMwJEjR9ClSxdMnz4dFStWxK1bt5CRkVHgz9ennEtiYiI0NDSwf/9+XL58GdeuXSv0Z0pTUxO7du1CdHQ0zp49ixcvXgAAqlevjt27d+PRo0dISkrClClTsHPnTsycOVPhEGceY2Nj7Ny5E+np6XBycsKhQ4cQEhKCuLg4nD17FkeOHME333yDkJAQAIC6ujp27NiB+/fv5/vcUxYGolLkxo0bWLFiBdzc3BAeHo74+HgEBgZiwIABGDFiBDIyMvI9L4yhoaH0IZeSkpLvfmy2trYAADMzM9ja2kJNTQ1aWlr5xsn7j0FPTw8A8N1336F3794ICgqCeM+vsdLX18fLly8BvDrfSS6Xw83NTTrG/abIyEi0bNnyrTfT/eKLLyCXy6XaAcDCwiLfh0de7V988QWSkpLw008/YcyYMRg0aBCePn36XrW/aeHChfn+kilsLg0bNoSVlRVatWqFnJycfEG2pOZy+fJlbN68WQoM73pPcnJy0L9/f8ybNw/q6uql6j15cy6GhoaQyWSwtraW6itsLqXlPQGALl264Nq1a0hMTMTFixcL/L3t3r073NzcpA+inTt3Ytu2bQgODsazZ8+km26+jZ2dHYyMjFCnTh1oaWkVOJdatWpBU1NTmkvXrl1x+PBh9OrVC+fOnfvgeRgYGEBTUxPGxsaoUqUKkpOTpXn8+++/OHHiBJydnaX1S8s8Vq5cCT8/P4W2wuYSHx+P8uXLAwA8PT1x+fJlqKur48cff4SHhwfOnTtX4B0RPtVcDA0N0aJFCwBA8+bNcf369UI/C1JTU+Hn54cWLVrg/PnzePToEYD/+9m2tLREcnIyMjIyYGpqClNTU1hYWBQ4H+D/Pk8MDAyQlZWFe/fuoWHDhgAABwcH3LlzB8CrczCBgj/3lIWBqBSpXr06Bg8ejKioKJw6dQpeXl5wdHREWFgYzM3NsW/fvnzPC9O4cWMcOHAAubm5uHr1KqysrBSWv/7B9rYPuYKWtWnTBqtWrUJiYuJ7n1zq6OiIAwcO4MWLF8jMzISGhgaioqIQFRVVYP+rV69i3bp1aNu2Lc6cOYOVK1e+V+1vBrQ3l1WrVg3Lly9Hy5YtCzwh+n3cu3cPPXr0wPTp0zFlyhTExcUVOhcACAoKwv79+1GuXDlpr0ZJziU5ORlDhw7FsmXLpMNM73pPfv75Z3Tt2lU6tFRa3pOC5pJ30uqzZ8+kQ3+FzQVQ/nsCvDrZNW+ccuXKQUdHp8Df23Xr1iEqKgrm5ubSOnK5HLq6urhx4wYuXrwovS8//vhjvu3k1fkhc9HW1sasWbPw559/YvLkyR88j7z3JCMjA//++y8MDQ2lechkMty6dQtt27ZFZGQkhg8fXirmAQC3bt3C9OnT4e/vj507d2L37t2FzsXc3FxafuLECVStWhUA0LFjR0RFRcHJyQnt2rUrsbl8+eWXuHjxIgDg4sWLsLa2LvSzYM+ePWjSpAkOHjyIunXrSjW8uW0tLS08evQIjx49Qlxc3Fvn8+a/rayspMPHZ86ckV4fNbVX8aOgzz1lUalbd5RGy5cvx969ewG8ujJg8uTJ+Ouvv6CmpoalS5ciODgYsbGxUFNTw8CBAzFo0CCF56/z8PDAmTNn4O7ujgULFsDb2xu9evXC6tWrMWTIEIXzLT5Gx44dkZaWhvLly6NGjRr5lj9+/Bjdu3dHTEwM3N3dsXnzZnz77bfo0aMHfv31V0yaNCnfOoGBgfj777+xc+dOjB49GsOHD8fw4cMBvDqfqXfv3sVSe1BQEKKjo5GZmYnVq1e/s39Bc4mIiADw6pCNjo5Ovr+W3pyLt7c3vvrqK6irq8PX17fQv4A/1VwWLFiA2NhY+Pr6AngVFgp7Tx4+fCidV7Z8+XL89NNPpeY9KWgus2fPxs6dOwEAf/zxxzvn0rp1a6W/J8CrD6Np06YhNzcXrq6uqFGjBqpUqVLo722PHj2kvU/z58+HnZ0djh07BuDV+zJt2rRimcvWrVsxf/58pKWl4ZdffvngeaxYsQKLFy+GTCbLF26++OILnDx5EsCr82mmTJkCc3Nzpc8D+L+fn7t372LKlCnw8PAodC4A0Lt3bzx+/Bi6uroICwsDAAwZMgTXr19HnTp1MHPmzBKbS+PGjbFy5Uq4ubmhbt26aNCgAWrXrv3Wn6nGjRtj0qRJOHz4cKF7/MeMGYN27drBzs6uwD1Eb2NhYYEGDRqgadOmkMvlWLduHc6ePSst9/b2xqBBgxQ+914/r6wk8dYdREREpPJ4yIyIiIhUHgMRERERqTwGIiIiIlJ5DERERESk8hiIiIiISOUxEBEREZHKYyAiIiIilcdAREQf5O7du5DJZIiJifmg9UJDQ/PdPobyK+rrm5mZierVq0tfblhUffr0gbe390eN8TpnZ2ds2rSp2MYj+lQYiIhKKZlMVugjODhY2SW+lbW1NWbNmqXQ5uvrixs3bpR4LUUNGJ+bRYsWwcbGBk2aNPmocWbPnv3ed4V/H+PGjcNPP/2E3NzcYhuT6FNgICIqpeLi4qTHrFmzIJfLFdpev4WAEEK6h1dppaurm+8mqqQo755gH0oIgXnz5qF///4fXYOhoWGx7slr164dnj9/jl27dhXbmESfAgMRUSllbm4uPfLu5p73/Nq1ayhXrhx27doFBwcHaGtr48iRI7h9+zY6duwIMzMzGBgYoHHjxtK98oBXNzl1cnLKt60GDRpg4sSJ0vNly5bB1tYWOjo6qF27NhYsWPDedbu5ueHevXsYOXKktDcLyH/ILDg4GA0bNsSKFStQpUoVGBgYYMiQIcjJycHUqVNhbm4OU1NT/PbbbwrjJyUlYcCAAahYsSLkcjlatWol3TyyIHn3RWrUqBFkMhnc3NwAALm5uZg4cSIqVaoEbW1tNGzYULpPXUHCw8NhZGSEnJwcAEBMTAxkMhl++uknqc+AAQPQs2dP6fmmTZtQp04daGtrw9raGtOnT1cY09raGpMmTULv3r0hl8vx7bff5ttuTk4O+vXrh9q1a+P+/fsF1nbmzBncvn0bnp6eUlvenrENGzagWbNm0NXVRePGjXHjxg2cOnUKjo6OMDAwQLt27aQ7nAP5D5m5ublh+PDhCAwMhLGxMczNzRX2TgohEBwcjCpVqkBbWxuWlpbSPe8AQF1dHe3bty/yzZSJSowgolIvJCREGBoaSs8PHDggAIj69euLPXv2iFu3boknT56ImJgYsWjRInHx4kVx48YNMW7cOKGjoyPu3bsnhBDi0qVLAoC4deuWNFZe282bN4UQQqxevVpYWFiITZs2iTt37ohNmzYJY2NjERoaKoQQIjY2VgAQ586dK7DWJ0+eiEqVKomJEyeKuLg4ERcXV+AcgoKChIGBgejSpYu4fPmy2LZtm9DS0hIeHh5i2LBh4tq1a2LFihUCgDh+/Li0nru7u+jQoYM4deqUuHHjhvjhhx+EiYmJePLkSYH1nDx5UgAQe/fuFXFxcVK/GTNmCLlcLv766y9x7do1ERgYKDQ1NcWNGzcKHCcpKUmoqamJU6dOCSGEmDVrlqhQoYJwcnKS+lSvXl0sXbpUCCHE6dOnhZqampg4caK4fv26CAkJEbq6uiIkJETqb2VlJeRyuZg2bZq4deuWuHXrlsLrm56eLjp16iQaNWokEhMTC6wrby61a9dWaMsbp3bt2iIiIkJcuXJFODs7CwcHB+Hm5iaOHDkizp49K6pXry6+++47aT1/f3/RsWNH6XmLFi2EXC4XwcHB4saNGyIsLEzIZDKxZ88eIYQQGzduFHK5XOzcuVPcu3dPnDhxQixZskShloULFworK6u31k9UGjAQEX0G3haItmzZ8s5169SpI+bOnSs9b9CggZg4caL0fOzYsQof6tWqVRNr165VGGPSpEnCxcVFCPHuQCTEqw/6mTNnFjqHoKAgoaenJ1JSUqQ2Dw8PYW1tLXJycqS2WrVqicmTJwshhDh8+LCQy+UiPT1dYexq1aqJxYsXF1jL2+q1tLQUv/32m0Jb48aNxZAhQ946L3t7e/Hnn38KIYTw9vYWv/32m9DS0hLPnz8X//77rwAgBapvvvlGfPXVVwrrjx49WtjZ2UnPrayshLe3d4H1Hj58WLRu3Vo0bdpUJCUlvbUmIYT4/vvvRatWrQocZ9myZVLbX3/9JQCIffv2SW2TJ08WtWrVkp4XFIiaNm2qMHbjxo3FmDFjhBBCTJ8+XdSsWVNkZma+tb6tW7cKNTU1hfeVqLThITOiz5ijo6PC89TUVPz444+wtbWFkZERDAwMcPXqVYVDLX5+fli7di2AV4c7/vrrL/j5+QEA0tLScPv2bfTv3x8GBgbS49dff8Xt27eLvX5ra2uUK1dOem5mZgY7OzuoqakptCUmJgIAzp8/j9TUVJiYmCjUFxsb+0H1paSk4OHDh3B1dVVod3V1xdWrV9+6XosWLRAVFQUhBA4fPozOnTvD1tYWR44cwcGDB2FpaYkaNWoAAK5evVrg+Ddv3pQOuwH538M8PXr0QFpaGvbs2QNDQ8NC5/Py5Uvo6OgUuKx+/frSv83MzAAA9erVU2jLe33f5vUxAMDCwkJap2vXrnj58iWqVq2KgQMH4p9//sl3Ppuuri5yc3ORkZFR6HaIlElD2QUQUdHp6+srPP/xxx8RGRmJadOmoXr16tDV1UWXLl0UTtbt0aMHxowZg7Nnz+Lly5d48OABfH19AbwKVACwdOnSfOcaqaurF3v9mpqaCs9lMlmBbXlXKKWmpsLCwgJRUVH5xiqJS/rd3NywYsUKnD9/Hpqamqhduzbc3NwQFRWFZ8+eoUWLFh885pvvYZ727dtj9erViI6ORqtWrQodo0KFCrh48WKBy15/PfPO53qz7V1XgBX2nlSuXBnXr1/H3r17ERkZiSFDhuDPP//EwYMHpfWePn0KfX196OrqFrodImViICIqQ44ePYo+ffqgU6dOAF4FiLt37yr0qVSpElq0aIE1a9bg5cuX+Oqrr6Srv8zMzGBpaYk7d+5Ie42KQktLS2EvSHGxt7dHfHw8NDQ0YG1t/d61AFCoRy6Xw9LSEkePHlUIMUePHsWXX3751rGaNWuG58+fY+bMmdJ6bm5umDJlCp49e4YffvhB6mtra4ujR48qrH/06FHUrFnzvcLl4MGDUbduXXz99dfYsWNHoWGrUaNGWLhwIYQQUugpSbq6uujQoQM6dOiAgIAA1K5dGxcvXoS9vT0A4NKlS2jUqFGJ10X0IRiIiMqQGjVqYPPmzejQoQNkMhl++eWXAv/69/PzQ1BQEDIzMzFz5kyFZRMmTMDw4cNhaGiItm3bIiMjA6dPn8azZ88watSo96rD2toahw4dQvfu3aGtrY0KFSoUy/zc3d3h4uICb29vTJ06FTVr1sTDhw+xY8cOdOrUqcDDT6amptDV1UVERAQqVaoEHR0dGBoaYvTo0QgKCkK1atXQsGFDhISEICYmBmvWrHnr9suXL4/69etjzZo1mDdvHgCgefPm6NatG7KyshRCyw8//IDGjRtj0qRJ8PX1RXR0NObNm/dBV+wNGzYMOTk58PLywq5du9C0adMC+7Vs2RKpqam4fPky6tat+97jF4fQ0FDk5OTAyckJenp6WL16NXR1dWFlZSX1OXz4MNq0aVOidRF9KJ5DRFSGzJgxA+XLl0eTJk3QoUMHeHh4SH+lv65Lly548uQJXrx4ke9biQcMGIBly5YhJCQE9erVQ4sWLRAaGipdvv4+Jk6ciLt376JatWqoWLHix05LIpPJsHPnTjRv3hx9+/ZFzZo10b17d9y7d086P+ZNGhoamDNnDhYvXgxLS0t07NgRADB8+HCMGjUKP/zwA+rVq4eIiAhs27ZNOgfobVq0aIGcnBzp8n1jY2PY2dnB3NwctWrVkvrZ29tjw4YNWLduHerWrYvx48dj4sSJ6NOnzwfNecSIEZgwYQLat2//1m+hNjExQadOnQoNc5+KkZERli5dCldXV9SvXx979+7F9u3bYWJiAgD477//cOzYMfTt27fEayP6EDIhhFB2EURE9HEuXLiAr776Crdv34aBgYGyy5GMGTMGz549w5IlS5RdClGhuIeIiKgMqF+/Pv744w/ExsYquxQFpqammDRpkrLLIHon7iEiIiIilcc9RERERKTyGIiIiIhI5TEQERERkcpjICIiIiKVx0BEREREKo+BiIiIiFQeAxERERGpPAYiIiIiUnkMRERERKTy/h+dJ1nyUaTOoAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axe = plt.subplots()\n", - "\n", - "\n", - "label = x\n", - "index = np.arange(len(label))-0.3\n", - "ti=index\n", - "for a in area:\n", - "\n", - " values1 = df_total[\"{}_2021\".format(a)]\n", - " ti+=0.3\n", - " axe.bar(ti, values1, width=0.3,label=\"{}\".format(a),alpha=.8)\n", - "axe.set_xticks(index-0.3)\n", - "axe.set_xticklabels(label)\n", - "\n", - "plt.xticks(fontsize=5.5)\n", - "plt.ylabel(\"Number\")\n", - "plt.xlabel(\"Travel time to work (mins)\")\n", - "plt.title(\"2021 Travel Time to Work by Area\")\n", - "# plt.title(\"2021 Percentage of total number of travel time to work\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axe = plt.subplots()\n", - "\n", - "\n", - "label = x\n", - "index = np.arange(len(label))-0.3\n", - "ti=index\n", - "for a in area:\n", - "\n", - " values1 = df_bus[\"{}_2021\".format(a)]\n", - " ti+=0.3\n", - " axe.bar(ti, values1, width=0.3,label=\"{}\".format(a),alpha=.8)\n", - "axe.set_xticks(index-0.3)\n", - "axe.set_xticklabels(label)\n", - "\n", - "plt.xticks(fontsize=5.5)\n", - "plt.ylabel(\"Number\")\n", - "plt.xlabel(\"Travel time to work (mins)\")\n", - "plt.title(\"2021 Travel Time to Work by Area (Bus)\")\n", - "# plt.title(\"2021 Percentage of total number of travel time to work\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_15876\\1959876970.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", - " df_total_per[\"{}_{}\".format(a,y)][0]\n" - ] - }, - { - "data": { - "text/plain": [ - "0.05645126801522474" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_total_per[\"{}_{}\".format(a,y)][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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xIpGIlcvlbE5ODnv+/HkWALtnzx6tbY8fP66z3N7evsxjWbhwIQuAPXz4sE4bNHnbP/30E8vj8djz589rrd+6dSsLgL148SK3DAArFArZiIgIbtm///7LAmA3bdrELavoNc3NzdVZ5uvryzo5OXGPExMTWT6fzw4YMECr3OLFi1kAWrnGX375JWtgYKCVH8+yLPvZZ5+xenp6bGxsrM7+NJRKJSsUCtnevXuzKpWKW75582ad/HzNa5aUlFRufRrl5ZgeOXKEBcAuW7ZMa/ngwYNZhmG0ntfycqrLev4uX76s8zmtbE615rhe/IwMHDiQNTc35x5rxhGU9T5/8bOuqXPixIncsuLiYrZRo0YswzDsihUruOVpaWmsWCzWOlZN221tbdnMzExu+a+//soCYDds2MCybMl72NXVlfX19dUah5Cbm8s6OjqyvXr10mnTsGHDKnw+NKZPn84C0PpcZGVlsY6OjqyDg4PW+wUAGxQU9NI6Kzovjh49mgXAfvbZZzrrynrNly9fzjIMw8bExHDLvLy82LZt22qVu3LlitZ7oyrPWW3kVNf2e6Mq56uylJdTHRwczAJgf/75Z25ZYWEh6+XlxUqlUq334YuUSiULgP32229ZlmXZ9PR0lsfjsUOGDGHlcjlXbtq0aayZmRn3vFd2n5rPnpGREatUKrX2XTqnOisri+3WrRtrYWHB3rx5s8LnoXS9pT/Tmvfh0qVLtcq2bt1a571Vlm7durHu7u4sy7Jsu3bt2PHjx7MsW/JaCoVCdvfu3WXmgVf2vFadnOr8/Hytz6vm2EUikdZxVva8U157y/pM1vT5JA2jxlPqPXjwAEFBQfDy8sLo0aO55ZpLGSKRSGcbTS7Wi5c7GkJubi6GDBkCsViMFStWaK3Ly8urVvvVajVGjBiB9PR0bNq0qfYbDaBp06awtLSEo6MjPvroI7i4uODo0aOQSCQ4cOAAjI2N0atXLyQnJ3N/bdu2hVQqxZkzZ7TqcnR0hK+vr9ayQ4cOoWXLlhg4cKDOvjWXaA8cOIBmzZrBzc1Naz+a3oUX9+Pj48P1mgIlPW1GRkZa6TEVKZ3nnZGRgeTkZHTr1g2PHz9GRkYGAOD06dMoLi7Gxx9/rLXt1KlTdeo7cOAAvL29YWpqqtV+Hx8fqFQqnDt3rty2nDp1CoWFhZg+fbrWlYwJEybAyMhI61J/bQgJCYGenh7Xc6Xx6aefgmVZHDt27KV1lH7+ioqKkJKSAhcXF5iYmOjMaFMVkyZN0nrs7e2NlJSUGs1G8+GHH3L/19PTQ7t27cCyLMaPH88tNzExQdOmTct8/4waNUorTWzw4MGwtrbmrpDdunULjx49wvDhw5GSksK99jk5OejZsyfOnTunkwL04nGWJyQkBB06dNBKEZFKpZg4cSKio6PLvfReU2X1XJV+zXNycpCcnIxOnTqBZVncvHmTW/f+++/j+vXr3BUvANi/fz9EIhHee+89ANV7zupCdd8bVT1fVVZISAisrKwwbNgwbplAIMC0adOQnZ2Ns2fPlrutpaUl3NzcuHPNxYsXoaenh9mzZ0OhUODRo0cASnqqu3Tpwp17q7rPwMDAcscqZWRkoHfv3njw4AFCQ0PRqlWraj0PGmWdDyp7jtcYPnw4Dh8+jMLCQhw8eBB6enplfhcBdXdeA0riF835XaVSISUlBVKpFE2bNi2z7pedd15sb0WfSY3aeD5J/alR+kdiYiL69u0LY2Nj7o2voXnjlJWrprkMX5eD4ZKSkrTyjqRSKXe5TkOlUuGDDz7AvXv3cOzYMZ1LOWKxuFrtnzp1Ko4fP44ff/wRLVu2rOmhlOnQoUMwMjKCQCBAo0aNtILVR48eISMjo8xLZcDzgXMajo6OOmUiIyMRGBhYYRsePXqE+/fvl3uyfnE/pXPrNUxNTXXyvMtz8eJFLFq0CJcvX9bK2QVKvhiMjY25vFAXFxet9WZmZtwl7NLtv337dqXbX5pmP02bNtVaLhQK4eTkVGZ+ak3ExMTAxsZGZzyB5jJmZfaXl5eH5cuXY+fOnYiLi9NKYdD8KKmOF19XzfOclpYGIyOjWqnT2NgY+vr6OqlUxsbGSElJ0dn+xWm5GIaBi4sLl2epCVZKdwS8KCMjQ+s9U9bnpCwxMTHo2LGjzvLSr5XmUn9t4fP5aNSokc7y2NhYLFy4EL///rvO56z0az5kyBDMnDmTywllWRYHDhyAn58f9xpW5zmrC9V9b1T1fFVZMTExcHV11UkTrOxn09vbmwu6zp8/j3bt2qFdu3YwMzPD+fPnIZfL8e+//2L48OHV3mdF793p06cjPz8fN2/e1ErhrA59fX2d57cq53iNDz74ALNmzcKxY8ewZ88e9OvXr9yxVHV1XgNKOsg2bNiAb7/9FlFRUVoxhSbVsrSXnXeAyn8mgdp7Pkn9qXZQnZGRAT8/P6Snp+P8+fM6Aam1tTUAICEhQWfbhIQEmJmZldkLXFvat2+vdWJZtGiRzgCxCRMm4M8//8SePXu43orSNDN1vEiz7MVjBkoGEn377bdYsWIFRo4cWcOjKF/Xrl3LzdVWq9WQyWTYs2dPmetf/JBW98eNWq2Gh4cH1q1bV+b6xo0baz0u/aOrNLaC/FSNyMhI9OzZE25ubli3bh0aN24MoVCIkJAQrF+/vlo9ZGq1Gr169cKcOXPKXN+kSZMq1/kqmzp1Knbu3Inp06fDy8sLxsbGYBgGH3zwQY16GF/2upY3AK+iwTZl1VmT98+LNMe7evXqcnvmXvwR/irPiFO6R01DpVKhV69eSE1Nxdy5c+Hm5gYDAwPExcVhzJgxWq+5jY0NvL298euvv2L+/Pn4559/EBsbq5UXX53nrC5U971R1fNVfenSpQu2b9+Ox48f4/z58/D29gbDMOjSpQv33apWq+Ht7V3tfVT03n3vvfewb98+rFixAj/++KPO+6gqynsdqsra2hrdu3fH2rVrcfHixQpn/Kir8xpQMoPZF198gXHjxuHLL7+EmZkZeDwepk+fXq26q/KZBGrv+ST1p1pBdX5+PgICAvDw4UOcOnUKzZs31ylja2sLS0tLXLt2TWfdlStXanyJ6WX27NmjlZ7h5OSktX727NnYuXMngoODtS6hldaqVSucP38earVa60QTFhYGiUSiE3R98803WLx4MaZPn465c+fW4tFUjbOzM06dOoXOnTtXOxBwdnbGf//999Iy//77L3r27Flu4FRV5dXzxx9/oKCgAL///rtWT9WLl2w1s7BERERo9c6kpKTo/Lp3dnZGdnY2fHx8qtxOzX7Cw8O13luFhYWIioqqVp1A+cdvb2+PU6dOISsrS6vH5sGDB1rtqaiOgwcPYvTo0VqzIuTn59f5jXY0PZcv7qe2e/NL0/SqarAsi4iICG5wn+bKjpGRUbVfq/LY29sjPDxcZ3lZr1VlVefzdefOHTx8+BC7d+/WGgh58uTJMsu///77+PjjjxEeHo79+/dDIpEgICCAW18Xz1ltnTcqo6bnq4o+m7dv39b5nqjs660Jlk+ePImrV6/is88+A1DScbJlyxbY2NjAwMAAbdu2rbV9ljZgwAD07t0bY8aMgaGhYZkzKjWE4cOH48MPP4SJiQn8/f3LLVfZ81pFr3lF58wePXrghx9+0Fqenp5eZqfWy847Vf1MktdPlX+SqlQqvP/++7h8+TIOHDhQ4ZzIgYGB+PPPP7WmlDl9+jQePnzITW9TVzp37gwfHx/ur3Tgs3r1aqxZswbz58+vcGT24MGDoVAocPjwYW5ZcnIyDhw4gICAAK2e9v3792PatGkYMWJEuT0h9WXo0KFQqVT48ssvddYVFxdXKogKDAzEv//+i99++01nnab3Z+jQoYiLi8P27dt1yuTl5SEnJ6fKbdfMrftiGzW/2F+8tLdz506tcj179gSfz9f5Yti8ebPOvoYOHYrLly/jxIkTOuvS09NRXFxcbjt9fHwgFAqxceNGrTb98MMPyMjIQN++fcvdtiLlHb+/vz9UKpXOcaxfvx4Mw8DPz0+rjrJeYz09PZ1e3U2bNtX59ExGRkawsLDQyVH/9ttv62yfP/74o870mAkJCdzz1LZtWzg7O2PNmjXIzs7W2b6su75Wlr+/P65cuYLLly9zy3JycrBt2zY4ODiU2QnxMprZPKryA6iszwzLstiwYUOZ5QMDA6Gnp4dffvkFBw4cQL9+/bTmuq6L56y893tdqOn5ysDAoMx0An9/fyQmJmrdQbe4uBibNm2CVCpFt27dKqzX0dERtra2WL9+PYqKitC5c2cAJcF2ZGQkDh48iHfeeQd8/vM+sJru80WjRo3Cxo0bsXXr1gbtECpt8ODBWLRoEb799ludaXxLq+x5raL3WlXOmQcOHEBcXFyZbXnZeaeqn0ny+qlyT/Wnn36K33//HQEBAUhNTdW62QsA/N///R/3//nz5+PAgQPo0aMHPvnkE2RnZ2P16tXw8PDQuskKUDIVWExMDJcre+7cOSxbtgwAMHLkSO6Xd0ZGBjf4TzM36ubNm2FiYgITExNMmTKlwvb/9ttvmDNnDlxdXdGsWTOd9vfq1YubTmfw4MF45513MHbsWNy7dw8WFhb49ttvoVKpsGTJEm6bK1euYNSoUTA3N0fPnj110i46deqkFdRv3rwZ6enpiI+PB1DSC/v06VMAJZeyjI2NKzyGl+nWrRs++ugjLF++HLdu3ULv3r0hEAjw6NEjHDhwABs2bOBuZlOe2bNn4+DBgxgyZAjGjRuHtm3bIjU1Fb///ju2bt2Kli1bYuTIkfj1118xadIknDlzBp07d4ZKpcKDBw/w66+/cnNfV4WmN+bzzz/HBx98AIFAgICAAPTu3RtCoRABAQH46KOPkJ2dje3bt0Mmk2ml6MjlcnzyySdYu3Yt+vfvjz59+uDff//FsWPHYGFhodUjMXv2bPz+++/o168fxowZg7Zt2yInJwd37tzBwYMHER0dXW6KjaWlJebNm4clS5agT58+6N+/P8LDw/Htt9+iffv2Wp+D6hz/tGnT4OvrCz09PXzwwQcICAhAjx498PnnnyM6OhotW7bEX3/9hf/973+YPn26Vk5927ZtcerUKaxbtw42NjZwdHREx44d0a9fP/z0008wNjZG8+bNcfnyZZw6darM3MDa9uGHH2LFihX48MMP0a5dO5w7dw4PHz6ss/2ZmZmhS5cuGDt2LBQKBYKDg+Hi4oIJEyYAAHg8Hr7//nv4+fnB3d0dY8eOha2tLeLi4nDmzBkYGRnhjz/+qNa+P/vsM/zyyy/w8/PDtGnTYGZmht27dyMqKgqHDh2q1uV1sViM5s2bY//+/WjSpAnMzMzQokWLCnOz3dzc4OzsjFmzZiEuLg5GRkY4dOhQufmYMpkMPXr0wLp165CVlYX3339fa31dPGflvd/rQk3PV23btsX+/fsxc+ZMtG/fHlKpFAEBAZg4cSK+++47jBkzBtevX4eDgwMOHjyIixcvIjg4uFL3VfD29sa+ffvg4eHBXdlp06YNDAwM8PDhQ618agC1ss8XTZkyBZmZmfj8889hbGyM+fPnV7mO2mRsbFypW9hX9rzWqlUr6OnpYeXKlcjIyIBIJOLue9C2bVts2bIFy5Ytg4uLC2QyGd59913069cPS5cuxdixY9GpUyfcuXMHe/bs0bnyrfGy805VP5PkNVTV6UK6devGAij370X//fcf27t3b1YikbAmJibsiBEj2MTExCrVW3oKHM1UPmX9VeYWspppmSqzL5Zl2dTUVHb8+PGsubk5K5FI2G7durFXr17VKqOZPqq8vxenEtNMY1fW38umoKrK9Gzbtm1j27Zty4rFYtbQ0JD18PBg58yZw8bHx2u1pW/fvmVun5KSwk6ZMoW1tbVlhUIh26hRI3b06NFscnIyV6awsJBduXIl6+7uzopEItbU1JRt27Ytu2TJEjYjI4Mrh3KmDrO3t9eZ/u3LL79kbW1tWR6Pp/Wc/P7776ynpyerr6/POjg4sCtXrmR37Nih87wVFxezX3zxBWtlZcWKxWL23XffZe/fv8+am5uzkyZN0tpXVlYWO2/ePNbFxYUVCoWshYUF26lTJ3bNmjVsYWHhS5/jzZs3s25ubqxAIGDlcjk7efJkNi0tTatMVV6z4uJidurUqaylpSXLMIzWZyorK4udMWMGa2NjwwoEAtbV1ZVdvXq1zq3pHzx4wHbt2pUVi8Va0wimpaWxY8eOZS0sLFipVMr6+vqyDx480HkNqjql3ovHVdZ0arm5uez48eNZY2Nj1tDQkB06dCg3nVhZ06a9WOfo0aNZAwMDnTaUnoardNt/+eUXdt68eaxMJmPFYjHbt29fremqNG7evMkOGjSINTc3Z0UiEWtvb88OHTqUPX369EvbVJHIyEh28ODBrImJCauvr8926NCB/fPPP3XKlfe5KMulS5fYtm3bskKhUOt5K++5YVmWvXfvHuvj48NKpVLWwsKCnTBhAjeVZVlTHG7fvp0FwBoaGrJ5eXll1lmZ56yyU+pV9H6v7fcGy1b+fFWW7Oxsdvjw4ayJiYnO941CoeA+W0KhkPXw8KjSVKnffPMNC4CdPHmy1nIfHx8WgNZzW5V9ar4vV69erbN9ebcpnzNnDguA3bx5c7ntLW9KvbJeB83r9jJlvV6VaXNlz2ssW/L+dnJyYvX09LTOcYmJiWzfvn1ZQ0NDFgA3vV5+fj776aefstbW1qxYLGY7d+7MXr58me3WrZvWFHxVOe9U9jNZ0+eTNAyGZasxyoeQ10x6ejpMTU2xbNkyboJ/QgghpKZCQ0PRo0cPHDhw4KVXgcmbrcbzVBPyqilr/nDN3RE1t58lhBBCCKlNtXKbckJeJfv378euXbvg7+8PqVSKCxcu4JdffkHv3r25QUCEEEIIIbWJgmryxvH09ASfz8eqVauQmZnJDV7UDHwlhBBCCKltlFNNCCGEEEJIDVFONSGEEEIIITVEQTUhhBBCCCE1RDnVZVCr1YiPj4ehoWG93kaXEEIIIdXHsiyysrJgY2NT6RstqVQqFBUV1XHLyOtKIBBwd8N8GQqqyxAfH4/GjRs3dDMIIYQQUg1PnjxBo0aNKizDsiwSExPLvEU5IaWZmJjAysrqpR2tFFSXQXOL1ydPnsDIyKiBW0MIIYSQysjMzETjxo0rdat2TUAtk8kgkUjoyjTRwbIscnNzoVQqAQDW1tYVlqegugyaD5aRkREF1YQQQshr5mUBskql4gJqc3PzemoVeR2JxWIAgFKphEwmqzAVhAYqEkIIIeStosmhlkgkDdwS8jrQvE9elntPQTUhhBBC3kqU8kEqo7LvEwqqCSGEEEIIqSEKqgkhhBBCyGtrzJgxGDBgQEM3g4JqQgghhJDXwasSPALAV199hU6dOkEikcDExKTMMrGxsejbty8kEglkMhlmz56N4uLiWm/Lhg0bsGvXrlqvt6po9g9CCCGEEFIlhYWFGDJkCLy8vPDDDz/orFepVOjbty+srKxw6dIlJCQkYNSoURAIBPj6669rtS3Gxsa1Wl91UU81IYQQQsgb4L///oOfnx+kUinkcjlGjhyJ5ORkbv3Bgwfh4eEBsVgMc3Nz+Pj4ICcnBwAQGhqKDh06wMDAACYmJujcuTNiYmLK3deSJUswY8YMeHh4lLn+r7/+wr179/Dzzz+jVatW8PPzw5dffolvvvkGhYWFZW4THR0NhmHw66+/wtvbG2KxGO3bt8fDhw9x9epVtGvXDlKpFH5+fkhKSuK2e7EHv3v37pg2bRrmzJkDMzMzWFlZYfHixdx6lmWxePFi2NnZQSQSwcbGBtOmTavMU1whCqoJIYQQ8tZjWRa5hcX1/seybK20Pz09He+++y5at26Na9eu4fjx41AoFBg6dCgAICEhAcOGDcO4ceNw//59hIaGYtCgQWBZFsXFxRgwYAC6deuG27dv4/Lly5g4cWKNZke5fPkyPDw8IJfLuWW+vr7IzMzE3bt3K9x20aJFWLBgAW7cuAE+n4/hw4djzpw52LBhA86fP4+IiAgsXLiwwjp2794NAwMDhIWFYdWqVVi6dClOnjwJADh06BDWr1+P7777Do8ePcKRI0fK/XFQFZT+QQghhJC3Xl6RCs0Xnqj3/d5b6guJsObh2ObNm9G6dWut1IodO3agcePGePjwIbKzs1FcXIxBgwbB3t4eALhAMjU1FRkZGejXrx+cnZ0BAM2aNatRexITE7UCagDc48TExAq3nTVrFnx9fQEAn3zyCYYNG4bTp0+jc+fOAIDx48e/NIfa09MTixYtAgC4urpi8+bNOH36NHr16oXY2FhYWVnBx8cHAoEAdnZ26NChQ3UOUwv1VBNCCCGEvOb+/fdfnDlzBlKplPtzc3MDAERGRqJly5bo2bMnPDw8MGTIEGzfvh1paWkAADMzM4wZMwa+vr4ICAjAhg0bkJCQ0GDH4unpyf1fE4iX7kmWy+XcrcMrUwdQcotxzTZDhgxBXl4enJycMGHCBPz222+1MoCSeqoJIYQQ8tYTC/Rwb6lvg+y3NmRnZyMgIAArV67UWWdtbQ09PT2cPHkSly5dwl9//YVNmzbh888/R1hYGBwdHbFz505MmzYNx48fx/79+7FgwQKcPHkS77zzTrXaY2VlhStXrmgtUygU3LqKCAQC7v+aFJQXl6nV6krX8eI2jRs3Rnh4OE6dOoWTJ0/i448/xurVq3H27Fmd7aqCeqoJIYQQ8tZjGAYSIb/e/2rrro5t2rTB3bt34eDgABcXF60/AwMD7hg7d+6MJUuW4ObNmxAKhfjtt9+4Olq3bo158+bh0qVLaNGiBfbu3Vvt9nh5eeHOnTtaPconT56EkZERmjdvXv0DrSVisRgBAQHYuHEjQkNDcfnyZdy5c6dGdVJPNSGEEELqDcuyyM6+D5HICkKhWUM357WTkZGBW7duaS0zNzdHUFAQtm/fjmHDhnGzXkRERGDfvn34/vvvce3aNZw+fRq9e/eGTCZDWFgYkpKS0KxZM0RFRWHbtm3o378/bGxsEB4ejkePHmHUqFHltiM2NhapqamIjY2FSqXi2uTi4gKpVIrevXujefPmGDlyJFatWoXExEQsWLAAQUFBEIlEdfgMvdyuXbugUqnQsWNHSCQS/PzzzxCLxVyueXVRUE0IIYSQOsWyLHJyHkKh+BMKZQjy8qLh6roAdo3HNnTTXjuhoaFo3bq11rLx48fj+++/x8WLFzF37lz07t0bBQUFsLe3R58+fcDj8WBkZIRz584hODgYmZmZsLe3x9q1a+Hn5weFQoEHDx5g9+7dSElJgbW1NYKCgvDRRx+V246FCxdi9+7d3GNNm86cOYPu3btDT08Pf/75JyZPngwvLy8YGBhg9OjRWLp0ad08MVVgYmKCFStWYObMmVCpVPDw8MAff/wBc3PzGtXLsLU1l8sbJDMzE8bGxsjIyICRkVFDN4cQQgh5LWVnP4RSGQKFMgS5uZHcch5PBLvG4+Hs/Gmt7q+y39/5+fmIioqCo6Mj9PX1a7UN5M1T2fcL9VQTQgghpNbk5ERCoTwKpTIEOTmPuOUMI4S5eVfIZX1hYfEu+HxpA7aSkNpHQTUhhBBCaiQ3NwoKRUkgnZ0Tzi1nGAHMzbtCJvOHpUVP8PmGDdhKQuoWBdWEEEIIqbLc3GgutSM7+z63nGH4MDPrArnMHxYWvSAQUBoleTtQUE0IIYSQSsnLi4VCeQxK5VFkZT2/1TTD8GFm2gkyWV9YWvpAIDBpuEYS0kAoqCaEEEJIufLy4qBUHoVCGYKsrOfz+DKMHkxNvCCT+0Nm2RsCgWkDtpKQhkdBNSGEEEK05OfHQ6k8BoUyBJmZt0qt4cHU9B3IZf6wtOwNobBmU5AR8iahoJoQQgghyC9IhFJ5DErFUWRk3iy1hoGJSQfIZX0hk/lCKLRosDYS8iqjoJoQQgh5SxUUKKBUHodCGYKMjGul1jAwMWkPmcwfMss+EIksG6yNhLwuKKgmhBBC3iIFBUlQJh2HUhmC9PSrAJ7fA87YuC3kMn/IZH4QieQN10hCXkMUVBNCCCFvuMLCZCiT/oJScRRp6VcAqLl1xkatIZP3hcyyD/T1rRuukYQ80717d7Rq1QrBwcEN3ZQq4TV0AwghhBBS+woLUxEX9wtu3ByJ8xe8EB7+BdLS/wGghpFRS7i4zEPnTufRrt1B2DUeSwH1a+DcuXMICAiAjY0NGIbBkSNHdMqwLIuFCxfC2toaYrEYPj4+ePTokW5lL5g2bRratm0LkUiEVq1aVVg2IiIChoaGMDExqd6BvMThw4fx5Zdf1knddYl6qgkhhJA3RFFRGpKSTkKhDEFa2iWwrIpbZ2jo8Sy1wx9icaMGbCWprpycHLRs2RLjxo3DoEGDyiyzatUqbNy4Ebt374ajoyO++OIL+Pr64t69e9DX16+w/nHjxiEsLAy3b98ut0xRURGGDRsGb29vXLp0qUbHUx4zM7M6qbeuUU81IYQQ8horKspAfPxB3Lo1FucvvIP7D+YhNfU8WFYFQ6k7nJ1mo5PXGXRofwT29hMpoH6N+fn5YdmyZRg4cGCZ61mWRXBwMBYsWID33nsPnp6e+PHHHxEfH19mr3ZpGzduRFBQEJycnCost2DBAri5uWHo0KEvbW9oaCgYhsGJEyfQunVriMVivPvuu1AqlTh27BiaNWsGIyMjDB8+HLm5udx23bt3x/Tp07nHDg4O+PrrrzFu3DgYGhrCzs4O27Zt49YXFhZiypQpsLa2hr6+Puzt7bF8+fKXtq+2UU81IYQQ8popLs7ieqRTUy+AZYu4dVJpM26woUTi2ICtfM2wLFCU+/JytU0gARimVqqKiopCYmIifHx8uGXGxsbo2LEjLl++jA8++KBG9f/99984cOAAbt26hcOHD1d6u8WLF2Pz5s2QSCQYOnQohg4dCpFIhL179yI7OxsDBw7Epk2bMHfu3HLrWLt2Lb788kvMnz8fBw8exOTJk9GtWzc0bdoUGzduxO+//45ff/0VdnZ2ePLkCZ48eVKjY60OCqoJIYSQ10BxcRaSkk9DqQxBSsp5sGwht87AoMmzQLovDAwq7mkk5SjKBb62qf/9zo8HhAa1UlViYiIAQC7XnrlFLpdz66orJSUFY8aMwc8//wwjI6Mqbbts2TJ07twZADB+/HjMmzcPkZGRXK/44MGDcebMmQqDan9/f3z88ccAgLlz52L9+vU4c+YMmjZtitjYWLi6uqJLly5gGAb29vbVPMqaoaCaEEIIeUUVF2cjOfnvkkA69SzU6ueBtETiArm8L2QyP0gNXBuwleR14ufnh/PnzwMA7O3tcffu3UptN2HCBAwfPhxdu3at8j49PT25/8vlckgkEq00E7lcjitXrlS6DoZhYGVlBaVSCQAYM2YMevXqhaZNm6JPnz7o168fevfuXeV21hQF1YQQQsgrRKXKRXLy31AoQ5CSEgq1uoBbJ5E4PbuzoT+k0iYN2Mo3kEBS0mvcEPutJVZWVgAAhUIBa+vns7koFApuRo/vv/8eeXl5JbsWCCpd999//43ff/8da9asAVCSv61Wq8Hn87Ft2zaMGzeu3G1L74dhGJ39MgwDtVr94mbl1vHiNm3atEFUVBSOHTuGU6dOYejQofDx8cHBgwcrfXy1gYJqQgghpIGpVHlITgmFUhmC5OS/oVbnc+vEYvuSQFreF1KDpmBqKf+WvIBhai0No6E4OjrCysoKp0+f5oLozMxMhIWFYfLkyQAAW1vbatV9+fJlqFTPZ5P53//+h5UrV+LSpUvVrrM2GRkZ4f3338f777+PwYMHo0+fPkhNTa3XmUQoqCaEEEIagEqVj5SUs1Aojz4LpPO4dWJ9O8jkfSGX+UMqbUaBNAEAZGdnIyIignscFRWFW7duwczMDHZ2dmAYBtOnT8eyZcvg6urKTalnY2ODAQMGVFh3REQEsrOzkZiYiLy8PNy6dQsA0Lx5cwiFQjRr1kyr/LVr18Dj8dCiRYvaPswqW7duHaytrdG6dWvweDwcOHAAVlZWdTaPdnkoqCaEEELqiUpVgNTUc1AoQ5CcfBoqVQ63Tl+/ETePtKFhCwqkiY5r166hR48e3OOZM2cCAEaPHo1du3YBAObMmYOcnBxMnDgR6enp6NKlC44fP/7SOao//PBDnD17lnvcunVrACWBu4ODQ+0eSC0zNDTEqlWr8OjRI+jp6aF9+/YICQkBj1e/M0czLMuy9brH10BmZiaMjY2RkZFR5RGuhBBCSGlqdQFSUi9AqQhBUvIpqFTZ3DqRyBpymT/k8n4wNPSgQLqGKvv9nZ+fj6ioKDg6Or402CSksu8X6qkmhBBCaplaXYjU1ItQKkOQlHwSxcVZ3DqRyAoymT/kMn8YGbWiQJqQNwQF1YQQQkgtUKuLkJZ2CQplCJKS/kJxcSa3TiSUQybzg0zuD2Oj1mAYuqExIW8aCqoJIYSQalKri5GW/g+UiqNQJv2F4uJ0bp1QaAmZrA9ksr4wMW5LgTQhbzgKqgkhhJAqUKuLkZ4exvVIFxWlcusEAnPIZH0gl/WFiUk7MIxeA7aUEFKfKKgmhBBCXoJlVUhPvwqFMgRK5XEUFaVw6wQCM8gsfSGT+cPEpAN4PPpqJeRtRJ98QgghpAwsq0Z6+jUolSFQJh1DYWEyt47PN4FM5vusR7ojBdKEEAqqCSGEEA2WVSMj48azHuljKCxUcuv4fGNYWvaGXOYPU1Mv8HiVv8UzIeTNR0E1IYSQtxrLqpGZeYsLpAsKErl1fL4hLC16Qyb3h5lpJ/B4wgZsKSHkVUZBNSGEkLcOy7LIzLoNpeIoFMoQFBQkcOv09KSwtPSBXNYXZmadweOJGrClhJDXBQXVhBBC3gosyyIr686zHukQ5OfHcev09AxgaeEDmcwfZmbe0NOjQJqQhjJmzBikp6fjyJEjDd2UKqFJMwkhhLyxSgLpu4iIXI3Ll9/F1WsDERu7Hfn5cdDTk0Au6wcPj2/h3eUK3N3XwdLShwJq8so6d+4cAgICYGNjA4ZhXhp0Tpo0CQzDIDg4+KV1T5s2DW3btoVIJEKrVq3KLPPrr7+iVatWkEgksLe3x+rVq6t+EJWwYcMG7Nq1q07qrkvUU00IIeSNwrIssrMfQKksSe3Iy4vh1vF4YlhY9IBc1hfm5t2gpyduwJYSUjU5OTlo2bIlxo0bh0GDBlVY9rfffsM///wDGxubStc/btw4hIWF4fbt2zrrjh07hhEjRmDTpk3o3bs37t+/jwkTJkAsFmPKlClVPpaKGBsb12p99YV6qgkhhLz2SgLpcEQ+Xo9/wnrjytV+iI7Zgry8GPB4Ilha9kEL943o6n0FHi02QSbrQwE1ee34+flh2bJlGDhwYIXl4uLiMHXqVOzZswcCQeVmqdm4cSOCgoLg5ORU5vqffvoJAwYMwKRJk+Dk5IS+ffti3rx5WLlyJViWLXOb6OhoMAyDX3/9Fd7e3hCLxWjfvj0ePnyIq1evol27dpBKpfDz80NSUhK33ZgxYzBgwADucffu3TFt2jTMmTMHZmZmsLKywuLFi7n1LMti8eLFsLOzg0gkgo2NDaZNm1ap465Nr0RQ/c0338DBwQH6+vro2LEjrly5Um7Z7du3w9vbG6ampjA1NYWPj49O+TFjxoBhGK2/Pn361PVhEEIIqWfZOY/w+PEG/BPWB2FX/BEdvRm5uY/B4wlhadkb7u7B8O5yFZ4e30Au7ws9PUlDN5m8oliWRW5Rbr3/lReQVpdarcbIkSMxe/ZsuLu711q9BQUF0NfX11omFovx9OlTxMTElLNViUWLFmHBggW4ceMG+Hw+hg8fjjlz5mDDhg04f/48IiIisHDhwgrr2L17NwwMDBAWFoZVq1Zh6dKlOHnyJADg0KFDWL9+Pb777js8evQIR44cgYeHR80OuBoaPP1j//79mDlzJrZu3YqOHTsiODgYvr6+CA8Ph0wm0ykfGhqKYcOGoVOnTtDX18fKlSvRu3dv3L17F7a2tly5Pn36YOfOndxjkYhy5Agh5E2Qk/OYS+3IyXnILWcYIczNu0Iu84eFxbvg8w0bsJXkdZNXnIeOezvW+37DhodBIqi9H3srV64En8+v9Z5aX19fzJgxA2PGjEGPHj0QERGBtWvXAgASEhLg4OBQ7razZs2Cr68vAOCTTz7BsGHDcPr0aXTu3BkAMH78+JfmUHt6emLRokUAAFdXV2zevBmnT59Gr169EBsbCysrK/j4+EAgEMDOzg4dOnSo+UFXUYMH1evWrcOECRMwduxYAMDWrVtx9OhR7NixA5999plO+T179mg9/v7773Ho0CGcPn0ao0aN4paLRCJYWVnVbeMJIYTUi9zcKG7WjuzsB9xyhhHA3MwbMpk/LC19KJAmb7Xr169jw4YNuHHjBhiGKbOMn58fzp8/DwCwt7fH3bt3K1X3hAkTEBkZiX79+qGoqAhGRkb45JNPsHjxYvB4FSc+eHp6cv+Xy+UAoNWTLJfLoVQqdbYrrw4AsLa25rYZMmQIgoOD4eTkhD59+sDf3x8BAQHg8+s3zG3QoLqwsBDXr1/HvHnzuGU8Hg8+Pj64fPlyperIzc1FUVERzMzMtJaHhoZCJpPB1NQU7777LpYtWwZzc/My6ygoKEBBQQH3ODMzsxpHQwghpDbl5sZAqTwGpTIEWdnPv/gZhg8zs84lgbRFbwgERg3YSvKmEPPFCBse1iD7rS3nz5+HUqmEnZ0dt0ylUuHTTz9FcHAwoqOj8f333yMvLw8AKp1vDQAMw2DlypX4+uuvkZiYCEtLS5w+fRoAys3D1ii9H02w/+IytVpd6Tpe3KZx48YIDw/HqVOncPLkSXz88cdYvXo1zp49W6VjrKkGDaqTk5OhUqm4Xy0acrkcDx48KGcrbXPnzoWNjQ18fHy4ZX369MGgQYPg6OiIyMhIzJ8/H35+frh8+TL09PR06li+fDmWLFlSs4MhhBBSY3l5T6BUhkChDEFW1n/ccobRg6lpJ8hlfWFp2QsCgUnDNZK8kRiGqdU0jIYwcuRIrXgIKEnbGDlyJJcRUDpVtjr09PS4On755Rd4eXnB0tKyRnXWBrFYjICAAAQEBCAoKAhubm64c+cO2rRpU29taPD0j5pYsWIF9u3bh9DQUK3k+Q8++ID7v4eHBzw9PeHs7IzQ0FD07NlTp5558+Zh5syZ3OPMzEw0bty4bhtPCCEEAJCfH8+ldmRm/ltqDQ9mpl7PUjt6Qyg0K7cOQt4G2dnZiIiI4B5HRUXh1q1bMDMzg52dHczNzXWuygsEAlhZWaFp06YV1h0REYHs7GwkJiYiLy8Pt27dAgA0b94cQqEQycnJOHjwILp37478/Hzs3LkTBw4cwNmzZ2v9OKtq165dUKlU6NixIyQSCX7++WeIxWLY29vXazsaNKi2sLCAnp4eFAqF1nKFQvHSfOg1a9ZgxYoVOHXqlE6ezYucnJxgYWGBiIiIMoNqkUhEAxkJIaQe5efHQ6k8DoUyBJmZN0ut4cHUtCNkMn/ILHtDKLRosDYS8qq5du0aevTowT3WdAiOHj26xjdL+fDDD7UC5NatWwMoCdw1gxB3796NWbNmgWVZeHl5ITQ0tEEGBL7IxMQEK1aswMyZM6FSqeDh4YE//vij3LTfusKwtT2XSxV17NgRHTp0wKZNmwCUTAVjZ2eHKVOmlDlQEQBWrVqFr776CidOnMA777zz0n08ffoUdnZ2OHLkCPr37//S8pmZmTA2NkZGRgaMjChXjxBCakNBgQJK5TEolCHIyLheag0DE5MOkMv8YSnrAxEF0qSaKvv9nZ+fj6ioKDg6OupME0fIiyr7fmnw9I+ZM2di9OjRaNeuHTp06IDg4GDk5ORwuT+jRo2Cra0tli9fDqBkqpiFCxdi7969cHBwQGJiIgBAKpVCKpUiOzsbS5YsQWBgIKysrBAZGYk5c+bAxcWFm86FEEJI/SgoUEKZdBxKRQjSM64B0PTjMDAxbgeZ3B8yyz4QiXSnUCWEkNdJgwfV77//PpKSkrBw4UIkJiaiVatWOH78ODd4MTY2Vmuqli1btqCwsBCDBw/WqmfRokVYvHgx9PT0cPv2bezevRvp6emwsbFB79698eWXX1KKByGE1IOCwmQkKU9AoTyK9PQreB5IA8bGbUpSO2R+0BfRtKeEkDdHg6d/vIoo/YMQQqqmsDAFyqQTUCpDkJYWBuD59FhGRq0hl/lDJusDfX2bhmskeeNR+gepC69N+gchhJDXU1FRGpRJf0GpOIq09H/AsipunZGh57PUDn+IxTWbwosQQl4HFFQTQgiptKKidCQlnYRCeRRpaZe0AmlDwxbPeqT9IRbTtKSEkLcLBdWEEEIqVFSUieTkkkA6NfUiWLaYWyeVNucCaYmkfueEJYSQVwkF1YQQQnQUF2chKekUlMoQpKSeB8sWceukUjfIZP6Qy/whkTg2YCsJIeTVQUE1IYQQAEBxcTaSk/+GQnkUKSnnwLKF3DoDA1fIZH0hl/nDwMC5AVtJCCGvJgqq61Fe3hPk5cVCKJJBJJSDzzcEwzAN3SxCyFusuDgHySl/l/RIp4RCrX4eSEskzpDL+kIm84NU2qQBW0kIIa8+CqrrUVLSX3gU8TX3mMfTh0gkg0hkBZFQBpFI/izgLvl/yTo59PQkDdhqQsibRqXKRXJKKJSKECSnnIFanc+tk0gcS/VIN6Ef/oSQescwDH777TcMGDCgoZtSJRRU1yOenhgGBq4oKFCguDgTanU+8vJikZcXW+F2fL4hhEJNkF3Syy0SySDUBN5CK4hEFuDx6OY2hJCyqVR5SEk5C4XyKJKTz0CtzuPWicV2z3qk+0IqdaNAmpBX1PLly3H48GE8ePAAYrEYnTp1wsqVK9G0aVOuTH5+Pj799FPs27cPBQUF8PX1xbfffsvdVK8soaGhWL9+Pa5cuYLMzEy4urpi9uzZGDFiRJnl9+3bh2HDhuG9997DkSNHavswkZCQAFNT01qvt65RUF2PGtkORyPb4QBKvuAKCpQoKFSisEBR8v+CRBQUKp/9X4GCAgXU6jwUF2ehuDgLubkRFdYvEJg9C7JLBdxcL3hJr7dAYA4ej152Qt4GKlUBUlPPQqEMQXLyaahUudw6ff3GkMtLeqSl0uYUSBPyGjh79iyCgoLQvn17FBcXY/78+ejduzfu3bsHAwMDAMCMGTNw9OhRHDhwAMbGxpgyZQoGDRqEixcvllvvpUuX4Onpiblz50Iul+PPP//EqFGjYGxsjH79+mmVjY6OxqxZs+Dt7V1nx2ll9XrebZXuqFiGV+WOiizLQqXK1gqyS4JuBQoLlCgo1ATjSq0BRRXjQSi04IJskUj+QupJSTAuEJiCYXgvr44Q8kpRqwuQknIeSmUIkpJPQ6XK5tbp69tys3YYGnpQIE3eOG/bHRWTkpIgk8lw9uxZdO3aFRkZGbC0tMTevXsxePBgAMCDBw/QrFkzXL58Ge+8806l6+7bty/kcjl27NjBLVOpVOjatSvGjRuH8+fPIz09vcKe6sWLF+PIkSOYNm0aFi9ejNTUVIwaNQqbNm3C2rVrsW7dOqjVanzyySf4/PPPue1Kp39ER0fD0dERhw4dwqZNmxAWFgZXV1ds3boVXl5eAICYmBhMmTIFFy5cQGFhIRwcHLB69Wr4+/tX8RktG91R8Q3AMAz4fEPw+YYVjrZnWRbFxenIL1CU6vV+IQAvUKCgMAmAGoWFShQWKpGV9V8F+xbo9noL5drBuEgOPT0pfTET0sDU6kKkpl6EQvknkpJOaQXSIpF1yTzS8r4wMvSkzysh5WBZFmxe3ssL1jJGLK725zIjIwMAYGZmBgC4fv06ioqK4OPjw5Vxc3ODnZ1dlYPqjIwMNGvWTGvZ0qVLIZPJMH78eJw/f75S9URGRuLYsWM4fvw4IiMjMXjwYDx+/BhNmjTB2bNncenSJYwbNw4+Pj7o2LFjufV8/vnnWLNmDVxdXfH5559j2LBhiIiIAJ/PR1BQEAoLC3Hu3DkYGBjg3r17kEqllT7W2kJB9RuAYRgIBKYQCEwBqVu55VhWhcLClHIC7ufBeFFRKli2CPn5ccjPj6tw3zye+HmgXbrHWyTXCsL19MS1fdiEvNXU6kKkpl2CUhGCpOSTKC7O5NaJRFaQyfwgl/nDyKgVXXUipBLYvDyEt2lb7/tteuM6GEnVJyRQq9WYPn06OnfujBYtWgAAEhMTIRQKYWJiolVWLpcjMTGx0nX/+uuvuHr1Kr777jtu2YULF/DDDz/g1q1bVW7njh07YGhoiObNm6NHjx4IDw9HSEgIeDwemjZtipUrV+LMmTMVBtWzZs1C3759AQBLliyBu7s7IiIi4ObmhtjYWAQGBsLDwwMA4OTkVKU21hYKqt8iDKPHDXasiFpdiMLC5GcpJ6VzvTVpJ8pSgy3zkJcXg7y8mArr1BlsWWrGE5FI9mydJXg8YW0eMiFvFLW6CGlp/0ChPIqkpL9QXJzBrRMKZVwgbWzchgJpQt5wQUFB+O+//3DhwoUqbefu7o6YmJLvbG9vbxw7dkxr/ZkzZzB27Fhs374d7u7uAICsrCyMHDkS27dvh4WFRZX25+DgAENDQ+6xXC6Hnp4eeDye1jKlUllhPZ6entz/ra2tAQBKpRJubm6YNm0aJk+ejL/++gs+Pj4IDAzUKl9fKKgmOng8IfT1baCvb1NhOW6w5bOe7kKttJOaDLZ8nm4i1OoFLwnGhUJzMIxebR4yIa8stboY6elhXCBdVJTGrRMKLSCz9INM5g8Tk7b0uSCkBhixGE1vXG+Q/VbVlClT8Oeff+LcuXNo1KgRt9zKygqFhYVIT0/X6q1WKBTc4L+QkBAUFZXcIVX8wr7Pnj2LgIAArF+/HqNGjeKWR0ZGIjo6GgEBAdwytVoNAODz+QgPD4ezc9lpqgKBQPt4GabMZZr6ylN6G026jGabDz/8EL6+vjh69Cj++usvLF++HGvXrsXUqVMrrLO2UVBNqk1PTwyJxB4SiX25ZZ4PtlQ87/kufB5wF5YKwlm2CEVFqSgqSkV29v0K9syDSGj5PODWmttbXpIDLtQMtqT8UfL6YVkV0tKvQKk4CmXSCRQVpXLrBAIzyGR9IJf1hYlJewqkCaklDMNUKw2jPrEsi6lTp+K3335DaGgoHB0dtda3bdsWAoEAp0+fRmBgIAAgPDwcsbGx3KA+e/uyv7NDQ0PRr18/rFy5EhMnTtRa5+bmhjt37mgtW7BgAbKysrBhwwY0bty4tg6x2ho3boxJkyZh0qRJmDdvHrZv305BNXmzaA+2dCm3HMuyKCpKq3CKwZLUk5LBlgWFJb3jWVl3yq2TYYQQiSwrnGKQBluSVwXLqpCefg0KZQiSko6jsDCZWycQmMLS0hdymT9MTDrStJiEvKWCgoKwd+9e/O9//4OhoSGXJ21sbAyxWAxjY2OMHz8eM2fOhJmZGYyMjDB16lR4eXlVOEjxzJkz6NevHz755BMEBgZy9QqFQpiZmUFfX5/L29bQ9IS/uLwhTJ8+HX5+fmjSpAnS0tJw5swZnUGW9YHOzOSVwDAMhEIzCIVmlRxsmajV6609xaBmsGVhFQdbWmn1egtfmPGEBluS2sayamRk3IBC+SeUyuMoLEzi1vH5JpBZ9oZM3hemJh3B4wkqqIkQ8jbYsmULAKB79+5ay3fu3IkxY8YAANavXw8ej4fAwECtm79UZPfu3cjNzcXy5cuxfPlybnm3bt0QGhpam4dQJ1QqFYKCgvD06VMYGRmhT58+WL9+fb23g+apLsOrMk81qT61ugAFBckoLCx7isH8AgUKCxUoLs6qdJ18vhGX3y18YcaT5/neFjTYklSIZdXIyLwJpSIESuUxFBQquHV8vhEsLXtDLvOHqWknCqQJqaK3bZ5qUj9onmryVuPxRBCLbSEW21ZYTqXK5W6gozXYslQQXjLYMh/FxZkoLs5ETs6jCussd7Bl6WkHabDlW4VlWWRm/guF8iiUyhAUFDyf2orPN4SlRS/IZP4wM+tMP8oIIeQ1RUE1eavp6UkgkThAInEot4zuYEsFF4QXFChrabCldq83DbZ8/bEsi6ysOyWBtCIE+QXx3Do9PSksLXwgk/vD3KwLeDxRA7aUEEJIbaCgmpCXqPxgS3XJYMsXe725wZYleeAlA9CqMthSVuEUgyKRjAZbviJYlkVW9l0oFSFQKEOQn/+EW6enZwALi56Qy/xhZtYVenoUSBNCyJuEgmpCagnD8CAUmkMoNIchyh91rFYXo6go5aVTDD4fbPkU+flPK9y3np4EQmHFUwyWBN802LK2sSyL7Oz7UChDoFQeRV5eLLeOxxPDwuJdyGV9YW7eDXp6lLtJCCFvKgqqCalnPB6fC3grUnqwZb4m4Naa5UTJDbZUqXKRlxeNvLzoCuvUHWxpxfWCa2Y5ocGWL8eyLHJyHkKh+BMKZYjW887j6cPC4l3IZP6wMO9OP2QIIeQtQUE1Ia+o0oMtjSsoVzLYUnuWE+3Uk5K0k6oPtixjikGu19sKQqHZWzfYMjv7IZTKktSO3NxIbjmPJ4K5eXfIZf4wN+8BPt+gAVtJCCGkIVBQTchrrmSwpSMkEsdyy7Asi+LirBdmONH0epcecJn0wmDLe+XWyTB6EAoty5liUM6lovD5Jq91vndOTiQ3a0fpHyM8nhDmZt1KeqQt3gWfL23AVhJCCGloFFQT8hZgGAYCgREEAiPAwLXccmUNtszXyvVWcIMtWVb1rBc8Eahguu8XB1u+OMOJ5vGrNNgyNzcKCkVJIJ2dE84tZxghzM29IZP5w9KiJ/h8wwZsJSGEkFcJBdWEEE71BluWN8WgAkVFadUcbCnXyvUuHYDX1WC/3NxoLrWj9HSIDCOAmVkXyGX+sLDwKflhQgghhLyAgmpCSJVVbbBlknbA/ULaSUGBAipVdhUGWxrrBtwv9oILLSt1N8K8vFgolMegVB5FVtZdbjnD8GFm2gkyWV9YWvaCQFBRVjshhJDa5ODggOnTp2P69OkN3ZQqoaCaEFJnSgZbNoJY3KjCcsXFOSjk5vNWlJrh5FkOeKHmzpYFKC7OQHFxxksGWzIv3Nmy9BSDMuTmPoZCcVRrjnCG0YOpaSfIZf7PAmnTWnoWCCGkdixfvhyHDx/GgwcPIBaL0alTJ6xcuRJNmzblynTv3h1nz57V2u6jjz7C1q1by603NDQU69evx5UrV5CZmQlXV1fMnj0bI0aM4MoUFRVh+fLl2L17N+Li4tC0aVOsXLkSffr0qfXjvHr1KgwMXr8B3xRUE0IaHJ9vAD6/8oMtC8qbYvBZ6gnLlqSnFBWlVDjYEuDB1PSdZ4G0L4RCs9o/OEIIqSVnz55FUFAQ2rdvj+LiYsyfPx+9e/fGvXv3tILQCRMmYOnSpdxjiURSYb2XLl2Cp6cn5s6dC7lcjj///BOjRo2CsbEx+vXrBwBYsGABfv75Z2zfvh1ubm44ceIEBg4ciEuXLqF169a1epyWlpa1Wl+9YYmOjIwMFgCbkZHR0E0hhFSRWq1iCwqS2MzMu2xS0t/s07h9bOTjjez9+5+zt/6dwIZdCWBv3BjJPnm6hy0oSGro5hJCalFlv7/z8vLYe/fusXl5efXUsrqhVCpZAOzZs2e5Zd26dWM/+eSTGtft7+/Pjh07lntsbW3Nbt68WavMoEGD2BEjRpRbx86dO1ljY2P2jz/+YJs0acKKxWI2MDCQzcnJYXft2sXa29uzJiYm7NSpU9ni4mJuO3t7e3b9+vXcYwDs9u3b2QEDBrBisZh1cXFh//e//3HrU1NT2eHDh7MWFhasvr4+6+Liwu7YsaPGz4FGZd8v1FNNCHmjlAy2tIBQaAFDw+YN3RxCyGuCZVkUF6rrfb98Ia/aMx9lZGQAAMzMtK+y7dmzBz///DOsrKwQEBCAL7744qW91WXV3azZ8wHrBQUF0NfXHiguFotx4cKFCuvJzc3Fxo0bsW/fPmRlZWHQoEEYOHAgTExMEBISgsePHyMwMBCdO3fG+++/X249S5YswapVq7B69Wps2rQJI0aMQExMDMzMzPDFF1/g3r17OHbsGCwsLBAREYG8vLwqHW9toKCaEEIIIW+94kI1tn1y9uUFa9nEDd0gEFX9RlpqtRrTp09H586d0aJFC2758OHDYW9vDxsbG9y+fRtz585FeHg4Dh8+XOm6f/31V1y9ehXfffcdt8zX1xfr1q1D165d4ezsjNOnT+Pw4cNQqVQV1lVUVIQtW7bA2dkZADB48GD89NNPUCgUkEqlaN68OXr06IEzZ85UGFSPGTMGw4YNAwB8/fXX2LhxI65cuYI+ffogNjYWrVu3Rrt27QCUDHRsCBRUE0IIIYS8ZoKCgvDff//p9BRPnDiR+7+Hhwesra3Rs2dPREZGwtnZGe7u7oiJiQEAeHt749ixY1rbnzlzBmPHjsX27dvh7u7OLd+wYQMmTJgANzc3MAwDZ2dnjB07Fjt27KiwnRKJhAuoAUAul8PBwQFSqVRrmVKprLAeT09P7v8GBgYwMjLitpk8eTICAwNx48YN9O7dGwMGDECnTp0qrK8uUFBNCCGEkLceX8jDxA3dGmS/VTVlyhT8+eefOHfuHBo1qnh2pY4dOwIAIiIi4OzsjJCQEBQVFQEoSd8o7ezZswgICMD69esxatQorXWWlpY4cuQI8vPzkZKSAhsbG3z22WdwcnKqcP8Cgfb0piU3I9NdplZXnHpT0TZ+fn6IiYlBSEgITp48iZ49eyIoKAhr1qypsM7aRkE1IYQQQt56DMNUKw2jPrEsi6lTp+K3335DaGgoHB3LnzFJ49atWwAAa2trAIC9vX2Z5UJDQ9GvXz+sXLlSq7f7Rfr6+rC1tUVRUREOHTqEoUOHVv1A6oClpSVGjx6N0aNHw9vbG7Nnz6agmhBCCCGE6AoKCsLevXvxv//9D4aGhkhMTAQAGBsbQywWIzIyEnv37oW/vz/Mzc1x+/ZtzJgxA127dtVKn3jRmTNn0K9fP3zyyScIDAzk6hUKhdwgyLCwMMTFxaFVq1aIi4vD4sWLoVarMWfOnLo/8JdYuHAh2rZtC3d3dxQUFODPP//UGmRZX6p+zYEQQgghhNS7LVu2ICMjA927d4e1tTX3t3//fgAlQfCpU6fQu3dvuLm54dNPP0VgYCD++OOPCuvdvXs3cnNzsXz5cq16Bw0axJXJz8/HggUL0Lx5cwwcOBC2tra4cOECTExM6vKQK0UoFGLevHnw9PRE165doaenh3379tV7O5hn8/+RUjIzM2FsbIyMjAwYGRk1dHMIIYQQUgmV/f7Oz89HVFQUHB0ddaaJI+RFlX2/UE81IYQQQgghNURBNSGEEEIIITVEQTUhhBBCCCE1REE1IYQQQgghNURBNSGEEEIIITVEQTUhhBBCCCE1REE1IYQQQgghNURBNSGEEEIIITVEQTUhhBBCCCE1REE1IYQQQgh5ZTAMgyNHjjR0M6qMgmpCCCGEkNfAli1b4OnpCSMjIxgZGcHLywvHjh3TKpOfn4+goCCYm5tDKpUiMDAQCoWiwnrDw8PRo0cPyOVy6Ovrw8nJCQsWLEBRUVGZ5fft2weGYTBgwIDaOjQtCQkJ8PPzq5O66xK/oRtACCGEEEJerlGjRlixYgVcXV3Bsix2796N9957Dzdv3oS7uzsAYMaMGTh69CgOHDgAY2NjTJkyBYMGDcLFixfLrVcgEGDUqFFo06YNTExM8O+//2LChAlQq9X4+uuvtcpGR0dj1qxZ8Pb2rrPjtLKyqrO66xL1VBNCCCGEvAYCAgLg7+8PV1dXNGnSBF999RWkUin++ecfAEBGRgZ++OEHrFu3Du+++y7atm2LnTt34tKlS1yZsjg5OWHs2LFo2bIl7O3t0b9/f4wYMQLnz5/XKqdSqTBixAgsWbIETk5OL23v4sWL0apVK+zYsQN2dnaQSqX4+OOPoVKpsGrVKlhZWUEmk+Grr77S2q50+kd0dDQYhsHhw4fRo0cPSCQStGzZEpcvX+bKx8TEICAgAKampjAwMIC7uztCQkIq+7TWmlciqP7mm2/g4OAAfX19dOzYEVeuXCm37Pbt2+Ht7Q1TU1OYmprCx8dHpzzLsli4cCGsra0hFovh4+ODR48e1fVhEEIIIeQ1xbIsivLz6/2PZdlqtVelUmHfvn3IycmBl5cXAOD69esoKiqCj48PV87NzQ12dnZaQejLRERE4Pjx4+jWrZvW8qVLl0Imk2H8+PGVrisyMhLHjh3D8ePH8csvv+CHH35A37598fTpU5w9exYrV67EggULEBYWVmE9n3/+OWbNmoVbt26hSZMmGDZsGIqLiwEAQUFBKCgowLlz53Dnzh2sXLkSUqm00m2sLQ2e/rF//37MnDkTW7duRceOHREcHAxfX1+Eh4dDJpPplA8NDcWwYcPQqVMn6OvrY+XKlejduzfu3r0LW1tbAMCqVauwceNG7N69G46Ojvjiiy/g6+uLe/fuQV9fv74PkRBCCCGvuOKCAmwcPbje9ztt90EIqhCb3LlzB15eXsjPz4dUKsVvv/2G5s2bAwASExMhFAphYmKitY1cLkdiYuJL6+7UqRNu3LiBgoICTJw4EUuXLuXWXbhwAT/88ANu3bpV6bYCgFqtxo4dO2BoaIjmzZujR48eCA8PR0hICHg8Hpo2bYqVK1fizJkz6NixY7n1zJo1C3379gUALFmyBO7u7oiIiICbmxtiY2MRGBgIDw8PAKhUL3pdaPCe6nXr1mHChAkYO3Ysmjdvjq1bt0IikWDHjh1llt+zZw8+/vhjtGrVCm5ubvj++++hVqtx+vRpACW/NIODg7FgwQK899578PT0xI8//oj4+PjXciQpIYQQQohG06ZNcevWLYSFhWHy5MkYPXo07t27V+nt3d3dIZVKIZVKdQYD7t+/Hzdu3MDevXtx9OhRrFmzBgCQlZWFkSNHYvv27bCwsKhSex0cHGBoaMg9lsvlaN68OXg8ntYypVJZYT2enp7c/62trQGA22batGlYtmwZOnfujEWLFuH27dtVamNtadCe6sLCQly/fh3z5s3jlvF4PPj4+FT6MkVubi6KiopgZmYGAIiKikJiYqLWpQ9jY2N07NgRly9fxgcffFC7B0EIIYSQ1x5fJMK03QcbZL9VIRQK4eLiAgBo27Ytrl69ig0bNuC7776DlZUVCgsLkZ6ertVbrVAouMF/ISEh3KweYrFYq+7GjRsDAJo3bw6VSoWJEyfi008/RWRkJKKjoxEQEMCVVavVJe3n8xEeHg5nZ+cy2ysQCLQeMwxT5jJNfeUpvQ3DMFpt+PDDD+Hr64ujR4/ir7/+wvLly7F27VpMnTq1wjprW4MG1cnJyVCpVJDL5VrL5XI5Hjx4UKk65s6dCxsbGy6I1lzeKKvO8i59FBQUoKCggHucmZlZ6WMghBBCyOuPYZgqpWG8KtRqNRfDtG3bFgKBAKdPn0ZgYCCAkunyYmNjubxre3v7StdbVFQEtVoNNzc33LlzR2v9ggULkJWVhQ0bNnDBeENq3LgxJk2ahEmTJmHevHnYvn372xVU19SKFSuwb98+hIaG1ihXevny5ViyZEkttowQQgghpHbNmzcPfn5+sLOzQ1ZWFvbu3YvQ0FCcOHECQMmV+fHjx2PmzJkwMzODkZERpk6dCi8vL7zzzjvl1rtnzx4IBAJ4eHhAJBLh2rVrmDdvHt5//30IBAIIBAK0aNFCaxtNT/iLyxvC9OnT4efnhyZNmiAtLQ1nzpxBs2bN6r0dDRpUW1hYQE9PT2dS8tKXKcqzZs0arFixAqdOndLKs9Fsp1AouJwbzeNWrVqVWde8efMwc+ZM7nFmZuYr8auLEEIIIURDqVRi1KhRSEhIgLGxMTw9PXHixAn06tWLK7N+/XrweDwEBgaioKAAvr6++Pbbbyusl8/nY+XKlXj48CFYloW9vT2mTJmCGTNm1PUh1QqVSoWgoCA8ffoURkZG6NOnD9avX1/v7WDY6s7lUks6duyIDh06YNOmTQBKLjfY2dlhypQp+Oyzz8rcZtWqVfjqq69w4sQJnV9eLMvCxsYGs2bNwqeffgqgJEiWyWTYtWtXpXKqMzMzYWxsjIyMDBgZGdXwCAkhhBBSHyr7/Z2fn4+oqCg4OjrSrGDkpSr7fmnw9I+ZM2di9OjRaNeuHTp06IDg4GDk5ORg7NixAIBRo0bB1tYWy5cvBwCsXLkSCxcuxN69e+Hg4MDlSWtGsjIMg+nTp2PZsmVwdXXlptSzsbGps9tpEkIIIYSQt1uDB9Xvv/8+kpKSsHDhQiQmJqJVq1Y4fvw4N9AwNjZWa9qVLVu2oLCwEIMHa88luWjRIixevBgAMGfOHOTk5GDixIlIT09Hly5dcPz4cfo1SgghhBBC6kSDp3+8iij9gxBCCHn9UPoHqQuVfb80+M1fCCGEEEIIed1RUE0IIYQQQkgNUVBNCCGEEEJIDVFQTQghhBBCSA1RUE0IIYQQQkgNUVBNCCGEEEJIDVFQTQghhBBCXhkMw+DIkSMN3Ywqo6CaEEIIIeQ1sGXLFnh6esLIyAhGRkbw8vLCsWPHtMp0794dDMNo/U2aNKnS+4iIiIChoSFMTEzKLbNv3z4wDFNnd6pOSEiAn59fndRdlyioJoQQQgh5DTRq1AgrVqzA9evXce3aNbz77rt47733cPfuXa1yEyZMQEJCAve3atWqStVfVFSEYcOGwdvbu9wy0dHRmDVrVoVlasrKygoikajO6q8rFFQTQggh5K3HsizUhap6/6vKja0DAgLg7+8PV1dXNGnSBF999RWkUin++ecfrXISiQRWVlbcX2XvDr1gwQK4ublh6NChZa5XqVQYMWIElixZAicnp5fWt3jxYrRq1Qo7duyAnZ0dpFIpPv74Y6hUKqxatQpWVlaQyWT46quvtLYrnf4RHR0NhmFw+PBh9OjRAxKJBC1btsTly5e58jExMQgICICpqSkMDAzg7u6OkJCQSh1zbeLX+x4JIYQQQl4xbJEa8Qsv1ft+bZZ2AiPUq/J2KpUKBw4cQE5ODry8vLTW7dmzBz///DOsrKwQEBCAL774AhKJpML6/v77bxw4cAC3bt3C4cOHyyyzdOlSyGQyjB8/HufPn69UOyMjI3Hs2DEcP34ckZGRGDx4MB4/fowmTZrg7NmzuHTpEsaNGwcfHx907Nix3Ho+//xzrFmzBq6urvj8888xbNgwREREgM/nIygoCIWFhTh37hwMDAxw7949SKXSSrWvNlFQTQghhBDymrhz5w68vLyQn58PqVSK3377Dc2bN+fWDx8+HPb29rCxscHt27cxd+5chIeHlxsoA0BKSgrGjBmDn3/+udxe7QsXLuCHH37ArVu3qtRetVqNHTt2wNDQEM2bN0ePHj0QHh6OkJAQ8Hg8NG3aFCtXrsSZM2cqDKpnzZqFvn37AgCWLFkCd3d3REREwM3NDbGxsQgMDISHhwcAVKoXvS5QUE0IIYSQtx4j4MFmaacG2W9VNG3aFLdu3UJGRgYOHjyI0aNH4+zZs1xgPXHiRK6sh4cHrK2t0bNnT0RGRsLZ2Rnu7u6IiYkBAHh7e+PYsWOYMGEChg8fjq5du5a5z6ysLIwcORLbt2+HhYVFldrr4OAAQ0ND7rFcLoeenh54PJ7WMqVSWWE9np6e3P+tra0BAEqlEm5ubpg2bRomT56Mv/76Cz4+PggMDNQqX18oqCaEEELIW49hmGqlYdQ3oVAIFxcXAEDbtm1x9epVbNiwAd99912Z5TW9vxEREXB2dkZISAiKiooAAGKxGEBJ6sfvv/+ONWvWAHiWX65Wg8/nY9u2bWjTpg2io6MREBDA1atWqwEAfD4f4eHhcHZ2LnP/AoFA6zHDMGUu09RXntLbMAyj1YYPP/wQvr6+OHr0KP766y8sX74ca9euxdSpUyuss7ZRUE0IIYQQ8ppSq9UoKCgod70mXUPTu2tvb69T5vLly1CpVNzj//3vf1i5ciUuXboEW1tbiMVi3LlzR2ubBQsWICsrCxs2bEDjxo1r4UhqpnHjxpg0aRImTZqEefPmYfv27RRUE0IIIYQQXfPmzYOfnx/s7OyQlZWFvXv3IjQ0FCdOnABQMihw79698Pf3h7m5OW7fvo0ZM2aga9euFaZDNGvWTOvxtWvXwOPx0KJFC25Z6f8D4OaxfnF5Q5g+fTr8/PzQpEkTpKWl4cyZMzrHVB8oqCaEEEIIeQ0olUqMGjUKCQkJMDY2hqenJ06cOIFevXoBKEkNOXXqFIKDg5GTk4PGjRsjMDAQCxYsaOCW1y2VSoWgoCA8ffoURkZG6NOnD9avX1/v7WDYqkyQ+JbIzMyEsbExMjIyKj23IyGEEEIaVmW/v/Pz8xEVFQVHR0fo6+vXYwvJ66iy7xe6+QshhBBCCCE1REE1IYQQQgghNURBNSGEEEIIITVEQXU9SkpKQmhoKOLj4186HyMhhBBCCHl90Owf9ej+/fsIDQ1FaGgopFIpXF1d4erqCmdnZ4hEooZuHiGEEEIIqaZqB9Xp6ek4ePAgIiMjMXv2bJiZmeHGjRuQy+WwtbWtzTa+MaysrODm5obIyEhkZ2fj5s2buHnzJng8Huzt7dGkSRO4urpW+RaghBBCCCGkYVUrqL59+zZ8fHxgbGyM6OhoTJgwAWZmZjh8+DBiY2Px448/1nY73whNmjRBkyZNUFxcjJiYGDx8+BCPHj1CamoqoqKiEBUVhRMnTsDMzAyurq5o0qQJ7O3twefTBQVCCCGEkFdZtaK1mTNnYsyYMVi1ahUMDQ255f7+/hg+fHitNe5Nxefz4ezsDGdnZ/j5+SElJYULsKOjo5GamoqwsDCEhYVBKBTCycmJSxWhebMJIYQQQl491Qqqr169iu+++05nua2tLRITE2vcqLeNubk5vLy84OXlhYKCAjx+/JgLsrOzs/HgwQM8ePAAQEkKiSZNxNbWFjwejTUlhBBCyJvDwcEB06dPx/Tp0xu6KVVSrYhMJBIhMzNTZ/nDhw9haWlZ40a9zUQiEZo1a4b33nsPM2fOxMSJE9GjRw8uTz0xMRHnzp3DDz/8gDVr1uDw4cP477//kJeX18AtJ4QQQkh9WbFiBRiG0Qk8u3fvDoZhtP4mTZpUYV3R0dE62zAMg3/++YcrU1RUhKVLl8LZ2Rn6+vpo2bIljh8/XheHhqtXr2LixIl1UnddqlZPdf/+/bF06VL8+uuvAACGYRAbG4u5c+ciMDCwVhv4NuPxeLCxsYGNjQ26deuG7OxsRERE4NGjR4iIiEBubi5u376N27dvg2EY2NnZcbnYlpaWYBimoQ+BEEIIIbVMkzHg6elZ5voJEyZg6dKl3GOJRFKpek+dOgV3d3fusbm5Off/BQsW4Oeff8b27dvh5uaGEydOYODAgbh06RJat25dzSMp2+vaQVutnuq1a9ciOzsbMpkMeXl56NatG1xcXGBoaIivvvqqtttInpFKpWjVqhWGDBmCOXPmYMyYMejUqRMsLS3BsixiYmJw6tQpfPvttwgODsbRo0fx8OFDFBUVNXTTCSGEEFILsrOzMWLECGzfvh2mpqZllpFIJLCysuL+Kjsey9zcXGs7gUDArfvpp58wf/58+Pv7w8nJCZMnT4a/vz/Wrl1bbn27du2CiYkJ/vzzTzRt2hQSiQSDBw9Gbm4udu/eDQcHB5iammLatGlQqVTcdg4ODggODuYeMwyD77//HgMHDoREIoGrqyt+//13bn1aWhpGjBgBS0tLiMViuLq6YufOnZU65tpUrZ5qY2NjnDx5EhcuXMDt27eRnZ2NNm3awMfHp7bbR8qhp6cHBwcHODg4oHfv3khLS8OjR4/w8OFDREVFISMjA1evXsXVq1fB5/Ph6OjI5WKbmJg0dPMJIYSQVwrLsg3SCSUQCKp0ZTkoKAh9+/aFj48Pli1bVmaZPXv24Oeff4aVlRUCAgLwxRdfVKq3un///sjPz0eTJk0wZ84c9O/fn1tXUFAAfX19rfJisRgXLlyosM7c3Fxs3LgR+/btQ1ZWFgYNGoSBAwfCxMQEISEhePz4MQIDA9G5c2e8//775dazZMkSrFq1CqtXr8amTZswYsQIxMTEwMzMDF988QXu3buHY8eOwcLCAhEREQ2SFlujudq6dOmCLl261FZbSA2YmpqiQ4cO6NChAwoLCxEVFcUF2ZmZmXj06BEePXoEAJDJZFyaSKNGjaCnp9fArSeEEEIaVlFREb7++ut63+/8+fMhFAorVXbfvn24ceMGrl69Wm6Z4cOHw97eHjY2Nrh9+zbmzp2L8PBwHD58uNxtpFIp1q5di86dO4PH4+HQoUMYMGAAjhw5wgXWvr6+WLduHbp27QpnZ2ecPn0ahw8f1uphLktRURG2bNkCZ2dnAMDgwYPx008/QaFQQCqVonnz5ujRowfOnDlTYVA9ZswYDBs2DADw9ddfY+PGjbhy5Qr69OmD2NhYtG7dGu3atQNQ0tPdEKoVVG/cuLHM5QzDQF9fHy4uLujatSsFaw1EKBSiadOmaNq0KViWhVKp5GYTefLkCZRKJZRKJS5evMi9Xq6urnBxcYGBgUFDN58QQgghL3jy5Ak++eQTnDx5UqfHuLTSA/w8PDxgbW2Nnj17IjIyEs7OznB3d0dMTAwAwNvbm+vdnTlzJrdd+/btER8fj9WrV3NB9YYNGzBhwgS4ubmBYRg4Oztj7Nix2LFjR4XtlkgkXEANAHK5HA4ODpBKpVrLlEplhfWUzh83MDCAkZERt83kyZMRGBiIGzduoHfv3hgwYAA6depUYX11oVpB9fr165GUlITc3FwunyctLQ0SiQRSqRRKpRJOTk44c+YMGjduXKsNJlXDMAzkcjnkcjm8vb2Rm5uLyMhIPHz4kLs88t9//+G///4DADRq1IhLE7GysqLBjoQQQt4KAoEA8+fPb5D9Vsb169ehVCrRpk0bbplKpcK5c+ewefNmFBQUlNmZ2bFjRwBAREQEnJ2dERISwqW5iMXicvfXsWNHnDx5kntsaWmJI0eOID8/HykpKbCxscFnn30GJyenKh0fwzBlLlOr1VWuR7ONn58fYmJiEBISgpMnT6Jnz54ICgrCmjVrKqyztlUrqP7666+xbds2fP/999yvj4iICHz00UeYOHEiOnfujA8++AAzZszAwYMHa7XBpGYkEgk8PDzg4eEBtVqNp0+fcmkiCoUCT58+xdOnT/H333/D0NCQSxNxdHSESCRq6OYTQgghdYJhmEqnYTSEnj174s6dO1rLxo4dCzc3N8ydO7fc7IBbt24BAKytrQEA9vb2ldrfrVu3uG1K09fXh62tLYqKinDo0CEMHTq0CkdRdywtLTF69GiMHj0a3t7emD179usRVC9YsACHDh3S6s53cXHBmjVrEBgYiMePH2PVqlU0vd4rjsfjwc7ODnZ2dujZsycyMjK43OvHjx8jKysLN27cwI0bN7iBkZog28zMrKGbTwghhLw1DA0N0aJFC61lBgYGMDc355ZHRkZi79698Pf3h7m5OW7fvo0ZM2aga9eu5U6/BwC7d++GUCjkpsY7fPgwduzYge+//54rExYWhri4OLRq1QpxcXFYvHgx1Go15syZUwdHWzULFy5E27Zt4e7ujoKCAvz5559o1qxZvbejWkF1QkICiouLdZYXFxdzd1S0sbFBVlZWzVpH6pWxsTHatWuHdu3aoaioCDExMVwudlpaGiIjIxEZGYnjx4/D3NycSxOxs7MDn1+jMa+E1Ju8rEwU5uVBIBKBLxKBLxSCx6PxH4SQ159QKMSpU6cQHByMnJwcNG7cGIGBgViwYMFLt/3yyy8RExMDPp8PNzc37N+/H4MHD+bW5+fnY8GCBXj8+DGkUin8/f3x008/vRIzigmFQsybNw/R0dEQi8Xw9vbGvn376r0dDMuybFU36tu3LxITE/H9999zv2pu3ryJCRMmwMrKCn/++Sf++OMPzJ8/X+dSxesgMzMTxsbGyMjIqPTcjm8ylmWRnJzMpYnExsZq5T4JhUI4OzujSZMm3HzlhDQktUqFdEUi0hKeIjXuKVLj45Aa/xSp8U+Rn6V7N1i+QFgSYItEEAhFEIj0S/4vEoEvLPlXUGo9V05fn1tf7rb6+hS4E1JPKvv9nZ+fj6ioKDg6OlY46I8QoPLvl2p1L/7www8YOXIk2rZtyyWOFxcXo2fPnvjhhx8APJ+ehbz+GIaBpaUlLC0t0alTJ+Tn5yMyMpJLFcnJycH9+/dx//59ACVXKVxdXeHq6gobGxvweNW6xxAhL5Wfnc0Fy2nP/k2Ne4p0RSLUKt2raRp8gRDFRYXc4+KiwpLH2XV3dU1PIHgekGsCb+7xs2WlA3RuvX6pAF07qNes02xDgTshhDScavVUazx48AAPHz4EAG4KtzcB9VRXnlqtRkJCApcmEh8fr7XewMAALi4uaNKkCZydnalHgFSZWq1CplLJBc8lAXRJz3NuRnq52/FFIpha28LMphHMbJ79a9sYplY2EOjrg1WrUVxUiKKCAhQXFJT8W1iAooL858sKn68rKsh/tv75uqL8UsuePea2efZvfdLj87mgXROIv/i4dA+6doCuW64kWNdexqOpUskrjHqqSV2o7PulRkH1m4qC6urLyspCREQEHj58iMjISBQWPu8N1AyM1Ax2tLCwoCn7CKcgN0crYOZ6nRPjoSpjDIeG1MwcZjaNYGrT6HkAbdsIhmYWYBr4KgnLstpBNxfA52sH7YUFKMovVaYwH0X5BToBOhfwvxDcox5P41zgXjogr1SAXlZKTdk99hS4k+qioJrUhTpN/wCAp0+f4vfff0dsbKxW4AQA69atq2615DVnaGiI1q1bo3Xr1iguLkZsbCyXi52SkoLo6GhER0fj5MmTMDEx4QY7Ojg4VHquTvL6YtVqZCYncQEzl/OcEIectNRyt+MLhDC1tikJnG0bwczatqTX2doGQvHLb73bUBiGeRZc1t2XNsuyJekrpXvUXwjYi0v3oL+0R/550F66Lk3griouhqq4GAU5OXV2TDw9/gt56qIXAvLKB+jaKTXPA309GlxNCKll1TqrnD59Gv3794eTkxMePHiAFi1aIDo6GizLak1KTt5ufD4fTk5OcHJygq+vL1JTU7k0kejoaKSnp+PKlSu4cuUKBAIBHB0duSDb2Ni4oZtPaqAwP6+kxznuCVIT4koC6PinSEuI18plfpGBiemzNI1GMLV+FkDb2MLQwpLyhcvBMExJ4CkUQVxHY4RZloWqqEg35UXTy64JvkulwbzYQ68T7JcRwGsCd7WqGAW5xSjIrcvAXa+CNJhnaS9aAbqo3AC9zJQakQg8PT5djXumsLAQCoUC8fHx3J+XlxfFDOSNUq2get68eZg1axaWLFkCQ0NDHDp0CDKZDCNGjECfPn1qu43kDWFmZoZ33nkH77zzDgoLC/H48WOuFzsrKwsPHz7kcvTlcjmXJtKoUSMa7PgKYtVqZKUml8ysEac9WDA7NaXc7fT4fJhY2TwLmBs9S90oyXkWSQzq8QhIZTEMA75QCL5QCLG0biL30oH7iwF6pdJgdIL2F8o8S7Fh2ZKZi9QqFQrzclGYl1snxwMADI/3PPiuYGaZF/PWtQel6j+baeaFoF6oD4H+qxm4FxUV6QTQSUlJeDHbNC4ujoJq8kapVk61oaEhbt26BWdnZ5iamuLChQtwd3fHv//+i/feew/R0dF10NT6QznV9YtlWSgUCq4X++nTp1onX7FYDBcXF7i6usLFxQUSyat7uf9NVFSQj7SE+JJe51L5zmkJcRUOxJMYm5QMFCwVPJvZNIKRTEa9zqRBsCwLVXHxs+A7X7vHvLxe91IB+8sGpRYV5GsF7vWhJHCvIA2m1NSP5abFvCSlRo9ffuBeVFQEpVKpFUArlUqdABoomRXMxsYG1tbWsLGxga2tLaRSaa0+H5RTTepCneZUGxgYcHnU1tbWiIyMhLu7OwAgOTm5OlWStxjDMLCysoKVlRW6du2K3NxcbrBjREQE8vLycOfOHdy5cwcMw6BRo0ZcmohcLn/lemleRyzLIjst5VmaRpzWTBtZyUnlbsfT04OJ3LokXcNGO3jWr+UvS0JqimEY8AUC8AUC6KNu3p8sy0KtKtZOedEK0AtQXJBfbhoM1+teQYpNUUE+2Gf3CmDVahTm5aEwL69OjgcAGIbHzeMOiRQqfQmKBPoo0OOjAGVfRRTy9WAiNYCZkREsTE0gs7CAkbGx9rSSddZiQhpGtd7T77zzDi5cuIBmzZrB398fn376Ke7cuYPDhw/jnXfeqe02kreMRCKBp6cnPD09oVKp8PTpUy5NRKlU4smTJ3jy5AlOnz4NIyMjLk3E0dERQqGwoZv/SisqLEB6QvyzHucnpQLoOBTll/+lrG9o9MLUdCU5z8YyOQ34IqQUhmGgxxdAjy+AvkHd/bBUFReXk6eerxOgl5UGU7JN/gvB/vO6CgsKUCwQQqUvgVrf4Nm/YoDRDaKZ4iLw8nOhl58DXl7Jv0xxEfIAxD37K0u7gEHo9n/j6uw5IqS+VevbcN26dcjOzgYALFmyBNnZ2di/fz9cXV1p5g9Sq/T09GBvbw97e3v4+PggPT2du+nM48ePkZmZievXr+P69evQ09ODo6MjF2Sbmpo2dPMbBMuyyElPe34zFE3gHPcUmcnKcqdfY3g8mMitufzm0vnOEiMaOErIq0SPz4ceXwrUQuCuUqmgVCqRkJDApXCkKRRQqVQ6ZUUiISxMTGBqaAgTqQRGIhH4DAtVYWEFM8uUPdOM6BWeuYfUjl27dmH69OlIT09v6KbUi2oF1U5OTtz/DQwMsHXr1lprECEVMTExQfv27dG+fXsUFRUhOjqay8VOT09HREQEIiIicOzYMVhYWHBpInZ2dtB7w+a+LS4qQnpi/PO5nUvNtFHR4CuRgcGzgLlxSQBt2whm1o1gYmUFPT5Na0jIm0ylUiEpKQnx8fFcEJ2YmFhmAK2vrw8bGxutPGgTExNKuWtgcXFxmDt3Lo4dO4bc3Fy4uLhg586daNeuHYCSjpVFixZh+/btSE9PR+fOnbFlyxa4uro2cMvffNUOqq9evQpzc3Ot5enp6WjTpg0eP35cK40jpCICgYC7HTrLskhKSuLSRGJjY5GcnIzk5GRcunQJIpEIzs7OaNKkCVxcXGp9cExdYVkWeZkZWjdDSXsWOGcoFeUOiGIYHoxl8mdpGs8DZzPbRhAbGdOXIiFvAZVKheTkZJ0AuriMmymJRCIugNYE0aampnSueMWkpaWhc+fO6NGjB44dOwZLS0s8evRI68rsqlWrsHHjRuzevRuOjo744osv4Ovri3v37r3VgzKLiorq/H4Y1Qqqo6Ojy/xVW1BQgLi48rKnCKk7DMNAJpNBJpOhc+fOyMvLQ2RkJJcqkpubi3v37uHevXsAAFtbWy5NxMrKqsGn7FMVFyNdkVCq1/kpl/Ocn5Nd7nZCsUT7ZijPUjdMrGzAp5vpEPLWUKvVXACtCaITEhLKDaA1Pc+aANrMzIwC6NfAypUr0bhxY+zcuZNb5ujoyP2fZVkEBwdjwYIFeO+99wAAP/74I+RyOY4cOYIPPvigzHqPHz+OZcuW4b///oOenh68vLywYcMGODs7AyiJ+xwdHXHo0CFs2rQJYWFhcHV1xdatW+Hl5cXVs2vXLixcuBDJycnw9fVFly5dKjweTb379+/Hpk2bcO3aNbRo0QJ79uxBRkYGJk+ejAcPHsDb2xs//vgjLC0tAZS835ctW4Zt27YhKSkJzZo1w4oVK7hpnTX17tu3D99++y3CwsKwdetWjBkzBt9//z3Wrl2LqKgoODg4YNq0afj444+r8WroqtKUer///jsAYMCAAdi9e7fWDTpUKhVOnz6NkydPIjw8vFYa11BoSr03i1qtRnx8PJcmkpCQoLVeKpVyPd7Ozs4QiUR11pa8rMxndxB8Wmpu5zikKxK40fw6GAZGFrJSU9PZcrflNjChniRC3jZqtRopKSk6AXRRUZFOWaFQqBVA29jYwNTUtME7EupKTabUY1kWanXdzaJSHh5PXOnzePPmzeHr64unT5/i7NmzsLW1xccff4wJEyYAAB4/fgxnZ2fcvHkTrVq14rbr1q0bWrVqhQ0bNpRZ76FDh8AwDDw9PZGdnY2FCxciOjoat27dAo/H44JUNzc3rFmzBq6urvj8889x9epVREREgM/nIywsDJ06dcLy5csxYMAAHD9+HIsWLQLLsuXmVJeuNzg4GHZ2dhg3bhyKiopgaGiIZcuWQSKRYOjQofDx8cGWLVsAAOvXr8fixYvx3XffoXXr1tixYwfWr1+Pu3fvwtXVlavXwcEBa9euRevWraGvr4+///4bs2fPxubNm9G6dWvcvHkTEyZMwLp16zB69Ohyn/fKTqlXpaBa8yFkGEZnDkqBQMA1vl+/fpWt8pVEQfWbLSsri0sTefz4MTc9JFDyHre3t+dysS0sLKpcv1qlQroi8fktuEtNUZeflVnudgKRvna6hk1jmNnYwsTaBgJh3QX6hJBXl1qtRmpqqk4AXfq8pSEQCHQCaDMzszc2gC5LTYJqlSoXoWc96qupnO7d7kBPr3KDNjVtnTlzJoYMGYKrV6/ik08+wdatWzF69GhcunQJnTt3Rnx8PKytrbnthg4dCoZhsH///krtJzk5GZaWlrhz5w5312xHR0d8//33GD9+PADg3r17cHd3x/379+Hm5obhw4cjIyMDR48e5er54IMPcPz48ZcG1aXr3bdvH4YNG4bTp0/j3XffBQCsWLECu3btwoMHDwCUXG0OCgrC/Pnzubo6dOiA9u3b45tvvuHqDQ4OxieffMKVcXFxwZdffolhw4Zxy5YtW4aQkBBcunSp3OejTuapVj/rSXN0dMTVq1erFXC86JtvvsHq1auRmJiIli1bYtOmTejQoUOZZe/evYuFCxfi+vXriImJwfr16zF9+nStMosXL8aSJUu0ljVt2pR7IQgxNDREmzZt0KZNGxQXFyMmJoYLslNTUxEVFYWoqCicOHECZmZmXC+2g4MD+KWmj8vPzn5+I5RSOc/pikSoVbqXXLn9W1jq3EnQzLYRpKbm1OtMyFtMrVYjLS1N60YqFQXQVlZWWgG0ubn5WxVAv43UajXatWuHr7/+GgDQunVr/Pfff1xQXV2PHj3CwoULERYWhuTkZC7ei42NRYsWLbhynp6e3P81QbtSqYSbmxvu37+PgQMHatXr5eWF48ePv3T/peuVy+UAAA8PD61lSqUSQMkPp/j4eHTu3Fmrjs6dO+Pff//VWqYZvAkAOTk5iIyMxPjx47mefQAoLi7WyryoiWrlVEdFRdXKzvfv34+ZM2di69at6NixI4KDg+Hr64vw8HDIZDKd8rm5uXBycsKQIUMwY8aMcut1d3fHqVOnuMd8mkeXlIPP58PZ2RnOzs7o06cPUlJSuDSR6OhopKamIiwsDGFhYdDj8WAs1IMwPxfFiU9RkFb+rbj5IlFJj7PW3M6NYWplA8FbPFCEEFKCZdkyA+iCMu5SyufzdQJoCwsLCqBrGY8nRvdudxpkv5VlbW2N5s2bay1r1qwZDh06BACwsrICACgUCq2eaoVCoZUO8qKAgADY29tj+/btsLGxgVqtRosWLXR+0JUe6KfpBFKXl7pYBWXV++Ky6uzHwMCA+79mKujt27ejY8eOWuVqa3awakebp0+fxunTp6FUKnUOdMeOHZWqY926dZgwYQLGjh0LANi6dSuOHj2KHTt24LPPPtMpr5lKDUCZ6zU0JyBCKqsgN4fLb1bFP4VJ8lM0ylAgOSsbRRIpig2MoRIIkZqvBiAArBzBM5FDUlwIS6OSW++a25aka5jZNoKhmQUY+sIjhABcTumLAXR+fr5OWT6fD7lcrhNAv2lTgr6KGIapdBpGQ+ncubPOuLWHDx/C3t4eQEkmgZWVFU6fPs0F0ZmZmQgLC8PkyZPLrDMlJQXh4eHYvn07vL29AQAXLlyoctuaNWuGsLAwrWX//PNPlet5GSMjI9jY2ODixYvo1q0bt/zixYvlZjoA4D5Xjx8/xogRI2q9XUA1g+olS5Zg6dKlaNeuHaytrat1ybqwsBDXr1/HvHnzuGU8Hg8+Pj64fPlydZrFefToEWxsbKCvrw8vLy8sX74cdnZ25ZYvKCjQ6h3IzCw/75W8vli1GpnJSaWmpnuW85wQh5y01DK30QMgys2CiYCBvqkNCsVSpBcUISUzC2p9CbIhQbYaUKRmw8WsCAKeAHIDQwqoCXlLaQLo0jdSSUhIQF4ZtxHX09ODlZWVVh60paUlBdCkXDNmzECnTp3w9ddfY+jQobhy5Qq2bduGbdu2ASj5YTB9+nQsW7YMrq6u3JR6NjY2GDBgQJl1mpqawtzcHNu2bYO1tTViY2Mr7Lgsz7Rp09C5c2esWbMG7733Hk6cOFGp1I/qmD17NhYtWgRnZ2e0atUKO3fuxK1bt7Bnz54Kt1uyZAmmTZsGY2Nj9OnTBwUFBbh27RrS0tIwc+bMGrerWkH11q1bsWvXLowcObLaO05OToZKpeJyZzTkcnmN8p87duyIXbt2oWnTpkhISMCSJUvg7e2N//77D4aGhmVus3z5cp08bPL6KszP07kZSlr8U6QlxKO4SDc3UcPAxFTrFtwlgwVtYWhhCR5P+0suJycHERERePjwISIjI5Gbm4vbt2/j9u3bYBgGjRs35gY7ymQyypUm5A3EsiwyMjK0Auj4+PhyA2i5XK4VQMtkMgqgSZW0b98ev/32G+bNm4elS5dyg/FK97zOmTMHOTk5mDhxItLT09GlSxccP3683AF2PB4P+/btw7Rp09CiRQs0bdoUGzduRPfu3avUtnfeeQfbt2/HokWLsHDhQvj4+GDBggX48ssva3LIZZo2bRoyMjLw6aefQqlUonnz5vj9999feoObDz/8EBKJBKtXr8bs2bNhYGAADw8PnfF51VWl2T80zM3NceXKFW7+wuqIj4+Hra0tLl26pDXH4Zw5c3D27FmdSwgvcnBwwPTp01/6RKSnp8Pe3h7r1q3jRpa+qKye6saNG9PsH68wVq1GVmpyycwacdqDBbNTy8911uPzYWJlU2p6uueDBUUSg3K3q4hKpcKTJ0+4wY5JSUla642Njbk5sR0dHet88nlCSO1jWRaZmZk6AXRuru7dS3k8HnepWRNEy2QyGt9TD2oy+wch5amT2T80PvzwQ+zduxdffPFFtRuoyRFTKBRayxUKRa3mQ5uYmKBJkyaIiIgot4xIJKrTuYlJ9RUV5CMtIb6k17nU1HRpCXEoLmNAj4bE2KTU1HTP/4xkMp1e55rS09ODg4MDHBwc0KtXL6SlpXE3nYmKikJGRgauXbuGa9eugc/nw9HRkQuyTUxMarUthJDaoZlhoHQQnZOTo1OOx+NBJpNpBdByuZwCaELeQtX61Ofn52Pbtm04deoUPD09dXre1q1b99I6hEIh2rZti9OnT3N5Pmq1GqdPn8aUKVOq06wyZWdnIzIyskapKqRusSyL7LSUZ2kazwPn1PinyEpOKnc7np4eTOTWJekaNtrBs34D3obc1NQUHTp0QIcOHVBYWIjo6GhuRpGMjAwu4A4JCYGlpSWXJtK4cWO6FExIA8jKytIJoDUzBZSmuXNr6TsRyuVyuvpECAFQzaD69u3b3KjS//77T2tdVXJHZ86cidGjR6Ndu3bo0KEDgoODkZOTw80GMmrUKNja2mL58uUASgY3am4zXVhYiLi4ONy6dQtSqRQuLi4AgFmzZnFTw8THx2PRokXQ09PTmuibNIyiwgKkJ8Q/63F+UiqAjkNRfvl3sdI3NHpharqSnGdjmRx6r3hvkFAoRJMmTdCkSROwLAulUsmliTx58gRJSUlISkrCxYsXoa+vD2dnZzRp0gQuLi5aUwERQmpHdna21gDC+Ph4ZGVl6ZRjGAaWlpZaAbSVlRUF0ISQclUrIjlz5kyt7Pz9999HUlISFi5ciMTERLRq1QrHjx/nBi/GxsZqzcMZHx+P1q1bc4/XrFmDNWvWoFu3bggNDQUAPH36FMOGDUNKSgosLS3RpUsX/PPPP9z94kndYlkWOelpz2+G8ixwTot/iowkJVBOCj/D48FEbv38Ziil8p0lRrUzKXtDYxgGcrkccrkcXbp0QV5eHiIiIrie67y8PNy9exd3794FADRq1IhLE7GysqLBjoRUUU5Ojk4AXdbsTgzDwMLCQieAFgqFDdBqQsjrqloDFTUiIiIQGRmJrl27QiwWg2XZN+KLn25T/nLFRUVIT4zn5nYuPdNGYZ7uwB0NkYHBs4C5cUkAbdsIZtaNYGJlBT3+29sDpFarERcXx6WJJCYmaq03NDTk7uzo5OREYwAIeUFubq7OPNAZGRllli0rgKbP1JuBBiqSulCnAxVTUlIwdOhQnDlzBgzD4NGjR3BycsL48eNhamqKtWvXVrvh5NXBsizyMjOe5zjHlQwQTI17igylAixb9t2NGIYHY5n8WZrG88DZzLYRxEbGb8QPr9rG4/HQuHFjNG7cGD179kRmZiaXJvL48WNkZWXhxo0buHHjBvT09GBvb8/lYpubmzd08wmpV7m5uTrzQKenp5dZ1tzcXOtGKhRAk9Jq0K9I3iKVfZ9UK6ieMWMGBAIBYmNj0axZM275+++/j5kzZ1JQ/ZpRFRcjXZFQqtf5KZfznJ+jO1hHQyiWaN+C+9n/TaxswKe8wxoxMjJC27Zt0bZtWxQXFyM6OpoLstPS0vD48WM8fvwYx48fh7m5OZcmYmdnR7MOkDdKXl6eTgCdlpZWZlkzMzOdAJp6IUlZNLnxubm5EIsrf5tw8nbSTJ35sjEV1fr2/euvv3DixAk0atRIa7mrqytiYmKqUyWpB3lZmc/uIPi01NzOcUhXJIBVl93rDIaBkYWs1NR0ts9ynRvBwMSUep3rAZ/Ph4uLC1xcXNCnTx+kpKRwaSIxMTFISUlBSkoK/vnnHwiFQjg5OXG92OXd8IiQV1F+fr7OPNDlBdCmpqZaAbS1tTUF0KTS9PT0YGJiAqVSCQCQSCT0fUZ0sCyL3NxcKJVKmJiYvHSGrmoF1Tk5OZBIJDrLU1NT6bJaA1OrVEhXJD6/BXepKerys8q//bpApK+drmHTGGY2tjCxtoFASK/pq0IzoMrCwgKdOnVCfn4+Hj9+zAXZOTk5ePDgAXdXUmtra64X28bGRmvgLyENKT8/H4mJiVoBdGpqapllTU1Nte5EaG1tTb2LpMY098TQBNaElMfExKRS91Cp1kBFf39/tG3bFl9++SUMDQ1x+/Zt2Nvb44MPPoBarcbBgwer1ehXxeswUDE/O/v5jVBK5TynKxKhVhWXu52hhaXOnQTNbBtBampOv9Jfc2q1GgkJCVyaSHx8vNZ6iUTCDXZ0dnamoITUm4KCAp0AOiWl7DufmpiY6ATQZXXiEFKW6nx/q1QqFBUV1XHLyOtKIBBU+h4S1Qqq//vvP/Ts2RNt2rTB33//jf79++Pu3btITU3FxYsXa3T78lfBqxJUq9UqZCqVWjdD0cztnJuRXu52fJGopMdZa27nxjC1soGALo++NbKzs7np+iIjI1FQ6g6UmoGRmjQRS0tL+lFFakVhYaFOAJ2cnFxmWWNjY50AmuZnJzXxqnx/k7dTtafUy8jIwObNm/Hvv/8iOzsbbdq0QVBQEKytrWu7jfWuvj+UBbk5WgEz1+ucGA9Vcfm9zlIzcy6/mQugbRvB0MwCDF3mJ6WoVCrExsZyaSIvBjkmJiZcmoiDgwPd4IJUiiaALp0HnZycXOZIeSMjI60A2sbGhgJoUusoqCYNqUbzVL+p6upDmZ2WiqSYqGdT0z3LeU6IQ05a2XmEAMAXCGFqbVMSONs2gpm1bUmvs7UNhGK6JEqqJzU1lUsTiY6Ohkql4tbx+Xw4OTlxQbax8Ztx8x1SM0VFRToBdFJSUpkBtKGhIdfzrAmgpVJpA7SavG0oqCYNqVpB9c6dOyGVSjFkyBCt5QcOHEBubi5Gjx5daw1sCHX1oTz/y25cOXKgzHUGJqZat+AuGSxoC0MLS/B4lcvlIf/f3p2HyVHV6wN/q3pfpmdfMplJQjYSyDIhJCEgJLIYVmXzgqIs6tV7vbIYkO1B2eQHCggqKoISuBdw4eKCIrlABAREkWVCgCxkQtbZevbpvbvq/P6o7uqu6erZZ3qW9/M8/UxPdXV1zSQ19dap7zmHhiMWi2HPnj16qUjfGecqKir0MpGamppB15bR5BWPx9HS0mII0K2traYB2uv1ZgVojjpD+cJQTfk0rFC9cOFC/PznP8cnP/lJw/JXXnkFX/3qV7Fz585R28F8GKuDcvvrr+Cfv/uNHp4zOws63LwNSvknhEBLS4teJnLw4EFDkHI6nZg/fz4WLlyI+fPnT8oOZFNl5tfRkkgk0NLSYpjKu7W1FarJMJsej8dQ/1xdXc3gQhMKQzXl07BCtdPpxI4dOzBnzhzD8r1792Lx4sUIh8OjtX95wYOSSBMKhbB792589NFH2L17t+HYliQJNTU1eplIZWVl3sKqEAIipkDpjUMNxKD0xqAG4oavSiAOtTcGJRCDxW2Da1k53HXlsM30TpuQnUgk0NraagjQLS0tpgHa7XabBujp8ruiyYnnb8qnYY1TXVFRgffeey8rVG/dupVTJhNNIW63G8uWLcOyZcugKAoOHTqkt2K3tLTgwIEDOHDgAP7617/C5/PpQ/bNnTsXdrt9xJ+vRhUtJGcE4nRwjhteE/EcExiZUHpiCLx2CIHXDsFa7oJ7eTncdRWwlk2dYQYVRdEDdCpEt7S0GOrnU1wuV9ZEKoWFhQzQRERDMKyW6uuuuw6/+c1vsGnTJpxwwgkAtNKPL33pSzj//PNxzz33jPqOjide6RINrLu7W+/suGfPHiQyRqqxWCyYM2eOXotdUlKiv6bGlIxWZGM4NrYwxyBigw/KACDZLbAU2CB77bB4bZALMr/aIRfYYPHaEW8OIlTfivCHHUAi/Rm22gK468rhXlYOS8HILwrGi6Io8Pv9hgDd3NxsGqCdTmdWgC4qKmKApimB52/Kp2GF6lgshi9+8Yt46qmnYLVqjd2qquLiiy/Ggw8+OCotVPnEg5JoaOLhKPZsb8Cuj3Zh9/496A4aOzsWWbyYLZWjJl6KymgBZAx+yEfJJvcJxzZYCuymwVm2D60TpRpNIPx+O0Jb/Yh+1Amk/hpKgGN+Edx1FXAdWQrZOaybemNCURS0tbUZxoFuaWkxXNSkOByOrABdXFzMAE1TFs/flE9DDtVCCBw4cADl5eU4ePAg6uvr4XK5sHTpUsyePXus9nNc8aAkAkRChRKIQe3tW5ucXa8soukWUQGBLimEA3IbDshtaJa7IaT0nxmbsKBGlGK2owpzCqvh9flMAnM6OMuO8RltROmNIfSeH+F6P2IHetMvWGW4jiiBu64CzoXFkKzjNwa8qqpZAbq5uTlngO47kUpJSQkDNE0rPH9TPg05VKuqCqfTiQ8++AALFiwYq/3KKx6UNFWJhAolmKpPTtcpq73xrHplEck98ZApq5QssTCG44QD2BdqwsdtB9DQuBehcMjwturqar1MZMaMGZAnwMRFibYwQlv9CNW3IuHP6JzpssK9tAzuunLY5xRCkkcvsKqqivb29qwAbTZ9st1uz5pIpbi4eEL87ojyiedvyqdhlX8ceeSR+OUvf4ljjjlmLPYp73hQ0mQiFBVqMA4lGYyNgTkjOAfiUENDDMoWyVCLnGpBNqtTlpyWAVtFVVVFY2OjXovd1NRkeN3j8eijicydOxdOp3Oov45RJYRAvDGI0LutCG31Q+2N6a9ZCh1w1ZXDvbwcthmeIbUIq6qKjo4OQ4BuamoyDdA2my0rQJeUlDBAE5ng+ZvyaVih+k9/+hO+//3v42c/+xmWLFkyFvuVVzwoKd+EIrSgHIhldeozBOdADGpwiEFZloy1yF57unOf/lV7TXJZx7R8oLe3V590pqGhAbFYOrTKsozZs2frI4qUlZXltZRBqALRPd1aB8dtbYaSF2ulW+vguLwC1hLjhUAqQGdOpNLU1GT4WVNsNhuqqqoMAbq0tJQBmmiQeP6mfBpWqC4uLkYoFEIikYDdbofLZRyGqqMj97TbkwEPShoLQk0F5eyyi6zRMELxdKe5wZAB2ZOjM1+fwCy7rKNatjBaEokE9u/frw/Z197ebni9uLhYb8WePXs2bDZbnvYUEHEVkZ0dCL3bivCODkDR/rEEBCIzLeieqaLdEUSTX5vWOxqNZm3DarVmBeiysjIGaKIR4Pmb8mlYofqxxx7r93VOU07ThVAF1FC834lG9OAcHGJQlgDZk91xL7sznw2y2zYhg/JItLe362Ui+/btMwwPZ7PZMHfuXD1k5+M4FUKgs7MTh/YexIH39+DQoUPwRzoRk7LvHKQCdGYZR1lZGad8JxplPH9TPg0rVE91PCinN6EKqOGEcQzlXPXKwRgwlKGUJUB22wxlFpnhOLOjn+yZekF5uKLRKPbs2aOXivT29hper6ys1Ds71tTUjHprrxACXV1dhpkIGxsbEYlEsta1yBaUygUojXhQJnwoUwtQYi2A+0htghnngiJIFrZGE40Fnr8pn4YdqhsaGrBp0yY0NDTghz/8ISoqKvDcc89h1qxZOPLII0d7P8cVD8qpRwgBEU5kTDBiPjNfqrUZ6tAOC9ljNYbkPvXJenB22yBZGJRHQgiB5uZmvUzk4MGDhtddLhfmz5+PhQsXYt68eXC73UPefnd3d1aAzpyiPcVisaCystLQAl1RUQGLxYJ4awiheq2Do9KeDt+yxwrX0nK4V1TAPquAQ94RjSKevymfhhWqX3nlFZx22mk47rjj8Le//Q3bt2/H3Llzcdddd+Gtt97C//7v/47Fvo4bHpSTgxACIqIYWpCNM/IZW5dTda+DJbut2a3HfUa8sBQkW5TZ8pg3wWAQu3fvxq5du9DQ0GBoPZYkCbW1tXqZSEVFhSHECiHQ09Nj6EDY2NiIUCiU9TmyLKOyslIfAzoVoFMTYOUihEDsQC/C9X6E3vNrF21JlmIH3HUVcNeVw1bpGYXfBtH0xvM35dOwQvXatWvx2c9+Fhs3bkRBQQG2bt2KuXPn4s0338S5556b1XI02fCgzB8hBERU6ROK433GUk6/hsTQ/vtKTquh455huLjM4OyxjeskHzQ6FEXBgQMH9Fpsv99veL2wsBALFiyA2+3WA3QwGMzajizLqKioMAToysrKAQP0QIQiEG3o0kYQeb8dIpZRJz7Do83guLwc1iLHiD6HaLri+ZvyaVih2uv1Ytu2bTjssMMMoXrv3r1YtGiRaZ3hZMKDcvSpUUXvuJcuu0iF47jhNSSGUqQMSA5Ldm1yqkU5tSw1ljKD8rTS1dWll4l8/PHHpjMRSpKkB+hUiK6srBzz0UXUmILI9g6E6lsR2dWZvpMiAfY5hXCvKId7SRlkd/5GOSGabHj+pnwaVrNLUVERmpqacNhhhxmWv/vuu5g5c+ao7BhNfGpMyeq4l1WvnHxNxIcRlLPGUjYLzjZINo6gQOaKioqwevVqrF69GrFYDHv37sVHH32EeDyuB+iqqqq8DM8n2y1wL9cmj1GCcYTfb0OovhWxj3sQ+7gbsY+70fXHBjgPL4G7rhyuxSX8v05ENIENK1RfeOGFuO666/DUU09BkiSoqorXX38d11xzDS6++OLR3kcaRyKuZIx0YT7iReq1zFvXgyHZ5axwnCs4y3aGBxpddrsdCxcuxMKFC/O9K1ksHhu8a2bAu2YGEl0RhLf6EXrXj3hzEJEP2xH5sB2SwwLXkaVw11XAMa+IHV6JiCaYYZV/xGIxfOMb38Cjjz6KRCIBq9UKRVHw+c9/Ho8++uikH3t1qt0+EnHVMMGI+aQj2tfMWeIGQ7LJOWfmM9Qpe+2QHZP7/wXReIs3BxGq9yNU3wqlKz2BjOy1wb1MG0HEVuPlCCJESVPt/E2Ty5BCtaqquPvuu/HMM88gFoth2bJlOO+88xAIBLBixQosWLBgLPd13EyGg1IkVH2aarMRLwxBOTK0oAyrZAjEZmUXqeAs2S08oRONMaEKxPb3IFTvR/g9P9RQujbcWuqEKzWCSPnQhg8kmmomw/mbpq4hlX/ccccduOWWW3DyySfD5XLhySefhBACjzzyyFjt37QiFFXvuJc9lnJmYI5DhLM7XPXLIhlHusgqv0i+VmCH5GBQJppIJFmCY04hHHMKUXTWXEQ+6kLo3VZEPmxHoj2C3i370btlP2w1XriXV8C9vBwWnz3fu01ENK0MqaV6wYIFuOaaa/C1r30NAPDiiy/ijDPOQDgcHvUZzPJprK504y1BxA4E+pRdpINzZuvToMiSsSa574gXmZ35XFYGZaIpRo0qiHzYro0g8lFnenZPCXDMK9I6OC4pg+wc2VCARJMFW6opn4YUqh0OB3bv3o3a2lp9mdPpxO7du1FTUzMmO5gPY3VQ9ry4Dz0v7u9/JRlaaUVGx73s4JysUXZZOY01TWsikYAaDEINBKAEgtrzYCD5fQBqalkgADWYXiYScUgWK2CRIVmskCwWwGJJfk0us1oAObnMaoEkW9LLrJnrZ7xmsWoTAVksfbYva69ZLYAsQ7ImPzPHNrXtZrwvtU2rFZIsA5lfLdo21WAc4W1tCNX7EdvXk/4lWSW4FpVoU6QvKuGwkjSlMVRTPg2p+SKRSMDpdBqW2Ww2xOPxHO+gTNZKDxwLiw3huO/kIwzKNNVpU8aHoQaDGcE3kAy+GcuSQVhblvw++VBCQS0cm0wdPm1lhHzZUw5r9UpYK4+C7KlE+P12bbKZRARq9w6oPTsgIo1aWNcDep+Lg8zQ3vfiwOziYygXITkuGCCbbTPjIsSwDeNz/edIbtOwjIhoHAyppVqWZZx22mlwONKzff3pT3/CiSeeCI8nPcXu7373u9Hdy3HGK12ibCIeTwfcZNjVQ26fVmElmBGMU2E5YxnUoY1bPhDJbofs9WoPjwcWjyf9vdejLfN6IXu0ZZLNCqEogKJkfFUBJQGRUCDU5LKEAqjGr5nvE0oCUNSMr4q2DcOyBJBQIFQVSCS096lKclnyq2Gbffcr/Xw4vze5sAa2mjWwzlwF2V2iL1fDnUgc/BfiB9+E2j3AHbTJrk8oN14wmAR0S5+LA5NlsPa5OEhdmAz2DkjqQkO2ZF/Q6Hc7TC5CMi805Mxl2vtNf45cFyFTsCSQ52/KpyG1VF9yySVZy77whS+M2s4Q0egSQkCEQlCCQUOLcH+lEYawHEwvE6M9U6ok6SFY9npg8XgNwVj2GoOwYVnmeh4PZPv06JQnVFUL2qmArqoQiYTxoiAzvCvpiwOhJBBvjiO2N47YIQUyimFf8CnYF3wKsluFrSIBa1kUsiN5EZF5cZB5odH34qPvxYGa/rz0hYbJRUiiz89hcvGhv8/k4sNwwWEyU6ZBIqH9ngAMeQzZqUyS9IsL07Ii04uDQVyEGO525LgIsVjgWXsMvOvW5fu3QDRqhhSqN23aNFb7QUQZRCymBeFgn9Ze09KIPq3CoYxyiWBw9FuFHQ69BdhiCLxeYxDuu0wPwV5YvB5ILhdvzQ+RJMtaKyUAZNwxHCqRUBHZ2YlQfSvC2zughoDoXjuie+2wzyqAu64CrmVlsHgnz8WK8UIjGe6Tgdv0QiN1cZAZ0PvemVAULYyntml2F8KwLZOLkMy7HKZ3JrIvPrK3lf7Zsi9oTC5C+lx85P6lCSAeh4jH83KxIdntDNU0pbBLONEoEaoKNRTuvzQiR0uxoVwiGISIRgf+wKGQ5Ywg7Em3/pouSwXjjBKKZBCWPR5IeZjSm0aXZJXhOrIUriNLoUYSCH+gjSAS3d2F2P5exPb3ouvPDXDML9ZGEDmyFLJjYp8uJFkG7HZMvYKGkRFCaBcFmUE78y5HnwuG1IVG1l2BzAuNvssMdyZyXYRkX9C4Vx2d718P0aia2H8licaB3iqc2REuVS6RWRqRuSwVhA3rBbWWn1EkuVwmNcJeyB63sTQis25YX55eJrlcU7J+kkZOdlrhWVkJz8pKKD0xhN7TZnCMHwwguqsT0V2d6LLJcC5OjiCysJgjiEwikiSlyzOIaEwNa5ryqY4dHSY+rVU4pIdZQ+tvf6URwWBWgBajPXqNxQLZ600HYbPSCLMa4WTJhMWbfp9k5XUv5Ue8LYxwfStC9X4k2tKjrMhuK1xLy+Cuq4B9to+jFdGEwvM35RNDtQkelGNHjcWMLcKDKI3QWoqDhgCtBoOjvm+S250sg/D0KY3wZi/LqA+WvV5jEHY62SpMU4YQAvFDAYTebUXoPT/U3vRFqKXIAffycrhXVMBW5elnK0Tjg+dvyieGahM8KI2EoqRbhfUOcxktwn3LJcxKI5LrYLRbha3W7NKIgWqEPSbL3G62ChMNQKgC0YYuhOr9CL/fBhFNd4KzVrrhXqFNkW4tdvazFaKxw/M35RNDtYmpcFAKISCi0ezSiGCucomgcVi1jACthkKjvn+y2z204dP61Ain3ivZ7WwVJsoDEVcQ3tGBUL0fkR0dgJI+ldjn+LQRRJaWweJhx1YaP1Ph/E2TF0O1iXwelEJRjKHXbHxhs050WeMLBwceu3WobLY+o0IMbkg1vTQitZ7bzU4zRFOIGooj/H5yBJGPu9ODQcsSnAuL4V5RDufiUsh2Hvc0thiqKZ94v3scBV57HYG//lULwqblEiGI0W4VliRjq/CgaoT7jCucej5NJtggoqGR3TZ4VlfBs7oKie4owluTI4g0BhHZ0YHIjg5IdhmuI8vgqiuHc34xJAvvMBHR1MJQPY4iH36IziefHNS6Q5122aw0QvZ4Ibs5wQYRjR9roQMFJ9Sg4IQaxFtDCCVHEFE6Ilpnx3dbIXtscC1LjiAyq4AlXEQ0JbD8w8RY3T4Kvf02gq+/zmmXiWhaEUIgdqAXoXdbEX6vDWowYwSREifcdeVw11XAVuHO417SVMDyD8onhmoTPCiJiMaGUFREdydHEPmgDSKm6q/Zqj1w12kjiFgKhz8NO01fPH9TPjFUm+BBSUQ09tSYgsj2doTe9SOyqxNQk6cjCXAcVqiNILKkFLKbI4jQ4PD8TfnEUG2CByUR0fhSgnGEt7UhVN+K2N6e9AsWCc7DS+BeUQ7XolJINvYRodx4/qZ8YkdFIiLKO4vHBu8xM+A9ZgYSnRGEtvoRercViZYQIh+2I/JhOySHBa4lZXDXlcMxr4hTpBPRhMKWahO80iUimhjizUFt1JB6P5TuqL5cLrDBvSzZwbHGyxFECADP35RfDNUmeFASEU0sQhWI7etBqL4V4W1tUEPpya2sZS6468rhqquArcyVx72kfOP5m/Ip78VpP/nJTzBnzhw4nU6sWbMGb775Zs51P/jgA5x33nmYM2cOJEnC/fffP+JtEhHRxCfJEhyHFaL4nAWYceMalF58BFzLyyHZZCTawuh5cT9a7nkLLQ+8i97XDkHpjeV7l4lomslrqP7Nb36DjRs34uabb8Y777yD5cuXY8OGDWhtbTVdPxQKYe7cubjrrrtQVVU1KtskIqLJRbLKcB1RitLPLcKMm9ag+N8WwrGwGJCB+MEAuv+8B03/75/w/3Ibgm+1QI0kBt4oEdEI5bX8Y82aNVi1ahUeeOABAICqqqitrcXll1+O66+/vt/3zpkzB1dddRWuuuqqUdtmCm8fERFNPkpvDOH3/Aht9SO2vzf9glWGa3EJ3HXlcB5eAsma95u0NEZ4/qZ8ytvoH7FYDG+//TZuuOEGfZksyzj55JPxxhtvjOs2o9EootF0B5ienp6c6xIR0cRkKbDDe9xMeI+biUR7GKF6P0L1rUj4wwhva0N4WxskpxXuZWVwLS+H47BCjiBCRKMmb6G6ra0NiqKgsrLSsLyyshI7duwY123eeeeduPXWW4f1mURENPFYS13wnTQLBSfWIt4YRKi+FaGtfqg9MQTfbEbwzWZYfHa4UlOkz/BwBBEiGhGOUw3ghhtuwMaNG/Xve3p6UFtbm8c9IiKi0SBJEuwzvbDP9KLwtMMQ/bgboXdbEX6/DUpPDIG/HULgb4dgrXBpU6TXVcBa4sz3bhPRJJS3UF1WVgaLxYKWlhbD8paWlpydEMdqmw6HAw6HY1ifSUREk4MkS3DOK4JzXhHEZ+YjsrNDG6JvRwcSrWH0PL8PPc/vg31WAdwrKuBaWgaL157v3SaiSSJvvTXsdjtWrlyJLVu26MtUVcWWLVuwdu3aCbNNIiKaeiSbDNeSMpR+4QhU33QMis9fAMf8IkACYvt70fXHBjT9v3+ibdP7CL3bCjWq5HuXiWiCy2v5x8aNG3HJJZfg6KOPxurVq3H//fcjGAzisssuAwBcfPHFmDlzJu68804AWkfEDz/8UH9+6NAh1NfXw+v1Yv78+YPaJhERUSbZaYXn6Cp4jq6C0hPTpkjf2or4wQAiOzsR2dkJySbDeUQp3Csq4FxQBMnCEUSIyCivofqCCy6A3+/Hd77zHTQ3N6Ourg6bN2/WOxru378fspz+w9XY2IgVK1bo399zzz245557sG7dOrz88suD2iYREVEuFp8dBcfPRMHxMxH3h/QRRJT2CMJb/Qhv9UN2W+FaVg53XTnss3wcQYSIAHCaclMc55KIiFKEEIgfDKRHEAnE9dcsRY5kB8dy2Ko8edxLAnj+pvxiqDbBg5KIiMwIRSDa0KV1cPygHSKj1tpW5UkO0VcOaxFHEMkHnr8pnxiqTfCgJCKigYi4gvD2DoTq/Yjs7ACU9OnUfphPa8FeWgbZbcvjXk4vPH9TPjFUm+BBSUREQ6GG4gi934bQu37EPu5Ov2CR4FxYDHddBZyLSyDbLfnbyWmA52/KJ07+QkRENEKy2wbv6hnwrp6BRFcU4a1aB8d4UxCR7R2IbO+AZLfAdaQ2gohjXhEkCzs4Ek0lbKk2wStdIiIaDfGWYHoEkc6ovlz22uBeVg5XXTnstQWcIn2U8PxN+cRQbYIHJRERjSYhBGL7e7Up0rf5oQYT+muWUifcy8vhXlEBW7k7j3s5+fH8TfnEUG2CByUREY0VoaiIfKSNIBL5oB0iruqv2WZ64a4rh3t5OSw+Rx73cnLi+ZvyiaHaBA9KIiIaD2pMQeTDdm0EkV2dgJo8JUuAY24h3HUVcC0pg+xiF6jB4Pmb8omh2gQPSiIiGm9KIIbwtjaE6v2I7etJv2CV4Dq8BK66CrgWlUCycYr0XHj+pnxiqDYxVgflk9ufxC+2/QKyJMMqW2GRLKbPZUmGRbKkn8sWWCVr7tf7eW6RLf2/nrFO6nnqs/TlA71usk6udWWJJwMiooEkOiIIbW1F6F0/Eq0hfbnktMC1pAzuunI45hZxivQ+GKopn3g/aRwF40H4w/5870ZeSZByh/E+gbzf1wd4nuvCxfC6ZM35XJb7f92wbuZnpd6X8XywF1Hs/U9EKdYSJ3yfnIWC9bWINwUR2upHuL4VSncMobdaEHqrBbLPDvcybQZH20wv/4YQ5Rlbqk2M1ZVue7gdbeE2JEQCiqpAFSoSakL7mrmsz3NVqFBURXuuDvB6n+cJNQFFGD8r17qKqkARyUfGc/19mctzrJtan4Zu0HcuBnFnIvMiY6A7EzkvaEbyfDDrDnQRxbsbRAZCFYjt7UGovhWhbW0Q4fTfWmu5SxtBpK4C1jJXHvcyv9hSTfnEUG2CB+XIZYXwwQTygZ4P932D2IbZBUjmerlez3nh0udiJPM5DY0EaVzuTAzrAqRP+De7IBroszJLpPotrTK5IOEFx/QlEioiuzoRqm9F+MMOIJExgkhtgTaCyLJyWArsedzL8cfzN+UTQ7UJHpQ0VoQQA4buVJBP3ZlQhPG54fXB3HkYyzsTfe5SjORiSIB/ioYq84JjqBcLg+0bMdi7J2b9N0ZyhyPXZw3255IledqUQ6iRBMIftCNU34ro7i7oh5IEOOYXJUcQKYXsmPoVnzx/Uz4xVJvgQUk0/jKDfGYZlFkAz1Uypd95MCmTGuguRd8LnIEuega6eMhVnmW6XZZTjYnBliX1d+Ewkv4dZn0zBts5fbifJYcE5O1hiG29EI2R9C/DKsGxuBiuunK4F5ZCtlny9w8zhnj+pnya+petRDQppFoXbbIt37sy4Qx0wTFgX4w+fSpGcqch12cNpv9Gf5+Vtd0c/U36blcVas7fW2r7yL3K1FUIzHCV45PdR+OTPatRE6tEdFsHots6cEAO4jXfu/hb0TvY5d0PSZaGdJch18VCrjsguS5SVletxidmfiLfvymiUcOWahO80iUimhyEEIMvORphgM9VUjXgRYqafVdkKJ81UPlUf307tF8SMD9Si0/2rMa6npUoTRTpv79WawdeKXwLf/X9C3sdh4BxrJj5ytKv4MqjrhzVbfL8TfnElmoiIpq0JEmCVbLCCiswNSsahi11wWEI80oC8Y97EH+vC+r2XlRES/DZ9k/hs+2fglpmReIIB6KLbEgUYMAA319H7qxyKpOLhRUVK/L9KyIaVQzVRDRlCVWgpz2M9kNBtB8KoP1QEB2NASTiKgpKnPAWO+AtcerPC0qc8JY44eCU0DQFpC44AMBuyRgF5Igi4IhZEHEV4R0dCNe3IryjA3JbAva/JWD/G2Cf7dM6OC4rh8XDkiyiwWD5hwnePiKafMKBmBaeDwbQ3pgM0E1BJKJDH8bQ7rSkw3ZG4C4occBb7ISn2AGLhcPZ0dShhhMIv9+mjSCypzs9gogswbmwGO66cjiPKIVsn9i3A3j+pnxiqDbBg5Jo4krEFHQ0BbUA3RhAR7IFOtQTM13fYpVRPMON0plelFZ7UTrTA6vDgkBnBIGOKAIdEfR2RtHbEUGgI4JoaBCjbUiAx2c3BO9U4Na+d8DpsU2bId1oalG6owht9SO01Y/4oYC+XLLLcB1RClddBZwLiiBNwAtLnr8pnxiqTfCgJMo/oQp0t4XRkQzPWgt0EN2tIeT6q+Urc2rheaYXJdUelM70oqjCBXkIJ/9YJIFAZzJsd0TSzzsj6O2IItAZgZoY+M+m1SYbWrn14F3iREGxttw6wVv9iOKtIW0Gx3o/lI70EH2yxwrX0nK4V1TAPqtgwlxA8vxN+cRQbYIHJdH4CvXEkq3OqdrngFa6ETMfC83psaG0xpNsefaiZKYHJTM8sDvHvhZaqALhQFxv2da+amG7N9nqHc7Rat6Xq8CWbt3OrO8ucaCg2Am3zw5JnhhhhaY3IQRiB3oRrvcj9J4faiCuv2YpdsBdVwF3XTlslZ487iXP35RfDNUmeFASjY14TEFnU7rTYCpAh3vjputbbDJKZnhQWu1ByUytdKN0plcLmxOkZcxMIq5oLdyZLd7JwJ36PtcFQybZImlhu5/gbWenShpnQhGINnQh9G4rwh+0Q8TS/RZsMzxaB8e6clgLHeO+bzx/Uz4xVJvgQUk0Mqoq0OMP66G5vVEL0N3+MExnI5cAX5kLpdUelNaka58LK9yQp2BLrRAC0VDCWF6it3xrrd7BrmjOMpdMdpfVWFbSp7bbU8ROlTR21JiCyPYOhOpbEdnZCajJ/7QSYJ9TCPeKcriXlEF2j88IIjx/Uz4xVJvgQUk0eKGemCE8dxwKoKMxiETcvCXWVWBDSXW61bm0Wqt/tjlYX5xJVVQEu2N62A5kdKZMBe/BdKqUJMBT5Ei3chdnd6x0eKwTuuWfJgclGNdGEHm3FbG9PekXLBKch5fAXVcO1+ISSGM4RTrP35RPDNUmeFASZYvHFHQ0pks2UmM+D1i6kQrPyYfbZzddn4YuFkkg0BFFb2cku2Nl8rmqDKJTpV02Gbc7Hby9JQ5YxzAI0dST6IogVO9HuN6PeHNQXy45LHAdWQp3XQUc84ogWUb3Yo7nb8onhmoTPChpOlNVge7WUMaQdcnSjbbcpRuF5a5kq3M6QPvKXVOydGMyEapAqDemBe+OiN6ZMjN457oo6stVYMsatzuzzMRdwE6VZC7eHEyPINIV1Ze76spReuGiUf0snr8pnxiqTfCgpOlACIFQTwwdh4JoO5Qc77lRmzBF6ad0Qy/ZSLZAl8xg6cZkloilOlWmy0r6lpwMqlOlVYK3KHP4QGefkhPHuIzOQhOXUAVi+3u0Fuz3/Cj69Dy46ypG9TN4/qZ8Yqg2wYOSpppYJIGOpmB6yLrkjIORgHkrpdUm6+M8p4asK61m6cZ0JIRANKh1quw7bneqvjvUPbhOlQ63NV1WkjGKSarF21NoH9KY4jR5iYR2oSZZR/ffm+dvyic2GxBNIaqiotsfNgxX134ogJ62iOn6kgQUVriNQ9ZVs3SD0iRJgtNrg9NrQ/msAtN1FEVFsCtqKDMx1npHEQsnEA0lEA1p/yfNP0vrVJk9fGC6vtvhZqfKqWC0wzTRRMBQTTQJCSEQ6o6lx3tu1IJKZ1MISiJH6YbPbqh5Lk1OmMJZ/WikLBYZvlIXfKWunOvEwolkyI5mj2iSXK6qQh/fOxerw4KC4n6Cd5ETFhsDGxGNP4ZqogkuFkmkR91IDlnXfiiISDBH6YZdTg9ZlzF0nauApRuUP3aXFaUurR7fjKoKhHtiWcFbLznp1DpVJqIKOptD6GwO5fwsl8+eDt5ZU8U74fLa2KmSiEYdQzXRBKEqKrpawnqrc2rIugFLNzLHe57pQWGZi4GBJh1ZluAp0iarwWHm66Q6VWaWlfQdSjARVxHuiSHcE0Prvl7T7VisMjzFDhQkZ6Xs27HSW8xOlUQ0dPyrQTTOhBAIdsX08NyRGrquKQg1Yd7by+2zJ2caTJdvFFe5WbpB04rVbkFRpRtFlW7T14UQiATjfYYQNAbvYHcUSkJFjz+MHn8452c5PNb0sIGG8bu15+xUSUR9MVQTjaFYOKFP0d2RMV13rpnwrA6LFpz1joNa+YbLy9INooFIkgSX1w6X1567U2Ui2anSMISgcar4WERBNJhANBhA24EcnSplCZ4ie9ZEOZklJ+xUSTS9MFQTjQJFUdHVEsoYsk772tueu3SjqNKNkmovymo8yRpoL3ylTpZuEI0hi1WGr8wFX1nuTpXRcMLQum0Y0aQjgmBnslNlhzbiCdBtuh2bw5LuQFmS0eJdnA7eFo6CQTRlMFQTDYFWuhHtM2RdEJ0tuUs3PIX25FjP6c6DxTPcnPaZaIJyuKxwJO8UmVFVbfSd9EQ56TrvVAiPBOKIRxV0NgXR2RQ03Q4kwF1g7xO8tbG7Uy3grgIbW7uJJgmGaqIcouGEoWSj/VAAHY3BnKUbNofFMGFKKkA7vbZx3nMiGkuyLGm11cUOVM0tNF0nHlOMwwZ2RNCb7EyZWqbEVYR6Ygj1xNC61/yzLFY5Y+hAY213KnhzRlOiiYGhmqY9RVHR1RzSZxnsOBRA26FA8rZuNkmWUFTpzhqyrqCEpRtEpLHZLSiu8qC4ymP6uhACkUA8q6U7c6r4UE8MSkKb0Km7n06VTo/NMDNlqqU7Fb7dhQ5O5kQ0DhiqadoQQptYIrNso6MxgM7mEFQlR+lGkcMQnktmelFS5eHkEkQ0IpIkwVVgh6vAjorZ5uukOlX2benODN7xiIJIMI5IMJ6zU2VquMLMshLjVPFOOFyMA0QjxaOIpqRoKG6YKCXVCh0L5yjdcFoMrc6lM7XOg04PSzeIKD+G3KmyT+AOdEQR6NI6VfYm12nK1anSackO3BlDCXqK2KmSaCAM1TSpKQkVnc2hZL1zQO9AmGuaY1mWUFTlNg5ZV+1BQamTnYGIaNIZXKfKqF7HrZebZIxoEgnGEY8o6GgMoqMxd6dKj8+eHLXEvGOl08tOlTS9MVTTpCCE1tLSkdHq3H4ogK7mEFTVvHTDW+zIGrKuuNLN0g0imja0TpVaEM7ZqTKqpFu3MztWJlu9Ax3ahDnB7hiC3TG0fNxjuh2LTTZMkGM2aY6NE1bRFMZQTRNONBTPGrKuozGAWEQxXd/utKSHrEuOvlFS7WHpBhHRINgcA3eqDPfGTYcQTE2cE+qJQYlr4/V3tYRyfpbTa9ND9ryjKnD4mqqx+rGIxh1DNeWNElfR2RLMCNBaeB6wdCNjuLrSGi+8xQ7eciQiGiOSJMHts8Pts6Nits90HSWuItCV7EjZadKxsiOCeFRBJBBHJBCHf38vSqrNQzzRZMVQTWNOCIHe9og23vPBgF6+0d3ST+lGiSNZ75zuPFhU6WZHGSKiCchik1FY7kJhuXmnSiEEYuGEYUr48tnmU8kTTVYM1TSqIsG43urc3hjQJ0+J5yrdcFmzhqwrrfbA4WbpBo2iRAyIdAGRbiDcBUgy4CkDvBWALffICkQ0OiRJgsNtg8NtQ1mNeadKosmOoZqGRYmr6GjuM2TdwQCC3THT9WWLhOIqd7LjoFefeZClGzQoQgDxsDEYR7rSXwdaFs9d4wl7QTpge8q1R9bzCm0dZyHA/69ERGSCoZr6JVSBnvZI1pB1Xa1hiBylGwUlznSrc7IVuqjKDYuFpRvTmhBAtFcLu5nhN9w1uGWK+QXbkDgKtWAsVCDYqm0z1qs9Oj8e+P0Wezpg9xvCywF3KSBzpAMioumCoZp0kUCydCMjPHc0BhGPmpduONxWvcW5NGPMZztn5pq6VCUZdocTjLsBYf5/adAkGXAWacHYVaQ9T30daJmz0BhyhQCiPUDADwT9WsgOtALBNpPnfi14KzGg56D2GMy+ukv7D96Zz62Okf1uiIgor5h+pqFEXEFnUyg9ZF2jFqBDuUo3rBKKqzwZtc9aC7SniKUbk5ISzxGCOwcIxt1aCIX5HYpBs9iHH4wdBaNXfiFJ2radhUDZ/IHXj4e18J0ZwvXv+zwPdSRbw5PrDoajcHAt4J7y0f09EBHRqJgQofonP/kJ7r77bjQ3N2P58uX48Y9/jNWrV+dc/6mnnsK3v/1t7N27FwsWLMD3vvc9nH766frrl156KR577DHDezZs2IDNmzeP2c8wEWmlG+GsIev6Ld0odeotzqnW58JKF0s3Jpp4ePBlE32XxXPMmDYUNvfwg7HNNTkDoc0FFM3SHgNREkCovf/gnRnQ1TgQ7dYeHQ0Db9/qHHwZiqsEkHn8EhGNtbyH6t/85jfYuHEjHnzwQaxZswb3338/NmzYgJ07d6KioiJr/b///e/43Oc+hzvvvBNnnnkmnnzySZx99tl45513sGTJEn29U089FZs2bdK/dzim9q3VcCCmheeMIes6moJI5Crd8FgNrc6lM70omcHSjXEjBBALDK/TXbgLUMzH8h4Sh69P4M0slSjqJxgXslRhIBYrUFCpPQYihPbvaihD6ed5PAgkIkD3fu0xEMmihe/BlKG4ywCrfcQ/PhHRdCQJIUZ4L3dk1qxZg1WrVuGBBx4AAKiqitraWlx++eW4/vrrs9a/4IILEAwG8ec//1lfdswxx6Curg4PPvggAK2luqurC3/4wx+GtU89PT0oLCxEd3c3fD7zge7zJRFT0NEUNA5ZdyiIUE/u0o2SGVrZRkkyPJdWe+EpsrN0Y6RUVWtZHE4wjnQDamJkny/JyfKFoiG2GBdrgdrCC6hJKRbMLkPJ9TzcOfTtO4sGV4LiKQccHBqNJpaJfP6mqS+vZ9VYLIa3334bN9xwg75MlmWcfPLJeOONN0zf88Ybb2Djxo2GZRs2bMgK0C+//DIqKipQXFyME088Ed/97ndRWlpqus1oNIpoNN3y19PTM8yfaPQIVaC7LYyO1HB1yfDc3RpCrssgX5lTL9lIdSAsqnBBZulGbko83fEu3AVEOocQjEehvli2Db5sou8yewFv609Hdo/2KJ4z8LpKPN3ZcjBlKEJJ/t/uAtp2Dbx9m3vwHTFdxZOz7IeIaJDyGqrb2tqgKAoqK423SCsrK7Fjxw7T9zQ3N5uu39zcrH9/6qmn4txzz8Vhhx2GhoYG3HjjjTjttNPwxhtvwGLJHuLqzjvvxK233joKP9HwhHpiyVbnoN55sKMpiERMNV3f6bEZJkpJhWi7c5q2PMYjQ6gt7jIG41hg5J9vdQ0/GNvcDBo0diw2wDdDewxEVZNlKK39B+9Ua3girI3/3bVPewxEthpbub3JmnBPRfZzd6m270REk8iUTGEXXnih/nzp0qVYtmwZ5s2bh5dffhknnXRS1vo33HCDofW7p6cHtbW1o75f8ZiCzqZ0p8HUyBvhHKUbFquM4hnu9HTdNVqAdvumWOmGENot7eGOX5yIjHwf7AXDD8asL6apQJYBd4n2wKL+100dswPVf6eep8qdepu0x2C4SkxavDNDeMZyzopJRBNAXkN1WVkZLBYLWlpaDMtbWlpQVVVl+p6qqqohrQ8Ac+fORVlZGXbv3m0aqh0Ox7h0ZHzzmT2of/FA9gsS4CtzGUbcKJ3pQWH5JCrdUFVtuLXhjl+sxke4A1L2mMSDCsbJ5awvJho8SdLqqR1eoGTuwOsnYhnDELZltIabPA+1acMRhju0h9/8rqWB3Tv4MhTOiklEYySvScJut2PlypXYsmULzj77bABaR8UtW7bgG9/4hul71q5diy1btuCqq67Sl73wwgtYu3Ztzs85ePAg2tvbMWPGIG6BjqGSai+cXpthvOeSmR5t1I2JULqhJLRgHO4cuNNd32Ac7dFOhCMhWzMCb+EAI1D0Webwsb6YaKKy2oHCmdpjIKqi/Q0yDd6ZZSh+bbkS1cq4YoEhzIrZtwwl12gonBWTiAYv70lu48aNuOSSS3D00Udj9erVuP/++xEMBnHZZZcBAC6++GLMnDkTd955JwDgyiuvxLp163DvvffijDPOwK9//Wu89dZbeOihhwAAgUAAt956K8477zxUVVWhoaEB1157LebPn48NGzbk7ecEgMOPqcKitVVjW7qRiA5/mLZY78g/3+ocIBj3s8zuYQsS0XQnp4YALANwRP/rCgFEe9MBu98yFL928a/EgJ5D2mNAkhas+6v/Tj33lAM252j8Bohoksp7qL7gggvg9/vxne98B83Nzairq8PmzZv1zoj79++HnNECeeyxx+LJJ5/ETTfdhBtvvBELFizAH/7wB32MaovFgvfeew+PPfYYurq6UF1djU996lO4/fbb8z5WtSwPIjAKoXX+GW4wToRHvqN27/CDMU8qRDReJAlw+rRH6byB14+HjVPP91eSEmoHILRylFDb4PbH4eu/E2ZmazhnxSSacvI+TvVENGbjXB58G9j/94GD8ajUF/uGEIyLM5YVstc9EZGS0Gq6+6v/znw+1L/bVmefMpTy7OCdes5ZMQeN41RTPuW9pXpa2fNX4K/fHdy6kmXwrcN9lzkK+QeYiGgkLFYt0HqzZ/bNkpoVUw/b/j4lKX2exwLJWTEPaI+BSLI222W/o6FktIZzVkyivGCoHk9Vy4Cl/za4YGz38tYgEdFkIEna5DauYqBswcDrx0ImYbtP/XfqtXCH1gk8mGwZHwxnYf/135nPOSsm0ahh+YcJ3j4iIqIJQYlr9d2DKUMJ+rXxwIfC5u5T812e43mF1vAzwe+C8vxN+cSWaiIioonKYgMKqrTHQFKzYuot4K39l6TEQ8lZMfdrj4HI1mQZSq7gnaoRT7aEs38OTTMM1URERFNB5qyY5YcPvH40MHD9d+p5pEtrBQ80a4/BcBWblJ5kBO/ywwc3agvRJMFQPY56I3EoqkChyza1phknIqLJR58V87CB19VnxfQPHMSDbYBITuIT7gTadppvc+03gA13jO7PRJRHDNXj6PfvHsJ3/vgBCpxWzCpx64/ajOfVRS7YrRO7Zo2IiKaZIc2KmZxmfqAW8NL5Y7/fROOIoXoctQViAIDeSAIfNPbgg8aerHVkCZhR6EJtics0dJd47GzlJiKiiUuW07NiVizO994QjRuO/mFiLHsPh2MKDnaGsL8j/TiQ8TwSV/t9v8duMYTsWaXp0F1T7ILDahnV/SUiIposOPoH5RNbqseZy27BgsoCLKgsyHpNCAF/IJoO2e1hQ+hu7okgGFOwo7kXO5p7s94vSUCVz2kM3Rkt3WVetnITERERjQW2VJuYqFe6kbiCg51hQ8t2ZugOxZR+3++yWfqUk7gwqzTVyu2G08ZWbiIimrwm6vmbpge2VE8iTpsF8yu8mF+RPQOWEALtwZgesg8YQncYjd1hhOMKdrb0YmdLdis3AFT6HFk13KlHeYGDrdxEREREObCl2sRUvNKNJhQ0dkWMrdvt6eAdiPY/C5fTJqO2OLvj5KxSN2qL3XDZ2cpNRET5NRXP3zR5sKV6mnBYLTiszIPDyjxZrwkh0BWK5+w82dgVRiSu4qPWAD5qDZhuv7zAYTpayawSNyoKHJBltnITERHR1MVQTZAkCcUeO4o9diyvLcp6Pa6oaOwKm4bufe0h9EYS8PdG4e+N4u19nVnvt1tl1BabDBGYbOX2OPjfkIiIiCY3phkakM0iY3apB7NLs1u5AaC7Tyt3Zug+1BVGLKGiwR9Egz9o+v4yr93Qup35vNLnhIWt3EREU4KqCnSF42gLRFHosqHS58z3LhGNGtZUm2BN1uhJKCqauiM5Q3dXKN7v++0WGTXFLtPQXVviQoHTNk4/CRERmYkrKjqCMfh7o2gPxtDWG0VbQHu0B2LwB6JoC8TQFoiiIxiDomqx45snL8SVJy8Y1X3h+ZvyiS3VNKasFhm1ySB8nMnr3eF41mglqdB9sDOMmKJiT1sQe9rMW7lLPHbDEIGZnSlnFDphtXDKdyKioYrEFdOQnArHqeftgSg6B2gcMVPkZoMITT0M1ZRXhS4bCmcWYsnMwqzXFFWgqTvcp+Nk+vuOYEx/bD3QlfV+qyxhZrKW22xCnEIX/6gT0fQghEAgmkiH4t4o2kwCc3vy60AjQvUlS0CJx4Eyrx3lBQ6UeR0o9dhRlnxe5rUnvzpQ4rHDbmWDB009DNU0YVlkCTXF2sQ0mJf9em8kjgPJkN136veDHVor9752rTOlmUKXLeeIJTOKnLCxlZuIJrDM+mSzkNyutyprX6MJdUjbt1tkLQwPEJLLvHYUu+0c5YmmPdZUm2BN1uSnqgItvRF9LG5jeUkYbYFov++3yBKqi5w5Q3ehy8bJcIho1A23PnmwPHaLaUgu99pRmhGSywocKHBYJ93fOZ6/KZ8Yqk3woJz6gtEEDnaGszpOph6xAVp0CpzWrHKS1PPqIhdvbRKRbjzqkzNDcnkyGBtCcvL5VJ+oi+dvyieGahM8KKc3VRXwB6JawDZp6W7t7b+VW5aAGYXpcblnlRpDd7GbrdxEkxnrkycunr8pnxiqTfCgpP6EY0pWDXdm6I7E+2/l9jqsyZCdPSHOzGIXHNap3ZJENBGxPnlq4Pmb8okdFYmGyGW3YEFlARZUFmS9JoTWyq2H7Hbj6CXNPREEoglsb+rB9qaerPdLEjDD5zTWcGe0dJd67GzlJhok1icT0XhiS7UJXunSWInEFRzsDJuOy72/I4RQTOn3/W67JecQgTXFLjhtbOWmqY31ydQfnr8pn9hSTTSOnDYL5ld4Mb/Cm/WaEALtwVg6ZLcbQ3dTTwShmIIdzb3Y0dxruv0qn9MYukvT43SXex1sKaMJRwiB3mgiXV7Rmzsksz6ZiCYytlSb4JUuTUTRhIJDncZyktQ43fs7QgOGDadNzjlEYE2xm61uNGr61if7DTXJxpDsD0QHHG2nL9YnUy48f1M+saWaaJJwWC2YW+7F3HLzVu6uUDxn58nGrjAicRW7WgLY1RIw3X5FgSMdsvuE7ooCB4PJNMf6ZCKi/jFUE00BkiSh2GNHsceO5bVFWa/HFRWNXWHT0L2vPYTeSAKtvVG09kbx1r7OrPfbrTJqi7NHK5lV6kZtsRseB/+UTEasTyYiGj08ExJNAzaLjNmlHswu9Zi+3t2nlTszdB/qCiOWUNHgD6LBHzR9f5nXbigpyXxe5XOylXucsD6ZiCh/WFNtgjVZRGkJRUVTdyRn6O4aoAXTbpFRU+wyD92lbnjZyt0v1icTDR7P35RPPJsRUb+sFhm1ySB8nMnr3eE4DphM9X6gI4SDnWHEFBV72oLY02beyl3iyWzldhlC94xCFyxTMMRl1ie3mYTkNtYnExFNOgzVRDQihS4bCmcWYsnMwqzXFFWgqTvcp+Nk+vuOYEx/bD3QlfV+m0XCzCKX6bjcs0rd8Dlt4/ATDs5g65PbAtEBW/fNsD6ZiGhiY6gmojFjkSXUFGtD9mFe9uu9kbg+LOCBHK3ce9tD2NseMt1+kdtmOkSg1srthNUy/Jpe1icTEdFQMFQTUd4UOG04otqGI6qzax8VVaClJ2I6ROCBjhDaAjF0heLoCnXjvYPdWe+3yFort1nodthk1icTEdGoYqgmognJIkuoLnKhusiFY+aWZr0ejCZwoDM98+TBTuOQgbGEqj8fCdYnExHRYDBUE9Gk5HFYsajKh0VV2a3cqirgD0S1UN3eZ1zujhDiisr6ZCIiGlUM1UQ05ciyhEqfE5U+J1bNKcn37hAR0TTAni9ERERERCPEUE1ERERENEIM1UREREREI8RQTUREREQ0QgzVREREREQjxFBNRERERDRCDNVERERERCPEUE1ERERENEIM1UREREREI8RQTUREREQ0QgzVREREREQjxFBNRERERDRCDNVERERERCPEUE1ERERENELWfO/ARCSEAAD09PTkeU+IiIhosFLn7dR5nGg8MVSb6O3tBQDU1tbmeU+IiIhoqHp7e1FYWJjv3aBpRhK8nMuiqioaGxtRUFAASZJGdds9PT2ora3FgQMH4PP5RnXbRDQwHoNE+TWWx6AQAr29vaiuroYss8KVxhdbqk3Isoyampox/Qyfz8cTOlEe8Rgkyq+xOgbZQk35wss4IiIiIqIRYqgmIiIiIhohhupx5nA4cPPNN8PhcOR7V4imJR6DRPnFY5CmKnZUJCIiIiIaIbZUExERERGNEEM1EREREdEIMVQTEREREY3QtA7VL7/8MpxOJwKBAADgJz/5CRYvXmy67uOPP276fCzdfvvtOPbYY3H88cfjgw8+AAC88847OO6443DiiSeiqakJAHDJJZeguLgY//jHPwBoM0kdffTRKCoqGpf9JBqJqXocAsAzzzyDk046CevXr0dzc/O47C/RUE2FYzAYDOKkk07CCSecgC984QtQVZXnQhp/Yhp76aWXRF1dnXj66aeFEEJcdNFF4pRTTjFdd8OGDabPx9LevXuFEELs3r1bfPGLXxRCCHHOOecIv98v/vnPf4qrr75aCCFEU1OTuPnmm8Ubb7whhBAiHo+Ltra2cdtPopGYqsdhIBAQX/ziF4WqquOyn0TDNRWOwUgkIpqamoQQQtx0001iy5YtPBfSuJvWLdUAcNppp+H5559HIBCA2+2GLMt4+OGHsW7dOqxduxYNDQ3YvHkz3nzzTaxfvx5vv/22/vzZZ5/NWhcA1q9fj4suuggrV67UW63OO+88HH/88bjkkksAaC0DZ511Fs444wycfvrpECaDsMyePRsAYLPZ9OnSY7EYysrKsHr1amzfvh0AUFVVZXif1WpFaWnp2PzCiMbAVDwO33jjDcTjcZxyyim48sorTbdNNFFM9mPQ4XDox2BqPZ4LabxN+1Dt8/kQCASwefNmnHLKKQCAiy66CK+88gruvfdePPzwwzj11FOxevVqvPzyy1i5cqX+/IwzzshaFwCam5tx33334bnnnsNdd90FAPif//kfvPrqq3A4HNixY4f+2c8++yzmzZuH9957L+c+3nzzzfja174GAFBVVV+e+ZxoMpuKx2Frays6OzvxwgsvwOv14v/+7/9G/osiGiNT5Rj0+/3YsmULTjjhhNH9BRENgjXfOzARrF69GnfeeSdeeukl/PKXv8QzzzyDn/70p0gkEjj88MP7fa/ZutXV1aioqAAARKNRJBIJXH755di9ezcOHDiAiy66CACwZMkSAMDMmTPR1dVluv3HHnsMtbW1OPbYYwFAv0oHAIvFMqKfm2gimWrHYWFhIY4//nhIkoQTTjgBO3bswKmnnjr4XwjROJvsx6CiKPjyl7+MBx54gOdHyguGagDnnHMO/H4/fD4fAK2TxksvvYQ333wTDz30EADjASzL6QZ+s3UbGxvh9/sBAHa7HfX19fB4PHjllVfw1a9+Vb+9lblNs1te77zzDn73u9/h97//vb7M4XCgvb0de/bswaJFi0brV0CUd1PtOFy5ciWeeOIJAMC2bduwYMGCof9SiMbRZD8Gb7zxRnz2s5/F0qVLR+X3QTRU0778AwBqa2tx++2369+vW7cOJ5xwAn7729/qyxYtWoRzzz0X27Ztw0knnYQzzzwTW7ZsMV13xowZuPrqq3Hqqafiuuuuw+GHH44333wTZ555JlpbWwe9XzfccAP27duHE088EVdddRUA7Y/Gpz/9aVx77bX45je/CQC49tpr8d///d+44oor8NRTTwEANmzYgLfeegsnn3wydu3aNZJfD9G4mGrHYVVVFZYsWYJ169bhrbfewplnnjnC3xDR2JrMx2BjYyN+9KMf4Ze//CXWr1+PzZs3A+C5kMYXpykfA6eeeqp+QBNRfvA4JMovHoM03bD8Y4LYuXOn3gEDAJYuXYof//jHedwjoumHxyFRfvEYpMmMLdVERERERCPEmmoiIiIiohFiqCYiIiIiGiGGaiIiIiKiEWKoJiIiIiIaIYZqIiIiIqIRYqgmon7t3bsXkiShvr5+SO979NFHUVRUNCb7NJUM9/cbi8Uwf/58/P3vfx/R51966aU4++yzR7SNTMcccwyefvrpUdseEdFkwVBNNEFIktTv45Zbbsn3LuY0Z84c3H///YZlF1xwQV5mMBtuSJ1sHnzwQRx22GE49thjR7SdH/7wh3j00UdHZ6cA3HTTTbj++uuhquqobZOIaDJgqCaaIJqamvTH/fffD5/PZ1h2zTXX6OsKIZBIJPK4twNzuVyoqKjI925MaLFYbFjvE0LggQcewJe//OUR70NhYeGo3lE47bTT0Nvbi+eee27UtklENBkwVBNNEFVVVfqjsLAQkiTp3+/YsQMFBQV47rnnsHLlSjgcDrz22mtoaGjAZz7zGVRWVsLr9WLVqlV48cUX9W3eeOONWLNmTdZnLV++HLfddpv+/S9+8QssXrwYTqcTixYtwk9/+tNB7/f69euxb98+fPOb39Rb1YHs8o9bbrkFdXV1eOSRRzBr1ix4vV58/etfh6Io+P73v4+qqipUVFTgjjvuMGy/q6sLX/nKV1BeXg6fz4cTTzwRW7duzbk/hx12GABgxYoVkCQJ69evBwCoqorbbrsNNTU1cDgcqKur63cK5T//+c8oKiqCoigAgPr6ekiShOuvv15f5ytf+Qq+8IUv6N8//fTTOPLII+FwODBnzhzce++9hm3OmTMHt99+Oy6++GL4fD589atfzfpcRVHwpS99CYsWLcL+/ftN9+3tt99GQ0MDzjjjDH1ZqoX+t7/9LY4//ni4XC6sWrUKu3btwr/+9S8cffTR8Hq9OO200+D3+/X39S3/WL9+Pa644gpce+21KCkpQVVVleEuiRACt9xyC2bNmgWHw4Hq6mpcccUV+usWiwWnn346fv3rX+f83RIRTUmCiCacTZs2icLCQv37l156SQAQy5YtE88//7zYvXu3aG9vF/X19eLBBx8U27ZtE7t27RI33XSTcDqdYt++fUIIId5//30BQOzevVvfVmrZRx99JIQQ4vHHHxczZswQTz/9tNizZ494+umnRUlJiXj00UeFEEJ8/PHHAoB49913Tfe1vb1d1NTUiNtuu000NTWJpqYm05/h5ptvFl6vV5x//vnigw8+EM8884yw2+1iw4YN4vLLLxc7duwQjzzyiAAg/vGPf+jvO/nkk8VZZ50l/vWvf4ldu3aJq6++WpSWlor29nbT/XnzzTcFAPHiiy+KpqYmfb0f/OAHwufziV/96ldix44d4tprrxU2m03s2rXLdDtdXV1ClmXxr3/9SwghxP333y/KysrEmjVr9HXmz58vHn74YSGEEG+99ZaQZVncdtttYufOnWLTpk3C5XKJTZs26evPnj1b+Hw+cc8994jdu3eL3bt3G36/kUhEnHPOOWLFihWitbXVdL9SP8uiRYsMy1LbWbRokdi8ebP48MMPxTHHHCNWrlwp1q9fL1577TXxzjvviPnz54v/+I//0N93ySWXiM985jP69+vWrRM+n0/ccsstYteuXeKxxx4TkiSJ559/XgghxFNPPSV8Pp/4y1/+Ivbt2yf++c9/ioceesiwLz/72c/E7Nmzc+4/EdFUxFBNNAHlCtV/+MMfBnzvkUceKX784x/r3y9fvlzcdttt+vc33HCDIRjOmzdPPPnkk4Zt3H777WLt2rVCiIFDtRBaWLzvvvv6/Rluvvlm4Xa7RU9Pj75sw4YNYs6cOUJRFH3Z4YcfLu68804hhBCvvvqq8Pl8IhKJGLY9b9488fOf/9x0X3Ltb3V1tbjjjjsMy1atWiW+/vWv5/y5jjrqKHH33XcLIYQ4++yzxR133CHsdrvo7e0VBw8eFAD0UP75z39enHLKKYb3f+tb3xJHHHGE/v3s2bPF2Wefbbq/r776qjjppJPEJz7xCdHV1ZVzn4QQ4sorrxQnnnii6XZ+8Ytf6Mt+9atfCQBiy5Yt+rI777xTHH744fr3ZqH6E5/4hGHbq1atEtddd50QQoh7771XLFy4UMRisZz798c//lHIsmz4dyUimupY/kE0iRx99NGG7wOBAK655hosXrwYRUVF8Hq92L59u6Fs4KKLLsKTTz4JQLt1/6tf/QoXXXQRACAYDKKhoQFf/vKX4fV69cd3v/tdNDQ0jPr+z5kzBwUFBfr3lZWVOOKIIyDLsmFZa2srAGDr1q0IBAIoLS017N/HH388pP3r6elBY2MjjjvuOMPy4447Dtu3b8/5vnXr1uHll1+GEAKvvvoqzj33XCxevBivvfYaXnnlFVRXV2PBggUAgO3bt5tu/6OPPtJLSIDsf8OUz33ucwgGg3j++edRWFjY788TDofhdDpNX1u2bJn+vLKyEgCwdOlSw7LU7zeXzG0AwIwZM/T3fPazn0U4HMbcuXPx7//+7/j973+fVd/vcrmgqiqi0Wi/n0NENJVY870DRDR4Ho/H8P0111yDF154Affccw/mz58Pl8uF888/39AB7nOf+xyuu+46vPPOOwiHwzhw4AAuuOACAFooB4CHH344q/baYrGM+v7bbDbD95IkmS5LjRwRCAQwY8YMvPzyy1nbGo/h+tavX49HHnkEW7duhc1mw6JFi7B+/Xq8/PLL6OzsxLp164a8zb7/himnn346Hn/8cbzxxhs48cQT+91GWVkZtm3bZvpa5u8zVd/ed9lAI3P0929SW1uLnTt34sUXX8QLL7yAr3/967j77rvxyiuv6O/r6OiAx+OBy+Xq93OIiKYShmqiSez111/HpZdeinPOOQeAFkL37t1rWKempgbr1q3DE088gXA4jFNOOUUflaOyshLV1dXYs2eP3no9HHa73dAaO1qOOuooNDc3w2q1Ys6cOYPeFwCG/fH5fKiursbrr79uCMKvv/46Vq9enXNbxx9/PHp7e3Hffffp71u/fj3uuusudHZ24uqrr9bXXbx4MV5//XXD+19//XUsXLhwUBco//mf/4klS5bg05/+NJ599tl+A/uKFSvws5/9DEIIPTiPJ5fLhbPOOgtnnXUW/uu//guLFi3Ctm3bcNRRRwEA3n//faxYsWLc94uIKJ8YqokmsQULFuB3v/sdzjrrLEiShG9/+9umrZAXXXQRbr75ZsRiMdx3332G12699VZcccUVKCwsxKmnnopoNIq33noLnZ2d2Lhx46D2Y86cOfjb3/6GCy+8EA6HA2VlZaPy85188slYu3Ytzj77bHz/+9/HwoUL0djYiGeffRbnnHOOaSlFRUUFXC4XNm/ejJqaGjidThQWFuJb3/oWbr75ZsybNw91dXXYtGkT6uvr8cQTT+T8/OLiYixbtgxPPPEEHnjgAQDACSecgH/7t39DPB43BN+rr74aq1atwu23344LLrgAb7zxBh544IEhjaRy+eWXQ1EUnHnmmXjuuefwiU98wnS9T37ykwgEAvjggw+wZMmSQW9/NDz66KNQFAVr1qyB2+3G448/DpfLhdmzZ+vrvPrqq/jUpz41rvtFRJRvrKkmmsR+8IMfoLi4GMceeyzOOussbNiwQW8tzHT++eejvb0doVAoa/a8r3zlK/jFL36BTZs2YenSpVi3bh0effRRfWi6wbjtttuwd+9ezJs3D+Xl5SP9sXSSJOEvf/kLTjjhBFx22WVYuHAhLrzwQuzbt0+vF+7LarXiRz/6EX7+85+juroan/nMZwAAV1xxBTZu3Iirr74aS5cuxebNm/HMM8/oNdG5rFu3Doqi6EPzlZSU4IgjjkBVVRUOP/xwfb2jjjoKv/3tb/HrX/8aS5YswXe+8x3cdtttuPTSS4f0M1911VW49dZbcfrpp+ecLbG0tBTnnHNOvxcEY6WoqAgPP/wwjjvuOCxbtgwvvvgi/vSnP6G0tBQAcOjQIfz973/HZZddNu77RkSUT5IQQuR7J4iIaGjee+89nHLKKWhoaIDX68337uiuu+46dHZ24qGHHsr3rhARjSu2VBMRTULLli3D9773PXz88cf53hWDiooK3H777fneDSKicceWaiIiIiKiEWJLNRERERHRCDFUExERERGNEEM1EREREdEIMVQTEREREY0QQzURERER0QgxVBMRERERjRBDNRERERHRCDFUExERERGNEEM1EREREdEI/X+Wj9L8WDrxagAAAABJRU5ErkJggg==", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for a in area:\n", - " fig, axe = plt.subplots()\n", - " index = range(len(label))\n", - "\n", - " for i in range(len(label)):\n", - " data = df_total_per[[\"{}_2011\".format(a), \"{}_2016\".format(a), \"{}_2021\".format(a)]].iloc[i]\n", - " axe.plot(data, label=label[i])\n", - "\n", - " plt.xticks(fontsize=5.5)\n", - " plt.ylabel(\"Percentage\")\n", - " plt.xlabel(\"Travel time to work (mins)\")\n", - " plt.title(\"2011-2021 Percentage of total number of travel time to work in {}\".format(a))\n", - " plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for a in area:\n", - " fig, axe = plt.subplots()\n", - " index = range(len(label))\n", - "\n", - " for i in range(len(label)):\n", - " data = df_bus_per[[\"{}_2011\".format(a), \"{}_2016\".format(a), \"{}_2021\".format(a)]].iloc[i]\n", - " axe.plot(data, label=label[i])\n", - "\n", - " plt.xticks(fontsize=5.5)\n", - " plt.ylabel(\"Percentage\")\n", - " plt.xlabel(\"Travel time to work (mins)\")\n", - " plt.title(\"2011-2021 Percentage of total number of travel time to work in {} (Bus)\".format(a))\n", - " plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n", - " plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ds701", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 17ab279e0ba839085aa24efad24975b853689a64 Mon Sep 17 00:00:00 2001 From: laowangbushilaowang <53445096+laowangbushilaowang@users.noreply.github.com> Date: Tue, 28 Nov 2023 22:33:35 -0500 Subject: [PATCH 08/39] Delete fa23-team/data_preprocess_q2.ipynb --- fa23-team/data_preprocess_q2.ipynb | 1180 ---------------------------- 1 file changed, 1180 deletions(-) delete mode 100644 fa23-team/data_preprocess_q2.ipynb diff --git a/fa23-team/data_preprocess_q2.ipynb b/fa23-team/data_preprocess_q2.ipynb deleted file mode 100644 index 550a715..0000000 --- a/fa23-team/data_preprocess_q2.ipynb +++ /dev/null @@ -1,1180 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Census Tract 1.01, Suffolk County, Massachusetts!!EstimateCensus Tract 1.01, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 1.02, Suffolk County, Massachusetts!!EstimateCensus Tract 1.02, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 2.01, Suffolk County, Massachusetts!!EstimateCensus Tract 2.01, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 2.02, Suffolk County, Massachusetts!!EstimateCensus Tract 2.02, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 3.01, Suffolk County, Massachusetts!!EstimateCensus Tract 3.01, Suffolk County, Massachusetts!!Margin of Error...Census Tract 9816, Suffolk County, Massachusetts!!EstimateCensus Tract 9816, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 9817, Suffolk County, Massachusetts!!EstimateCensus Tract 9817, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 9818, Suffolk County, Massachusetts!!EstimateCensus Tract 9818, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 9819, Suffolk County, Massachusetts!!EstimateCensus Tract 9819, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 9901.01, Suffolk County, Massachusetts!!EstimateCensus Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error
Label (Grouping)
Total:1,034±2182,533±7312,716±3792,150±2501,659±250...0±130±1312±180±130±13
Less than 10 minutes26±38175±17965±61125±8145±40...0±130±130±130±130±13
10 to 14 minutes110±65148±93154±73241±10189±53...0±130±130±130±130±13
15 to 19 minutes126±61630±459323±138137±69285±134...0±130±130±130±130±13
20 to 24 minutes84±49182±91325±129360±118156±91...0±130±130±130±130±13
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5 rows × 470 columns

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" - ], - "text/plain": [ - " Census Tract 1.01, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 1,034 \n", - "    Less than 10 minutes 26 \n", - "    10 to 14 minutes 110 \n", - "    15 to 19 minutes 126 \n", - "    20 to 24 minutes 84 \n", - "\n", - " Census Tract 1.01, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±218 \n", - "    Less than 10 minutes ±38 \n", - "    10 to 14 minutes ±65 \n", - "    15 to 19 minutes ±61 \n", - "    20 to 24 minutes ±49 \n", - "\n", - " Census Tract 1.02, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 2,533 \n", - "    Less than 10 minutes 175 \n", - "    10 to 14 minutes 148 \n", - "    15 to 19 minutes 630 \n", - "    20 to 24 minutes 182 \n", - "\n", - " Census Tract 1.02, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±731 \n", - "    Less than 10 minutes ±179 \n", - "    10 to 14 minutes ±93 \n", - "    15 to 19 minutes ±459 \n", - "    20 to 24 minutes ±91 \n", - "\n", - " Census Tract 2.01, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 2,716 \n", - "    Less than 10 minutes 65 \n", - "    10 to 14 minutes 154 \n", - "    15 to 19 minutes 323 \n", - "    20 to 24 minutes 325 \n", - "\n", - " Census Tract 2.01, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±379 \n", - "    Less than 10 minutes ±61 \n", - "    10 to 14 minutes ±73 \n", - "    15 to 19 minutes ±138 \n", - "    20 to 24 minutes ±129 \n", - "\n", - " Census Tract 2.02, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 2,150 \n", - "    Less than 10 minutes 125 \n", - "    10 to 14 minutes 241 \n", - "    15 to 19 minutes 137 \n", - "    20 to 24 minutes 360 \n", - "\n", - " Census Tract 2.02, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±250 \n", - "    Less than 10 minutes ±81 \n", - "    10 to 14 minutes ±101 \n", - "    15 to 19 minutes ±69 \n", - "    20 to 24 minutes ±118 \n", - "\n", - " Census Tract 3.01, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 1,659 \n", - "    Less than 10 minutes 45 \n", - "    10 to 14 minutes 89 \n", - "    15 to 19 minutes 285 \n", - "    20 to 24 minutes 156 \n", - "\n", - " Census Tract 3.01, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±250 \n", - "    Less than 10 minutes ±40 \n", - "    10 to 14 minutes ±53 \n", - "    15 to 19 minutes ±134 \n", - "    20 to 24 minutes ±91 \n", - "\n", - " ... \\\n", - "Label (Grouping) ... \n", - "Total: ... \n", - "    Less than 10 minutes ... \n", - "    10 to 14 minutes ... \n", - "    15 to 19 minutes ... \n", - "    20 to 24 minutes ... \n", - "\n", - " Census Tract 9816, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 0 \n", - "    Less than 10 minutes 0 \n", - "    10 to 14 minutes 0 \n", - "    15 to 19 minutes 0 \n", - "    20 to 24 minutes 0 \n", - "\n", - " Census Tract 9816, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±13 \n", - "    Less than 10 minutes ±13 \n", - "    10 to 14 minutes ±13 \n", - "    15 to 19 minutes ±13 \n", - "    20 to 24 minutes ±13 \n", - "\n", - " Census Tract 9817, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 0 \n", - "    Less than 10 minutes 0 \n", - "    10 to 14 minutes 0 \n", - "    15 to 19 minutes 0 \n", - "    20 to 24 minutes 0 \n", - "\n", - " Census Tract 9817, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±13 \n", - "    Less than 10 minutes ±13 \n", - "    10 to 14 minutes ±13 \n", - "    15 to 19 minutes ±13 \n", - "    20 to 24 minutes ±13 \n", - "\n", - " Census Tract 9818, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 12 \n", - "    Less than 10 minutes 0 \n", - "    10 to 14 minutes 0 \n", - "    15 to 19 minutes 0 \n", - "    20 to 24 minutes 0 \n", - "\n", - " Census Tract 9818, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±18 \n", - "    Less than 10 minutes ±13 \n", - "    10 to 14 minutes ±13 \n", - "    15 to 19 minutes ±13 \n", - "    20 to 24 minutes ±13 \n", - "\n", - " Census Tract 9819, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 0 \n", - "    Less than 10 minutes 0 \n", - "    10 to 14 minutes 0 \n", - "    15 to 19 minutes 0 \n", - "    20 to 24 minutes 0 \n", - "\n", - " Census Tract 9819, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±13 \n", - "    Less than 10 minutes ±13 \n", - "    10 to 14 minutes ±13 \n", - "    15 to 19 minutes ±13 \n", - "    20 to 24 minutes ±13 \n", - "\n", - " Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 0 \n", - "    Less than 10 minutes 0 \n", - "    10 to 14 minutes 0 \n", - "    15 to 19 minutes 0 \n", - "    20 to 24 minutes 0 \n", - "\n", - " Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error \n", - "Label (Grouping) \n", - "Total: ±13 \n", - "    Less than 10 minutes ±13 \n", - "    10 to 14 minutes ±13 \n", - "    15 to 19 minutes ±13 \n", - "    20 to 24 minutes ±13 \n", - "\n", - "[5 rows x 470 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "suffk2021=pd.read_csv('./data/q2/2021.csv',index_col=\"Label (Grouping)\")\n", - "suffk2016=pd.read_csv('./data/q2/2016.csv',index_col=\"Label (Grouping)\")\n", - "suffk2011=pd.read_csv('./data/q2/2011.csv',index_col=\"Label (Grouping)\")\n", - "suffk2021.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "Roxbuy_list=['805','806.','804.01','806.01','801','814','803','813','815','904','819','820','821,9803']\n", - "Mattapan_list=['9811','1011.01','1011.02','1010.01','1010.02','1009']\n", - "Dorchester_list=['907','913','912','911','914','909.01','915','910.01','902','918','916','917'\n", - " ,'901','919','920','921.01','924','923','922','1006.01','1001','1005'\n", - " ,'1006.03','1002','1003','1008','1006.01','1007']\n", - "\n", - "# select the data which column name contain elements in Roxbuy_list\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "selected_columns = [col for col in suffk2021.columns if any(item in col for item in Roxbuy_list)]\n", - "Roxbury_data_2021 = suffk2021[selected_columns]\n", - "\n", - "selected_columns2 = [col for col in suffk2021.columns if any(item in col for item in Mattapan_list)]\n", - "\n", - "Mattapan_data_2021 = suffk2021[selected_columns2]\n", - "\n", - "selected_columns3 = [col for col in suffk2021.columns if any(item in col for item in Dorchester_list)]\n", - "\n", - "Dorchester_data_2021 = suffk2021[selected_columns3]\n", - "\n", - "\n", - "# output the data to csv file\n", - "Roxbury_data_2021.to_csv('./data/q2/Roxbury_data_2021.csv')\n", - "Mattapan_data_2021.to_csv('./data/q2/Mattapan_data_2021.csv')\n", - "Dorchester_data_2021.to_csv('./data/q2/Dorchester_data_2021.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# 813 miss here\n", - "Roxbuy_list=['805','806.','804.01','806.01','801','814','803','815','904','819','820','821,9803']\n", - "Mattapan_list=['9811','1011.01','1011.02','1010.01','1010.02','1009']\n", - "Dorchester_list=['907','913','912','911','914','909.01','915','910.01','902','918','916','917'\n", - " ,'901','919','920','921.01','924','923','922','1006.01','1001','1005'\n", - " ,'1006.03','1002','1003','1008','1006.01','1007']\n", - "\n", - "# select the data which column name contain elements in Roxbuy_list\n", - "\n", - " \n", - "\n", - "selected_columns = [col for col in suffk2016.columns if any(item in col for item in Roxbuy_list)]\n", - "Roxbury_data_2016 = suffk2016[selected_columns]\n", - "\n", - "selected_columns2 = [col for col in suffk2016.columns if any(item in col for item in Mattapan_list)]\n", - "\n", - "Mattapan_data_2016 = suffk2016[selected_columns2]\n", - "\n", - "selected_columns3 = [col for col in suffk2016.columns if any(item in col for item in Dorchester_list)]\n", - "\n", - "Dorchester_data_2016 = suffk2016[selected_columns3]\n", - "\n", - "\n", - "# output the data to csv file\n", - "Roxbury_data_2016.to_csv('./data/q2/Roxbury_data_2016.csv')\n", - "Mattapan_data_2016.to_csv('./data/q2/Mattapan_data_2016.csv')\n", - "Dorchester_data_2016.to_csv('./data/q2/Dorchester_data_2016.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# 813 missing \n", - "Roxbuy_list=['805','806.','804.01','806.01','801','814','803','815','904','819','820','821,9803']\n", - "Mattapan_list=['9811','1011.01','1011.02','1010.01','1010.02','1009']\n", - "Dorchester_list=['907','913','912','911','914','909.01','915','910.01','902','918','916','917'\n", - " ,'901','919','920','921.01','924','923','922','1006.01','1001','1005'\n", - " ,'1006.03','1002','1003','1008','1006.01','1007']\n", - "\n", - "# select the data which column name contain elements in Roxbuy_list\n", - "\n", - "selected_columns = [col for col in suffk2011.columns if any(item in col for item in Roxbuy_list)]\n", - "Roxbury_data_2011 = suffk2011[selected_columns]\n", - "\n", - "selected_columns2 = [col for col in suffk2011.columns if any(item in col for item in Mattapan_list)]\n", - "\n", - "Mattapan_data_2011 = suffk2011[selected_columns2]\n", - "\n", - "selected_columns3 = [col for col in suffk2011.columns if any(item in col for item in Dorchester_list)]\n", - "\n", - "Dorchester_data_2011 = suffk2011[selected_columns3]\n", - "\n", - "\n", - "# output the data to csv file\n", - "Roxbury_data_2011.to_csv('./data/q2/Roxbury_data_2011.csv')\n", - "Mattapan_data_2011.to_csv('./data/q2/Mattapan_data_2011.csv')\n", - "Dorchester_data_2011.to_csv('./data/q2/Dorchester_data_2011.csv')" - 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Census Tract 901, Suffolk County, Massachusetts!!EstimateCensus Tract 901, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 902, Suffolk County, Massachusetts!!EstimateCensus Tract 902, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 907, Suffolk County, Massachusetts!!EstimateCensus Tract 907, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 909.01, Suffolk County, Massachusetts!!EstimateCensus Tract 909.01, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 910.01, Suffolk County, Massachusetts!!EstimateCensus Tract 910.01, Suffolk County, Massachusetts!!Margin of Error...Census Tract 1006.01, Suffolk County, Massachusetts!!EstimateCensus Tract 1006.01, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 1006.03, Suffolk County, Massachusetts!!EstimateCensus Tract 1006.03, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 1007, Suffolk County, Massachusetts!!EstimateCensus Tract 1007, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 1008, Suffolk County, Massachusetts!!EstimateCensus Tract 1008, Suffolk County, Massachusetts!!Margin of ErrorCensus Tract 9901.01, Suffolk County, Massachusetts!!EstimateCensus Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error
Label (Grouping)
Total:1,758±282695±1682,032±2711,440±2491,485±209...2,664±454971±1742,161±2552,973±3660±95
Less than 10 minutes70±530±95160±8468±5763±40...30±35158±109184±103305±1340±95
10 to 14 minutes118±7755±46197±92167±10471±44...313±114163±91322±130318±1580±95
15 to 19 minutes161±93171±84250±111160±75113±75...96±80111±63219±83381±1560±95
20 to 24 minutes374±166127±82260±121154±81300±107...381±14349±42299±120585±2560±95
..................................................................
25 to 29 minutes0±950±9523±380±950±95...0±950±950±950±950±95
30 to 34 minutes0±950±9521±360±9512±21...0±950±950±9515±240±95
35 to 44 minutes0±950±950±950±950±95...0±950±950±950±950±95
45 to 59 minutes0±950±950±950±950±95...0±950±950±950±950±95
60 or more minutes0±950±950±950±950±95...0±950±9517±250±950±95
\n", - "

120 rows × 56 columns

\n", - "
" - ], - "text/plain": [ - " Census Tract 901, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 1,758 \n", - "    Less than 10 minutes 70 \n", - "    10 to 14 minutes 118 \n", - "    15 to 19 minutes 161 \n", - "    20 to 24 minutes 374 \n", - "... ... \n", - "        25 to 29 minutes 0 \n", - "        30 to 34 minutes 0 \n", - "        35 to 44 minutes 0 \n", - "        45 to 59 minutes 0 \n", - "        60 or more minutes 0 \n", - "\n", - " Census Tract 901, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±282 \n", - "    Less than 10 minutes ±53 \n", - "    10 to 14 minutes ±77 \n", - "    15 to 19 minutes ±93 \n", - "    20 to 24 minutes ±166 \n", - "... ... \n", - "        25 to 29 minutes ±95 \n", - "        30 to 34 minutes ±95 \n", - "        35 to 44 minutes ±95 \n", - "        45 to 59 minutes ±95 \n", - "        60 or more minutes ±95 \n", - "\n", - " Census Tract 902, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 695 \n", - "    Less than 10 minutes 0 \n", - "    10 to 14 minutes 55 \n", - "    15 to 19 minutes 171 \n", - "    20 to 24 minutes 127 \n", - "... ... \n", - "        25 to 29 minutes 0 \n", - "        30 to 34 minutes 0 \n", - "        35 to 44 minutes 0 \n", - "        45 to 59 minutes 0 \n", - "        60 or more minutes 0 \n", - "\n", - " Census Tract 902, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±168 \n", - "    Less than 10 minutes ±95 \n", - "    10 to 14 minutes ±46 \n", - "    15 to 19 minutes ±84 \n", - "    20 to 24 minutes ±82 \n", - "... ... \n", - "        25 to 29 minutes ±95 \n", - "        30 to 34 minutes ±95 \n", - "        35 to 44 minutes ±95 \n", - "        45 to 59 minutes ±95 \n", - "        60 or more minutes ±95 \n", - "\n", - " Census Tract 907, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 2,032 \n", - "    Less than 10 minutes 160 \n", - "    10 to 14 minutes 197 \n", - "    15 to 19 minutes 250 \n", - "    20 to 24 minutes 260 \n", - "... ... \n", - "        25 to 29 minutes 23 \n", - "        30 to 34 minutes 21 \n", - "        35 to 44 minutes 0 \n", - "        45 to 59 minutes 0 \n", - "        60 or more minutes 0 \n", - "\n", - " Census Tract 907, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±271 \n", - "    Less than 10 minutes ±84 \n", - "    10 to 14 minutes ±92 \n", - "    15 to 19 minutes ±111 \n", - "    20 to 24 minutes ±121 \n", - "... ... \n", - "        25 to 29 minutes ±38 \n", - "        30 to 34 minutes ±36 \n", - "        35 to 44 minutes ±95 \n", - "        45 to 59 minutes ±95 \n", - "        60 or more minutes ±95 \n", - "\n", - " Census Tract 909.01, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 1,440 \n", - "    Less than 10 minutes 68 \n", - "    10 to 14 minutes 167 \n", - "    15 to 19 minutes 160 \n", - "    20 to 24 minutes 154 \n", - "... ... \n", - "        25 to 29 minutes 0 \n", - "        30 to 34 minutes 0 \n", - "        35 to 44 minutes 0 \n", - "        45 to 59 minutes 0 \n", - "        60 or more minutes 0 \n", - "\n", - " Census Tract 909.01, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±249 \n", - "    Less than 10 minutes ±57 \n", - "    10 to 14 minutes ±104 \n", - "    15 to 19 minutes ±75 \n", - "    20 to 24 minutes ±81 \n", - "... ... \n", - "        25 to 29 minutes ±95 \n", - "        30 to 34 minutes ±95 \n", - "        35 to 44 minutes ±95 \n", - "        45 to 59 minutes ±95 \n", - "        60 or more minutes ±95 \n", - "\n", - " Census Tract 910.01, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 1,485 \n", - "    Less than 10 minutes 63 \n", - "    10 to 14 minutes 71 \n", - "    15 to 19 minutes 113 \n", - "    20 to 24 minutes 300 \n", - "... ... \n", - "        25 to 29 minutes 0 \n", - "        30 to 34 minutes 12 \n", - "        35 to 44 minutes 0 \n", - "        45 to 59 minutes 0 \n", - "        60 or more minutes 0 \n", - "\n", - " Census Tract 910.01, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±209 \n", - "    Less than 10 minutes ±40 \n", - "    10 to 14 minutes ±44 \n", - "    15 to 19 minutes ±75 \n", - "    20 to 24 minutes ±107 \n", - "... ... \n", - "        25 to 29 minutes ±95 \n", - "        30 to 34 minutes ±21 \n", - "        35 to 44 minutes ±95 \n", - "        45 to 59 minutes ±95 \n", - "        60 or more minutes ±95 \n", - "\n", - " ... \\\n", - "Label (Grouping) ... \n", - "Total: ... \n", - "    Less than 10 minutes ... \n", - "    10 to 14 minutes ... \n", - "    15 to 19 minutes ... \n", - "    20 to 24 minutes ... \n", - "... ... \n", - "        25 to 29 minutes ... \n", - "        30 to 34 minutes ... \n", - "        35 to 44 minutes ... \n", - "        45 to 59 minutes ... \n", - "        60 or more minutes ... \n", - "\n", - " Census Tract 1006.01, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 2,664 \n", - "    Less than 10 minutes 30 \n", - "    10 to 14 minutes 313 \n", - "    15 to 19 minutes 96 \n", - "    20 to 24 minutes 381 \n", - "... ... \n", - "        25 to 29 minutes 0 \n", - "        30 to 34 minutes 0 \n", - "        35 to 44 minutes 0 \n", - "        45 to 59 minutes 0 \n", - "        60 or more minutes 0 \n", - "\n", - " Census Tract 1006.01, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±454 \n", - "    Less than 10 minutes ±35 \n", - "    10 to 14 minutes ±114 \n", - "    15 to 19 minutes ±80 \n", - "    20 to 24 minutes ±143 \n", - "... ... \n", - "        25 to 29 minutes ±95 \n", - "        30 to 34 minutes ±95 \n", - "        35 to 44 minutes ±95 \n", - "        45 to 59 minutes ±95 \n", - "        60 or more minutes ±95 \n", - "\n", - " Census Tract 1006.03, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 971 \n", - "    Less than 10 minutes 158 \n", - "    10 to 14 minutes 163 \n", - "    15 to 19 minutes 111 \n", - "    20 to 24 minutes 49 \n", - "... ... \n", - "        25 to 29 minutes 0 \n", - "        30 to 34 minutes 0 \n", - "        35 to 44 minutes 0 \n", - "        45 to 59 minutes 0 \n", - "        60 or more minutes 0 \n", - "\n", - " Census Tract 1006.03, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±174 \n", - "    Less than 10 minutes ±109 \n", - "    10 to 14 minutes ±91 \n", - "    15 to 19 minutes ±63 \n", - "    20 to 24 minutes ±42 \n", - "... ... \n", - "        25 to 29 minutes ±95 \n", - "        30 to 34 minutes ±95 \n", - "        35 to 44 minutes ±95 \n", - "        45 to 59 minutes ±95 \n", - "        60 or more minutes ±95 \n", - "\n", - " Census Tract 1007, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 2,161 \n", - "    Less than 10 minutes 184 \n", - "    10 to 14 minutes 322 \n", - "    15 to 19 minutes 219 \n", - "    20 to 24 minutes 299 \n", - "... ... \n", - "        25 to 29 minutes 0 \n", - "        30 to 34 minutes 0 \n", - "        35 to 44 minutes 0 \n", - "        45 to 59 minutes 0 \n", - "        60 or more minutes 17 \n", - "\n", - " Census Tract 1007, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±255 \n", - "    Less than 10 minutes ±103 \n", - "    10 to 14 minutes ±130 \n", - "    15 to 19 minutes ±83 \n", - "    20 to 24 minutes ±120 \n", - "... ... \n", - "        25 to 29 minutes ±95 \n", - "        30 to 34 minutes ±95 \n", - "        35 to 44 minutes ±95 \n", - "        45 to 59 minutes ±95 \n", - "        60 or more minutes ±25 \n", - "\n", - " Census Tract 1008, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 2,973 \n", - "    Less than 10 minutes 305 \n", - "    10 to 14 minutes 318 \n", - "    15 to 19 minutes 381 \n", - "    20 to 24 minutes 585 \n", - "... ... \n", - "        25 to 29 minutes 0 \n", - "        30 to 34 minutes 15 \n", - "        35 to 44 minutes 0 \n", - "        45 to 59 minutes 0 \n", - "        60 or more minutes 0 \n", - "\n", - " Census Tract 1008, Suffolk County, Massachusetts!!Margin of Error \\\n", - "Label (Grouping) \n", - "Total: ±366 \n", - "    Less than 10 minutes ±134 \n", - "    10 to 14 minutes ±158 \n", - "    15 to 19 minutes ±156 \n", - "    20 to 24 minutes ±256 \n", - "... ... \n", - "        25 to 29 minutes ±95 \n", - "        30 to 34 minutes ±24 \n", - "        35 to 44 minutes ±95 \n", - "        45 to 59 minutes ±95 \n", - "        60 or more minutes ±95 \n", - "\n", - " Census Tract 9901.01, Suffolk County, Massachusetts!!Estimate \\\n", - "Label (Grouping) \n", - "Total: 0 \n", - "    Less than 10 minutes 0 \n", - "    10 to 14 minutes 0 \n", - "    15 to 19 minutes 0 \n", - "    20 to 24 minutes 0 \n", - "... ... \n", - "        25 to 29 minutes 0 \n", - "        30 to 34 minutes 0 \n", - "        35 to 44 minutes 0 \n", - "        45 to 59 minutes 0 \n", - "        60 or more minutes 0 \n", - "\n", - " Census Tract 9901.01, Suffolk County, Massachusetts!!Margin of Error \n", - "Label (Grouping) \n", - "Total: ±95 \n", - "    Less than 10 minutes ±95 \n", - "    10 to 14 minutes ±95 \n", - "    15 to 19 minutes ±95 \n", - "    20 to 24 minutes ±95 \n", - "... ... \n", - "        25 to 29 minutes ±95 \n", - "        30 to 34 minutes ±95 \n", - "        35 to 44 minutes ±95 \n", - "        45 to 59 minutes ±95 \n", - "        60 or more minutes ±95 \n", - "\n", - "[120 rows x 56 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Dorchester_data_2011" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} From d6072526af1529892ddfae1119adbf323dcdf540 Mon Sep 17 00:00:00 2001 From: laowangbushilaowang <53445096+laowangbushilaowang@users.noreply.github.com> Date: Tue, 28 Nov 2023 22:34:06 -0500 Subject: [PATCH 09/39] wbh create and upload --- fa23-team/README.md | 76 +- fa23-team/q2-final.ipynb | 2192 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 2267 insertions(+), 1 deletion(-) create mode 100644 fa23-team/q2-final.ipynb diff --git a/fa23-team/README.md b/fa23-team/README.md index e82061c..b6cd892 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -1 +1,75 @@ -Update this README with information on how to run your project. \ No newline at end of file +## Q2 + +### q2-final.ipynb + +All data should be downloaded from: https://www2.census.gov/programs-surveys/acs/data/pums/ + +Because the data before 2016 do not have area information: PUMA, so we choose 5 years data in MA from 2016-2021. + +#### Description of variables: +2016-2018: https://data.census.gov/mdat/#/search?ds=ACSPUMS5Y2018 (Can be searched on this website) + +2019-2022: https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2022.pdf + +##### The variables I used: + +PUMA: + + Public use microdata area code (PUMA) based on 2020 Census definition + (areas with population of 100,000 or more, use with ST for unique code) + 00100..81003 .Public use microdata area codes + +JWTRNS: + + Means of transportation to work + bb .N/A (not a worker-not in the labor force, including persons + .under 16 years; unemployed; employed, with a job but not at + .work; Armed Forces, with a job but not at work) + 01 .Car, truck, or van + 02 .Bus + 03 .Subway or elevated rail + 04 .Long-distance train or commuter rail + 05 .Light rail, streetcar, or trolley + 06 .Ferryboat + 07 .Taxicab + 08 .Motorcycle + 09 .Bicycle + 10 .Walked + 11 .Worked from home + 12 .Other method + +JWTR: + + Same as JWTRNS but data before 2019 use this name. + +RAC1P: + + Recoded detailed race code + 1 .White alone + 2 .Black or African American alone + 3 .American Indian alone + 4 .Alaska Native alone + 5 .American Indian and Alaska Native tribes specified; or + .American Indian or Alaska Native, not specified and no other + .races + 6 .Asian alone + 7 .Native Hawaiian and Other Pacific Islander alone + 8 .Some Other Race alone + 9 .Two or More Races + +JWMNP: + + Travel time to work + bbb .N/A (not a worker or worker who worked at home) + 1..200 .1 to 200 minutes to get to work (Top-coded) + + +_Data should be rename to psam_p25_year.csv and put them in the same dirt._ + +### Data processes include: + +1. Handle missing values by dropping lines with missing JWMNP. +2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf, create two dicts to store the area name and code of PUMA. +3. Find the areas which in all years to analysis their average. Calculate the time difference between black and white of different areas across these years. Use (average * 2 * 5 * 50) / 60 to calculate the travel time difference between B&W each year. +4. To draw Geography graph, I processed the result. Change all the areas names to zip codes, and then draw a graph using tableau. +5. Draw \ No newline at end of file diff --git a/fa23-team/q2-final.ipynb b/fa23-team/q2-final.ipynb new file mode 100644 index 0000000..9be9c6b --- /dev/null +++ b/fa23-team/q2-final.ipynb @@ -0,0 +1,2192 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data should be renamed as psam_p25_year.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_30912\\781117606.py:5: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2018 = pd.read_csv('./psam_p25_2018.csv')\n", + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_30912\\781117606.py:6: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2019 = pd.read_csv('./psam_p25_2019.csv')\n", + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_30912\\781117606.py:7: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2020 = pd.read_csv('./psam_p25_2020.csv')\n", + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_30912\\781117606.py:8: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2021 = pd.read_csv('./psam_p25_2021.csv')\n" + ] + } + ], + "source": [ + "# df_2014 = pd.read_csv('./psam_p25_2014.csv')\n", + "# df_2015 = pd.read_csv('./psam_p25_2015.csv')\n", + "df_2016 = pd.read_csv('./psam_p25_2016.csv')\n", + "df_2017 = pd.read_csv('./psam_p25_2017.csv')\n", + "df_2018 = pd.read_csv('./psam_p25_2018.csv')\n", + "df_2019 = pd.read_csv('./psam_p25_2019.csv')\n", + "df_2020 = pd.read_csv('./psam_p25_2020.csv')\n", + "df_2021 = pd.read_csv('./psam_p25_2021.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete rows in each DataFrame where the JWMNP column has a NaN value\n", + "# df_2014 = df_2014.dropna(subset=['JWMNP'])\n", + "# df_2015 = df_2015.dropna(subset=['JWMNP'])\n", + "df_2016 = df_2016.dropna(subset=['JWMNP'])\n", + "df_2017 = df_2017.dropna(subset=['JWMNP'])\n", + "df_2018 = df_2018.dropna(subset=['JWMNP'])\n", + "df_2019 = df_2019.dropna(subset=['JWMNP'])\n", + "df_2020 = df_2020.dropna(subset=['JWMNP'])\n", + "df_2021 = df_2021.dropna(subset=['JWMNP'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Common PUMA categories across all datasets: {400, 3601, 3602, 3603, 4500, 1300, 4000, 4901, 4902, 4903, 300, 3500, 301, 303, 302, 304, 3900, 701, 702, 703, 4800, 704, 1600, 200, 3400, 4301, 4302, 4303, 4700, 2400, 100, 3301, 3302, 3303, 3304, 3305, 3306, 1000, 1900, 1901, 1902, 4200, 2800, 504, 501, 502, 503, 1400, 505, 506, 507, 508}\n" + ] + } + ], + "source": [ + "# unique_pumas_2014 = set(df_2014['PUMA'].unique())\n", + "# unique_pumas_2015 = set(df_2015['PUMA'].unique())\n", + "unique_pumas_2016 = set(df_2016['PUMA'].unique())\n", + "unique_pumas_2017 = set(df_2017['PUMA'].unique())\n", + "unique_pumas_2018 = set(df_2018['PUMA'].unique())\n", + "unique_pumas_2019 = set(df_2019['PUMA'].unique())\n", + "unique_pumas_2020 = set(df_2020['PUMA'].unique())\n", + "unique_pumas_2021 = set(df_2021['PUMA'].unique())\n", + "\n", + "# Find the PUMA class common to all datasets\n", + "common_pumas = unique_pumas_2016.intersection(\n", + " unique_pumas_2017,\n", + " unique_pumas_2018,\n", + " unique_pumas_2019,\n", + " unique_pumas_2020,\n", + " unique_pumas_2021\n", + ")\n", + "\n", + "# Print the common PUMA categories\n", + "print(\"Common PUMA categories across all datasets:\", common_pumas)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "puma_dict_2010 = {\n", + " \"Berkshire County--Pittsfield City\": 100,\n", + " \"Franklin & Hampshire (North) Counties\": 200,\n", + " \"Worcester County (Central)--Worcester City\": 300,\n", + " \"Worcester County (Northeast)--Leominster, Fitchburg & Gardner Cities\": 301,\n", + " \"Worcester County (West Central)\": 302,\n", + " \"Worcester County (East Central)\": 303,\n", + " \"Worcester County (South)\": 304,\n", + " \"Worcester & Middlesex Counties (Outside Leominster, Fitchburg & Gardner Cities)\": 400,\n", + " \"Middlesex County (Outside Lowell City)\": 501,\n", + " \"Middlesex County (Far Northeast)--Lowell City\": 502,\n", + " \"Middlesex County--Waltham City, Lexington, Burlington, Bedford & Lincoln Towns\": 503,\n", + " \"Middlesex County (South)--Framingham Town, Marlborough City & Natick Town\": 504,\n", + " \"Middlesex County--Watertown Town City, Arlington, Belmont & Winchester Towns\": 505,\n", + " \"Middlesex County (East)--Cambridge City\": 506,\n", + " \"Middlesex County (East)--Somerville & Everett Cities\": 507,\n", + " \"Middlesex County (East)--Malden & Medford Cities\": 508,\n", + " \"Essex County (Northwest)--Lawrence, Haverhill & Methuen Town Cities\": 701,\n", + " \"Essex County (Central)--Amesbury Town City\": 702,\n", + " \"Essex County (East)--Salem, Beverly, Gloucester & Newburyport Cities\": 703,\n", + " \"Essex County (South)--Lynn City, Swampscott & Nahant Towns\": 704,\n", + " \"Peabody City, Danvers, Reading, North Reading & Lynnfield Towns\": 1000,\n", + " \"Billerica, Andover, Tewksbury & Wilmington Towns\": 1300,\n", + " \"Middlesex (West Central) & Worcester (East) Counties\": 1400,\n", + " \"Hampden (West & East) & Hampshire (South) Counties--Northampton City\": 1600,\n", + " \"Hampden County (Central)--Springfield City\": 1900,\n", + " \"Hampden County (West of Springfield City)--Westfield & Holyoke Cities\": 1901,\n", + " \"Hampden County (East of Springfield City)--Chicopee City\": 1902,\n", + " \"Middlesex (Far Southwest), Norfolk (Northwest) & Worcester (Far East) Counties\": 2400,\n", + " \"Woburn, Melrose Cities, Saugus, Wakefield & Stoneham Towns\": 2800,\n", + " \"Boston City--Allston, Brighton & Fenway\": 3301,\n", + " \"Boston City--Back Bay, Beacon Hill, Charlestown, East Boston, Central & South End\": 3302,\n", + " \"Boston City--Dorchester & South Boston\": 3303,\n", + " \"Boston City--Mattapan & Roxbury\": 3304,\n", + " \"Boston City--Hyde Park, Jamaica Plain, Roslindale & West Roxbury\": 3305,\n", + " \"Suffolk County (North)--Revere, Chelsea & Winthrop Town Cities\": 3306,\n", + " \"Middlesex (Southeast) & Norfolk (Northeast) Counties--Newton City & Brookline Town\": 3400,\n", + " \"Norfolk (Northeast) & Middlesex (Southeast) Counties (West of Boston City)\": 3500,\n", + " \"Norfolk County (Southwest)--Greater Franklin Town City\": 3601,\n", + " \"Norfolk County (Central)--Randolph, Norwood, Dedham, Canton & Holbrook Towns\": 3602,\n", + " \"Norfolk County (Northeast)--Quincy City & Milton Town\": 3603,\n", + " \"Weymouth Town, Braintree Town Cities, Hingham, Hull & Cohasset Towns\": 3900,\n", + " \"Plymouth & Norfolk Counties--Brockton City, Stoughton & Avon Towns\": 4000,\n", + " \"Attleboro City, North Attleborough, Swansea, Seekonk, Rehoboth & Plainville Towns\": 4200,\n", + " \"Bristol (Outside New Bedford City) & Plymouth (South) Counties\": 4301,\n", + " \"Bristol County (Central)--Fall River City & Somerset Town\": 4302,\n", + " \"Bristol County--Taunton City, Mansfield, Norton, Raynam, Dighton & Berkley Towns\": 4303,\n", + " \"Bristol County (South)--New Bedford City & Fairhaven Town\": 4500,\n", + " \"Barnstable County (West)--Inner Cape Cod Towns & Barnstable Town City\": 4700,\n", + " \"Barnstable (East), Dukes & Nantucket Counties--Outer Cape Cod Towns\": 4800,\n", + " \"Plymouth & Bristol Counties (Outside Brockton City)\": 4901,\n", + " \"Plymouth County (Central)\": 4902,\n", + " \"Plymouth County (East)--Plymouth, Marshfield, Scituate, Duxbury & Kingston Towns\": 4903\n", + "}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "puma_dict_2020 = {\n", + " # Updated and added data for 2020\n", + " \"Berkshire County--Pittsfield City\": 100,\n", + " \"Franklin & Hampshire (West North East) Counties\": 201,\n", + " \"Hampshire County (South)--Northampton\": 301,\n", + " \"Hampden County (West)--Westfield & Holyoke\": 401,\n", + " \"Hampden County (Central)--Springfield\": 402,\n", + " \"Hampden County (East)--Chicopee\": 403,\n", + " \"Worcester County (Northwest)--Gardner\": 501,\n", + " \"Worcester County (Northeast)--Fitchburg Leominster & Lunenburg\": 502,\n", + " \"Worcester County (East Central)--Outside Worcester City\": 503,\n", + " \"Worcester City (East)\": 504,\n", + " \"Worcester City (West)\": 505,\n", + " \"Worcester County (West Central)--Outside Worcester City\": 506,\n", + " \"Worcester County (South)\": 507,\n", + " \"Middlesex County (Northwest)--Outside Lowell\": 601,\n", + " \"Middlesex County--Lowell\": 602,\n", + " \"Middlesex County (Northeast)--Billerica Tewksbury & Dracut\": 603,\n", + " \"Middlesex County (West Central)\": 604,\n", + " \"Middlesex County--Framingham & Marlborough\": 605,\n", + " \"Middlesex County (Southwest)\": 606,\n", + " \"Middlesex County--Lexington Burlington & Wilmington\": 607,\n", + " \"Middlesex County--Woburn Melrose & Reading\": 608,\n", + " \"Middlesex County (East)--Malden & Medford\": 609,\n", + " \"Middlesex County (East)--Somerville & Everett\": 610,\n", + " \"Middlesex County (East)--Cambridge\": 611,\n", + " \"Middlesex County--Watertown Arlington & Belmont\": 612,\n", + " \"Middlesex County--Newton & Waltham\": 613,\n", + " \"Essex County (Northwest)--Haverhill & Methuen\": 701,\n", + " \"Essex County (West)--Lawrence & Andover\": 702,\n", + " \"Essex County (North)--Newburyport Amesbury & North Andover\": 703,\n", + " \"Essex County (Central)--Peabody Danvers & Saugus\": 704,\n", + " \"Essex County--Lynn & Nahant\": 705,\n", + " \"Essex County (South Coastline)--Salem Beverly & Gloucester\": 706,\n", + " \"Norfolk County (Northwest)--Needham Wellesley & Westwood\": 902,\n", + " \"Norfolk County--Quincy\": 903,\n", + " \"Norfolk County (Northeast)--Weymouth Braintree Randolph & Cohasset\": 904,\n", + " \"Norfolk County (Central)--Norwood Stoughton & Canton\": 905,\n", + " \"Norfolk County (Southwest)--Franklin Walpole & Foxborough\": 906,\n", + " \"Bristol County--Attleboro North Attleborough & Swansea\": 1001,\n", + " \"Bristol County--Taunton Easton & Mansfield\": 1002,\n", + " \"Bristol County (Central)--Fall River Somerset & Acushnet\": 1003,\n", + " \"Bristol County (South)--New Bedford Dartmouth & Westport\": 1004,\n", + " \"Plymouth County--Brockton\": 1101,\n", + " \"Plymouth County (West)--Bridgewater Abington & Whitman\": 1102,\n", + " \"Plymouth County (Central)--Middleborough, Wareham & Hanson\": 1103,\n", + " \"Plymouth County (East)--Plymouth, Marshfield & Duxbury\": 1104,\n", + " \"Barnstable County (West)--Barnstable & Sandwich\": 1201,\n", + " \"Barnstable County (East)--Yarmouth, Dennis & Chatham\": 1202,\n", + " \"Dukes & Nantucket Counties--Martha's Vineyard & Nantucket Islands\": 1301,\n", + " \"Worcester County (Central)--Worcester City\": 300,\n", + " \"Worcester County (West Central)\": 302,\n", + " \"Worcester County (East Central)\": 303,\n", + " \"Worcester & Middlesex Counties (Outside Leominster, Fitchburg & Gardner Cities)\": 400,\n", + " \"Middlesex County (Outside Lowell City)\": 501,\n", + " \"Middlesex County (Far Northeast)--Lowell City\": 502,\n", + " \"Middlesex County--Waltham City, Lexington, Burlington, Bedford & Lincoln Towns\": 503,\n", + " \"Middlesex County (South)--Framingham Town, Marlborough City & Natick Town\": 504,\n", + " \"Middlesex County--Watertown Town City, Arlington, Belmont & Winchester Towns\": 505,\n", + " \"Middlesex County (East)--Malden & Medford Cities\": 508,\n", + " \"Essex County (Northwest)--Lawrence, Haverhill & Methuen Town Cities\": 701,\n", + " \"Essex County (Central)--Amesbury Town City\": 702,\n", + " \"Essex County (East)--Salem, Beverly, Gloucester & Newburyport Cities\": 703,\n", + " \"Essex County (South)--Lynn City, Swampscott & Nahant Towns\": 704,\n", + " \"Peabody City, Danvers, Reading, North Reading & Lynnfield Towns\": 1000,\n", + " \"Billerica, Andover, Tewksbury & Wilmington Towns\": 1300,\n", + " \"Middlesex (West Central) & Worcester (East) Counties\": 1400,\n", + " \"Hampden (West & East) & Hampshire (South) Counties--Northampton City\": 1600,\n", + " \"Hampden County (Central)--Springfield City\": 1900,\n", + " \"Hampden County (West of Springfield City)--Westfield & Holyoke Cities\": 1901,\n", + " \"Hampden County (East of Springfield City)--Chicopee City\": 1902,\n", + " \"Middlesex (Far Southwest), Norfolk (Northwest) & Worcester (Far East) Counties\": 2400,\n", + " \"Woburn, Melrose Cities, Saugus, Wakefield & Stoneham Towns\": 2800,\n", + " \"Boston City--Allston, Brighton & Fenway\": 3301,\n", + " \"Boston City--Back Bay, Beacon Hill, Charlestown, East Boston, Central & South End\": 3302,\n", + " \"Boston City--Dorchester & South Boston\": 3303,\n", + " \"Boston City--Mattapan & Roxbury\": 3304,\n", + " \"Boston City--Hyde Park, Jamaica Plain, Roslindale & West Roxbury\": 3305,\n", + " \"Suffolk County (North)--Revere, Chelsea & Winthrop Town Cities\": 3306,\n", + " \"Middlesex (Southeast) & Norfolk (Northeast) Counties--Newton City & Brookline Town\": 3400,\n", + " \"Norfolk (Northeast) & Middlesex (Southeast) Counties (West of Boston City)\": 3500,\n", + " \"Norfolk County (Southwest)--Greater Franklin Town City\": 3601,\n", + " \"Norfolk County (Central)--Randolph, Norwood, Dedham, Canton & Holbrook Towns\": 3602,\n", + " \"Norfolk County (Northeast)--Quincy City & Milton Town\": 3603,\n", + " \"Weymouth Town, Braintree Town Cities, Hingham, Hull & Cohasset Towns\": 3900,\n", + " \"Plymouth & Norfolk Counties--Brockton City, Stoughton & Avon Towns\": 4000,\n", + " \"Attleboro City, North Attleborough, Swansea, Seekonk, Rehoboth & Plainville Towns\": 4200,\n", + " \"Bristol (Outside New Bedford City) & Plymouth (South) Counties\": 4301,\n", + " \"Bristol County (Central)--Fall River City & Somerset Town\": 4302,\n", + " \"Bristol County--Taunton City, Mansfield, Norton, Raynham, Dighton & Berkley Towns\": 4303,\n", + " \"Bristol County (South)--New Bedford City & Fairhaven Town\": 4500,\n", + " \"Barnstable County (West)--Inner Cape Cod Towns & Barnstable Town City\": 4700,\n", + " \"Barnstable (East), Dukes & Nantucket Counties--Outer Cape Cod Towns\": 4800,\n", + " \"Plymouth & Bristol Counties (Outside Brockton City)\": 4901,\n", + " \"Plymouth County (Central)\": 4902,\n", + " \"Plymouth County (East)--Plymouth, Marshfield, Scituate, Duxbury & Kingston Towns\": 4903\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "# List of dataframes that have been read\n", + "dataframes = {\n", + " # '2014': df_2014,\n", + " # '2015': df_2015,\n", + " '2016': df_2016,\n", + " '2017': df_2017,\n", + " '2018': df_2018,\n", + "}\n", + "\n", + "# Initializes a dictionary to store averages\n", + "average_jwmnp_values = {}\n", + "\n", + "# Iterate over each DataFrame\n", + "for year, df in dataframes.items():\n", + " # Filter out the rows with PUMA values in common pumas\n", + " # df_common_pumas = df[df['PUMA'].isin(common_pumas)]\n", + "\n", + " # Filter the data whose JWTRNS is 02\n", + " df_filtered = df[df['JWTR'] == 2]\n", + "\n", + " # Group each PUMA value\n", + " grouped = df_filtered.groupby('PUMA')\n", + "\n", + " # Traverse each PUMA group\n", + " for puma, group in grouped:\n", + " # Iterate over the value of RAC1P\n", + " for rac1p_value in [1, 2]:\n", + " # Apply further screening criteria\n", + " condition = group['RAC1P'] == rac1p_value\n", + "\n", + " # Calculate the average\n", + " average_jwmnp = group[condition]['JWMNP'].mean()\n", + "\n", + " # Store results\n", + " average_jwmnp_values[f\"{year}, PUMA={puma}, RAC1P={rac1p_value}\"] = average_jwmnp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average JWMNP for 2016, PUMA=100, RAC1P=1: 43.516129032258064\n", + "Average JWMNP for 2016, PUMA=100, RAC1P=2: 20.0\n", + "Average JWMNP for 2016, PUMA=200, RAC1P=1: 24.849056603773583\n", + "Average JWMNP for 2016, PUMA=200, RAC1P=2: 37.5\n", + "Average JWMNP for 2016, PUMA=300, RAC1P=1: 36.19642857142857\n", + "Average JWMNP for 2016, PUMA=300, RAC1P=2: 37.93939393939394\n", + "Average JWMNP for 2016, PUMA=301, RAC1P=1: 27.95\n", + "Average JWMNP for 2016, PUMA=301, RAC1P=2: 37.5\n", + "Average JWMNP for 2016, PUMA=302, RAC1P=1: 61.5\n", + "Average JWMNP for 2016, PUMA=302, RAC1P=2: 50.0\n", + "Average JWMNP for 2016, PUMA=303, RAC1P=1: 25.0\n", + "Average JWMNP for 2016, PUMA=303, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=304, RAC1P=1: 46.25\n", + "Average JWMNP for 2016, PUMA=304, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=400, RAC1P=1: 24.285714285714285\n", + "Average JWMNP for 2016, PUMA=400, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=501, RAC1P=1: 62.714285714285715\n", + "Average JWMNP for 2016, PUMA=501, RAC1P=2: 15.0\n", + "Average JWMNP for 2016, PUMA=502, RAC1P=1: 30.0\n", + "Average JWMNP for 2016, PUMA=502, RAC1P=2: 25.0\n", + "Average JWMNP for 2016, PUMA=503, RAC1P=1: 44.979166666666664\n", + "Average JWMNP for 2016, PUMA=503, RAC1P=2: 78.25\n", + "Average JWMNP for 2016, PUMA=504, RAC1P=1: 39.5\n", + "Average JWMNP for 2016, PUMA=504, RAC1P=2: 26.2\n", + "Average JWMNP for 2016, PUMA=505, RAC1P=1: 41.18343195266272\n", + "Average JWMNP for 2016, PUMA=505, RAC1P=2: 37.857142857142854\n", + "Average JWMNP for 2016, PUMA=506, RAC1P=1: 32.0045871559633\n", + "Average JWMNP for 2016, PUMA=506, RAC1P=2: 43.483870967741936\n", + "Average JWMNP for 2016, PUMA=507, RAC1P=1: 38.68681318681319\n", + "Average JWMNP for 2016, PUMA=507, RAC1P=2: 44.333333333333336\n", + "Average JWMNP for 2016, PUMA=508, RAC1P=1: 45.18518518518518\n", + "Average JWMNP for 2016, PUMA=508, RAC1P=2: 43.888888888888886\n", + "Average JWMNP for 2016, PUMA=701, RAC1P=1: 29.107142857142858\n", + "Average JWMNP for 2016, PUMA=701, RAC1P=2: 24.0\n", + "Average JWMNP for 2016, PUMA=702, RAC1P=1: 62.46666666666667\n", + "Average JWMNP for 2016, PUMA=702, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=703, RAC1P=1: 59.44736842105263\n", + "Average JWMNP for 2016, PUMA=703, RAC1P=2: 36.42857142857143\n", + "Average JWMNP for 2016, PUMA=704, RAC1P=1: 50.383720930232556\n", + "Average JWMNP for 2016, PUMA=704, RAC1P=2: 45.388888888888886\n", + "Average JWMNP for 2016, PUMA=1000, RAC1P=1: 27.5\n", + "Average JWMNP for 2016, PUMA=1000, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=1300, RAC1P=1: 60.095238095238095\n", + "Average JWMNP for 2016, PUMA=1300, RAC1P=2: 90.0\n", + "Average JWMNP for 2016, PUMA=1400, RAC1P=1: 40.833333333333336\n", + "Average JWMNP for 2016, PUMA=1400, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=1600, RAC1P=1: 37.72727272727273\n", + "Average JWMNP for 2016, PUMA=1600, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=1900, RAC1P=1: 40.56818181818182\n", + "Average JWMNP for 2016, PUMA=1900, RAC1P=2: 33.20454545454545\n", + "Average JWMNP for 2016, PUMA=1901, RAC1P=1: 41.958333333333336\n", + "Average JWMNP for 2016, PUMA=1901, RAC1P=2: 30.0\n", + "Average JWMNP for 2016, PUMA=1902, RAC1P=1: 41.68421052631579\n", + "Average JWMNP for 2016, PUMA=1902, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=2400, RAC1P=1: 36.0\n", + "Average JWMNP for 2016, PUMA=2400, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=2800, RAC1P=1: 53.71739130434783\n", + "Average JWMNP for 2016, PUMA=2800, RAC1P=2: 45.0\n", + "Average JWMNP for 2016, PUMA=3301, RAC1P=1: 37.90879478827362\n", + "Average JWMNP for 2016, PUMA=3301, RAC1P=2: 39.56521739130435\n", + "Average JWMNP for 2016, PUMA=3302, RAC1P=1: 31.429268292682927\n", + "Average JWMNP for 2016, PUMA=3302, RAC1P=2: 34.69565217391305\n", + "Average JWMNP for 2016, PUMA=3303, RAC1P=1: 30.308970099667775\n", + "Average JWMNP for 2016, PUMA=3303, RAC1P=2: 44.34375\n", + "Average JWMNP for 2016, PUMA=3304, RAC1P=1: 42.604166666666664\n", + "Average JWMNP for 2016, PUMA=3304, RAC1P=2: 46.96320346320346\n", + "Average JWMNP for 2016, PUMA=3305, RAC1P=1: 43.05\n", + "Average JWMNP for 2016, PUMA=3305, RAC1P=2: 52.568627450980394\n", + "Average JWMNP for 2016, PUMA=3306, RAC1P=1: 41.07142857142857\n", + "Average JWMNP for 2016, PUMA=3306, RAC1P=2: 41.0\n", + "Average JWMNP for 2016, PUMA=3400, RAC1P=1: 35.904411764705884\n", + "Average JWMNP for 2016, PUMA=3400, RAC1P=2: 32.44444444444444\n", + "Average JWMNP for 2016, PUMA=3500, RAC1P=1: 40.23529411764706\n", + "Average JWMNP for 2016, PUMA=3500, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=3601, RAC1P=1: 32.5\n", + "Average JWMNP for 2016, PUMA=3601, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=3602, RAC1P=1: 46.666666666666664\n", + "Average JWMNP for 2016, PUMA=3602, RAC1P=2: 48.333333333333336\n", + "Average JWMNP for 2016, PUMA=3603, RAC1P=1: 48.04545454545455\n", + "Average JWMNP for 2016, PUMA=3603, RAC1P=2: 58.0\n", + "Average JWMNP for 2016, PUMA=3900, RAC1P=1: 75.85714285714286\n", + "Average JWMNP for 2016, PUMA=3900, RAC1P=2: 35.0\n", + "Average JWMNP for 2016, PUMA=4000, RAC1P=1: 40.526315789473685\n", + "Average JWMNP for 2016, PUMA=4000, RAC1P=2: 55.127272727272725\n", + "Average JWMNP for 2016, PUMA=4200, RAC1P=1: 37.22222222222222\n", + "Average JWMNP for 2016, PUMA=4200, RAC1P=2: 30.0\n", + "Average JWMNP for 2016, PUMA=4301, RAC1P=1: 56.18181818181818\n", + "Average JWMNP for 2016, PUMA=4301, RAC1P=2: 15.0\n", + "Average JWMNP for 2016, PUMA=4302, RAC1P=1: 47.68421052631579\n", + "Average JWMNP for 2016, PUMA=4302, RAC1P=2: 84.5\n", + "Average JWMNP for 2016, PUMA=4303, RAC1P=1: 70.9375\n", + "Average JWMNP for 2016, PUMA=4303, RAC1P=2: 18.333333333333332\n", + "Average JWMNP for 2016, PUMA=4500, RAC1P=1: 48.12\n", + "Average JWMNP for 2016, PUMA=4500, RAC1P=2: 36.25\n", + "Average JWMNP for 2016, PUMA=4700, RAC1P=1: 84.05555555555556\n", + "Average JWMNP for 2016, PUMA=4700, RAC1P=2: 24.0\n", + "Average JWMNP for 2016, PUMA=4800, RAC1P=1: 63.473684210526315\n", + "Average JWMNP for 2016, PUMA=4800, RAC1P=2: 104.5\n", + "Average JWMNP for 2016, PUMA=4901, RAC1P=1: 60.2\n", + "Average JWMNP for 2016, PUMA=4901, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=4902, RAC1P=1: 62.77777777777778\n", + "Average JWMNP for 2016, PUMA=4902, RAC1P=2: nan\n", + "Average JWMNP for 2016, PUMA=4903, RAC1P=1: 79.43243243243244\n", + "Average JWMNP for 2016, PUMA=4903, RAC1P=2: 37.5\n", + "Average JWMNP for 2017, PUMA=100, RAC1P=1: 46.41935483870968\n", + "Average JWMNP for 2017, PUMA=100, RAC1P=2: 18.333333333333332\n", + "Average JWMNP for 2017, PUMA=200, RAC1P=1: 27.14516129032258\n", + "Average JWMNP for 2017, PUMA=200, RAC1P=2: 15.0\n", + "Average JWMNP for 2017, PUMA=300, RAC1P=1: 39.17307692307692\n", + "Average JWMNP for 2017, PUMA=300, RAC1P=2: 37.0625\n", + "Average JWMNP for 2017, PUMA=301, RAC1P=1: 31.63157894736842\n", + "Average JWMNP for 2017, PUMA=301, RAC1P=2: 37.5\n", + "Average JWMNP for 2017, PUMA=302, RAC1P=1: 49.888888888888886\n", + "Average JWMNP for 2017, PUMA=302, RAC1P=2: 50.0\n", + "Average JWMNP for 2017, PUMA=303, RAC1P=1: 27.5\n", + "Average JWMNP for 2017, PUMA=303, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=304, RAC1P=1: 50.5\n", + "Average JWMNP for 2017, PUMA=304, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=400, RAC1P=1: 22.428571428571427\n", + "Average JWMNP for 2017, PUMA=400, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=501, RAC1P=1: 62.714285714285715\n", + "Average JWMNP for 2017, PUMA=501, RAC1P=2: 15.0\n", + "Average JWMNP for 2017, PUMA=502, RAC1P=1: 29.4390243902439\n", + "Average JWMNP for 2017, PUMA=502, RAC1P=2: 14.333333333333334\n", + "Average JWMNP for 2017, PUMA=503, RAC1P=1: 46.68181818181818\n", + "Average JWMNP for 2017, PUMA=503, RAC1P=2: 78.25\n", + "Average JWMNP for 2017, PUMA=504, RAC1P=1: 45.0\n", + "Average JWMNP for 2017, PUMA=504, RAC1P=2: 23.75\n", + "Average JWMNP for 2017, PUMA=505, RAC1P=1: 41.33136094674556\n", + "Average JWMNP for 2017, PUMA=505, RAC1P=2: 31.11111111111111\n", + "Average JWMNP for 2017, PUMA=506, RAC1P=1: 32.84186046511628\n", + "Average JWMNP for 2017, PUMA=506, RAC1P=2: 41.08\n", + "Average JWMNP for 2017, PUMA=507, RAC1P=1: 41.5398773006135\n", + "Average JWMNP for 2017, PUMA=507, RAC1P=2: 47.96774193548387\n", + "Average JWMNP for 2017, PUMA=508, RAC1P=1: 42.1764705882353\n", + "Average JWMNP for 2017, PUMA=508, RAC1P=2: 38.0\n", + "Average JWMNP for 2017, PUMA=701, RAC1P=1: 30.30952380952381\n", + "Average JWMNP for 2017, PUMA=701, RAC1P=2: 18.0\n", + "Average JWMNP for 2017, PUMA=702, RAC1P=1: 62.857142857142854\n", + "Average JWMNP for 2017, PUMA=702, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=703, RAC1P=1: 59.3\n", + "Average JWMNP for 2017, PUMA=703, RAC1P=2: 51.833333333333336\n", + "Average JWMNP for 2017, PUMA=704, RAC1P=1: 49.62337662337662\n", + "Average JWMNP for 2017, PUMA=704, RAC1P=2: 45.29032258064516\n", + "Average JWMNP for 2017, PUMA=1000, RAC1P=1: 37.0\n", + "Average JWMNP for 2017, PUMA=1000, RAC1P=2: 15.0\n", + "Average JWMNP for 2017, PUMA=1300, RAC1P=1: 50.25\n", + "Average JWMNP for 2017, PUMA=1300, RAC1P=2: 90.0\n", + "Average JWMNP for 2017, PUMA=1400, RAC1P=1: 34.0\n", + "Average JWMNP for 2017, PUMA=1400, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=1600, RAC1P=1: 37.5\n", + "Average JWMNP for 2017, PUMA=1600, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=1900, RAC1P=1: 35.6875\n", + "Average JWMNP for 2017, PUMA=1900, RAC1P=2: 32.16216216216216\n", + "Average JWMNP for 2017, PUMA=1901, RAC1P=1: 38.18181818181818\n", + "Average JWMNP for 2017, PUMA=1901, RAC1P=2: 30.0\n", + "Average JWMNP for 2017, PUMA=1902, RAC1P=1: 41.68421052631579\n", + "Average JWMNP for 2017, PUMA=1902, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=2400, RAC1P=1: 36.0\n", + "Average JWMNP for 2017, PUMA=2400, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=2800, RAC1P=1: 57.651162790697676\n", + "Average JWMNP for 2017, PUMA=2800, RAC1P=2: 46.666666666666664\n", + "Average JWMNP for 2017, PUMA=3301, RAC1P=1: 38.47962382445141\n", + "Average JWMNP for 2017, PUMA=3301, RAC1P=2: 39.21875\n", + "Average JWMNP for 2017, PUMA=3302, RAC1P=1: 31.830917874396135\n", + "Average JWMNP for 2017, PUMA=3302, RAC1P=2: 36.12\n", + "Average JWMNP for 2017, PUMA=3303, RAC1P=1: 30.570093457943926\n", + "Average JWMNP for 2017, PUMA=3303, RAC1P=2: 44.40833333333333\n", + "Average JWMNP for 2017, PUMA=3304, RAC1P=1: 41.51960784313726\n", + "Average JWMNP for 2017, PUMA=3304, RAC1P=2: 46.84439359267734\n", + "Average JWMNP for 2017, PUMA=3305, RAC1P=1: 43.007407407407406\n", + "Average JWMNP for 2017, PUMA=3305, RAC1P=2: 54.11214953271028\n", + "Average JWMNP for 2017, PUMA=3306, RAC1P=1: 42.90291262135922\n", + "Average JWMNP for 2017, PUMA=3306, RAC1P=2: 43.75\n", + "Average JWMNP for 2017, PUMA=3400, RAC1P=1: 36.49242424242424\n", + "Average JWMNP for 2017, PUMA=3400, RAC1P=2: 33.833333333333336\n", + "Average JWMNP for 2017, PUMA=3500, RAC1P=1: 43.5\n", + "Average JWMNP for 2017, PUMA=3500, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=3601, RAC1P=1: 43.75\n", + "Average JWMNP for 2017, PUMA=3601, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=3602, RAC1P=1: 51.714285714285715\n", + "Average JWMNP for 2017, PUMA=3602, RAC1P=2: 69.52941176470588\n", + "Average JWMNP for 2017, PUMA=3603, RAC1P=1: 47.78048780487805\n", + "Average JWMNP for 2017, PUMA=3603, RAC1P=2: 58.0\n", + "Average JWMNP for 2017, PUMA=3900, RAC1P=1: 78.5625\n", + "Average JWMNP for 2017, PUMA=3900, RAC1P=2: 35.0\n", + "Average JWMNP for 2017, PUMA=4000, RAC1P=1: 43.05555555555556\n", + "Average JWMNP for 2017, PUMA=4000, RAC1P=2: 56.4\n", + "Average JWMNP for 2017, PUMA=4200, RAC1P=1: 45.625\n", + "Average JWMNP for 2017, PUMA=4200, RAC1P=2: 30.0\n", + "Average JWMNP for 2017, PUMA=4301, RAC1P=1: 53.583333333333336\n", + "Average JWMNP for 2017, PUMA=4301, RAC1P=2: 12.5\n", + "Average JWMNP for 2017, PUMA=4302, RAC1P=1: 55.42857142857143\n", + "Average JWMNP for 2017, PUMA=4302, RAC1P=2: 72.25\n", + "Average JWMNP for 2017, PUMA=4303, RAC1P=1: 67.36842105263158\n", + "Average JWMNP for 2017, PUMA=4303, RAC1P=2: 15.0\n", + "Average JWMNP for 2017, PUMA=4500, RAC1P=1: 44.84\n", + "Average JWMNP for 2017, PUMA=4500, RAC1P=2: 37.857142857142854\n", + "Average JWMNP for 2017, PUMA=4700, RAC1P=1: 83.44444444444444\n", + "Average JWMNP for 2017, PUMA=4700, RAC1P=2: 32.5\n", + "Average JWMNP for 2017, PUMA=4800, RAC1P=1: 59.54545454545455\n", + "Average JWMNP for 2017, PUMA=4800, RAC1P=2: 87.6\n", + "Average JWMNP for 2017, PUMA=4901, RAC1P=1: 61.54545454545455\n", + "Average JWMNP for 2017, PUMA=4901, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=4902, RAC1P=1: 57.0\n", + "Average JWMNP for 2017, PUMA=4902, RAC1P=2: nan\n", + "Average JWMNP for 2017, PUMA=4903, RAC1P=1: 80.57142857142857\n", + "Average JWMNP for 2017, PUMA=4903, RAC1P=2: 37.5\n", + "Average JWMNP for 2018, PUMA=100, RAC1P=1: 40.77777777777778\n", + "Average JWMNP for 2018, PUMA=100, RAC1P=2: 18.333333333333332\n", + "Average JWMNP for 2018, PUMA=200, RAC1P=1: 26.25925925925926\n", + "Average JWMNP for 2018, PUMA=200, RAC1P=2: 22.5\n", + "Average JWMNP for 2018, PUMA=300, RAC1P=1: 34.56603773584906\n", + "Average JWMNP for 2018, PUMA=300, RAC1P=2: 41.04\n", + "Average JWMNP for 2018, PUMA=301, RAC1P=1: 30.5\n", + "Average JWMNP for 2018, PUMA=301, RAC1P=2: 60.0\n", + "Average JWMNP for 2018, PUMA=302, RAC1P=1: 46.9\n", + "Average JWMNP for 2018, PUMA=302, RAC1P=2: 50.0\n", + "Average JWMNP for 2018, PUMA=303, RAC1P=1: 27.5\n", + "Average JWMNP for 2018, PUMA=303, RAC1P=2: nan\n", + "Average JWMNP for 2018, PUMA=304, RAC1P=1: 57.833333333333336\n", + "Average JWMNP for 2018, PUMA=304, RAC1P=2: 15.0\n", + "Average JWMNP for 2018, PUMA=400, RAC1P=1: 22.428571428571427\n", + "Average JWMNP for 2018, PUMA=400, RAC1P=2: 15.0\n", + "Average JWMNP for 2018, PUMA=501, RAC1P=1: 70.66666666666667\n", + "Average JWMNP for 2018, PUMA=501, RAC1P=2: nan\n", + "Average JWMNP for 2018, PUMA=502, RAC1P=1: 28.789473684210527\n", + "Average JWMNP for 2018, PUMA=502, RAC1P=2: 15.75\n", + "Average JWMNP for 2018, PUMA=503, RAC1P=1: 43.63636363636363\n", + "Average JWMNP for 2018, PUMA=503, RAC1P=2: 67.6\n", + "Average JWMNP for 2018, PUMA=504, RAC1P=1: 41.0\n", + "Average JWMNP for 2018, PUMA=504, RAC1P=2: 30.0\n", + "Average JWMNP for 2018, PUMA=505, RAC1P=1: 41.85142857142857\n", + "Average JWMNP for 2018, PUMA=505, RAC1P=2: 34.23076923076923\n", + "Average JWMNP for 2018, PUMA=506, RAC1P=1: 33.42512077294686\n", + "Average JWMNP for 2018, PUMA=506, RAC1P=2: 42.5\n", + "Average JWMNP for 2018, PUMA=507, RAC1P=1: 40.627027027027026\n", + "Average JWMNP for 2018, PUMA=507, RAC1P=2: 40.40625\n", + "Average JWMNP for 2018, PUMA=508, RAC1P=1: 41.351351351351354\n", + "Average JWMNP for 2018, PUMA=508, RAC1P=2: 38.689655172413794\n", + "Average JWMNP for 2018, PUMA=701, RAC1P=1: 36.829787234042556\n", + "Average JWMNP for 2018, PUMA=701, RAC1P=2: 20.0\n", + "Average JWMNP for 2018, PUMA=702, RAC1P=1: 64.94444444444444\n", + "Average JWMNP for 2018, PUMA=702, RAC1P=2: nan\n", + "Average JWMNP for 2018, PUMA=703, RAC1P=1: 61.26829268292683\n", + "Average JWMNP for 2018, PUMA=703, RAC1P=2: 50.857142857142854\n", + "Average JWMNP for 2018, PUMA=704, RAC1P=1: 48.14492753623188\n", + "Average JWMNP for 2018, PUMA=704, RAC1P=2: 43.65384615384615\n", + "Average JWMNP for 2018, PUMA=1000, RAC1P=1: 39.166666666666664\n", + "Average JWMNP for 2018, PUMA=1000, RAC1P=2: 15.0\n", + "Average JWMNP for 2018, PUMA=1300, RAC1P=1: 51.785714285714285\n", + "Average JWMNP for 2018, PUMA=1300, RAC1P=2: nan\n", + "Average JWMNP for 2018, PUMA=1400, RAC1P=1: 57.714285714285715\n", + "Average JWMNP for 2018, PUMA=1400, RAC1P=2: nan\n", + "Average JWMNP for 2018, PUMA=1600, RAC1P=1: 35.810810810810814\n", + "Average JWMNP for 2018, PUMA=1600, RAC1P=2: 4.0\n", + "Average JWMNP for 2018, PUMA=1900, RAC1P=1: 39.84615384615385\n", + "Average JWMNP for 2018, PUMA=1900, RAC1P=2: 34.02777777777778\n", + "Average JWMNP for 2018, PUMA=1901, RAC1P=1: 38.0\n", + "Average JWMNP for 2018, PUMA=1901, RAC1P=2: 30.0\n", + "Average JWMNP for 2018, PUMA=1902, RAC1P=1: 46.285714285714285\n", + "Average JWMNP for 2018, PUMA=1902, RAC1P=2: 35.0\n", + "Average JWMNP for 2018, PUMA=2400, RAC1P=1: 48.0\n", + "Average JWMNP for 2018, PUMA=2400, RAC1P=2: nan\n", + "Average JWMNP for 2018, PUMA=2800, RAC1P=1: 55.31578947368421\n", + "Average JWMNP for 2018, PUMA=2800, RAC1P=2: 46.666666666666664\n", + "Average JWMNP for 2018, PUMA=3301, RAC1P=1: 39.51764705882353\n", + "Average JWMNP for 2018, PUMA=3301, RAC1P=2: 38.484848484848484\n", + "Average JWMNP for 2018, PUMA=3302, RAC1P=1: 32.995\n", + "Average JWMNP for 2018, PUMA=3302, RAC1P=2: 35.0\n", + "Average JWMNP for 2018, PUMA=3303, RAC1P=1: 31.195121951219512\n", + "Average JWMNP for 2018, PUMA=3303, RAC1P=2: 44.243478260869566\n", + "Average JWMNP for 2018, PUMA=3304, RAC1P=1: 42.524752475247524\n", + "Average JWMNP for 2018, PUMA=3304, RAC1P=2: 47.0445434298441\n", + "Average JWMNP for 2018, PUMA=3305, RAC1P=1: 41.02158273381295\n", + "Average JWMNP for 2018, PUMA=3305, RAC1P=2: 54.95\n", + "Average JWMNP for 2018, PUMA=3306, RAC1P=1: 43.27272727272727\n", + "Average JWMNP for 2018, PUMA=3306, RAC1P=2: 41.8\n", + "Average JWMNP for 2018, PUMA=3400, RAC1P=1: 39.0354609929078\n", + "Average JWMNP for 2018, PUMA=3400, RAC1P=2: 32.8421052631579\n", + "Average JWMNP for 2018, PUMA=3500, RAC1P=1: 50.0\n", + "Average JWMNP for 2018, PUMA=3500, RAC1P=2: 40.0\n", + "Average JWMNP for 2018, PUMA=3601, RAC1P=1: 43.75\n", + "Average JWMNP for 2018, PUMA=3601, RAC1P=2: nan\n", + "Average JWMNP for 2018, PUMA=3602, RAC1P=1: 52.93333333333333\n", + "Average JWMNP for 2018, PUMA=3602, RAC1P=2: 73.58823529411765\n", + "Average JWMNP for 2018, PUMA=3603, RAC1P=1: 56.35897435897436\n", + "Average JWMNP for 2018, PUMA=3603, RAC1P=2: 51.2\n", + "Average JWMNP for 2018, PUMA=3900, RAC1P=1: 75.17647058823529\n", + "Average JWMNP for 2018, PUMA=3900, RAC1P=2: 26.666666666666668\n", + "Average JWMNP for 2018, PUMA=4000, RAC1P=1: 41.785714285714285\n", + "Average JWMNP for 2018, PUMA=4000, RAC1P=2: 62.5\n", + "Average JWMNP for 2018, PUMA=4200, RAC1P=1: 57.18181818181818\n", + "Average JWMNP for 2018, PUMA=4200, RAC1P=2: 30.0\n", + "Average JWMNP for 2018, PUMA=4301, RAC1P=1: 31.25\n", + "Average JWMNP for 2018, PUMA=4301, RAC1P=2: 12.5\n", + "Average JWMNP for 2018, PUMA=4302, RAC1P=1: 43.083333333333336\n", + "Average JWMNP for 2018, PUMA=4302, RAC1P=2: 72.25\n", + "Average JWMNP for 2018, PUMA=4303, RAC1P=1: 67.14285714285714\n", + "Average JWMNP for 2018, PUMA=4303, RAC1P=2: 15.0\n", + "Average JWMNP for 2018, PUMA=4500, RAC1P=1: 41.689655172413794\n", + "Average JWMNP for 2018, PUMA=4500, RAC1P=2: 41.666666666666664\n", + "Average JWMNP for 2018, PUMA=4700, RAC1P=1: 86.63157894736842\n", + "Average JWMNP for 2018, PUMA=4700, RAC1P=2: 20.0\n", + "Average JWMNP for 2018, PUMA=4800, RAC1P=1: 44.611111111111114\n", + "Average JWMNP for 2018, PUMA=4800, RAC1P=2: 64.8\n", + "Average JWMNP for 2018, PUMA=4901, RAC1P=1: 61.09090909090909\n", + "Average JWMNP for 2018, PUMA=4901, RAC1P=2: nan\n", + "Average JWMNP for 2018, PUMA=4902, RAC1P=1: 63.0\n", + "Average JWMNP for 2018, PUMA=4902, RAC1P=2: nan\n", + "Average JWMNP for 2018, PUMA=4903, RAC1P=1: 80.3529411764706\n", + "Average JWMNP for 2018, PUMA=4903, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=100, RAC1P=1: 41.392857142857146\n", + "Average JWMNP for 2019, PUMA=100, RAC1P=2: 17.5\n", + "Average JWMNP for 2019, PUMA=200, RAC1P=1: 31.596774193548388\n", + "Average JWMNP for 2019, PUMA=200, RAC1P=2: 21.666666666666668\n", + "Average JWMNP for 2019, PUMA=300, RAC1P=1: 34.0188679245283\n", + "Average JWMNP for 2019, PUMA=300, RAC1P=2: 43.04\n", + "Average JWMNP for 2019, PUMA=301, RAC1P=1: 28.541666666666668\n", + "Average JWMNP for 2019, PUMA=301, RAC1P=2: 60.0\n", + "Average JWMNP for 2019, PUMA=302, RAC1P=1: 32.0\n", + "Average JWMNP for 2019, PUMA=302, RAC1P=2: 50.0\n", + "Average JWMNP for 2019, PUMA=303, RAC1P=1: 30.0\n", + "Average JWMNP for 2019, PUMA=303, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=304, RAC1P=1: 62.18181818181818\n", + "Average JWMNP for 2019, PUMA=304, RAC1P=2: 15.0\n", + "Average JWMNP for 2019, PUMA=400, RAC1P=1: 34.57142857142857\n", + "Average JWMNP for 2019, PUMA=400, RAC1P=2: 15.0\n", + "Average JWMNP for 2019, PUMA=501, RAC1P=1: 45.0\n", + "Average JWMNP for 2019, PUMA=501, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=502, RAC1P=1: 27.342105263157894\n", + "Average JWMNP for 2019, PUMA=502, RAC1P=2: 18.6\n", + "Average JWMNP for 2019, PUMA=503, RAC1P=1: 46.72727272727273\n", + "Average JWMNP for 2019, PUMA=503, RAC1P=2: 62.4\n", + "Average JWMNP for 2019, PUMA=504, RAC1P=1: 37.8125\n", + "Average JWMNP for 2019, PUMA=504, RAC1P=2: 33.333333333333336\n", + "Average JWMNP for 2019, PUMA=505, RAC1P=1: 42.32960893854749\n", + "Average JWMNP for 2019, PUMA=505, RAC1P=2: 32.8125\n", + "Average JWMNP for 2019, PUMA=506, RAC1P=1: 34.13775510204081\n", + "Average JWMNP for 2019, PUMA=506, RAC1P=2: 43.333333333333336\n", + "Average JWMNP for 2019, PUMA=507, RAC1P=1: 41.29281767955801\n", + "Average JWMNP for 2019, PUMA=507, RAC1P=2: 41.77777777777778\n", + "Average JWMNP for 2019, PUMA=508, RAC1P=1: 40.18518518518518\n", + "Average JWMNP for 2019, PUMA=508, RAC1P=2: 35.5\n", + "Average JWMNP for 2019, PUMA=701, RAC1P=1: 38.38636363636363\n", + "Average JWMNP for 2019, PUMA=701, RAC1P=2: 53.0\n", + "Average JWMNP for 2019, PUMA=702, RAC1P=1: 69.11764705882354\n", + "Average JWMNP for 2019, PUMA=702, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=703, RAC1P=1: 63.39473684210526\n", + "Average JWMNP for 2019, PUMA=703, RAC1P=2: 38.833333333333336\n", + "Average JWMNP for 2019, PUMA=704, RAC1P=1: 48.90909090909091\n", + "Average JWMNP for 2019, PUMA=704, RAC1P=2: 46.19047619047619\n", + "Average JWMNP for 2019, PUMA=1000, RAC1P=1: 38.57142857142857\n", + "Average JWMNP for 2019, PUMA=1000, RAC1P=2: 15.0\n", + "Average JWMNP for 2019, PUMA=1300, RAC1P=1: 50.588235294117645\n", + "Average JWMNP for 2019, PUMA=1300, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=1400, RAC1P=1: 60.8\n", + "Average JWMNP for 2019, PUMA=1400, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=1600, RAC1P=1: 35.69444444444444\n", + "Average JWMNP for 2019, PUMA=1600, RAC1P=2: 4.0\n", + "Average JWMNP for 2019, PUMA=1900, RAC1P=1: 39.470588235294116\n", + "Average JWMNP for 2019, PUMA=1900, RAC1P=2: 36.24324324324324\n", + "Average JWMNP for 2019, PUMA=1901, RAC1P=1: 37.8\n", + "Average JWMNP for 2019, PUMA=1901, RAC1P=2: 18.5\n", + "Average JWMNP for 2019, PUMA=1902, RAC1P=1: 42.64705882352941\n", + "Average JWMNP for 2019, PUMA=1902, RAC1P=2: 35.0\n", + "Average JWMNP for 2019, PUMA=2400, RAC1P=1: 45.0\n", + "Average JWMNP for 2019, PUMA=2400, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=2800, RAC1P=1: 52.333333333333336\n", + "Average JWMNP for 2019, PUMA=2800, RAC1P=2: 50.0\n", + "Average JWMNP for 2019, PUMA=3301, RAC1P=1: 39.779761904761905\n", + "Average JWMNP for 2019, PUMA=3301, RAC1P=2: 37.56410256410256\n", + "Average JWMNP for 2019, PUMA=3302, RAC1P=1: 33.926108374384235\n", + "Average JWMNP for 2019, PUMA=3302, RAC1P=2: 31.875\n", + "Average JWMNP for 2019, PUMA=3303, RAC1P=1: 31.58934169278997\n", + "Average JWMNP for 2019, PUMA=3303, RAC1P=2: 44.73394495412844\n", + "Average JWMNP for 2019, PUMA=3304, RAC1P=1: 41.8921568627451\n", + "Average JWMNP for 2019, PUMA=3304, RAC1P=2: 46.18357487922705\n", + "Average JWMNP for 2019, PUMA=3305, RAC1P=1: 40.945578231292515\n", + "Average JWMNP for 2019, PUMA=3305, RAC1P=2: 53.83064516129032\n", + "Average JWMNP for 2019, PUMA=3306, RAC1P=1: 44.4344262295082\n", + "Average JWMNP for 2019, PUMA=3306, RAC1P=2: 46.578947368421055\n", + "Average JWMNP for 2019, PUMA=3400, RAC1P=1: 41.46666666666667\n", + "Average JWMNP for 2019, PUMA=3400, RAC1P=2: 31.692307692307693\n", + "Average JWMNP for 2019, PUMA=3500, RAC1P=1: 52.0\n", + "Average JWMNP for 2019, PUMA=3500, RAC1P=2: 40.0\n", + "Average JWMNP for 2019, PUMA=3601, RAC1P=1: 43.75\n", + "Average JWMNP for 2019, PUMA=3601, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=3602, RAC1P=1: 54.94117647058823\n", + "Average JWMNP for 2019, PUMA=3602, RAC1P=2: 81.26666666666667\n", + "Average JWMNP for 2019, PUMA=3603, RAC1P=1: 52.60526315789474\n", + "Average JWMNP for 2019, PUMA=3603, RAC1P=2: 54.333333333333336\n", + "Average JWMNP for 2019, PUMA=3900, RAC1P=1: 71.0\n", + "Average JWMNP for 2019, PUMA=3900, RAC1P=2: 36.666666666666664\n", + "Average JWMNP for 2019, PUMA=4000, RAC1P=1: 42.5\n", + "Average JWMNP for 2019, PUMA=4000, RAC1P=2: 58.8\n", + "Average JWMNP for 2019, PUMA=4200, RAC1P=1: 57.9\n", + "Average JWMNP for 2019, PUMA=4200, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=4301, RAC1P=1: 34.5\n", + "Average JWMNP for 2019, PUMA=4301, RAC1P=2: 12.5\n", + "Average JWMNP for 2019, PUMA=4302, RAC1P=1: 35.8\n", + "Average JWMNP for 2019, PUMA=4302, RAC1P=2: 41.25\n", + "Average JWMNP for 2019, PUMA=4303, RAC1P=1: 63.473684210526315\n", + "Average JWMNP for 2019, PUMA=4303, RAC1P=2: 30.0\n", + "Average JWMNP for 2019, PUMA=4500, RAC1P=1: 39.074074074074076\n", + "Average JWMNP for 2019, PUMA=4500, RAC1P=2: 42.5\n", + "Average JWMNP for 2019, PUMA=4700, RAC1P=1: 80.47058823529412\n", + "Average JWMNP for 2019, PUMA=4700, RAC1P=2: 55.0\n", + "Average JWMNP for 2019, PUMA=4800, RAC1P=1: 50.94117647058823\n", + "Average JWMNP for 2019, PUMA=4800, RAC1P=2: 64.8\n", + "Average JWMNP for 2019, PUMA=4901, RAC1P=1: 62.44444444444444\n", + "Average JWMNP for 2019, PUMA=4901, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=4902, RAC1P=1: 58.93333333333333\n", + "Average JWMNP for 2019, PUMA=4902, RAC1P=2: nan\n", + "Average JWMNP for 2019, PUMA=4903, RAC1P=1: 83.63636363636364\n", + "Average JWMNP for 2019, PUMA=4903, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=100, RAC1P=1: 38.0\n", + "Average JWMNP for 2020, PUMA=100, RAC1P=2: 22.5\n", + "Average JWMNP for 2020, PUMA=200, RAC1P=1: 32.49152542372882\n", + "Average JWMNP for 2020, PUMA=200, RAC1P=2: 25.0\n", + "Average JWMNP for 2020, PUMA=300, RAC1P=1: 32.870370370370374\n", + "Average JWMNP for 2020, PUMA=300, RAC1P=2: 40.04545454545455\n", + "Average JWMNP for 2020, PUMA=301, RAC1P=1: 27.36842105263158\n", + "Average JWMNP for 2020, PUMA=301, RAC1P=2: 60.0\n", + "Average JWMNP for 2020, PUMA=302, RAC1P=1: 30.555555555555557\n", + "Average JWMNP for 2020, PUMA=302, RAC1P=2: 20.0\n", + "Average JWMNP for 2020, PUMA=303, RAC1P=1: 30.0\n", + "Average JWMNP for 2020, PUMA=303, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=304, RAC1P=1: 54.45454545454545\n", + "Average JWMNP for 2020, PUMA=304, RAC1P=2: 15.0\n", + "Average JWMNP for 2020, PUMA=400, RAC1P=1: 42.4\n", + "Average JWMNP for 2020, PUMA=400, RAC1P=2: 17.5\n", + "Average JWMNP for 2020, PUMA=501, RAC1P=1: 45.0\n", + "Average JWMNP for 2020, PUMA=501, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=502, RAC1P=1: 27.310344827586206\n", + "Average JWMNP for 2020, PUMA=502, RAC1P=2: 23.25\n", + "Average JWMNP for 2020, PUMA=503, RAC1P=1: 50.885714285714286\n", + "Average JWMNP for 2020, PUMA=503, RAC1P=2: 63.0\n", + "Average JWMNP for 2020, PUMA=504, RAC1P=1: 33.63636363636363\n", + "Average JWMNP for 2020, PUMA=504, RAC1P=2: 35.0\n", + "Average JWMNP for 2020, PUMA=505, RAC1P=1: 41.94805194805195\n", + "Average JWMNP for 2020, PUMA=505, RAC1P=2: 32.857142857142854\n", + "Average JWMNP for 2020, PUMA=506, RAC1P=1: 34.67777777777778\n", + "Average JWMNP for 2020, PUMA=506, RAC1P=2: 47.10526315789474\n", + "Average JWMNP for 2020, PUMA=507, RAC1P=1: 41.225806451612904\n", + "Average JWMNP for 2020, PUMA=507, RAC1P=2: 48.3\n", + "Average JWMNP for 2020, PUMA=508, RAC1P=1: 40.6764705882353\n", + "Average JWMNP for 2020, PUMA=508, RAC1P=2: 32.23809523809524\n", + "Average JWMNP for 2020, PUMA=701, RAC1P=1: 37.395348837209305\n", + "Average JWMNP for 2020, PUMA=701, RAC1P=2: 60.6\n", + "Average JWMNP for 2020, PUMA=702, RAC1P=1: 67.33333333333333\n", + "Average JWMNP for 2020, PUMA=702, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=703, RAC1P=1: 60.611111111111114\n", + "Average JWMNP for 2020, PUMA=703, RAC1P=2: 40.1\n", + "Average JWMNP for 2020, PUMA=704, RAC1P=1: 50.43478260869565\n", + "Average JWMNP for 2020, PUMA=704, RAC1P=2: 42.5\n", + "Average JWMNP for 2020, PUMA=1000, RAC1P=1: 38.57142857142857\n", + "Average JWMNP for 2020, PUMA=1000, RAC1P=2: 52.5\n", + "Average JWMNP for 2020, PUMA=1300, RAC1P=1: 51.05263157894737\n", + "Average JWMNP for 2020, PUMA=1300, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=1400, RAC1P=1: 88.0\n", + "Average JWMNP for 2020, PUMA=1400, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=1600, RAC1P=1: 34.24242424242424\n", + "Average JWMNP for 2020, PUMA=1600, RAC1P=2: 4.0\n", + "Average JWMNP for 2020, PUMA=1900, RAC1P=1: 39.142857142857146\n", + "Average JWMNP for 2020, PUMA=1900, RAC1P=2: 34.588235294117645\n", + "Average JWMNP for 2020, PUMA=1901, RAC1P=1: 36.81818181818182\n", + "Average JWMNP for 2020, PUMA=1901, RAC1P=2: 18.5\n", + "Average JWMNP for 2020, PUMA=1902, RAC1P=1: 42.5\n", + "Average JWMNP for 2020, PUMA=1902, RAC1P=2: 28.333333333333332\n", + "Average JWMNP for 2020, PUMA=2400, RAC1P=1: 50.0\n", + "Average JWMNP for 2020, PUMA=2400, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=2800, RAC1P=1: 50.083333333333336\n", + "Average JWMNP for 2020, PUMA=2800, RAC1P=2: 51.25\n", + "Average JWMNP for 2020, PUMA=3301, RAC1P=1: 39.94097222222222\n", + "Average JWMNP for 2020, PUMA=3301, RAC1P=2: 36.46511627906977\n", + "Average JWMNP for 2020, PUMA=3302, RAC1P=1: 32.70786516853933\n", + "Average JWMNP for 2020, PUMA=3302, RAC1P=2: 30.0\n", + "Average JWMNP for 2020, PUMA=3303, RAC1P=1: 31.69858156028369\n", + "Average JWMNP for 2020, PUMA=3303, RAC1P=2: 44.423529411764704\n", + "Average JWMNP for 2020, PUMA=3304, RAC1P=1: 38.63855421686747\n", + "Average JWMNP for 2020, PUMA=3304, RAC1P=2: 44.91545189504373\n", + "Average JWMNP for 2020, PUMA=3305, RAC1P=1: 40.48\n", + "Average JWMNP for 2020, PUMA=3305, RAC1P=2: 53.48039215686274\n", + "Average JWMNP for 2020, PUMA=3306, RAC1P=1: 45.29245283018868\n", + "Average JWMNP for 2020, PUMA=3306, RAC1P=2: 45.833333333333336\n", + "Average JWMNP for 2020, PUMA=3400, RAC1P=1: 42.06956521739131\n", + "Average JWMNP for 2020, PUMA=3400, RAC1P=2: 29.75\n", + "Average JWMNP for 2020, PUMA=3500, RAC1P=1: 52.77777777777778\n", + "Average JWMNP for 2020, PUMA=3500, RAC1P=2: 40.0\n", + "Average JWMNP for 2020, PUMA=3601, RAC1P=1: 50.0\n", + "Average JWMNP for 2020, PUMA=3601, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=3602, RAC1P=1: 53.142857142857146\n", + "Average JWMNP for 2020, PUMA=3602, RAC1P=2: 90.33333333333333\n", + "Average JWMNP for 2020, PUMA=3603, RAC1P=1: 53.1578947368421\n", + "Average JWMNP for 2020, PUMA=3603, RAC1P=2: 39.0625\n", + "Average JWMNP for 2020, PUMA=3900, RAC1P=1: 68.38095238095238\n", + "Average JWMNP for 2020, PUMA=3900, RAC1P=2: 32.5\n", + "Average JWMNP for 2020, PUMA=4000, RAC1P=1: 43.05555555555556\n", + "Average JWMNP for 2020, PUMA=4000, RAC1P=2: 58.67741935483871\n", + "Average JWMNP for 2020, PUMA=4200, RAC1P=1: 61.0\n", + "Average JWMNP for 2020, PUMA=4200, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=4301, RAC1P=1: 43.0\n", + "Average JWMNP for 2020, PUMA=4301, RAC1P=2: 12.5\n", + "Average JWMNP for 2020, PUMA=4302, RAC1P=1: 46.44444444444444\n", + "Average JWMNP for 2020, PUMA=4302, RAC1P=2: 39.0\n", + "Average JWMNP for 2020, PUMA=4303, RAC1P=1: 55.4\n", + "Average JWMNP for 2020, PUMA=4303, RAC1P=2: 30.0\n", + "Average JWMNP for 2020, PUMA=4500, RAC1P=1: 35.38461538461539\n", + "Average JWMNP for 2020, PUMA=4500, RAC1P=2: 41.0\n", + "Average JWMNP for 2020, PUMA=4700, RAC1P=1: 72.88235294117646\n", + "Average JWMNP for 2020, PUMA=4700, RAC1P=2: 55.0\n", + "Average JWMNP for 2020, PUMA=4800, RAC1P=1: 43.285714285714285\n", + "Average JWMNP for 2020, PUMA=4800, RAC1P=2: 61.8\n", + "Average JWMNP for 2020, PUMA=4901, RAC1P=1: 67.4\n", + "Average JWMNP for 2020, PUMA=4901, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=4902, RAC1P=1: 58.09090909090909\n", + "Average JWMNP for 2020, PUMA=4902, RAC1P=2: nan\n", + "Average JWMNP for 2020, PUMA=4903, RAC1P=1: 78.96\n", + "Average JWMNP for 2020, PUMA=4903, RAC1P=2: 20.0\n", + "Average JWMNP for 2021, PUMA=100, RAC1P=1: 32.892857142857146\n", + "Average JWMNP for 2021, PUMA=100, RAC1P=2: 22.0\n", + "Average JWMNP for 2021, PUMA=200, RAC1P=1: 32.490566037735846\n", + "Average JWMNP for 2021, PUMA=200, RAC1P=2: 23.75\n", + "Average JWMNP for 2021, PUMA=300, RAC1P=1: 34.395833333333336\n", + "Average JWMNP for 2021, PUMA=300, RAC1P=2: 43.888888888888886\n", + "Average JWMNP for 2021, PUMA=301, RAC1P=1: 22.142857142857142\n", + "Average JWMNP for 2021, PUMA=301, RAC1P=2: nan\n", + "Average JWMNP for 2021, PUMA=302, RAC1P=1: 39.1\n", + "Average JWMNP for 2021, PUMA=302, RAC1P=2: 20.0\n", + "Average JWMNP for 2021, PUMA=303, RAC1P=1: 33.333333333333336\n", + "Average JWMNP for 2021, PUMA=303, RAC1P=2: nan\n", + "Average JWMNP for 2021, PUMA=304, RAC1P=1: 61.75\n", + "Average JWMNP for 2021, PUMA=304, RAC1P=2: 15.0\n", + "Average JWMNP for 2021, PUMA=400, RAC1P=1: 38.0\n", + "Average JWMNP for 2021, PUMA=400, RAC1P=2: 17.5\n", + "Average JWMNP for 2021, PUMA=501, RAC1P=1: 25.0\n", + "Average JWMNP for 2021, PUMA=501, RAC1P=2: nan\n", + "Average JWMNP for 2021, PUMA=502, RAC1P=1: 32.30434782608695\n", + "Average JWMNP for 2021, PUMA=502, RAC1P=2: 20.6\n", + "Average JWMNP for 2021, PUMA=503, RAC1P=1: 50.03333333333333\n", + "Average JWMNP for 2021, PUMA=503, RAC1P=2: 30.0\n", + "Average JWMNP for 2021, PUMA=504, RAC1P=1: 40.833333333333336\n", + "Average JWMNP for 2021, PUMA=504, RAC1P=2: 35.0\n", + "Average JWMNP for 2021, PUMA=505, RAC1P=1: 42.45652173913044\n", + "Average JWMNP for 2021, PUMA=505, RAC1P=2: 35.0\n", + "Average JWMNP for 2021, PUMA=506, RAC1P=1: 35.7956204379562\n", + "Average JWMNP for 2021, PUMA=506, RAC1P=2: 43.23809523809524\n", + "Average JWMNP for 2021, PUMA=507, RAC1P=1: 42.47727272727273\n", + "Average JWMNP for 2021, PUMA=507, RAC1P=2: 50.869565217391305\n", + "Average JWMNP for 2021, PUMA=508, RAC1P=1: 39.703125\n", + "Average JWMNP for 2021, PUMA=508, RAC1P=2: 32.85\n", + "Average JWMNP for 2021, PUMA=701, RAC1P=1: 36.292682926829265\n", + "Average JWMNP for 2021, PUMA=701, RAC1P=2: 89.33333333333333\n", + "Average JWMNP for 2021, PUMA=702, RAC1P=1: 71.3076923076923\n", + "Average JWMNP for 2021, PUMA=702, RAC1P=2: nan\n", + "Average JWMNP for 2021, PUMA=703, RAC1P=1: 60.82857142857143\n", + "Average JWMNP for 2021, PUMA=703, RAC1P=2: 45.125\n", + "Average JWMNP for 2021, PUMA=704, RAC1P=1: 46.54054054054054\n", + "Average JWMNP for 2021, PUMA=704, RAC1P=2: 43.333333333333336\n", + "Average JWMNP for 2021, PUMA=1000, RAC1P=1: 40.416666666666664\n", + "Average JWMNP for 2021, PUMA=1000, RAC1P=2: 52.5\n", + "Average JWMNP for 2021, PUMA=1300, RAC1P=1: 47.94117647058823\n", + "Average JWMNP for 2021, PUMA=1300, RAC1P=2: nan\n", + "Average JWMNP for 2021, PUMA=1400, RAC1P=1: 88.0\n", + "Average JWMNP for 2021, PUMA=1400, RAC1P=2: nan\n", + "Average JWMNP for 2021, PUMA=1600, RAC1P=1: 29.0625\n", + "Average JWMNP for 2021, PUMA=1600, RAC1P=2: 4.0\n", + "Average JWMNP for 2021, PUMA=1900, RAC1P=1: 43.46341463414634\n", + "Average JWMNP for 2021, PUMA=1900, RAC1P=2: 33.4\n", + "Average JWMNP for 2021, PUMA=1901, RAC1P=1: 39.57142857142857\n", + "Average JWMNP for 2021, PUMA=1901, RAC1P=2: 18.5\n", + "Average JWMNP for 2021, PUMA=1902, RAC1P=1: 44.411764705882355\n", + "Average JWMNP for 2021, PUMA=1902, RAC1P=2: 28.333333333333332\n", + "Average JWMNP for 2021, PUMA=2400, RAC1P=1: 52.5\n", + "Average JWMNP for 2021, PUMA=2400, RAC1P=2: nan\n", + "Average JWMNP for 2021, PUMA=2800, RAC1P=1: 51.8125\n", + "Average JWMNP for 2021, PUMA=2800, RAC1P=2: 51.25\n", + "Average JWMNP for 2021, PUMA=3301, RAC1P=1: 40.22406639004149\n", + "Average JWMNP for 2021, PUMA=3301, RAC1P=2: 37.92857142857143\n", + "Average JWMNP for 2021, PUMA=3302, RAC1P=1: 32.71518987341772\n", + "Average JWMNP for 2021, PUMA=3302, RAC1P=2: 31.071428571428573\n", + "Average JWMNP for 2021, PUMA=3303, RAC1P=1: 32.55416666666667\n", + "Average JWMNP for 2021, PUMA=3303, RAC1P=2: 47.37096774193548\n", + "Average JWMNP for 2021, PUMA=3304, RAC1P=1: 42.25\n", + "Average JWMNP for 2021, PUMA=3304, RAC1P=2: 45.1740614334471\n", + "Average JWMNP for 2021, PUMA=3305, RAC1P=1: 38.88181818181818\n", + "Average JWMNP for 2021, PUMA=3305, RAC1P=2: 53.32142857142857\n", + "Average JWMNP for 2021, PUMA=3306, RAC1P=1: 45.16842105263158\n", + "Average JWMNP for 2021, PUMA=3306, RAC1P=2: 45.76923076923077\n", + "Average JWMNP for 2021, PUMA=3400, RAC1P=1: 43.36190476190476\n", + "Average JWMNP for 2021, PUMA=3400, RAC1P=2: 30.833333333333332\n", + "Average JWMNP for 2021, PUMA=3500, RAC1P=1: 53.0\n", + "Average JWMNP for 2021, PUMA=3500, RAC1P=2: 40.0\n", + "Average JWMNP for 2021, PUMA=3602, RAC1P=1: 54.92857142857143\n", + "Average JWMNP for 2021, PUMA=3602, RAC1P=2: 94.9090909090909\n", + "Average JWMNP for 2021, PUMA=3603, RAC1P=1: 52.166666666666664\n", + "Average JWMNP for 2021, PUMA=3603, RAC1P=2: 42.5\n", + "Average JWMNP for 2021, PUMA=3900, RAC1P=1: 61.421052631578945\n", + "Average JWMNP for 2021, PUMA=3900, RAC1P=2: 35.0\n", + "Average JWMNP for 2021, PUMA=4000, RAC1P=1: 47.13333333333333\n", + "Average JWMNP for 2021, PUMA=4000, RAC1P=2: 59.57692307692308\n", + "Average JWMNP for 2021, PUMA=4200, RAC1P=1: 62.111111111111114\n", + "Average JWMNP for 2021, PUMA=4200, RAC1P=2: nan\n", + "Average JWMNP for 2021, PUMA=4301, RAC1P=1: 33.75\n", + "Average JWMNP for 2021, PUMA=4301, RAC1P=2: 12.5\n", + "Average JWMNP for 2021, PUMA=4302, RAC1P=1: 47.5\n", + "Average JWMNP for 2021, PUMA=4302, RAC1P=2: 35.0\n", + "Average JWMNP for 2021, PUMA=4303, RAC1P=1: 55.4\n", + "Average JWMNP for 2021, PUMA=4303, RAC1P=2: 30.0\n", + "Average JWMNP for 2021, PUMA=4500, RAC1P=1: 37.375\n", + "Average JWMNP for 2021, PUMA=4500, RAC1P=2: 28.333333333333332\n", + "Average JWMNP for 2021, PUMA=4700, RAC1P=1: 65.85714285714286\n", + "Average JWMNP for 2021, PUMA=4700, RAC1P=2: 90.0\n", + "Average JWMNP for 2021, PUMA=4800, RAC1P=1: 42.0\n", + "Average JWMNP for 2021, PUMA=4800, RAC1P=2: 54.75\n", + "Average JWMNP for 2021, PUMA=4901, RAC1P=1: 48.75\n", + "Average JWMNP for 2021, PUMA=4901, RAC1P=2: nan\n", + "Average JWMNP for 2021, PUMA=4902, RAC1P=1: 58.09090909090909\n", + "Average JWMNP for 2021, PUMA=4902, RAC1P=2: nan\n", + "Average JWMNP for 2021, PUMA=4903, RAC1P=1: 77.0\n", + "Average JWMNP for 2021, PUMA=4903, RAC1P=2: 20.0\n" + ] + } + ], + "source": [ + "# List of dataframes that have been read\n", + "dataframes = {\n", + " '2019': df_2019,\n", + " '2020': df_2020,\n", + " '2021': df_2021\n", + "}\n", + "\n", + "# average_jwmnp_values = {}\n", + "for year, df in dataframes.items():\n", + "\n", + " # df_common_pumas = df[df['PUMA'].isin(common_pumas)]\n", + " # Filter the data whose JWTRNS is 02\n", + " df_filtered = df[df['JWTRNS'] == 2]\n", + "\n", + " # Iterate over the value of RAC1P\n", + " grouped = df_filtered.groupby('PUMA')\n", + "\n", + " # Traverse each PUMA group\n", + " for puma, group in grouped:\n", + " # Iterate over the value of RAC1P\n", + " for rac1p_value in [1, 2]:\n", + " # Apply further screening criteria\n", + " condition = group['RAC1P'] == rac1p_value\n", + "\n", + " # Calculate the average\n", + " average_jwmnp = group[condition]['JWMNP'].mean()\n", + "\n", + " # Store results\n", + " average_jwmnp_values[f\"{year}, PUMA={puma}, RAC1P={rac1p_value}\"] = average_jwmnp\n", + "\n", + "# Print result\n", + "for key, value in average_jwmnp_values.items():\n", + " print(f\"Average JWMNP for {key}: {value}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearPUMARAC1PAverage JWMNP
02016100143.516129
12016100220.000000
22016200124.849057
32016200237.500000
42016300136.196429
...............
617202149012NaN
61820214902158.090909
619202149022NaN
62020214903177.000000
62120214903220.000000
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622 rows × 4 columns

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" + ], + "text/plain": [ + " Year PUMA RAC1P Average JWMNP\n", + "0 2016 100 1 43.516129\n", + "1 2016 100 2 20.000000\n", + "2 2016 200 1 24.849057\n", + "3 2016 200 2 37.500000\n", + "4 2016 300 1 36.196429\n", + ".. ... ... ... ...\n", + "617 2021 4901 2 NaN\n", + "618 2021 4902 1 58.090909\n", + "619 2021 4902 2 NaN\n", + "620 2021 4903 1 77.000000\n", + "621 2021 4903 2 20.000000\n", + "\n", + "[622 rows x 4 columns]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Suppose average_jwmnp_values is the dictionary you created earlier, with the keys containing year, PUMA, and RAC1P\n", + "# average_jwmnp_values = {...}\n", + "\n", + "# Convert the dictionary format to fit the DataFrame\n", + "data_for_df = []\n", + "for key, value in average_jwmnp_values.items():\n", + " year, puma_rac1p = key.split(\", PUMA=\")\n", + " puma, rac1p = puma_rac1p.split(\", RAC1P=\")\n", + " data_for_df.append({'Year': int(year), 'PUMA': int(puma), 'RAC1P': int(rac1p), 'Average JWMNP': value})\n", + "\n", + "# Create a DataFrame\n", + "df_average_jwmnp = pd.DataFrame(data_for_df)\n", + "\n", + "# Displays the DataFrame\n", + "df_average_jwmnp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearPUMARAC1PAverage JWMNP
02016Berkshire County--Pittsfield City143.516129
12016Berkshire County--Pittsfield City220.000000
22016Franklin & Hampshire (North) Counties124.849057
32016Franklin & Hampshire (North) Counties237.500000
42016Worcester County (Central)--Worcester City136.196429
...............
6172021Plymouth & Bristol Counties (Outside Brockton ...2NaN
6182021Plymouth County (Central)158.090909
6192021Plymouth County (Central)2NaN
6202021Plymouth County (East)--Plymouth, Marshfield, ...177.000000
6212021Plymouth County (East)--Plymouth, Marshfield, ...220.000000
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622 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Year PUMA RAC1P \\\n", + "0 2016 Berkshire County--Pittsfield City 1 \n", + "1 2016 Berkshire County--Pittsfield City 2 \n", + "2 2016 Franklin & Hampshire (North) Counties 1 \n", + "3 2016 Franklin & Hampshire (North) Counties 2 \n", + "4 2016 Worcester County (Central)--Worcester City 1 \n", + ".. ... ... ... \n", + "617 2021 Plymouth & Bristol Counties (Outside Brockton ... 2 \n", + "618 2021 Plymouth County (Central) 1 \n", + "619 2021 Plymouth County (Central) 2 \n", + "620 2021 Plymouth County (East)--Plymouth, Marshfield, ... 1 \n", + "621 2021 Plymouth County (East)--Plymouth, Marshfield, ... 2 \n", + "\n", + " Average JWMNP \n", + "0 43.516129 \n", + "1 20.000000 \n", + "2 24.849057 \n", + "3 37.500000 \n", + "4 36.196429 \n", + ".. ... \n", + "617 NaN \n", + "618 58.090909 \n", + "619 NaN \n", + "620 77.000000 \n", + "621 20.000000 \n", + "\n", + "[622 rows x 4 columns]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Invert the dictionary so that it maps from values to keys\n", + "puma_dict_2010_reversed = {v: k for k, v in puma_dict_2010.items()}\n", + "puma_dict_2020_reversed = {v: k for k, v in puma_dict_2020.items()}\n", + "\n", + "# Define replacement function\n", + "def replace_puma(row):\n", + " year = int(row['Year'])\n", + " puma = row['PUMA']\n", + "\n", + " if year <= 2019:\n", + " return puma_dict_2010_reversed.get(puma, puma) # Replace with the inverted puma_dict_2010\n", + " else:\n", + " return puma_dict_2020_reversed.get(puma, puma) # Replace with the inverted puma_dict_2020\n", + "\n", + "# Apply the replacement function to each line of df_average_jwmnp\n", + "df_average_jwmnp['PUMA'] = df_average_jwmnp.apply(replace_puma, axis=1)\n", + "\n", + "# View the updated DataFrame\n", + "df_average_jwmnp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearPUMARAC1PAverage JWMNP
02016Berkshire County--Pittsfield City143.516129
12016Berkshire County--Pittsfield City220.000000
42016Worcester County (Central)--Worcester City136.196429
52016Worcester County (Central)--Worcester City237.939394
82016Worcester County (West Central)161.500000
...............
6172021Plymouth & Bristol Counties (Outside Brockton ...2NaN
6182021Plymouth County (Central)158.090909
6192021Plymouth County (Central)2NaN
6202021Plymouth County (East)--Plymouth, Marshfield, ...177.000000
6212021Plymouth County (East)--Plymouth, Marshfield, ...220.000000
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552 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Year PUMA RAC1P \\\n", + "0 2016 Berkshire County--Pittsfield City 1 \n", + "1 2016 Berkshire County--Pittsfield City 2 \n", + "4 2016 Worcester County (Central)--Worcester City 1 \n", + "5 2016 Worcester County (Central)--Worcester City 2 \n", + "8 2016 Worcester County (West Central) 1 \n", + ".. ... ... ... \n", + "617 2021 Plymouth & Bristol Counties (Outside Brockton ... 2 \n", + "618 2021 Plymouth County (Central) 1 \n", + "619 2021 Plymouth County (Central) 2 \n", + "620 2021 Plymouth County (East)--Plymouth, Marshfield, ... 1 \n", + "621 2021 Plymouth County (East)--Plymouth, Marshfield, ... 2 \n", + "\n", + " Average JWMNP \n", + "0 43.516129 \n", + "1 20.000000 \n", + "4 36.196429 \n", + "5 37.939394 \n", + "8 61.500000 \n", + ".. ... \n", + "617 NaN \n", + "618 58.090909 \n", + "619 NaN \n", + "620 77.000000 \n", + "621 20.000000 \n", + "\n", + "[552 rows x 4 columns]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Gets the unique PUMA value for each year\n", + "unique_pumas_by_year = df_average_jwmnp.groupby('Year')['PUMA'].unique()\n", + "\n", + "# Find the PUMA values for all years\n", + "common_pumas = set(unique_pumas_by_year.iloc[0])\n", + "for pumas in unique_pumas_by_year[1:]:\n", + " common_pumas.intersection_update(pumas)\n", + "\n", + "# Filter out the rows that have these common PUMA values\n", + "df_common_pumas = df_average_jwmnp[df_average_jwmnp['PUMA'].isin(common_pumas)]\n", + "\n", + "# Displays the filtered DataFrame\n", + "df_common_pumas\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearPUMARAC1PAverage JWMNP
02016Berkshire County--Pittsfield City143.516129
12016Berkshire County--Pittsfield City220.000000
42016Worcester County (Central)--Worcester City136.196429
52016Worcester County (Central)--Worcester City237.939394
82016Worcester County (West Central)161.500000
...............
6112021Bristol County (South)--New Bedford City & Fai...228.333333
6122021Barnstable County (West)--Inner Cape Cod Towns...165.857143
6132021Barnstable County (West)--Inner Cape Cod Towns...290.000000
6142021Barnstable (East), Dukes & Nantucket Counties-...142.000000
6152021Barnstable (East), Dukes & Nantucket Counties-...254.750000
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360 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Year PUMA RAC1P \\\n", + "0 2016 Berkshire County--Pittsfield City 1 \n", + "1 2016 Berkshire County--Pittsfield City 2 \n", + "4 2016 Worcester County (Central)--Worcester City 1 \n", + "5 2016 Worcester County (Central)--Worcester City 2 \n", + "8 2016 Worcester County (West Central) 1 \n", + ".. ... ... ... \n", + "611 2021 Bristol County (South)--New Bedford City & Fai... 2 \n", + "612 2021 Barnstable County (West)--Inner Cape Cod Towns... 1 \n", + "613 2021 Barnstable County (West)--Inner Cape Cod Towns... 2 \n", + "614 2021 Barnstable (East), Dukes & Nantucket Counties-... 1 \n", + "615 2021 Barnstable (East), Dukes & Nantucket Counties-... 2 \n", + "\n", + " Average JWMNP \n", + "0 43.516129 \n", + "1 20.000000 \n", + "4 36.196429 \n", + "5 37.939394 \n", + "8 61.500000 \n", + ".. ... \n", + "611 28.333333 \n", + "612 65.857143 \n", + "613 90.000000 \n", + "614 42.000000 \n", + "615 54.750000 \n", + "\n", + "[360 rows x 4 columns]" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Find the PUMA value for the Average JWMNP line containing NaN\n", + "pumas_with_nan = df_common_pumas[df_common_pumas['Average JWMNP'].isna()]['PUMA'].unique()\n", + "\n", + "# Filter out all rows that do not contain these PUMA values\n", + "df_filtered = df_common_pumas[~df_common_pumas['PUMA'].isin(pumas_with_nan)]\n", + "\n", + "# Displays the filtered DataFrame\n", + "\n", + "df_filtered\n" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PUMADifference
0Barnstable (East), Dukes & Nantucket Counties-...22.398810
1Barnstable County (West)--Inner Cape Cod Towns...-32.806944
2Berkshire County--Pittsfield City-20.722052
3Boston City--Allston, Brighton & Fenway-1.104043
4Boston City--Back Bay, Beacon Hill, Charlestow...0.526289
5Boston City--Dorchester & South Boston13.601288
6Boston City--Hyde Park, Jamaica Plain, Roslind...12.479476
7Boston City--Mattapan & Roxbury4.615998
8Bristol (Outside New Bedford City) & Plymouth ...-29.127525
9Bristol County (Central)--Fall River City & So...11.384907
10Bristol County (South)--New Bedford City & Fai...-3.146034
11Essex County (East)--Salem, Beverly, Glouceste...-16.945450
12Essex County (Northwest)--Lawrence, Haverhill ...9.435414
13Essex County (South)--Lynn City, Swampscott & ...-4.613262
14Hampden County (Central)--Springfield City-5.758789
15Hampden County (West of Springfield City)--Wes...-14.471627
16Middlesex (Southeast) & Norfolk (Northeast) Co...-7.822485
17Middlesex County (East)--Malden & Medford Cities-4.685191
18Middlesex County (Far Northeast)--Lowell City-9.608660
19Middlesex County (South)--Framingham Town, Mar...-9.083144
20Middlesex County--Waltham City, Lexington, Bur...16.092722
21Middlesex County--Watertown Town City, Arlingt...-7.871956
22Norfolk County (Central)--Randolph, Norwood, D...23.938863
23Norfolk County (Northeast)--Quincy City & Milt...-1.169818
24Plymouth & Norfolk Counties--Brockton City, St...15.504190
25Suffolk County (North)--Revere, Chelsea & Wint...0.431524
26Weymouth Town, Braintree Town Cities, Hingham,...-38.260798
27Woburn, Melrose Cities, Saugus, Wakefield & St...-5.013363
28Worcester County (Central)--Worcester City5.299270
29Worcester County (West Central)-3.324074
\n", + "
" + ], + "text/plain": [ + " PUMA Difference\n", + "0 Barnstable (East), Dukes & Nantucket Counties-... 22.398810\n", + "1 Barnstable County (West)--Inner Cape Cod Towns... -32.806944\n", + "2 Berkshire County--Pittsfield City -20.722052\n", + "3 Boston City--Allston, Brighton & Fenway -1.104043\n", + "4 Boston City--Back Bay, Beacon Hill, Charlestow... 0.526289\n", + "5 Boston City--Dorchester & South Boston 13.601288\n", + "6 Boston City--Hyde Park, Jamaica Plain, Roslind... 12.479476\n", + "7 Boston City--Mattapan & Roxbury 4.615998\n", + "8 Bristol (Outside New Bedford City) & Plymouth ... -29.127525\n", + "9 Bristol County (Central)--Fall River City & So... 11.384907\n", + "10 Bristol County (South)--New Bedford City & Fai... -3.146034\n", + "11 Essex County (East)--Salem, Beverly, Glouceste... -16.945450\n", + "12 Essex County (Northwest)--Lawrence, Haverhill ... 9.435414\n", + "13 Essex County (South)--Lynn City, Swampscott & ... -4.613262\n", + "14 Hampden County (Central)--Springfield City -5.758789\n", + "15 Hampden County (West of Springfield City)--Wes... -14.471627\n", + "16 Middlesex (Southeast) & Norfolk (Northeast) Co... -7.822485\n", + "17 Middlesex County (East)--Malden & Medford Cities -4.685191\n", + "18 Middlesex County (Far Northeast)--Lowell City -9.608660\n", + "19 Middlesex County (South)--Framingham Town, Mar... -9.083144\n", + "20 Middlesex County--Waltham City, Lexington, Bur... 16.092722\n", + "21 Middlesex County--Watertown Town City, Arlingt... -7.871956\n", + "22 Norfolk County (Central)--Randolph, Norwood, D... 23.938863\n", + "23 Norfolk County (Northeast)--Quincy City & Milt... -1.169818\n", + "24 Plymouth & Norfolk Counties--Brockton City, St... 15.504190\n", + "25 Suffolk County (North)--Revere, Chelsea & Wint... 0.431524\n", + "26 Weymouth Town, Braintree Town Cities, Hingham,... -38.260798\n", + "27 Woburn, Melrose Cities, Saugus, Wakefield & St... -5.013363\n", + "28 Worcester County (Central)--Worcester City 5.299270\n", + "29 Worcester County (West Central) -3.324074" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a list to store the difference for each PUMA\n", + "data_for_df = []\n", + "puma_rac1p_avg = df_filtered.groupby(['PUMA', 'RAC1P'])['Average JWMNP'].mean().reset_index()\n", + "# Loop through each PUMA\n", + "for puma in puma_rac1p_avg['PUMA'].unique():\n", + " # Filter data for a specific PUMA\n", + " df_puma = puma_rac1p_avg[puma_rac1p_avg['PUMA'] == puma]\n", + "\n", + " # Make sure both RAC1P values are present\n", + " if 1 in df_puma['RAC1P'].values and 2 in df_puma['RAC1P'].values:\n", + " avg_jwmnp_rac1p1 = df_puma[df_puma['RAC1P'] == 1]['Average JWMNP'].iloc[0]\n", + " avg_jwmnp_rac1p2 = df_puma[df_puma['RAC1P'] == 2]['Average JWMNP'].iloc[0]\n", + "\n", + " # Calculated difference\n", + " diff = avg_jwmnp_rac1p2 - avg_jwmnp_rac1p1\n", + " data_for_df.append({'PUMA': puma, 'Difference': diff})\n", + "\n", + "# Create a DataFrame\n", + "df_puma_diff = pd.DataFrame(data_for_df)\n", + "\n", + "# Displays the DataFrame\n", + "df_puma_diff\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "df_puma_diff.to_csv(\"df_puma_diff\")" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Difference Regions\n", + "0 22.398810 Barnstable, Nantucket Counties\n", + "1 -32.806944 Barnstable County\n", + "2 -20.722052 Berkshire County\n", + "3 -1.104043 Boston City\n", + "4 0.526289 Boston City\n", + "5 13.601288 Boston City\n", + "6 12.479476 Boston City\n", + "7 4.615998 Boston City\n", + "8 -29.127525 Bristol, Plymouth\n", + "9 11.384907 Bristol County\n", + "10 -3.146034 Bristol County\n", + "11 -16.945450 Essex County\n", + "12 9.435414 Essex County\n", + "13 -4.613262 Essex County\n", + "14 -5.758789 Hampden County\n", + "15 -14.471627 Hampden County\n", + "16 -7.822485 Middlesex, Norfolk\n", + "17 -4.685191 Middlesex County\n", + "18 -9.608660 Middlesex County\n", + "19 -9.083144 Middlesex County\n", + "20 16.092722 Middlesex County\n", + "21 -7.871956 Middlesex County\n", + "22 23.938863 Norfolk County\n", + "23 -1.169818 Norfolk County\n", + "24 15.504190 Plymouth, Norfolk Counties\n", + "25 0.431524 Suffolk County\n", + "26 -38.260798 Weymouth Town, Braintree Town Cities, Hingham,...\n", + "27 -5.013363 Woburn, Melrose Cities, Saugus, Wakefield, Sto...\n", + "28 5.299270 Worcester County\n", + "29 -3.324074 Worcester County\n" + ] + } + ], + "source": [ + "def simplify_puma_name(name):\n", + " name_parts = name.split('--')[0].split('&')\n", + " simplified_names = [part.split('(')[0].strip() for part in name_parts]\n", + " return ', '.join(simplified_names)\n", + "df=df_puma_diff\n", + "df['Simplified PUMA'] = df['PUMA'].apply(simplify_puma_name)\n", + "\n", + "# Refactor the DataFrame to group regions with the same difference\n", + "grouped_data = {}\n", + "for _, row in df.iterrows():\n", + " diff = row['Difference']\n", + " puma = row['Simplified PUMA']\n", + " if diff in grouped_data:\n", + " grouped_data[diff].append(puma)\n", + " else:\n", + " grouped_data[diff] = [puma]\n", + "\n", + "# Create a new DataFrame\n", + "new_df_data = {'Difference': [], 'Regions': []}\n", + "for diff, pumas in grouped_data.items():\n", + " new_df_data['Difference'].append(diff)\n", + " new_df_data['Regions'].append(', '.join(pumas))\n", + "\n", + "new_df = pd.DataFrame(new_df_data)\n", + "\n", + "# Output the new DataFrame\n", + "print(new_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PUMA have maximum difference: Norfolk County (Central)--Randolph, Norwood, Dedham, Canton & Holbrook Towns,\n", + " Difference: 199.49052853464627\n", + "PUMA have minimum difference: Weymouth Town, Braintree Town Cities, Hingham, Hull & Cohasset Towns,\n", + " Difference: -318.8399793396892\n" + ] + } + ], + "source": [ + "# Find the maximum difference\n", + "max_diff = df_puma_diff['Difference'].max()\n", + "max_diff_puma = df_puma_diff[df_puma_diff['Difference'] == max_diff]['PUMA'].iloc[0]\n", + "\n", + "# Find the minimum difference\n", + "min_diff = df_puma_diff['Difference'].min()\n", + "min_diff_puma = df_puma_diff[df_puma_diff['Difference'] == min_diff]['PUMA'].iloc[0]\n", + "\n", + "# Output the maximum and minimum values\n", + "print(f\"PUMA have maximum difference: {max_diff_puma},\\n Difference: {(max_diff * 2 * 5 * 50) / 60}\")\n", + "print(f\"PUMA have minimum difference: {min_diff_puma},\\n Difference: {(min_diff * 2 * 5 * 50) / 60}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PUMA have maximum difference: Weymouth Town, Braintree Town Cities, Hingham, Hull & Cohasset Towns,\n", + " Difference: 318.8399793396892\n", + "PUMA have minimum difference: Suffolk County (North)--Revere, Chelsea & Winthrop Town Cities,\n", + " Difference: 3.596031796030038\n" + ] + } + ], + "source": [ + "# Calculate the Average JWMNP for each PUMA and RAC1P combination\n", + "puma_rac1p_avg = df_filtered.groupby(['PUMA', 'RAC1P'])['Average JWMNP'].mean().reset_index()\n", + "\n", + "# The dictionary is initialized to store the maximum and minimum differences for each PUMA\n", + "puma_diff = {}\n", + "\n", + "# Walk through each PUMA\n", + "for puma in puma_rac1p_avg['PUMA'].unique():\n", + " # Filter data for a specific PUMA\n", + " df_puma = puma_rac1p_avg[puma_rac1p_avg['PUMA'] == puma]\n", + "\n", + " # Make sure there are two RAC1P values\n", + " if df_puma.shape[0] == 2:\n", + " # Calculated difference\n", + " diff = abs(df_puma.iloc[0]['Average JWMNP'] - df_puma.iloc[1]['Average JWMNP'])\n", + " puma_diff[puma] = diff\n", + "\n", + "# Calculate the maximum and minimum differences\n", + "max_diff_puma = max(puma_diff, key=puma_diff.get)\n", + "min_diff_puma = min(puma_diff, key=puma_diff.get)\n", + "\n", + "max_diff = puma_diff[max_diff_puma]\n", + "min_diff = puma_diff[min_diff_puma]\n", + "\n", + "print(f\"PUMA have maximum difference: {max_diff_puma},\\n Difference: {(max_diff * 2 * 5 * 50) / 60}\")\n", + "print(f\"PUMA have minimum difference: {min_diff_puma},\\n Difference: {(min_diff * 2 * 5 * 50) / 60}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Load the CSV file\n", + "file_path = './df_puma_diff.csv'\n", + "df = pd.read_csv(file_path)\n", + "\n", + "# Display the first few rows of the dataframe to understand its structure\n", + "zip_codes = []\n", + "differences = []\n", + "\n", + "for _, row in df.iterrows():\n", + " # Splitting ZIP codes by space and comma, then stripping any whitespace\n", + " codes = [code.strip() for code in str(row['PUMA']).replace(',', ' ').split()]\n", + " zip_codes.extend(codes)\n", + " differences.extend([row['Difference']] * len(codes))\n", + "\n", + "# Creating a new DataFrame\n", + "df_separated = pd.DataFrame({\n", + " 'ZIP Code': zip_codes,\n", + " 'Difference': differences\n", + "})\n", + "df_separated[\"Difference\"]=(df_separated[\"Difference\"]* 2 * 5 * 50) / 60\n", + "df_separated.to_csv(\"zip.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Creating a donut chart with larger spaces between segments for a more 3D effect\n", + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "sizes = [199, 319] # The absolute values of the differences\n", + "labels = [\"Norfolk County (Central)\\nBlack > White by 199 hours\", \n", + " \"Weymouth Town & Others\\nWhite > Black by 319 hours\"]\n", + "colors = ['#505050', '#D3D3D3']\n", + "# Custom function to create space between segments\n", + "def make_explode(n):\n", + " return [0.05] * n\n", + "\n", + "explode = make_explode(len(sizes))\n", + "\n", + "# Creating the updated donut chart with larger spaces between segments\n", + "wedges, texts = ax.pie(sizes, labels=labels, colors=colors, startangle=140, \n", + " explode=explode, wedgeprops=dict(width=0.3, edgecolor='white'))\n", + "\n", + "# Draw a circle at the center of pie to make it look like a donut\n", + "centre_circle = plt.Circle((0,0),0.70,fc='white')\n", + "fig = plt.gcf()\n", + "fig.gca().add_artist(centre_circle)\n", + "\n", + "# Equal aspect ratio ensures that pie is drawn as a circle\n", + "ax.axis('equal') \n", + "\n", + "plt.title('Areas with Max Yearly Commute Time Differences (Hours)\\n(Black vs White Bus Commuters)')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Re-defining the data that was missing in the previous code block\n", + "\n", + "# Updated data for the donut chart\n", + "new_labels = [\"Weymouth Town & Others\\nDifference between B&W: 319 hours\", \n", + " \"Suffolk County (North)\\nRevere, Chelsea & Winthrop\\nDifference between B&W: 4 hours\"]\n", + "new_sizes = [319, 4] # Absolute values for the new differences\n", + "\n", + "# New colors for the updated chart\n", + "new_colors = ['#FF9999', '#99CCFF'] # Light red and light blue\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "# Creating the updated donut chart with the new colors\n", + "wedges, texts = ax.pie(new_sizes, labels=new_labels, colors=new_colors, startangle=140, \n", + " explode=explode, wedgeprops=dict(width=0.3, edgecolor='white'))\n", + "\n", + "# Draw a circle at the center of pie to make it look like a donut\n", + "centre_circle = plt.Circle((0,0),0.70,fc='white')\n", + "fig = plt.gcf()\n", + "fig.gca().add_artist(centre_circle)\n", + "\n", + "# Equal aspect ratio ensures that pie is drawn as a circle\n", + "ax.axis('equal') \n", + "\n", + "plt.title('Areas With Max And Min Yearly Commute Time Differences (Hours)\\n(Black & White Bus Commuters)')\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ds", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From cac676edd10d1002c54511de444a922dc5417422 Mon Sep 17 00:00:00 2001 From: laowangbushilaowang <53445096+laowangbushilaowang@users.noreply.github.com> Date: Tue, 28 Nov 2023 22:35:24 -0500 Subject: [PATCH 10/39] Bohan create and upload --- fa23-team/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fa23-team/README.md b/fa23-team/README.md index b6cd892..8ee4ac4 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -1,4 +1,4 @@ -## Q2 +## Q2 (Bohan Wang) ### q2-final.ipynb From 11998c288647b51ae9d81fd8fd2a383180a85e7f Mon Sep 17 00:00:00 2001 From: laowangbushilaowang <53445096+laowangbushilaowang@users.noreply.github.com> Date: Tue, 28 Nov 2023 22:36:59 -0500 Subject: [PATCH 11/39] Bohan create and upload --- fa23-team/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fa23-team/README.md b/fa23-team/README.md index 8ee4ac4..c0937d3 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -72,4 +72,4 @@ _Data should be rename to psam_p25_year.csv and put them in the same dirt._ 2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf, create two dicts to store the area name and code of PUMA. 3. Find the areas which in all years to analysis their average. Calculate the time difference between black and white of different areas across these years. Use (average * 2 * 5 * 50) / 60 to calculate the travel time difference between B&W each year. 4. To draw Geography graph, I processed the result. Change all the areas names to zip codes, and then draw a graph using tableau. -5. Draw \ No newline at end of file +5. Draw ring diagrams. \ No newline at end of file From a20fbb20daa056df03e58b46bde95e4f4360a1a0 Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 22:56:21 -0500 Subject: [PATCH 12/39] Rename q4_analisys.ipynb to q3_analisys.ipynb --- fa23-team/{q4_analisys.ipynb => q3_analisys.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename fa23-team/{q4_analisys.ipynb => q3_analisys.ipynb} (100%) diff --git a/fa23-team/q4_analisys.ipynb b/fa23-team/q3_analisys.ipynb similarity index 100% rename from fa23-team/q4_analisys.ipynb rename to fa23-team/q3_analisys.ipynb From 54e8bdc1688a3ae4f7346b2996406903aeca2359 Mon Sep 17 00:00:00 2001 From: yuli0316 <144358568+yuli0316@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:03:13 -0500 Subject: [PATCH 13/39] Q1_Final_Visualization for Question 1_Yu --- fa23-team/Q1_Final.ipynb | 678 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 678 insertions(+) create mode 100644 fa23-team/Q1_Final.ipynb diff --git a/fa23-team/Q1_Final.ipynb b/fa23-team/Q1_Final.ipynb new file mode 100644 index 0000000..7d6ca2d --- /dev/null +++ b/fa23-team/Q1_Final.ipynb @@ -0,0 +1,678 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_4827/26858416.py:1: DtypeWarning: Columns (109,110) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2013 = pd.read_csv('psam_p25_2013.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_4827/26858416.py:2: DtypeWarning: Columns (108,109) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2014 = pd.read_csv('psam_p25_2014.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_4827/26858416.py:3: DtypeWarning: Columns (108,109) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2015 = pd.read_csv('psam_p25_2015.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_4827/26858416.py:6: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2018 = pd.read_csv('psam_p25_2018.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_4827/26858416.py:7: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2019 = pd.read_csv('psam_p25_2019.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_4827/26858416.py:8: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2020 = pd.read_csv('psam_p25_2020.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_4827/26858416.py:9: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2021 = pd.read_csv('psam_p25_2021.csv')\n" + ] + } + ], + "source": [ + "df_2013 = pd.read_csv('psam_p25_2013.csv')\n", + "df_2014 = pd.read_csv('psam_p25_2014.csv')\n", + "df_2015 = pd.read_csv('psam_p25_2015.csv')\n", + "df_2016 = pd.read_csv('psam_p25_2016.csv')\n", + "df_2017 = pd.read_csv('psam_p25_2017.csv')\n", + "df_2018 = pd.read_csv('psam_p25_2018.csv')\n", + "df_2019 = pd.read_csv('psam_p25_2019.csv')\n", + "df_2020 = pd.read_csv('psam_p25_2020.csv')\n", + "df_2021 = pd.read_csv('psam_p25_2021.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete NAN in JWMNP\n", + "df_2013 = df_2013.dropna(subset=['JWMNP'])\n", + "df_2014 = df_2014.dropna(subset=['JWMNP'])\n", + "df_2015 = df_2015.dropna(subset=['JWMNP'])\n", + "df_2016 = df_2016.dropna(subset=['JWMNP'])\n", + "df_2017 = df_2017.dropna(subset=['JWMNP'])\n", + "df_2018 = df_2018.dropna(subset=['JWMNP'])\n", + "df_2019 = df_2019.dropna(subset=['JWMNP'])\n", + "df_2020 = df_2020.dropna(subset=['JWMNP'])\n", + "df_2021 = df_2021.dropna(subset=['JWMNP'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_2013.to_csv('psam_p25_2013')\n", + "df_2014.to_csv('psam_p25_2015')\n", + "df_2015.to_csv('psam_p25_2014')\n", + "df_2016.to_csv('psam_p25_2016')\n", + "df_2017.to_csv('psam_p25_2017')\n", + "df_2018.to_csv('psam_p25_2018')\n", + "df_2019.to_csv('psam_p25_2019')\n", + "df_2020.to_csv('psam_p25_2020')\n", + "df_2021.to_csv('psam_p25_2021')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "dataframes = {\n", + " '2013': df_2013,\n", + " '2014': df_2014,\n", + " '2015': df_2015,\n", + " '2016': df_2016,\n", + " '2017': df_2017,\n", + " '2018': df_2018,\n", + "}\n", + "\n", + "average_jwmnp_values = {}\n", + "\n", + "for year, df in dataframes.items():\n", + " # filter JWTRNS=02\n", + " df_filtered = df[df['JWTR'] == 2]\n", + "\n", + " for rac1p_value in [1, 2]:\n", + " condition = df_filtered['RAC1P'] == rac1p_value\n", + " average_jwmnp = df_filtered[condition]['JWMNP'].mean()\n", + " average_jwmnp_values[f\"{year}, RAC1P={rac1p_value}\"] = average_jwmnp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average JWMNP for 2013, RAC1P=1: 38.30573248407644\n", + "Average JWMNP for 2013, RAC1P=2: 47.769911504424776\n", + "Average JWMNP for 2014, RAC1P=1: 38.98692810457516\n", + "Average JWMNP for 2014, RAC1P=2: 45.735264735264735\n", + "Average JWMNP for 2015, RAC1P=1: 39.831717451523545\n", + "Average JWMNP for 2015, RAC1P=2: 46.01587301587302\n", + "Average JWMNP for 2016, RAC1P=1: 40.03766413268832\n", + "Average JWMNP for 2016, RAC1P=2: 45.56928508384819\n", + "Average JWMNP for 2017, RAC1P=1: 40.3446385748544\n", + "Average JWMNP for 2017, RAC1P=2: 45.95731153496821\n", + "Average JWMNP for 2018, RAC1P=1: 40.648849797023004\n", + "Average JWMNP for 2018, RAC1P=2: 46.08083560399637\n", + "Average JWMNP for 2019, RAC1P=1: 40.925135501355015\n", + "Average JWMNP for 2019, RAC1P=2: 45.685009487666036\n", + "Average JWMNP for 2020, RAC1P=1: 40.475467289719624\n", + "Average JWMNP for 2020, RAC1P=2: 44.53224043715847\n", + "Average JWMNP for 2021, RAC1P=1: 40.74730215827338\n", + "Average JWMNP for 2021, RAC1P=2: 44.737041719342606\n" + ] + } + ], + "source": [ + "dataframes = {\n", + " '2019': df_2019,\n", + " '2020': df_2020,\n", + " '2021': df_2021\n", + "}\n", + "\n", + "for year, df in dataframes.items():\n", + " # filter JWTRNS=02\n", + " df_filtered = df[df['JWTRNS'] == 2]\n", + "\n", + " for rac1p_value in [1, 2]:\n", + " condition = df_filtered['RAC1P'] == rac1p_value\n", + " average_jwmnp = df_filtered[condition]['JWMNP'].mean()\n", + " average_jwmnp_values[f\"{year}, RAC1P={rac1p_value}\"] = average_jwmnp\n", + " \n", + "for key, value in average_jwmnp_values.items():\n", + " print(f\"Average JWMNP for {key}: {value}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'2013, RAC1P=1': 319.2144373673036,\n", + " '2013, RAC1P=2': 398.0825958702065,\n", + " '2014, RAC1P=1': 324.8910675381264,\n", + " '2014, RAC1P=2': 381.1272061272061,\n", + " '2015, RAC1P=1': 331.9309787626962,\n", + " '2015, RAC1P=2': 383.46560846560845,\n", + " '2016, RAC1P=1': 333.647201105736,\n", + " '2016, RAC1P=2': 379.74404236540164,\n", + " '2017, RAC1P=1': 336.20532145711996,\n", + " '2017, RAC1P=2': 382.9775961247351,\n", + " '2018, RAC1P=1': 338.7404149751917,\n", + " '2018, RAC1P=2': 384.0069633666364,\n", + " '2019, RAC1P=1': 341.0427958446251,\n", + " '2019, RAC1P=2': 380.708412397217,\n", + " '2020, RAC1P=1': 337.29556074766356,\n", + " '2020, RAC1P=2': 371.10200364298726,\n", + " '2021, RAC1P=1': 339.5608513189448,\n", + " '2021, RAC1P=2': 372.80868099452175}" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_travel_time = {}\n", + "for key, average_jwmnp in average_jwmnp_values.items():\n", + " # Using the formula (Average JWMNP * 2 * 5 * 50) / 60\n", + " yearly_travel_time = (average_jwmnp * 2 * 5 * 50) / 60\n", + " total_travel_time[key] = yearly_travel_time\n", + "total_travel_time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### T Test" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearWhiteBlack
02013319.214437398.082596
12014324.891068381.127206
22015331.930979383.465608
32016333.647201379.744042
42017336.205321382.977596
52018338.740415384.006963
62019341.042796380.708412
72020337.295561371.102004
82021339.560851372.808681
\n", + "
" + ], + "text/plain": [ + " Year White Black\n", + "0 2013 319.214437 398.082596\n", + "1 2014 324.891068 381.127206\n", + "2 2015 331.930979 383.465608\n", + "3 2016 333.647201 379.744042\n", + "4 2017 336.205321 382.977596\n", + "5 2018 338.740415 384.006963\n", + "6 2019 341.042796 380.708412\n", + "7 2020 337.295561 371.102004\n", + "8 2021 339.560851 372.808681" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Organizing the data into a structured format for the table\n", + "years = sorted(set(key.split(\", \")[0] for key in total_travel_time.keys()))\n", + "table_data = {year: {'White': None, 'Black': None} for year in years}\n", + "\n", + "for key, value in total_travel_time.items():\n", + " year, race = key.split(\", \")\n", + " race = 'White' if race == 'RAC1P=1' else 'Black'\n", + " table_data[year][race] = value\n", + "\n", + "# Convert the dictionary to a pandas DataFrame\n", + "df = pd.DataFrame.from_dict(table_data, orient='index')\n", + "df.index.rename('Year', inplace=True)\n", + "df = df.reset_index()\n", + "\n", + "# Exporting to CSV\n", + "df.to_csv('T_test.csv')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-4.331535857081992, 0.00470223929757063)" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import ttest_ind\n", + "\n", + "# Converting 'Year' column to integers\n", + "df['Year'] = df['Year'].astype(int)\n", + "\n", + "pre_pandemic_data = df[(df['Year'] >= 2014) & (df['Year'] <= 2020)]\n", + "during_pandemic_data = df[df['Year'] >= 2020]\n", + "\n", + "# Calculate the difference in commute times for pre-pandemic and during-pandemic\n", + "pre_pandemic_diff = pre_pandemic_data['White'] - pre_pandemic_data['Black']\n", + "during_pandemic_diff = during_pandemic_data['White'] - during_pandemic_data['Black']\n", + "\n", + "# Perform t-test\n", + "t_stat, p_value = ttest_ind(pre_pandemic_diff, during_pandemic_diff, equal_var=False)\n", + "t_stat, p_value\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2013: 78.86815850290287,\n", + " 2014: 56.2361385890797,\n", + " 2015: 51.53462970291224,\n", + " 2016: 46.096841259665666,\n", + " 2017: 46.772274667615136,\n", + " 2018: 45.2665483914447,\n", + " 2019: 39.66561655259187,\n", + " 2020: 33.8064428953237,\n", + " 2021: 33.24782967557695}" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculating the difference in total travel time between RAC1P=2 and RAC1P=1 for each year\n", + "travel_time_difference = {}\n", + "\n", + "for year in range(2013, 2022): # Looping through the years 2017 to 2021\n", + " white = f\"{year}, RAC1P=1\"\n", + " black = f\"{year}, RAC1P=2\"\n", + "\n", + " # Calculate the difference if both keys exist in the dictionary\n", + " if white in total_travel_time and black in total_travel_time:\n", + " difference = total_travel_time[black] - total_travel_time[white]\n", + " travel_time_difference[year] = difference\n", + "travel_time_difference" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Extracting years and differences\n", + "years = list(travel_time_difference.keys())\n", + "differences = list(travel_time_difference.values())\n", + "\n", + "# Plotting the line graph\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Annual Travel Time Disparity between Black and White Riders 2013-2021')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Travel Time Difference(Hours)')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + "\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "199" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "average_difference_2017_2021 = round(sum(travel_time_difference[year] for year in range(2017, 2022)))\n", + "average_difference_2017_2021" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bus Commute Disparity between Black and White Riders(2017-2021): 40 hours\n" + ] + } + ], + "source": [ + "# Calculating the average of the yearly differences from 2017 to 2021\n", + "average_difference_2017_2021 = round(sum(travel_time_difference[year] for year in range(2017, 2022)) / 5)\n", + "print(f'Bus Commute Disparity between Black and White Riders(2017-2021):', average_difference_2017_2021, f'hours')" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "data = total_travel_time\n", + "\n", + "data_rac1p1 = {}\n", + "data_rac1p2 = {}\n", + "\n", + "for key, value in data.items():\n", + " year, rac1p = key.replace('Average JWMNP for ', '').split(', RAC1P=')\n", + " if rac1p == '1':\n", + " data_rac1p1[year] = value\n", + " elif rac1p == '2':\n", + " data_rac1p2[year] = value\n", + "\n", + "years_rac1p1 = list(data_rac1p1.keys())\n", + "values_rac1p1 = list(data_rac1p1.values())\n", + "\n", + "years_rac1p2 = list(data_rac1p2.keys())\n", + "values_rac1p2 = list(data_rac1p2.values())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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r14/IyEiGDBmCoiioVCqePn3KhQsX+OKLL5LtZ9KkSajVagYNGkRYWBhTpkyhU6dOHD161OBxnZyc8PX1pV+/frRq1Ur3A79ChQqA9j3z8ccfU7BgQYYNG4aNjQ2rV6+mZcuWrFu3Ltl7OT3CwsJ4+vRpiu/hpEaNGsW4ceNo0qQJTZo04d9//8Xb25vY2Fi97SIjI6lduzb379+nb9++FC5cmMOHDzN8+HCCgoKS9SNdtGgR0dHR9OnTBwsLC/LkycNvv/3GV199Rdu2bfn666+Jjo7mzJkzHD16lM8++yzD5/nw4UOsra2xtrbWLTt16hQffPBBsgCpWrVqLFiwgCtXruilU2fE8+fPiYqKIiwsjM2bN7N9+3Y6dOiQqX0llh/QCxJPnTqFjY0N7u7uycqfuD61dFND+xQ5WDa3iIl32IkTJ/SapzUajVKoUCHl66+/1tsuMeUnb968SkhIiG75pk2bFED5+++/dcu6deumAMqPP/6ot4/KlSsrVapU0T1PTOH4559/DB5r0aJFumWG0ur++usvBVAOHDigW5ZVqX1FihRRAGXHjh3JtjdUloYNGyrFixfXPQ8LC1MsLCyU7777Tm+7KVOmKCqVSrl9+7aiKNo0ABMTE2X8+PF62509e1YxNTXVW57VqX28kgKTmLrTp08f3bL4+HilUKFCikqlUiZNmqRb/vTpU8XKykpv33/++aeiVquVgwcP6h1n3rx5CmAwPSKplFL7ZsyYoQDKsmXLdMtiY2MVLy8vxdbWVgkPD091v4MHD1YA5d69e4qiaK8bS0tLJSYmRtm2bZtiYmKi28fs2bP1yvrNN98ogN45RUREKMWKFVOKFi2qS1dJvJaLFy+e7PpImtoXFBSklC1bVilevLhy69atVMutKCmn2rVs2VIxNzdXrl+/rlv24MEDJVeuXEqtWrWSHbtGjRq6VJX0HM/Qo1+/fsnSHV9Nc4uPj0+WWvT06VMlX758yueff65btnfvXgVQvvrqq2RlSHoMkqRTfffdd4parVYWL16c5nkoivY97O3trTx+/Fh5/PixcvbsWaVLly4GU7Re/SyKjY1VnJ2dlUqVKumdz4IFCxRA75wzct0DilqtVs6fP6+37ddff63Y2dml62/0qlevt9jYWKVcuXJK3bp19ZYDirm5uXLt2jXdstOnTyuAMmvWLN2yli1bKpaWlrrPKEXRprCZmJikmdqnKIoyZ84cvfdMQECAAii3b9/WpcIlnv+WLVsUQFm+fLnu9Yl/C3d3d726nzlzpgIoZ8+e1S179TMxtdS+evXqKeXLl1eio6N1yzQajVK9enWlVKlSaZ4XoPTs2VN5/PixEhwcrJw4cUJp1KiRwffnq99DwcHBirm5udK0aVO96/v7779XAL3P0Z9++kmxsbFRrly5orfPYcOGKSYmJsqdO3cURXn5XrWzs1OCg4P1tm3RooVStmzZNM8pPa5evapYWloqXbp00VtuY2Oj955OtHXr1hS/OxUlfal9ffv21X3uqNVqpW3btnq/OzIiJiZG8fDwUIoVK6bExcXpljdt2lTvOzvR8+fPFUAZNmxYqvutX7++Ymdnp0tJNURS+3IOSe0TRrN8+XLy5cvHJ598Amibtjt06MDKlSsNjnLToUMHvbu5NWvWBLTpT6969S5jzZo1DW6XHlZWVrr/R0dH8+TJEz766CMAg6MEZYVixYrpdSA2VJawsDCePHlC7dq1uXHjBmFhYQDY2dnRuHFjVq9erTdi3KpVq/joo48oXLgwAOvXr0ej0dC+fXuePHmie7i4uFCqVCmDKVHG1qtXL93/TUxM8PT0RFEUevbsqVvu4OBAmTJl9P6ea9aswd3dHTc3N71zqVu3LkCmz2Xbtm24uLjQsWNH3TIzMzO++uornj17xv79+1N9feJdxcS0LH9/f6pUqYK5uTleXl66dL7EdYkpP4nHrlatmt6dSVtbW/r06cOtW7d0aTSJunXrpnd9JHXv3j1q165NXFwcBw4cMDhiYHokJCSwa9cuWrZsqZc+mj9/fj777DMOHTqULKWwd+/emJiYpPsYffr0wc/PDz8/P9atW8eXX37J/PnzGThwYKqvMzEx0bXCaTQaQkJCiI+Px9PTU+99um7dOlQqFaNHj062j1dblxVFYcCAAcycOZNly5bRrVu3dJ/Hrl27cHJywsnJifLly/Pnn3/So0ePNAfMOHHiBMHBwXzxxRd6rYrdu3fH3t5eb9uMXve1a9fGw8NDb5mDgwPPnz/Hz88v3eeWKOn19vTpU8LCwqhZs6bBz8X69evrtZ5UqFABOzs73fs4ISGBnTt30rJlS91nFGhT2Ax9FhqS+F5JbAX29/enYMGCFC5cGDc3N/LkyaNL70ttoIkePXro1X1q3zVpCQkJYe/evbRv356IiAjd3+i///6jYcOGXL16lfv376e5n4ULF+Lk5ISzszOenp7s2bOHIUOGpPm+2L17N7Gxsfzvf//Tu74NDayzZs0aatasSe7cufWup/r165OQkMCBAwf0tm/Tpk2ygZEcHBy4d+9esrT7jIqMjKRdu3ZYWVkxadIkvXVRUVEGB9OwtLTUrc+sb775Bj8/P5YsWULjxo1JSEhI1nKXXgMGDODChQvMnj1bLzX5dco/YcIEdu/ezaRJk954xojIHEntE0aRkJDAypUr+eSTT7h586Zu+Ycffsj06dPZs2cP3t7eeq9J+uUK6IKqp0+f6i1P7PPw6ravbpdeISEhjB07lpUrVxIcHKy3LjF4yWrFihUzuNzf35/Ro0cTEBBAZGRksrIk/tDq0KEDGzduJCAggOrVq3P9+nVOnjypl5px9epVFEXRG+EoqaTpkm/Kq39je3t7LC0tk6Uw2Nvb6/VRu3r1KhcvXkxxtMNX/27pdfv2bUqVKpUshSQxJeP27dupvv7jjz9GpVLh7+/Pp59+ir+/Pw0aNAC0Pzg8PDx0y/z9/alataruB9zt27cNTgmQ9NjlypXTLU/pmgHtSF6mpqZcvHgRFxeXdJy5YY8fPyYyMlKXavpquTQaDXfv3tXrK5BauQwpVaoU9evX1z1v3bo1KpWKGTNm8Pnnn6easrNkyRKmT5/OpUuXiIuLM1iG69evU6BAAfLkyZNmWZYuXcqzZ8/w9fXVC6bT48MPP2TcuHEkJCRw7tw5xo0bx9OnT9McnS/xmnr1fWlmZpas72NGr3tDf4v+/fuzevVqGjduTMGCBfH29qZ9+/Y0atQozXPcsmUL48aNIzAwUK9fiqF051ff26D/ufz48WOioqIMfh6VKVOGbdu2pVmecuXK4eDgoBcsffzxx7oyeXl54e/vT+/evfH398fV1dVgudL7XZMe165dQ1EURo4cyciRIw1uExwcTMGCBVPdT4sWLRgwYIAuXXHChAlERkam2f8npevJyckpWZrp1atXOXPmzGtdT0OHDmX37t1Uq1aNkiVL4u3tzWeffab7O6RHQkICn376KRcuXGD79u0UKFBAb72VlZXBvq+JQ4KndEMpPdzc3HBzcwOga9eueHt74+Pjw9GjR1GpVISFhekFOubm5gY/S6ZOncpvv/3GTz/9pBuF9HXLv2rVKn744Qd69uyZqW4KIntIICWMYu/evQQFBbFy5UpWrlyZbP3y5cuTBVIp3dVO2uqS2nZJpdSvyVBLWPv27Tl8+DCDBw+mUqVK2NraotFoaNSokV4H3Kxk6IP0+vXr1KtXDzc3N37++WdcXV0xNzdn27Zt/PLLL3pl8fHxwdramtWrV1O9enVWr16NWq2mXbt2um00Gg0qlYrt27cbrLPEPjFvkqFypOfvrtFoKF++PD///LPBbZP2K3mT8ubNi5ubG4cOHeLZs2ecOXNGryWkevXqHDp0iHv37nHnzh06deqU6WOl9uOhdevWLF26lJkzZzJx4sRMHyMzXudHTaJ69eoxe/ZsDhw4kGIgtWzZMrp3707Lli0ZPHgwzs7OmJiYMHHixGQd8tPr448/JjAwkNmzZ9O+fft0BV+JHB0ddQFhw4YNcXNzo1mzZsycOTPNVoT0yuh1b+hv4ezsTGBgIDt37mT79u1s376dRYsW0bVr11QHVTl48CDNmzenVq1azJ07l/z582NmZsaiRYsMDiyQ3s/v16FWq/Hy8uLw4cMoioK/v7/eHFHVq1fnjz/+0AUjLVu2NLifrCxr4ufyoEGDUmxZS8+UH4UKFdJdT02aNMHR0ZEBAwbwySef6A268Do0Gg0NGjRgyJAhBteXLl1a77mh68nd3Z3Lly+zZcsWduzYwbp165g7dy6jRo3SDQ+flt69e7NlyxaWL1+ua11NKn/+/AaH905c9mrg9Tratm1L3759uXLlCmXKlOHrr7/We1/Url072UAxixcvZujQoXzxxRf88MMPBsv/zz//6Prrpaf8fn5+dO3alaZNmzJv3rwsOjvxJkggJYxi+fLlODs7M2fOnGTr1q9fz4YNG5g3b16W/AgzJPFO3Kuj8bzawvD06VP27NnD2LFjGTVqlG751atXjVKu1Pz999/ExMSwefNmvTumhtLWbGxsaNasGWvWrOHnn39m1apV1KxZU+8DukSJEiiKQrFixZJ9Qb5tSpQowenTp6lXr16mBv9I6TVFihThzJkzaDQavTu/ly5d0q1PS40aNfjjjz/YtWsXCQkJeqMwVa9enb/++kv3RZw0zahIkSIGR9XLyLET/e9//6NkyZKMGjUKe3t7hg0blu7XJuXk5IS1tXWK5VKr1UYJWuPj4wF49uxZitusXbuW4sWLs379er2/56spfCVKlGDnzp2EhISkGRiVLFmSKVOmUKdOHRo1asSePXvIlStXps6hadOm1K5dmwkTJtC3b19sbGwMbpf4d7169arej8i4uDhu3rxJxYoV9c7lda77RObm5vj4+ODj44NGo6F///7Mnz+fkSNHpvgjf926dVhaWrJz5069NKVFixZlqgxOTk5YWVkZ/GxNaXRJQxLnb9u8eTPBwcF6LSHVq1dnxIgRbNu2jaioqCydry2l+k9sRTQzM9NraX1dffv25ZdffuGHH36gVatWqX6GgfZ6Stqi+fjx42QtbCVKlODZs2evXU4bGxs6dOhAhw4diI2NpXXr1owfP57hw4fr0tdSMnjwYBYtWsSMGTNSbAWuVKkSBw8eTPa5fPToUaytrbP0+yyx9Skx+2TIkCF07txZt/7VVr1NmzbRq1cvWrdubfD3TWL5f//9dy5evKiXaps4mEnSgXESl7dq1QpPT09Wr16do+ZgFGmTPlIiy0VFRbF+/XqaNWtG27Ztkz0GDBhARETEaw0Nm5YiRYpgYmKSLOd77ty5es8T70y+eify1dGL3gRDZQkLC0vxh0uHDh148OABv//+O6dPn0428lDr1q0xMTFh7Nixyc5PURSjDe9uDO3bt+f+/fv89ttvydZFRUWlOVqcjY2NwTTNJk2a8PDhQ1atWqVbFh8fz6xZs7C1taV27dpplq1GjRokJCQwbdo0SpUqpZc2U716dZ49e8bcuXNRq9V6QVaTJk04duyYbihc0I4otWDBAooWLZqsr0taRo4cyaBBgxg+fHiyIfPTy8TEBG9vbzZt2qQ3vPKjR49YsWIFNWrUwM7OLlP7Ts3ff/8NoBdEGCob6L8/jh49qld/oO3XoSiKwbvjhlocKlSowLZt27h48SI+Pj6v1f9i6NCh/Pfffwav00Senp44OTkxb948vb4ZixcvTnbj53WveyDZ+1ytVutGm0tt6gATExNUKpVeK/6tW7dSHSEzNSYmJjRs2JCNGzdy584d3fKLFy+yc+fOdO8nMTiaPHky1tbWej9Kq1WrhqmpKVOmTNHbNiskjir36t/I2dmZOnXqMH/+fIOtKI8fP87U8UxNTfnuu++4ePEimzZtSnG7+vXrY2ZmxqxZs/Sub0PfYe3btycgIMBgfYeGhupuaKTm1evJ3NwcDw8PFEXRS7c1ZOrUqUybNo3vv/8+1REj27Zty6NHj1i/fr1u2ZMnT1izZg0+Pj6ZmozYUPp3XFwcS5cuxcrKSvd56+HhQf369XWPKlWq6LY/cOAAn376KbVq1WL58uUppl22aNECMzMzvd8biqIwb948ChYsqPc9cPHiRZo2bUrRokXZsmWL0W4uC+ORsFdkuc2bNxMREUHz5s0Nrv/oo49wcnJi+fLlrzXsaGrs7e1p164ds2bNQqVSUaJECbZs2ZLsw9TOzo5atWoxZcoU4uLiKFiwILt27dLr1/WmeHt76+4c9+3bl2fPnvHbb7/h7Oxs8Au6SZMm5MqVi0GDBmFiYkKbNm301pcoUYJx48YxfPhwbt26RcuWLcmVKxc3b95kw4YN9OnTh0GDBr2p03stXbp0YfXq1XzxxRf8888/fPzxxyQkJHDp0iVWr16tm5MrJVWqVGHVqlUMHDiQqlWrYmtri4+PD3369GH+/Pl0796dkydPUrRoUdauXYu/vz8zZsxIV+tE4o+1gIAAunfvrreudOnSODo6EhAQQPny5fU6Dw8bNoy//vqLxo0b89VXX5EnTx6WLFnCzZs3WbduXabmRpk6dSphYWF8+eWX5MqVS+/OanqNGzcOPz8/atSoQf/+/TE1NWX+/PnExMTofqC+jn///Zdly5YBEBERwZ49e1i3bh3Vq1dPlu6bVLNmzVi/fj2tWrWiadOm3Lx5k3nz5uHh4aHXkvXJJ5/QpUsXfv31V65evapL0T148CCffPIJAwYMSLbvjz76iE2bNtGkSRPatm3Lxo0bM9WHsHHjxpQrV46ff/6ZL7/80uA+zMzMGDduHH379qVu3bp06NCBmzdvsmjRomR9pF73ugftAC8hISHUrVuXQoUKcfv2bWbNmkWlSpWSDc+cVNOmTfn5559p1KgRn332GcHBwcyZM4eSJUty5syZDNcNaOdg2rFjBzVr1qR///66mxZly5ZN9z4T528LCAigTp06enfvra2tqVixIgEBATg4OOj1MXxdiT+2V61aRenSpcmTJw/lypWjXLlyzJkzhxo1alC+fHl69+5N8eLFefToEQEBAdy7d4/Tp09n6pjdu3dn1KhRTJ48OcU0xcR5FCdOnEizZs1o0qQJp06dYvv27cn6ng4ePJjNmzfTrFkzunfvTpUqVXj+/Dlnz55l7dq13Lp1K80ht729vXFxceHjjz8mX758XLx4kdmzZ9O0adNUPy83bNjAkCFDKFWqFO7u7rrPgEQNGjTQDYPftm1bPvroI3r06MGFCxdwdHRk7ty5JCQkJLtBcuDAAd0N08ePH/P8+XPGjRsHQK1atXRTaPTt25fw8HBq1apFwYIFefjwIcuXL+fSpUtMnz49zVT327dv07x5c1QqFW3btmXNmjV66ytUqKC7QVGoUCG++eYbpk6dSlxcHFWrVmXjxo0cPHiQ5cuX624KRURE0LBhQ54+fcrgwYOTzSFYokQJvLy8dM///vtv3bUUFxfHmTNndOfavHlz3fHFG/YmhwgU7wcfHx/F0tJSef78eYrbdO/eXTEzM1OePHmS4jDMipJ8GO1u3bopNjY2ybZLHF47qcePHytt2rRRrK2tldy5cyt9+/ZVzp07l2z483v37imtWrVSHBwcFHt7e6Vdu3bKgwcPkh07K4c/b9q0qcHtN2/erFSoUEGxtLRUihYtqkyePFn5448/Ujxup06dFECpX79+imVYt26dUqNGDcXGxkaxsbFR3NzclC+//FK5fPmybps3Nfz5qzPbp/T3rF27drIhdmNjY5XJkycrZcuWVSwsLJTcuXMrVapUUcaOHauEhYWlWtZnz54pn332meLg4KAAeuf66NEjpUePHoqjo6Nibm6ulC9fXu/6SI8CBQoogLJgwYJk65o3b64b4vtV169fV9q2bas4ODgolpaWSrVq1ZQtW7bobZM4ZPOaNWuSvT7p8OeJEhISlI4dOyqmpqbKxo0bUyxzau+7f//9V2nYsKFia2urWFtbK5988oly+PDhNI+dGkPDn5uamirFixdXBg8erEREROht/+rw5xqNRpkwYYJSpEgRxcLCQqlcubKyZcsWg9dufHy8MnXqVMXNzU0xNzdXnJyclMaNGysnT57UbYOBoco3bdqkmJqaKh06dNANP29Iau/hxYsX633GpDQVw9y5c5VixYopFhYWiqenp3LgwIFk56wo6b/uDZ2PoijK2rVrFW9vb8XZ2VkxNzdXChcurPTt21cJCgpK8fwSLVy4UClVqpRiYWGhuLm5KYsWLTL4WZvSsYsUKZLsM2L//v1KlSpVFHNzc6V48eLKvHnzDO4zNV5eXgqgfP/998nWffXVVwqgNG7cONm6lN5LhqbFMHRdHT58WFf2Vz/jrl+/rnTt2lVxcXFRzMzMlIIFCyrNmjVT1q5dm+b5pFR/iqIoY8aM0bt+DH0PJSQkKGPHjlXy58+vWFlZKXXq1FHOnTtnsP4jIiKU4cOHKyVLllTMzc0VR0dHpXr16sq0adOU2NhYvfow9Nkwf/58pVatWkrevHkVCwsLpUSJEsrgwYPT/AxO/Bun9Hj1/RESEqL07NlTyZs3r2Jtba3Url3b4GdNavtN+vf566+/lPr16yv58uVTTE1Nldy5cyv169dXNm3alGq5EyVeO+k5lqJo/yaJn1fm5uZK2bJl9abZUJTUp4TglaHrFeXl9C+GHhn9zhJZR6UoWdgTVAghhBBCCCHeA9JHSgghhBBCCCEySAIpIYQQQgghhMggCaSEEEIIIYQQIoMkkBJCCCGEEEKIDJJASgghhBBCCCEySAIpIYQQQgghhMggmZAX0Gg0PHjwgFy5cqFSqbK7OEIIIYQQQohsoigKERERFChQALU65XYnCaSABw8e4Orqmt3FEEIIIYQQQuQQd+/epVChQimul0AKyJUrF6CtLDs7u2wtS1xcHLt27cLb2xszM7NsLcu7SOrXuKR+jUvq17ikfo1L6te4pH6NS+rXuHJa/YaHh+Pq6qqLEVIigRTo0vns7OxyRCBlbW2NnZ1djriQ3jVSv8Yl9WtcUr/GJfVrXFK/xiX1a1xSv8aVU+s3rS4/MtiEEEIIIYQQQmSQBFJCCCGEEEIIkUESSAkhhBBCCCFEBkkfKSGEEEII8d5ISEggLi4uQ6+Ji4vD1NSU6OhoEhISjFSy99ebrl8zMzNMTExeez85JpCaNGkSw4cP5+uvv2bGjBkAREdH891337Fy5UpiYmJo2LAhc+fOJV++fLrX3blzh379+vHPP/9ga2tLt27dmDhxIqamOebUhBBCCCFENlMUhYcPHxIaGpqp17q4uHD37l2Zc9QIsqN+HRwccHFxea3j5Yho4/jx48yfP58KFSroLf/222/ZunUra9aswd7engEDBtC6dWv8/f0B7R2Fpk2b4uLiwuHDhwkKCqJr166YmZkxYcKE7DgVIYQQQgiRAyUGUc7OzlhbW2foB7RGo+HZs2fY2tqmOkGryJw3Wb+KohAZGUlwcDAA+fPnz/S+sj2QevbsGZ06deK3335j3LhxuuVhYWEsXLiQFStWULduXQAWLVqEu7s7R44c4aOPPmLXrl1cuHCB3bt3ky9fPipVqsRPP/3E0KFDGTNmDObm5tl1WkIIIYQQIodISEjQBVF58+bN8Os1Gg2xsbFYWlpKIGUEb7p+raysAAgODsbZ2TnTaX7ZHkh9+eWXNG3alPr16+sFUidPniQuLo769evrlrm5uVG4cGECAgL46KOPCAgIoHz58nqpfg0bNqRfv36cP3+eypUrGzxmTEwMMTExuufh4eGANj8zozmzWS3x+NldjneV1K9xSf0al9SvcUn9GpfUr3FJ/aYuJiYGRVGwtLREo9Fk+PWKouj+zczrReqyo34tLS1RFIWoqCgsLCz01qX3fZStgdTKlSv5999/OX78eLJ1Dx8+xNzcHAcHB73l+fLl4+HDh7ptkgZRiesT16Vk4sSJjB07NtnyXbt2YW1tndHTMAo/P7/sLsI7TerXuKR+jUvq17ikfo1L6te4pH4NMzU1xcXFhefPn79WsBkREZGFpRKvepP1GxsbS1RUFPv37yc+Pl5vXWRkZLr2kW2B1N27d/n666/x8/PD0tLyjR57+PDhDBw4UPc8PDwcV1dXvL29sbOze6NleVVcXBx+fn40aNAgR83s/K6Q+jUuqV/jkvo1Lqlf45L6NS6p39RFR0dz9+5dbG1tM/W7U1EUIiIiyJUrlww2YQTZUb/R0dFYWVlRq1atZNdEYrZaWrItkDp58iTBwcF88MEHumUJCQkcOHCA2bNns3PnTmJjYwkNDdVrlXr06BEuLi4AuLi4cOzYMb39Pnr0SLcuJRYWFsma8EA7FGJO+fDJSWV5F0n9GpfUr3FJ/RqX1K9xSf0al9SvYQkJCahUKtRqdab64CSmmyXuI6davHgx33zzTaojE3bv3p3Q0FA2btz4RsqUnuPVqVMHd3d35syZ88bqV61Wo1KpDL5n0vseyrZAql69epw9e1ZvWY8ePXBzc2Po0KG4urpiZmbGnj17aNOmDQCXL1/mzp07eHl5AeDl5cX48eN1HcVA26RtZ2eHh4fHmz0hIYQQQgjxzkvQKBy7GUJwRDTOuSypViwPJuq3p5Vq5syZuj5JoA1iKlWqpJt+yNjHe5dkWyCVK1cuypUrp7fMxsaGvHnz6pb37NmTgQMHkidPHuzs7Pjf//6Hl5cXH330EQDe3t54eHjQpUsXpkyZwsOHD/nhhx/48ssvDbY4CSGEEEIIkVk7zj3kp60XCQqL1i3Lb2/JaB8PGpXL/DDab5K9vf0bOU5iK+CbOF7SFsc3Kee2TQK//PILzZo1o02bNtSqVQsXFxfWr1+vW29iYsKWLVswMTHBy8uLzp0707VrV3788cdsLPVr0CSgun2IgiEBqG4fAo3MnC2EEEIIkRPsufwfX644pRdEATwMi6bfsn/ZcS4oy4+5ZcsWHBwcSEjQ/iYMDAxEpVIxbNgw3Ta9evWic+fOeq/buXMn7u7u2Nra0qhRI4KCXpate/futGzZUvf//fv3M3PmTFQqFSqVilu3bgFw7tw5GjdujK2tLfny5aNLly48efIkxbIuXrwYBwcHNm/ejIeHBxYWFty5c0fveADPnz+na9eu2Nrakj9/fqZPn55sXzExMQwaNIiCBQtiY2PDhx9+yL59+9I81r59+6hWrRo2NjY4ODjw8ccfc/v27fRWd4Zl+/DnSSWtINAOSzhnzhzmzJmT4muKFCnCtm3bjFyyN+DCZtgxFNPwB3gC3PYFuwLQaDJ4NM/u0gkhhBBCvFMURSEqLn03rePiE5jsdwNDCWoKoALGbL7AxyUd05XmZ2Vmkq5BFWrWrElERASnTp3C09OT/fv34+joqPebef/+/QwdOlT3PDIykmnTpvHnn3+iVqvp3LkzgwYNYvny5cn2P3PmTK5cuUK5cuV0DRFOTk6EhoZSt25devXqxS+//EJUVBRDhw6lffv27N27N8XyRkZGMnnyZH7//Xfy5s2r63qT1ODBg9m/fz+bNm3C2dmZ77//nn///Rd3d3fdNgMGDODChQusXLmSAgUKsGHDBho1asTZs2cpVaqUwWPlyZOHSpUq0bt3b/766y9iY2M5duyYUQevyFGB1HvrwmZY3RVefXuGB2mXt18qwZQQQgghRBaKikvAY9TOLNmXAjwMj6b8mF3p2v7Cjw2xNk/7Z7i9vT2VKlVi3759eHp6sm/fPr799lvGjh3Ls2fPCAsL49q1a9SuXVv3mri4OObNm0eJEiUAbVCSUraWvb095ubmWFtb6w3UNnv2bCpXrsyECRN0y/744w9cXV25cuUKpUuXNri/uLg45s6dS8WKFQ2uf/bsGQsXLmTZsmXUq1cPgCVLllCoUCHdNnfu3GHRokXcuXOHAgUKADBo0CB27NjBokWLdGV69VghISGEhYXRrFkz3bknDc6MIUen9r0XNAmwYyjJgih4uWzHMEnzE0IIIYR4D9WuXZt9+/ahKAoHDx6kdevWuLu7c+jQIfbv30+BAgV0rTQA1tbWukACIH/+/AQHB2fomKdPn+aff/7B1tZW93BzcwPg+vXrKb7O3NycChUqpLj++vXrxMbG8uGHH+qW5cmThzJlyuienz17loSEBEqXLq13/P379+sd+9Vj5cmTh+7du9OwYUN8fHyYOXOmXkqjMUiLVHa7fRjCH6SygQLh97XbFav5xoolhBBCCPEuszIz4cKPDdO17ZHrT/h8yck0t1vcoyrViuVJ17HTq06dOvzxxx+cPn0aMzMz3NzcqFOnDvv27ePp06d6rVGQfOhulUqV4VHznj17ho+PD5MnT062Ln/+lAfVsLKyeu1UumfPnmFiYsLJkycxMdGvJ1tb21SPtWjRIr766it27NjBqlWr+OGHH/Dz89MNVJfVJJDKbs8eZe12QgghhBAiTSqVKl3pdQA1SzmRL5c5wRGxBnOIVICLvSU1Szll+VDoif2kfvnlF13QVKdOHSZNmsTTp0/57rvvXmv/5ubmusEsEn3wwQesW7eOokWLYmqadeFCiRIlMDMz4+jRoxQuXBiAp0+fcuXKFV2wU7lyZRISEggODqZmzYw3IlSuXJnKlSszfPhwvLy8WLFihdECKUnty262+bJ2OyGEEEIIkaVM1CqG1C8OaIOmpBKfj/bxMMp8Urlz56ZChQosX76cOnXqAFCrVi3+/fdfrly5kqxFKqOKFi3K0aNHuXXrFk+ePEGj0fDll18SEhJCx44dOX78ONevX2fnzp306NEjWdCVEba2tvTs2ZPBgwezd+9ezp07R/fu3fWGLS9dujSdOnWia9eurF+/nps3b3Ls2DEmTpzI1q1bU9z3zZs3GT58OAEBAdy+fZtdu3Zx9epVo/aTkkAquxWprh2dL9nbMolc+bXbCSGEEEKIbFGvTF7mfFYZF3tLveUu9pb4dv7AqPNI1a5dm4SEBF0glSdPHjw8PHBxcdHrX5QZgwYNwsTEBA8PD5ycnHSDPPj7+5OQkIC3tzfly5fnm2++wcHB4bXnapo6dSo1a9bEx8eH+vXrU6NGDapUqaK3zaJFi+jatSvfffcdZcqUoWXLlhw/flzXimWItbU1ly5dok2bNpQuXZo+ffrw5Zdf0rdv39cqb2pUyrs61XAGhIeHY29vT1hYGHZ2dm++ALpR+8DgoBO5i0Hf/WD5ZiZQe5fFxcWxbds2mjRpkiyHWLw+qV/jkvo1Lqlf45L6NS6p39RFR0dz8+ZNihUrhqWlZdoveIVGoyE8PBw7OzsUVBy7GUJwRDTOuSypViyPUVqi3idJ6/dNTaqb2jWR3thAWqRyAo/m2iHO7V65k2HrDOa28PQmLGsLMRHZUz4hhBBCCAFo0/y8SuSlRaWCeJXIK0HUe0wCqZzCozl8c474zhs5UaQf8Z03wsBL0GM7WDrAvWOwvD3EPs/ukgohhBBCCPHek0AqJ1GboBSpwf08XihFaoDaBPJXgK4bwcIe7hyGvz6FuKjsLqkQQgghhBDvNQmk3gYFKkPnddo0v5sHYOVnEBed3aUSQgghhBDivSWB1NvCtSp0Wgtm1nB9r3ZwivjY7C6VEEIIIYQQ7yUJpN4mRbzgs9VgagVXd8LaHpAQl92lEkIIIYQQ4r0jgdTbplhN6LgCTCzg0hZY3xsS4rO7VEIIIYQQQrxXJJDKJF9fXypUqICdnR12dnZ4eXmxfft23frr16/TqlUrnJycsLOzo3379jx69EhvH1euXKFFixY4OjpiZ2dHjRo12LdvX6rHVRSFUUv2kX9WAlbjw6k/dBlXfT8DzctZpg3t959//snS8xdCCCGEEOJ9JoFUJhUqVIhJkyZx8uRJTpw4Qd26dWnRogXnz5/n+fPneHt7o1Kp2Lt3L/7+/sTGxuLj44NGo9Hto1mzZsTHx7N3715OnjxJxYoVadmyJU+fPk3xuFOmTOHXX39l3m9/cHTtLGzM1TQcuZbotf3gxb4N7bdZs2Y8fPjQ6PUihBBCCCHE+0ACqUzy8fGhSZMmlCpVitKlSzN+/HhsbW05cuQI/v7+3Lp1i8WLF1O+fHnKly/PkiVLOHHiBHv37gXgyZMnXL16lWHDhlGhQgVKlSrFpEmTiIyM5M6dOwaPqSgKM2bM4IcffqBFixZUaDGApYsX8SBCYePqZbD1W548fpzifs+dO/cmq0gIIYQQ4t2jSYCbB+HsWu2/SbKCjKFOnTp88803qW5TtGhRZsyY8UaPmR5jxoyhUqVKqW7TvXt3WrVq9drHyg4SSGWBhIQEVq5cyfPnz/Hy8iImJgaVSoWFhYVuG0tLS9RqNYcOHQIgb968lClThqVLl/L8+XPi4+OZP38+zs7OlChRwuBxbt68ycOHD6lfv75umf1HnfiwkjsB9xLg5GLyHpuc4n6rVKli3IoQQgghhHiXXfwbZpSDJc1gXU/tvzPKwYXN2V2yHGnQoEHs2bMnu4thNKbZXYC32dmzZ/Hy8iI6OhpbW1s2bNiAh4cHTk5O2NjYMHToUCZMmICiKAwbNoyEhASCgoIAUKlU7N69m5YtW5IrVy7UajXOzs78/fffum1elZialy9fPr3l+YqX42GYHXAJ1fHf2D2iCy1nHtHb744dO8idO7dR60MIIYQQ4l1ldm07qi39AEV/RXiQdlqa9kvBo3m2lC2nURSFhIQEbG1tsbW1fSPHMjV982GNtEi9hjJlyhAYGMjRo0fp168f3bp148KFCzg5ObFmzRr+/vtvbG1tsbe3JzQ0lA8++AC1WlvliqLw5Zdf4uzszMGDBzl27BgtW7akdevWhISEZLwwDkXAZ6Z2v+Pn46wO4+CBA7r9+vj4pBigCSGEEEK8dxQFYp+n7xEdjtU/Y0gWRGl3pP1nx1CIDk/f/hRD+0lZfHw8AwYMwN7eHkdHR0aOHImSyj5+/vlnypcvj42NDa6urvTv359nz57pbePv70+dOnWwtrYmd+7cNGzYMMV++lu3bsXe3p7ly5cbXL9v3z5UKhXbt2+nSpUqWFhYcOjQoWSpfQkJCQwcOBAHBwfy5s3LkCFDkp2HRqNh4sSJFCtWDCsrKypWrMjatWvTPNbp06f55JNPyJUrF3Z2dlSpUoUTJ06kVbWvRVqkXoO5uTklS5YEoEqVKhw/fpyZM2cyf/58vL29uX79Ok+ePMHU1BQHBwdcXFwoXrw4AHv37mXLli08ffoUOzs7AObOnYufnx///PMPnTt3TnY8FxcXAB49ekT+/Pl1yx89eqS9SKt0Y++R02y58gtPhz7CLu4gfDBct98lS5YwbNgwI9eKEEIIIcRbIC4SJhRI16ZptzwoEP4AJrmm79jfPwBzm/RtCyxZsoSePXty7NgxTpw4QZ8+fShcuDC9e/c2XF61ml9//ZVixYpx48YN+vfvz5AhQ5g7dy4AgYGB1KtXj88//5yZM2diamrKP//8Q0JC8v5eK1as4IsvvmDFihU0a9Ys1XIOGzaMadOmUbx4cXLnzp1sNOrp06ezePFi/vjjD9zd3Zk+fTobNmzgk08+0W0zceJEli1bxrx58yhVqhQHDhygc+fOODk5Ubt27RSPVatWLSpXroyvry8mJiYEBgZiZmaW3irOFAmkspBGoyEmJkZvmaOjI6ANnIKDg2neXNvkGxkZCaBroUqkUqn0RvZLqlixYri4uLBnzx5ddB8eHq5rEQOILPwJqGagVgH7J4GJKdQajFqtTnG/QgghhBAi53J1deWXX35BpVJRpkwZzp49yy+//JJiIJV0oIiiRYsybtw4vvjiC10gNWXKFDw9PXXPAcqWLZtsP3PmzGHEiBH8/fffekFMSn788UcaNGiQ4voZM2YwfPhwWrduDcC8efPYuXOnbn1MTAwTJkxg9+7deHl5AVC8eHEOHTrE/Pnz9crw6rHu3LnD4MGDcXNzA6BUqVJplvd1SSCVScOHD6dx48YULlyYiIgIVqxYwb59+3QXw6JFi3B3d8fJyYmAgAC+/vprvv32W8qUKQOAl5cXuXPnplu3bowaNQorKyt+++03bt26Rf/+/XXHcXNzY+LEibRq1QqVSsU333zDuHHjKFWqFMWKFWPkyJEUKFCAli1bJtlvHrodLsgot+tYrRnLb/N2cPPmTZo2bfrG60kIIYQQIkcys9a2DKWD5uYh1H+1T3vDTmuhSPX0HTsDPvroI1Qqle65l5cX06dPJyEhARMTk2Tb7969m4kTJ3Lp0iXCw8OJj48nOjqayMhIrK2tCQwMpF27dqkec+3atQQHB+Pv70/VqlXTVU5PT88U14WFhREUFMSHH36oW2Zqaoqnp6fuZv+1a9eIjIxMFozFxsZSuXLlVI81cOBAevXqxZ9//kn9+vVp165digO4ZRUJpDIpODiYrl27EhQUhL29PRUqVGDnzp26P/zly5cZPnw4ISEhFC1alBEjRvDtt9/qXu/o6MiOHTsYMWIEdevWJS4ujrJly7Ju3Tq9lqPLly8TFhamez5kyBCeP39Onz59CA0NpUaNGuzYsQNLS8vk+/3rGnHRkZR13s+myV9QsWLFN1Q7QgghhBA5nEqV/vS6EnXR2OZH9ewhKoP9pFRgVwBK1AV18sDmTbp16xbNmjWjX79+jB8/njx58nDo0CF69uxJbGws1tbWWFlZpbmfypUr8++///LHH3/g6empF8ilxMYm/emKhiT249q6dSsFCxbUW5d0NGxDxxozZgyfffYZW7duZfv27YwePZqVK1cadWh1CaQyaeHChamunzRpEpMmTUp1G09PT73mTIC4uDi2bdume/5qBzyVSsWPP/7Ijz/+mL797h0PB6ZA2J9wvBJU7ZVqmYQQQgghxCvUJkTVGY31ln6ACv1BJ14EGI0mGS2IOnr0qN7zI0eOUKpUKYOtUSdPnkSj0TB9+nRdF5LVq1frbVOhQgX27NnD2LFjUzxmiRIlmD59OnXq1MHExITZs2e/1jnY29uTP39+jh49Sq1atQDtIBonT57UtTZ5eHhgYWHBnTt30pVK+KrSpUtTunRpvv32Wzp27MiiRYuMGkjJqH3vuk++h4+/0f5/63fw79JsLY4QQgghxNsormRjlHZLwC6//gq7AkYf+vzOnTsMHDiQy5cv89dffzFr1iy+/vprg9uWLFmSuLg4Zs2axY0bN/jzzz+ZN2+e3jbDhw/n+PHj9O/fnzNnznDp0iV8fX158uSJ3nalS5fmn3/+Yd26dVkyQe/XX3/NpEmT2LhxI5cuXaJ///6Ehobq1ufKlYtBgwbx7bffsmTJEq5fv86///7LrFmzWLJkSYr7jYqKYsCAAezbt4/bt2/j7+/P8ePHcXd3f+0yp0ZapN51KhXUHwMJsXBkLmz+CkzMoeKn2V0yIYQQQoi3i7sPuDeD24fh2SOwzaftE2XkdL6uXbsSFRVFtWrVMDEx4euvv6ZPnz4Gt61YsSI///wzkydPZvjw4dSqVYuJEyfStWtX3TalS5dm165dfP/991SrVg0rKys+/PBDOnbsmGx/ZcqUYe/evbqWqenTp2f6PL777juCgoLo1q0barWazz//nFatWukFUz/99BNOTk5MnDiRGzdu4ODgwAcffMD333+f4n5NTEz477//6Nq1K48ePcLR0ZHWrVun2uKWFVRKaoPQvyfCw8Oxt7cnLCxMNxR5dklM7WvSpEnWDtmoKLBtEBz/HVRqaP0blG+bdft/SxitfgUg9WtsUr/GJfVrXFK/xiX1m7ro6Ghu3rxJsWLFdP3KM0Kj0RAeHo6dnV2yEZfF68uO+k3tmkhvbCBXwvtCpYLGU+GDbqBoYH0fuLApu0slhBBCCCHEW0kCqfeJWg3NZkClTqAkwNrP4dK2NF8mhBBCCCGE0CeB1PtGrYbms6B8O9DEw5pucNUvu0slhBBCCCHEW0UCqfeR2gRazgOPFtpBKFZ2guv/ZHephBBCCCGEeGtIIPW+MjGFNguhTFNIiIG/OsKtQ9ldKiGEEEIIo5Ex1kSirLgWJJB6n5mYQbtFUMob4qNgeXu4cyS7SyWEEEIIkaUSRzKMjIzM5pKInCLxWnidUS5lHqn3nakFtP8T/voUbvwDy9pC141QyDO7SyaEEEIIkSVMTExwcHAgODgYAGtra1QqVbpfr9FoiI2NJTo6WoY/N4I3Wb+KohAZGUlwcDAODg6YmGR+DjAJpASYWcKnK2BFe7h1EP5sDd02QYHK2V0yIYQQQogs4eLiAqALpjJCURSioqKwsrLKUAAm0ic76tfBwUF3TWSWBFJCy9waPlsFy9rAnQD4sxV0+xtcymd3yYQQQgghXptKpSJ//vw4OzsTFxeXodfGxcVx4MABatWqJRMeG8Gbrl8zM7PXaolKJIGUeMncBjqt0QZR947D0hbQfSs4u2d3yYQQQgghsoSJiUmGf0SbmJgQHx+PpaWlBFJG8LbWryR5Cn0WuaDTWm1aX+R/sKQ5PLma3aUSQgghhBAiR5FASiRn5QCd12vT+p4HwxIf+O96dpdKCCGEEEKIHEMCKWGYdR7osgmcPSAiSNsy9fRWdpdKCCGEEEKIHEECKZEym7zQdRM4lobwe9qWqdC72V0qIYQQQgghsl22BlK+vr5UqFABOzs77Ozs8PLyYvv27br1169fp1WrVjg5OWFnZ0f79u159OiR3j5CQkLo1KkTdnZ2ODg40LNnT549e/amT+XdZeusHb0vTwkIvaMNpsIfZHephBBCCCGEyFbZGkgVKlSISZMmcfLkSU6cOEHdunVp0aIF58+f5/nz53h7e6NSqdi7dy/+/v7Exsbi4+ODRqPR7aNTp06cP38ePz8/tmzZwoEDB+jTp082ntU7KJeLNphyKAJPb2rT/CIepf06IYQQQggh3lHZOvy5j4+P3vPx48fj6+vLkSNHuH//Prdu3eLUqVPY2dkBsGTJEnLnzs3evXupX78+Fy9eZMeOHRw/fhxPT08AZs2aRZMmTZg2bRoFChR44+f0zrIvqA2mFjeF/67C0ubaodFtHLO7ZEIIIYQQQrxxOWYeqYSEBNasWcPz58/x8vLi+vXrqFQqLCwsdNtYWlqiVqs5dOgQ9evXJyAgAAcHB10QBVC/fn3UajVHjx6lVatWBo8VExNDTEyM7nl4eDignQwsoxO0ZbXE42d3OQyyLQCd1mP6Z3NUjy+hLGlOfKcN2oEp3hI5un7fAVK/xiX1a1xSv8Yl9WtcUr/GJfVrXDmtftNbjmwPpM6ePYuXlxfR0dHY2tqyYcMGPDw8cHJywsbGhqFDhzJhwgQURWHYsGEkJCQQFBQEwMOHD3F2dtbbn6mpKXny5OHhw4cpHnPixImMHTs22fJdu3ZhbW2dtSeYSX5+ftldhBTZFPqGGlcnYBl8nufzvPEvOZR4U5vsLlaG5OT6fRdI/RqX1K9xSf0al9SvcUn9GpfUr3HllPqNjIxM13bZHkiVKVOGwMBAwsLCWLt2Ld26dWP//v14eHiwZs0a+vXrx6+//oparaZjx4588MEHqNWv17Vr+PDhDBw4UPc8PDwcV1dXvL29dWmE2SUuLg4/Pz8aNGiQs2d2flwDZVkLHCJv0fi/30j4bJ12Mt8c7q2p37eU1K9xSf0al9SvcUn9GpfUr3FJ/RpXTqvfxGy1tGR7IGVubk7JkiUBqFKlCsePH2fmzJnMnz8fb29vrl+/zpMnTzA1NcXBwQEXFxeKFy8OgIuLC8HBwXr7i4+PJyQkBBcXlxSPaWFhoZcymMjMzCxH/PEgZ5XFoALloNtmWNwM9YN/Ua/6VDuJr4VtdpcsXXJ8/b7lpH6NS+rXuKR+jUvq17ikfo1L6te4ckr9prcMOW4eKY1Go9d/CcDR0REHBwf27t1LcHAwzZs3B8DLy4vQ0FBOnjyp23bv3r1oNBo+/PDDN1ru91K+stB1I1jaw92jsKIDxKavKVQIIYQQQoi3Wba2SA0fPpzGjRtTuHBhIiIiWLFiBfv27WPnzp0ALFq0CHd3d5ycnAgICODrr7/m22+/pUyZMgC4u7vTqFEjevfuzbx584iLi2PAgAF8+umnMmLfm5K/InTZAEtbwu1DsLIjdFwJZlbZXTIhhBBCCCGMJlsDqeDgYLp27UpQUBD29vZUqFCBnTt30qBBAwAuX77M8OHDCQkJoWjRoowYMYJvv/1Wbx/Lly9nwIAB1KtXD7VaTZs2bfj111+z43TeXwWrQKe1sKw13NgHqzrDpyvANHn6pBBCCCGEEO+CbA2kFi5cmOr6SZMmMWnSpFS3yZMnDytWrMjKYonMKPwhfLYalreFa7thdTdovxRMzbO7ZEIIIYQQQmS5HNdHSrzFin4MHf8CU0u4sh3WfQ4JOWM+ACGEEEIIIbKSBFIiaxWvA58uBxNzuPg3bOgLmoTsLpUQQgghhBBZSgIpkfVK1of2f4LaDM6tg439JZgSQgghhBDvFAmkhHGUaQTtFoHKBM6shL+/Bo0mu0slhBBCCCFElpBAShiPuw+0+R1Uajj1J2z7DhQlu0slhBBCCCHEa5NAShhXudbQaj6gghN/wI5hEkwJIYQQQoi3ngRSwvgqtIcWs7X/PzoP/EZKMCWEEEIIId5qEkiJN6NyZ2g2Q/v/w7Ng708STAkhhBBCiLeWBFLizfHsAY2nav9/cDrsn5K95RFCCCGEECKTJJASb9aHfcB7vPb/+ybAwZ+ztzxCCCGEEEJkggRS4s2rPgDqjdb+f89YODw7e8sjhBBCCCFEBkkgJbJHzYFQ53vt/3eNgKMLsrc8QgghhBBCZIAEUiL71B4CNQdp/799MJxYlL3lEUIIIYQQIp0kkBLZR6WCuj9A9f9pn2/5Bk4ty9YiCSGEEEIIkR4SSInspVJBg5/gwy+0zzcNgDOrs7dMQgghhBBCpEECKZH9VCpoNAk8ewIKbOgL5zdkd6mEEEIIIYRIkQRSImdQqaDJNKjcBRQNrO0JF//O7lIJIYQQQghhkARSIudQq8FnJlT4FJQEWNMDLu/I7lIJIYQQQgiRjARSImdRm0DLuVCuDWjiYHUXuLY7u0slhBBCCCGEHgmkRM6jNoFW88HdBxJiYWUnuLE/u0slhBBCCCGEjgRSImcyMYM2f0DpxhAfDX99Crf8s7tUQgghMkOTgOr2IQqGBKC6fQg0CdldIiGEeG0SSImcy9Qc2i+BkvUhLhJWtIe7x7K7VEIIITLiwmaYUQ7TZS3xvO2L6bKWMKOcdrkQQrzFJJASOZupBXRYBsXrQOwzWNYG7p/M7lIJIYRIjwubYXVXCH+gvzw8SLtcgikhxFtMAimR85lZwad/QZEaEBMOf7aCoNPZXSrxKkndEUIkpUmAHUMBxcDKF8t2DJPPCiHEW8s0uwsgRLqYW8Nnq7QtUnePwNIW0H0r5Cub3SUToL2rvGMopuEP8AS47Qt2BaDRZPBont2lE0K8jvgYiA6H6LAXj9Ak/0/hERMOz4IhKiSVHSsQfh/OrIIKHbQDDQkhxFtEAinx9rCwhU5rtC1S90/AkubaYMrZLbtL9n5LTN159a5zYupO+6USTImcT69F1Q6K13p3ftjHRWsDm4wEQtFhL4On+Cjjlm9jP9g2GFzKQ/5KUKAyFKgEeUu+O38DIcQ7SQIp8XaxtIPO62Bpc21639Lm0H0bOJbM7pK9n9JM3VFpU3fcmsoPIpFz5eQWVUXRjlyq1yKUkWAoDBJisqYsFvZgmd6HHYTcgL+/Tnu/JhbaPrB3ArSPRGY2kL/Ci+CqkvZfx1LyWSKEyDEkkBJvHysH6LIRlvjAo3Paf3tshTzFs7tk7zaNBiIeaH8cJT7uHkveiVzPi9SdS1vAo8UbK6oQ6WbsFlVFgbgo/ZS3jAZDCbGvc4YvqLTBjS7QcUg7GLJIsr1FrowHMEU+hv2TtXVp8GaLShuwfhUIIdfhwSl4EAhBgfDwLMQ9NxxcuZR/GVgVqASOpSW4EkJkCwmkxNvJOg903QSLm8LjS9o0vx7bwKFwdpfs7aZJ0AY+ITfgv+svAqab2n+f3tTeGc+M1V21f5tC1cD1Q3CtBvnKgYl8BIlslN4W1cRRQ19NeUtvIKSJe/2yqtT6gU26A6IXrzHPBeo3PL6U2kTbqre6K6BCv55V2n8aTdJOdeHsrn1U+ky7XJMAT668DKweBMLDM9rg6u4R7SORmXWStMBK2tRACa6EEG+A/IoRby8bR+i6GRY3gf+uweJm0GM72BfM7pLlbAnxEHY3ScvSTe3d4JAb8PRW6ne/1abgUETb+pfYAnhsfjoOqoLQO9rHubXaRWbWULAKFKqqDa4KVQWbvK97dkKk3+3D6WtRneT6+sdSqVMJdhzSbhkyt33zgVBW8GiubdXbMVS/ru0KaIOolFr71CZJgquO2mWaBHhy9WVgFRQIQYnB1VHtI9GrwVX+StrgSm7eCCGykHyiiLdbrnzQ7W9Y1ETbYrLERzsAhV3+7C5Z9kqI0wYtSdPwEh9Pb6d+h9zEHHIXfRks5SkOeYpBnhJg76r/Q0STAJf+Tjt15wt/7Y+ee8df/OA5DjFhcOug9pEob8mXQZXrh+Dk9nb+eBQ5U8wzbcpY0Gnt4+aB9L9WZZJGy49D6uvNbUClMtqp5WgezcGtKfE3DhB4cCeVajbENDODeahNtIMLObtBxU+1yzQJ2htpSdMCUwquTK0MpAWWkeBKCJFp8ukh3n52BbTB1OIm2paVpS9G87N1zu6SGVd8LITefhkg6VLxbmiDKCWVuVlMLF4ER8WTP+wLpf8HTnpTd6xzQ4lPtA/Q9rd6cuXFD51jcO+Y9vl/17SPwOXa7SzsoVCVl8FVIU/tj1Ih0hIVqk0FSwyagk5rWzMMBvxp6LQWStZ/fwOhrKA2QSlSg/vnw6lYpEbWpd2pTcCpjPaRLLgK1E8LjH2m/ay5d+zl602twKXcK2mBElwJIdJHPinEu8HB9UXLVFPtD/KlLaDblrc/VSwuWptul6xl6TqE3QNFk/JrTa2StCa9CJLyltD+m6tA1rX0ZCZ1R61+eWe5SjftssgQuHfi5V3k+/9qW62u79U+AFCBswe4JqYDVtOek/zAfb89f/KiJSJJ0PT0luFtcxWA/BW1j3zlYdtA7XxHqbWolqgr19jbRC+46qBdptFogyu9tMDTL4Kr49pHoleDq/yVtK3jElwJIV4hnwri3ZG7KHTbrE3zC74Af7bQ9qGyzpPdJUtdbOSLYOn6K8HSTW2wlNoddDObFwGSgZalXPnf3I+/rEjdsc4Dpb21D9D25Qo+r22xuntMG1yF3tYuCz4PJxe/eF3eF4NYvAiuCnygncBZvHsUBSKC9AOmoNPafkyGOBR5GTTlr6QdSjtZS7Um7RZVGbTg7adWg1Np7aNCe+0yjSb5aIFBZyA2wkBwZakdICdpWqCTG5iYvfFTEULkHBJIiXdL3hIv0vyaavtCLGutHd0vu9PBYp5p+3DppeG9eB6RWmd3tKNt6QVKJV7+39Y559wpz+rUHRPTlz+Cq/XWLot4pE3LSexn9eAURP4HV7ZrH6Dty+JS/uXogK7VtH27cko9ifRRFG2K6qtB0/Ngw9vnLZkkaKoILhXSdxMls4MhiLefWq2dl8qxlIHgKjBJ69VpbXB1/4T2kcjUEvKV1W+5cnaX4EqI94gEUuLd41Ra2zK1uKn2h/ayNtBlA5hZo7p9iIIhAahu20FmOjunJjo8eYtSYhres0epv9bSXj9ASpqKZ51XgoBEufKBu4/2ARAfow2YE/ta3T36osUiUPtIHFEwV/6XA1i4VtP+0Da1yK6zEK/SaLTvlVfT86JDk2+rUmtbApIGTfnKaYf5zqysGgxBvP30gqt22mVJr88Hp15enzHhcP+k9pHIxCJ5WqAEV0K8sySQEu8mZ/cX80w106Zn/FYXYiIwjQjCE+C274s7zpMzdsc5KtTwSHghN+D549Rfa5UneV+lxEdOTz/MqUwttANQFPIEry+1rRhh9160Wr0IrB6e1QZXFzdrH6AdmTB/pRctVi+Cq1wu2Xoq742EeG0/xqQBU+JAAK9Sm0E+D/30PGcP46RuGmswhLeQr68vvr6+3Lp1C4CyZcsyatQoGjduDMDDhw8ZPHgwfn5+REREUKZMGUaMGEGbNm2S7SsmJoYPP/yQ06dPc+zYsWTrk4qOjua7775j5cqVxMTE0LBhQ+bOnUu+fPl023z11Vf4+/tz7tw53N3dCQwMzLLzTpFaDY4ltY/ybbXLNBptlsGDU+kLrvKVfSUt0F07f5YQ4q0mgZR4d7mUh64b4Y8m2h9urwoP0vaNaL9UP5iKDEkeJCWOiBcVkvoxbZwMj4SXpxhY5c7S0xMGqFTagUccXKHcix91sZHaHzpJg6vI/16O3hUwW7udQ+GXA1jIhMFZIz4WHl98JWg6B/FRybdN7IOSv+KLH5wV5cdmNilUqBCTJk2iVKlSKIrCkiVLaNGiBadOnaJs2bJ07dqV0NBQNm/ejKOjIytWrKB9+/acOHGCypUr6+1ryJAhFChQgNOnT6d53G+//ZatW7eyZs0a7O3tGTBgAK1bt8bf319vu88//5yjR49y5syZLD3vDFGrtTfE8pYwHFzp0gLPaAfNefCv9pHIxNxAWqBH5q93TYJxMy6EEAbJrwTxbnOpABY2EB9pYOWLjuWb+sP5jS/7MBlKJ0rKNl+SvkqvDCH+OulFwjjMraHox9oHaFutQm68HHb97jF4dP7lhMFn12i3S5ww2LXay+BKWg5TFhelrcek6XmPLhies8zcVvveTJqeJ5Ol5hg+Pj56z8ePH4+vry9HjhyhbNmyHD58GF9fX6pVqwbADz/8wC+//MLJkyf1Aqnt27eza9cu1q1bx/bt21M9ZlhYGAsXLmTFihXUrVsXgEWLFuHu7s6RI0f46KOPAPj1118BePz4cfYGUoakFlwlHS3wwekXwdWL1qzExqvMBlcXNsOOoZiGP3i9jAshRIbJt5Z4t90+nHbKXUwEnF+nvyxXgZctSUnT8HIXAwtb45X3DcmK1J3mzZsTGBhIcHAwuXPnpn79+owbNy5dx1cUhSZNmrBjxw42bNhAy5YtAVi8eDE9evQw+JpHjx7h7JwFc4OpVC9/7FTqqF0W/SIdRxdcpTFhcGJw9b5OGBwToT+xbdBpeHzZ8Nxllg76AVP+Str30vtYb2+hhIQE1qxZw/Pnz/Hy8gKgevXqrFq1iqZNm+Lg4MDq1auJjo6mTp06utc9evSI3r17s3HjRqyt007FPHnyJHFxcdSvX1+3zM3NjcKFCxMQEKALpN46SYOrxFZyRUmSFhj48uZDdArBlbOHflqgs8fLPp4XNr8YdfKV0V1TyrgQQmQpCaTEuy2tQR4SlWsLZVtqW5lyF33nh8/OitSdTz75hO+//578+fNz//59Bg0axKeffsqwYcPSPP6MGTNQGRhAo0OHDjRq1EhvWffu3YmOjs6aICollnYGJgy+rD/0+n9XU58w2LUaFPR891olo55q05OStjT9dx2Dw/LbOL0YZjxJ4ORQWAZLeQudPXsWLy8voqOjsbW1ZcOGDXh4eACwevVqOnToQN68eTE1NcXa2poNGzZQsmRJQHujpHv37nzxxRd4enrqbtik5uHDh5ibm+Pg4KC3PF++fDx8+DCrTy97qVQvb84lC64C9VuvosNeDp6TKLHvoEuFF/0+DU2RoQAq2DEM3JpKmp8QRpLhQCo0NJQNGzZw8OBBbt++TWRkJE5OTlSuXJmGDRtSvXp1Y5RTiMyxzZf2NgBVukOxmkYtSk6SFak73377re71RYoUYdiwYbRs2ZL4+PhUjx0YGMj06dM5ceIE+fPn11tnZWWFlZWV7vnjx4/Zu3cvCxcufK3zzTC1WjtgibP7KxMGH38ZWN0/mfaEwa4fan8svS2BxLPHL4KlwJdBU+htw9vaFXylpanim527TBhVmTJlCAwMJCwsjLVr19KtWzf279+Ph4cHI0eOJDQ0lN27d+Po6MjGjRtp3749Bw8epHz58syaNYuIiAiGDx+e3afx9tALrlprlymKdo5BvbTAQG36eeL7M1WKdo6124ffq+83Id6kdOdWPHjwgF69epE/f37GjRtHVFQUlSpVol69ehQqVIh//vmHBg0a4OHhwapVq9K1T19fXypUqICdnR12dnZ4eXnp5VE/fPiQLl264OLigo2NDR988AHr1umnYIWEhNCpUyfs7OxwcHCgZ8+ePHtmYPQn8X4qUl2bK05KP+5U2h+ERd7fGwAJCQmsXLnSYOpOSEgIGo2GlStXJkvdSSokJITly5fj5eWFqWnK92ciIyP57LPPmDNnDi4uaY+St3TpUqytrWnbtm2mzi1LWeeB0g2h3kjovgWG3YW+B6DJNCjfXjv5K8rLyYI39oNZH8DUErDiUzg4HW4d0g5+kd0UBcLuw6Vt8M9Ebfmmu8O0krC8Dez9SXunOzGIyl0UPFpAvVHQeR0MugYDL0DHv6DOMCjTWPs+e0NB1Ot+d9y6dYuePXtSrFgxrKysKFGiBKNHjyY2NjbV416/fp1WrVrh5OSEnZ0d7du359Ej/VbvK1eu0KJFCxwdHbGzs6NGjRr8888/WVsBb4C5uTklS5akSpUqTJw4kYoVKzJz5kyuX7/O7Nmz+eOPP6hXrx4VK1Zk9OjReHp6MmfOHAD27t1LQEAAFhYWmJqa6lqqvLy8mDlzpsHjubi4EBsbS2hoqN7yR48epeuz4p2kUmnTy8u2ggZjtSPRDr0FX5+GdouhdKO09qCV3swMIUSGpbtFqnLlynTr1o2TJ0/qmvdfFRUVxcaNG5kxYwZ3795l0KBBqe4zK9KLOnXqRFBQEH5+fsTFxdGjRw/69OnDihUrMlAN4p2lNtF2uF3dFW0wlTQF4sWPvkaT3su0h9dJ3Uk0dOhQZs+eTWRkJB999BEbNmzg6NGjKR7z22+/pXr16rRo0SJdZVy4cCGfffaZXitVjpHmhMHHtHePX50wWG2qHZ0uoxMGZ3ZULkXRBkRBp19OLhp0GiKfGNhYpZ0/R29i2/I5bsTJ1/3uuHTpEhqNhvnz51OyZEnOnTtH7969iYiIoHbt2gaP+fz5c7y9valYsSJ792pbIEeOHImPjw9HjhxB/aLPV7NmzShVqhR79+7FysqKGTNm0KxZM65fv/5WBwQajYaYmBgiI7U3AtSv9HEzMTFBo9EA2gEhkvaXfPDgAQ0bNmT58uUp3uisUqUKZmZm7NmzR9cX8/Lly9y5c0d3g0eg/ZzIXVT7sHaEKzvSfo2tEdOihXjfKen05MmT9G6aqe0T5c6dW/n9998VRVEUGxsbZenSpXrr8+TJo/z222+KoijKhQsXFEA5fvy4bv327dsVlUql3L9/P93HDAsLUwAlLCwsU2XOSrGxscrGjRuV2NjY7C7Ku+X8JkWZ7qYoo+1ePqa7a5e/p2JiYpSrV68qJ06cUIYNG6Y4Ojoq58+fVxRFUQYMGKBUq1ZN2b17txIYGKiMGTNGsbe3V86cOaO3j8ePHyuXL19Wdu3apXz88cdK48aNlQ0bNhi8fjdt2qSULFlSiYiI0C0DlA0bNhgs3+HDhxVAOXHiRNad9JsWF60od44pyuHZirKqi6JMK6N/DSY+ppVRlJWdFcV/lnb7uGj9/Ri8ft2SX78JCYry+IqinFmjKDtHKMriZooy0dXwMcfkVpQ5Xoqy/gtFOTJPUW4HKEp0hPK2ysh3hyFTpkxRihUrluLn786dOxW1Wq33PREaGqqoVCrFz89PURTt+wFQDhw4oNsmPDxcAXTbvA2GDRum7N+/X7l586Zy5swZZdiwYYpKpVJ27dqlxMbGKiVLllRq1qypHD16VLl27Zoybdo0RaVSKVu3bjW4v5s3byqAcuzYMV393rt3TylTpoxy9OhR3XZffPGFUrhwYWXv3r3KiRMnFC8vL8XLy0tvX1evXlVOnTql9O3bVyldurRy6tQp5dSpU0pMTIxR6yRHSoh/8blgb/g9nvj4vaGiPAjM7tK+9eT3mXHltPpNb2yQ7hapvHnzZihAy+j2mRkZKCAgAAcHBzw9PXX7qV+/Pmq1mqNHj9KqVSuDx4qJiSEmJkb3PDw8HIC4uDji4gwM1fsGJR4/u8vxzinVGEp4k3DzEOcCdlPOqz4mxV5Muvme1rVKpaJIkSIAVKhQgWPHjvHLL7/w3XffMXv2bN3dfQAPDw8OHDjArFmzdOk7APb29tjb21OsWDFKlixJ8eLFqV27Nt7e3smOt3v3bq5fv56sM3mbNm2oUaMGu3fv1lu+YMECKlasSIUKFd7i94MaXCppH559tK1D4fdR3TuG6v4J7b8Pz6J6ZcJgxcQcJX8llIKeoFKjPjIHUPQSVJXwIFjdBY1nL1CpUT08g+rRWVSxz5OVQjExR3FyB5cKKC4VUFwqoji7g5mBlr63rK4TEhJYu3Ytz58/x9PTk7i4OLy8vFi5ciXe3t44ODiwZs0aoqOj+fjjj1O8lkJCQnTXpqFtnj9/jkqlQq1W69abmJigVqvZv38/tWvXxs7OjtKlS7N48WLKly+PhYUFc+fOxdnZ+a26jh8+fEjXrl0JCgrC3t6e8uXLs3XrVt1376ZNmxgxYgQ+Pj48e/aMEiVKsHDhQho0aGDwHBOXJfafjIuLIzIyksuXLxMeHq5bP2XKFED7mRATE0ODBg2YNWuW3j579uzJgQMHdM8Ts1OuXLlC0aJFs7wucjpVgwmYrOsBqFAlybhQEjMw1Oao7gagzK+NpnIXNLW/BxvHbCvv20x+nxlXTqvf9JZDpSiKoeFeUrVkyRIcHR1p2rQpoJ1wb8GCBXh4ePDXX3/pfpylx6vpRStWrKBJkyaAdmCLDh06sGvXLl160Zo1a3Q/0iZMmMCSJUu4fPmy3j6dnZ0ZO3Ys/fr1M3jMMWPGMHbs2GTLV6xYka5hWoV4F40cORJHR0datGjBN998w6xZs3B1ddWtHzNmDE5OTnz55ZcGX//48WN69+7NTz/9RPny5ZOtf/r0qe6mRaKvv/6aXr16UbVqVfLlezkwSFRUFD169KBLly66z5l3lYkmBofIm+R5dpXckdfI8/waFvERmd5fvMqccOvChFoVJcy6CKFWRYmwLIiifrcGab116xbDhg0jNjYWS0tLBg4cqLup9uzZM6ZNm0ZgYCAmJiZYWFgwePDgZJPFJgoKCuK7776je/fuBm8CgHaeo379+lG3bl26dOmCoigsXbqUbdu24e3tTf/+/QF48uQJEydO5MaNG6hUKuzt7Rk5ciTFixc3TkWI917+0OOUv7ccq7iXE8ZHmuXhXKFOhFoXw+P+KgqFalOu40ysueTSkptO9VFU79ZnghBZKbFPd1hYGHZ2KY/Gm6lAqkyZMvj6+lK3bl0CAgKoX78+v/zyC1u2bMHU1JT169ene1+xsbHcuXNHNzLQ77//rhsZ6H//+x/Hjh1jwoQJupGBfvnlF93IQJkNpAy1SLm6uvLkyZNUK+tNiIuLw8/PjwYNGmBmZpatZXkXSf1qjRgxgkaNGuHq6kpERAQrV65k2rRpbN26ldq1a1OxYkVcXFyYPHkyefLkYfPmzQwbNoyNGzfSuHFjjh07xokTJ6hevTq5c+fmxo0bjBkzhkePHjF58mSaNGlCcHAwDRs2ZNGiRVStWtVgOczNzVmzZk2yPlOLFi3i66+/5s6dO8lasN55igJPb6C6dwLVxc2YXNuZ5ksS3HxQSjdGcamona/mHQuaDEn87ggPD2fdunUsWrSI3bt34+HhwTfffMPx48f56aefyJs3L5s3b+bXX39l7969yYL8+/fvU79+fWrVqsXs2bNT/Xzw8/Pjf//7Hzdv3kStVtOhQwcuXrxI1apVmT17Noqi0KZNG+Lj4xk2bBhWVlb88ccfbNmyhcOHDycbpfJ9I5+/RqRJMJxx8YLqTgAmu75H9egsAIpjaRIajEcp/kl2lfitI9evceW0+g0PD8fR0THNQCpT37Z3797VdTrfuHEjbdq0oU+fPnz88ccpjuqVksSRgUDb2fT48ePMnDmTIUOGMHv2bM6dO6dLL6pYsSIHDx5kzpw5zJs3DxcXF4KDg/X2Fx8fT0hISKqdei0sLLCwsEi23MzMLEf88SBnleVd9L7X73///cfnn3+uS92pUKECO3fupEGDBgBs27aNYcOG0apVK549e0bJkiVZsmQJzZtrJ3a0s7Nj06ZN/Pjjjzx//pz8+fPTqFEjhg4dyunTp3V1e+XKFWJjY1Ota1NT02TrFy9eTOvWrXFycjJSDeRw+dy0D3NLSEcgZVK2JZTPASMbvkFmZma4u7sD8OGHH/Lvv/8yd+5chgwZwty5c/W+Ozw9PTl8+DALFixg3rx5un08ePAAb29vqlevzu+//05CQoJu34au2SZNmtCkSROePHmCqakpDg4OuLi4ULJkSd1ACdu2bePp06e6L95q1apRqlQpVqxYka451t4H7/vnr3GYQYna3L/8nIolaiev3xK1oO9++Hcp7P0J1ZMrmP7VDso0Ae9x2hswIl3k+jWunFK/6S1DpgIpW1tb/vvvPwoXLsyuXbsYOHAgAJaWlkRFRWVmlzoZGRnIy8uL0NBQTp48SZUqVQDtsKsajYYPP/zwtcohxLssrXmZSpUqlWyqgaTKly+vG7ksqbi4OE6f1s5tUrRoUdJq8E5p/eHDh1N93XsjvfOgpXe7d1hGvjtA2xL1ySefUKVKFRYtWoRardYFUmlxdNT2Mdm7dy/BwcG6GwwpHVutVusdW4hsoTYBzx7ayef3T4FjC+DyNri2Gz7qD7UGgUWu7C6lEG+VdM8jlVSDBg3o1asXvXr14sqVK7o+TefPn89QZ8/hw4dz4MABbt26xdmzZxk+fDj79u2jU6dOuLm5UbJkSfr27cuxY8e4fv0606dPx8/Pj5YtWwLg7u5Oo0aN6N27N8eOHcPf358BAwbw6aefUqBAgcycmhBC5BwyD5pBr/vdcf/+ferUqUPhwoWZNm0ajx8/5uHDhzx8+FB3jPv37+Pm5saxY8d0yxYtWsSRI0e4fv06y5Yto127dnz77beUKVMG0N7cy507N926deP06dNcuXKFwYMHc/PmzXe+r594i1jlhkYTod9hKFEXEmLBfwbMqgKBK0CCfiHSLVOB1Jw5c6hevTqPHz9m3bp1uhH6Tp48SceOHdO9n+DgYLp27UqZMmWoV68ex48f16UXmZmZsW3bNpycnPDx8aFChQosXbqUJUuW6AI3gOXLl+Pm5ka9evVo0qQJNWrUYMGCBZk5LZGDZMVkzePHj6d69epYW1unu5/N+vXr8fb2Jm/evKhUKgIDA5Nt07dvX0qUKIGVlRVOTk60aNGCS5cuvc7pCmFY4jxoQPJg6v2dB+11vzv8/Py4du0ae/bsoVChQuTPn5/8+fNTuHBh3THi4uK4fPmyrpUJtPMatWzZEnd3d3788UdGjBjBtGnTdOsdHR3ZsWMHz549o27dunh6enLo0CE2bdpExYoV31wFCZEeTmWg83rouBJyF9NO3LuxHyxsAPdOZHfphHgrZHiwifj4eCZMmMDnn39OoUKFjFWuNyo8PBx7e/s0O5S9CXFxcWzbto0mTZrkiBzR7PL3339jYmKiN+Hm1KlTdUNye3t7ExoayuzZs3UTbo4ePVpvsubRo0fj4ODAvXv3WLhwIaGhoWnW759//snNmzcpUKAAvXv35tSpU1SqVElvmwULFuDm5kbhwoUJCQlhzJgxBAYGcvPmTUxM3q8ftK+S69dILmyGHUMh/MHLZXYFtUGUR/PsK9c7Rq5f45L6Na7Xqt/4GDgyFw5Mg9gXkyZX/Azqj4Zcb+9E0llJrl/jymn1m97YIMN9pExNTZkyZQpdu3Z9rQIKkRofHx+95+PHj8fX15cjR45QtmxZDh8+jK+vL9WqVQPghx9+4JdffuHkyZO6QCpxiPvFixen+7hdunQBtEMrp6RPnz66/xctWpRx48ZRsWJFbt26RYkS0mFXGIFHc3BrSvyNAwQe3Emlmg0xLV7rvWuJEkIYiakF1PgWKnaE3WPh9Art4+Jmbd+pj/prtxFC6MlUal+9evXYv39/VpdFCIMSEhJYuXKlwcmaQ0JC0Gg0rFy5Um+y5jfl+fPnLFq0iGLFiunNuSREllOboBSpwf08XihFakgQJcRbKitS10NCQujUqRN2dnY4ODjQs2dPnj17luIxb926hUqlMvhYs2aNbruvRkygyrhjWEyMotJCjbZ1avcYmPMhXNqmnZ5BCKGTqVH7GjduzLBhwzh79ixVqlTBxsZGb33iCEZCvI5XJ2vesGEDHh4eAKxevZoOHTqQN29e3WTNGzZs0A2lb2yJwyw/f/6cMmXK4Ofnh7m5+Rs5thBCiLdXoUKFmDRpkl7qeosWLXSp6127diU0NJTNmzfrUtfbt2+vl7reqVMngoKC8PPzIy4ujh49etCvX78U+6m7uroSFBSkt2zBggVMnTqVxo0b6y3//PPPOXr0KGfOnIGWQ2H3aHh6E1Z21A5O0WiStn+VECJzgVTiDO4///xzsnUqlSrdQ8gKkZoyZcoQGBiom6y5W7duusmaR44cSWhoKLt379ZN1ty+fXvdZM3G1qlTJxo0aEBQUBDTpk2jffv2+Pv7Y2lpafRjCyGEeHu9bur6xYsX2bFjB8ePH8fT0xOAWbNm0aRJExo2bGjwmCYmJsnm19ywYQPt27fH1tZWt+zXX38F4PHjx9pAqlJHcG+m7Tt1ZC5c3wtzvaBaH6gzDKwcsqpahHgrZSq1T6PRpPiQIEpklcTJmqtUqcLEiROpWLEiM2fO5Pr168yePZs//viDevXqUbFiRUaPHo2npydz5sx5I2Wzt7enVKlS1KpVi7Vr13Lp0iU2bNjwRo4thBDi3ZCZ1PWAgAAcHBx0QRRA/fr1UavVXLlyJV3HPXnyJIGBgfTs2TPtjS1yQYOx0P+IdgJfJQGO+sKsD+DEItDI7z7x/spUICVEdsjohJtviqIoKIpCTEzMGz+2EEKIt8/Zs2extbXFwsKCL774IlnqelxcHHnz5sXCwoK+ffvqpa4/fPgQZ2dnvf2ZmpqSJ08enj59mq7jL1y4EHd3d6pXz8AcdHlLQMe/tEOmO5aByP9gyzewoDbclknUxfspU6l9P/74Y6rrR40alanCCJFo+PDhNG7cmMKFCxMREcGKFSvYt28fO3fu1Jtwc9q0aeTNm5eNGzfi5+fHli1bdPu4c+cOISEh3Llzh4SEBAIDA4mLiyMqKkq3jZubGxMnTqRVq1YAuu0fPNAOM3358mUAXFxccHFx4caNG6xatQpvb2+cnJy4d+8ekyZNwsrKSm9+M/F+8/X1xdfXVzf6Y9myZRk1ahSNGzfm1q1bFCtWzODrVq9eTbt27QDYs2cPI0eO5OzZs9jY2NCtWzfGjBmT4jHTu99E//33HxUrVuT+/fs8ffo03XOtCSFeX3amrkdFRbFixQpGjhyZuR2UrAf9/OH47/DPRHh4FhY1hrKtocGP4CADL4n3R6YCqVdTmOLi4rh58yampqaUKFFCAinx2hIn3AwKCsLe3p4KFSroJtwE2LZtG8OGDcPHx4dnz55RsmTJZJM1jxo1iiVLluieJ3bS/emnn3TLLl++TFhYmO755s2b6dGjh+75p59+CmjnpBozZgyWlpYcPHiQGTNm8PTpU/Lly0etWrU4fPhwsjuE4v2VWmdyNze3NDt9nz59miZNmjBixAiWLl3K/fv3+eKLL4iLi6N27doGj5mRzuQAPXv2pEKFCty/fz+LzloIkV6JqesAVapU4fjx48ycOZMhQ4Ywe/Zszp07R9myZQGoWLEiBw8eZM6cOcybNw8XFxeCg4P19hcfH09ISAi5c+dO89hr164lMjLy9aaxMTGDj/pB+XawdxycXAzn18Pl7VDjG/j4azCzyvz+hXhLZCqQOnXqVLJl4eHhdO/eXXdnX4jXsXDhwlTXlypVKtlwsK9avHhxsjmkEid8S/TqfNTdu3ene/fuKe6zQIECeq8XwpC0OpOn1el71apVVKhQQXdTqmTJkkyZMoX27dvrOqC/Kr2dyUHbYhYaGsqoUaP0hl0WQmSPjKSue3l5ERoaysmTJ6lSpQoAe/fuRaPRULp06TSPtXDhQpo3b46Tk9PrF9zGEXxmgOfnsH0o3DkM+ybCqWXg/RN4tASV6vWPI0QOlWV9pOzs7Bg7dmzmm4qFEOIdZKgzeVKGOn3HxMQkGwHSysqK6Ohorl27lq7jptSZ/MKFC/z4448sXbo02Y81IYTxDR8+nAMHDnDr1i3Onj3L8OHD2bdvH506ddJLXT927BjXr19n+vTp+Pn50bJlSwDc3d1p1KgRvXv35tixY/j7+zNgwADat29Pnjx5ALh//z5ubm4cO3ZM79jXrl3jwIED9OrVy2DZrl27RmBgIA8fPiQqKorAwEACAwOJjY1N/aTyV4Ae26DtH2BXCMLuwprusLiZNvVPiHdUln6LhoWF6aVJCSHE+yq1zuRJGer03bBhQw4fPsxff/1FQkIC9+/f1/VNfZ3O5DExMXTs2JGpU6dSuHDh1zxDIURmJKaulylThnr16nH8+HFd6rqZmRnbtm3DyckJHx8fKlSowNKlS5Olri9fvhw3Nzfq1atHkyZNqFGjBr6+vrr1cXFxXL58WdfCleiPP/6gUKFCeHt7Gyxbr169qFy5MvPnz+fKlStUrlyZypUr6/oNp0qlgnJtYMBxqD0MTC3h9iGYXwu2fAvP/8tchQmRg2UqtS9xnoFEiqIQFBTEn3/+aTAXXwgh3jepdSZPlFKnb29vb6ZOncoXX3xBly5dsLCwYOTIkRw8eDBdrUgp7Xf48OG4u7vTuXPnrDlJIUSGZUXqep48eVixYoXesri4ON3/ixYtmix1HWDChAlMmDAhxf3u27cv1eOmi7k1fDIcKncCv1FwfgOc+APOrYNPRmjTAE3MXv84QuQAmQqkfvnlF73narUaJycnunXrxvDhw7OkYEII8TZLqTP5/Pnzdduk1ul74MCBfPvttwQFBZE7d25u3brF8OHDyZcvX5rHTmm/e/fu5ezZs6xduxZ42UfQ0dGRESNGMHbs2EyfrxBC6HEoDO0WQ9VesH0YPDoL24dog6pGk6DEJ9ldQiFeW6YCqZs3b2Z1OYQQ4p2W2Jk8qbQ6fatUKgoUKADAX3/9haurK8WLF0/zWCntd926dXrD/x8/fpzPP/+cgwcPUqJEiYyekhBCpK1oDei7Xzuy395x8PgS/NkSyjSFhuMhj+FpG4R4G2QqkErq3r17gHa4XyGEEKnPg5YosdN3SqNATp06lUaNGqFWq1m/fj2TJk1ixYoVmJiYANrO5PXq1WPp0qV6I/mltt9Xg6UnT54A2s7rMo+UEMJo1CZQtSeUaw37JsGx3+DyVrjmB14DoOZ3YGGb9n6EyGEyNdiERqPhxx9/xN7eniJFilCkSBEcHBz46aefdMNzCiHE+yq1zuSJ0ur0vX37dmrWrImnpydbt25l06ZNtGjRQrc+s53JhRAi21jlhsaTtRP6Fq8DCbFw6GeYVQVOrwT5DSneMplqkRoxYgQLFy5k0qRJfPzxxwAcOnSIMWPGEB0dzfjx47O0kEII8TZJqzM5pN3pe+/evcmWZUVn8qTq1KljcB9CCGFUzu7QZSNc3gY7v4ent2BDXzj+uzbQKlglu0soRLpkKpBasmQJv//+O82bN9ctq1ChAgULFqR///4SSAkhhBBCiJSpVODWFErUgyNz4MB0uHccfqsLlTpBvdGQK+3BdYTITplK7QsJCcHNzS3Zcjc3N0JCQl67UEIIIYQQ4j1gZqntI/W/k1DhU+2ywOXadD//mRCfxmTAQmSjTAVSFStWZPbs2cmWz549m4oVK752oYQQQgghxHvELj+0ng89d0OBDyA2QjsP1dyP4PIOkDRkkQNlKrVvypQpNG3alN27d+Pl5QVAQEAAd+/eTXEEKiGEEEIIIVLlWhV67YHTf8HuMRByHf7qACXrQ8OJ4FQ6u0sohE6mWqRq167NlStXaNWqFaGhoYSGhtK6dWsuX75MzZo1s7qMQgghhBDifaFWQ+VO2nS/j78GtRlc2w2+XrBzBESHZXcJhQBeYx6pAgUKyKASQgghhBDCOCztoMGP8EE37eh+V3ZAwGztUOn1RkHlzto5qoTIJhkKpM6cOZOu7SpUqJCpwgghhBBCCKEnbwn4bBVc3Q07hsF/V+Hvr+DEQmg8BQp/lN0lFO+pDAVSlSpVQqVS6eYdUalUAHrzkKhUKhISErKwiEIIIYQQ4r1Xqj4UD4BjC2DfJAg6DX80hHJttS1X9gWzu4TiPZOhQOrmzZu6/yuKQrly5di2bRtFihTJ8oIJIYQQQgihx8QMvL6E8u1h70/w71I4t1Y7uW+NgVB9AJhZZXcpxXsiQ4HUqwGTSqWiUKFCEkgJIYQQQog3x9YJmv8Knp9r0/3uBMA/4+DUUvAeB+7NtZP+ipxPk4Dq9iEKhgSgum0HxWu9NX3fMjVqnxBCCCGEENmuQCXosR3aLAS7ghB6B1Z3hSU+8Oh8dpdOpOXCZphRDtNlLfG87YvpspYwo5x2+VtAAikhhBBCCPH2UqmgfFsYcBxqDQFTS7h1EObVgK3fQWRIdpdQGHJhszboDX+gvzw8SLv8LQimXjuQUkmzqRBCCCGEyG7mNlB3BHx5DDxagKKB47/Dr5Xh6AJIiM/uEopEmgTYMRRQDKx8sWzHMO12OViG+khVrlxZL3CKiorCx8cHc3Nzve3+/fffrCmdEEIIIYQQGZG7CLRfCjcPwPZhEHwetg+Gk4ug0SQoXju7Syiu7U7eEqVHgfD7cPswFKv5xoqVURkKpFq2bKn3vEWLFllZFiGEEEIIIbJGsVrQ9wD8uxj2joPgC7C0Obj7aAekyF00u0v47omPhWcPtel5EUkfD7WBU8RD7fPYZ+nb37NHxi3va8pQIDV69GhjlUMIIYQQQoisZWIKVXtB2dawbyIcXwgX/4Yru6D6/6DmQG1KoEidRgORT/SDocRH+ItAKeIBRP6Xtce1zZe1+8tiGQqkhBBCCCGEeOtY54EmU6FKD23fm5v74eA0CFwBDcZC+Xbv53DpigIx4a+0GD145flDbSuTJp19zEzMIVf+Fw8XsCug/TdXgZfPrR3B9yNtEGawn5RKu12R6ll5tlku3YFUo0aNGDNmDB999FGq20VERDB37lxsbW358ssvX7uAQgghhBBCZIl8HtB1E1zaAjtHQOhtWN9bOyhF48lQoHJ2lzDrxEW/kmaXNEhKsizueTp3qAJb55dBkl3+JAFTkqDJKnf6gtJGk7Wj86FCP5h68dpGk3L8fFLpDqTatWtHmzZtsLe3x8fHB09PTwoUKIClpSVPnz7lwoULHDp0iG3bttG0aVOmTp1qzHILIYQQQgiRcSqVtp9UyQYQMBsOToe7R2HBJ1C5M9QbpQ0YcipNAjx/nCStzlCaXRBEZWDYd0t7/RajXC76QZJdfrBx1qZKZhWP5tpBQXYM1R94wq6ANojyaJ51xzKSdNdGz5496dy5M2vWrGHVqlUsWLCAsLAwQDsEuoeHBw0bNuT48eO4u7sbrcBCCCGEEEK8NjNLqDUIKn0GfqPh7Go49Sdc2AS1h0C1vmBqDpoEVLcPUTAkANVtOyheyzgtJYoC0aGG0+ySLnv2CJR0Dgtuapk8rU4vSHrxf3PrrD+f9PBoDm5Nib9xgMCDO6lUsyGmxqpfI8hQWGlhYUHnzp3p3LkzAGFhYURFRZE3b17MzMyMUkAhhBBCCCGMxq4AtPlNOyjFjqHw4BTs+gFOLtHOR3V6BabhD/AEuO37osVkcsZaTOKiXmlBevhKK9KLZfFR6dufSq0diEEvzc5AK5KlQ87v+6U2QSlSg/vnw6lYpMZbE0RBJgebCA8Px87ODnt7e+zt7fXWXbt2jZIlS2ZJ4YQQQgghhHgjCn8IvfZC4HLYMxb+u6odkOJV4UHavj3tl0KZJvA82ECa3SvDfUeHpr8cVrlftiDpBUlJltk6v1UBx7sqU4FU06ZN8fPzw9LSUm/55cuXqVevHvfu3cuSwgkhhBBCCPHGqNXwQRdwawozyqcw39GLgRHWdNOm4xkcdc4AU6sXQVFiml3S9LokQZKZZdr7EjlCpgIpW1tbWrduzebNmzE11e7i4sWL1K1bl/bt22dpAYUQQgghhHijHp1Pe9JYRaP9V2WSJK0uaSvSK2l2FnY5P81OZEimAqn169dTv359OnXqxMqVKzl//jz16tWjU6dO/Pzzz1ldRiGEEEIIId6cZ4/St12Tn8Gzu6TZvafUmXmRlZUVW7du5fLly7Rv35569erRtWvXDAdRvr6+VKhQATs7O+zs7PDy8mL79u0A3Lp1C5VKZfCxZs0a3T7u3LlD06ZNsba2xtnZmcGDBxMfn84Jw4QQQgghhHiVbb70bedUWoKo91i6A6nw8HC9h1qtZtWqVRw9epQ2bdowcuRI3br0KlSoEJMmTeLkyZOcOHGCunXr0qJFC86fP4+rqytBQUF6j7Fjx2Jra0vjxo0BSEhIoGnTpsTGxnL48GGWLFnC4sWLGTVqVMZrQgghhBBCCIAi1bWj85FSKp4K7Apqt3uPvG4jyOnTp+nYsSOurq5YWVnh7u7OzJkz0zzulStXaNGiBY6OjtjZ2VGjRg3++ecfvW2++uorqlSpgoWFBZUqVcryczck3al9Dg4OqAzkdSqKwrx585g/fz6KoqBSqUhISN/Y9j4+PnrPx48fj6+vL0eOHKFs2bK4uLjord+wYQPt27fH1tYWgF27dnHhwgV2795Nvnz5qFSpEj/99BNDhw5lzJgxmJubp/f0hBBCCCGE0FKbaIc4X90VbTCVdECJF7+HG01671qjEhtBSpUqhaIoLFmyhBYtWnDq1Cnc3NwICgrS237BggVMnTpV1why8uRJnJ2dWbZsGa6urhw+fJg+ffoAULRo0RSP26xZM0qVKsXevXuxsrJixowZNGvWjOvXr+vFC59//jlHjx7lzJkzWX/yBqQ7kHo16stqCQkJrFmzhufPn+Pl5ZVs/cmTJwkMDGTOnDm6ZQEBAZQvX558+V42vzZs2JB+/fpx/vx5KleubPBYMTExxMTE6J4ntqLFxcURFxeXVaeUKYnHz+5yvKukfo1L6te4pH6NS+rXuKR+jUvq1whKNUbVZhEmu75HFfFAt1ixK0BCg/EopRrDe1bfjRo10ns+ZswYfH198ff3p3Tp0uTNm1dv/fr162nbti0WFhbExcXRpUsXunTpolvfoUMH/P39Wb9+PQMHDjR4/T558oSrV68yf/583N3dAfjpp5+YO3cugYGB1KtXD4Dp06cD8PDhQ06fPv1a74X0vjbdgVTt2rUBiI+PZ8KECXz++ecUKlQoc6VL4uzZs3h5eREdHY2trS0bNmzAw8Mj2XYLFy7E3d2d6tVfNqE+fPhQL4gCdM8fPnyY4jEnTpzI2LFjky3ftWsX1tbZNLPzK/z8/LK7CO80qV/jkvo1Lqlf45L6NS6pX+OS+s1qaigxgbzPLmMZF0q0mQP/2ZaBG2q4sS27C5etEhISOHz4MBEREcTGxrJtm359XLt2TZfK9+q6pC5evKgLXAxdv4qiULBgQSZOnEjPnj0xMzPj77//xt7env/++y/Zvq9evUp4eHiqx0xLZGRkurbL8Kh9pqamTJ06la5du2a4UIaUKVOGwMBAwsLCWLt2Ld26dWP//v16wVRUVBQrVqxg5MiRWXLM4cOHM3DgQN3z8PBwXF1d8fb2xs7OLkuOkVlxcXH4+fnRoEEDzMzMsrUs7yKpX+OS+jUuqV/jkvo1Lqlf45L6Na64uIZSvy+cPXuWWrVq6RpB1q1bp0vdS+p///sfbm5uer+5XxUQEIC/vz/r1q1DUZQU67dixYq0bduWjh07olarcXZ2ZteuXQazz06cOMHFixdp0qRJps8xvWM+ZGr487p167J///5UcxnTy9zcnJIlSwJQpUoVjh8/zsyZM5k/f75um7Vr1xIZGZkseHNxceHYsWN6yx49eqRblxILCwssLCySLTczM8sxb46cVJZ3kdSvcUn9GpfUr3FJ/RqX1K9xSf0al9QvlCtXTq8RpGfPngYbQVauXMnIkSNTrK9z587Rpk0bRo8eTePGjdm2bZvB+lUUhW+++YZ8+fJx8OBBrKys+P3332ndujXHjx8nf/78etubmJigUqle6++U3tdmKpBq3Lgxw4YN4+zZs1SpUgUbGxu99c2bN8/MbgHQaDR6/ZdAm9bXvHlznJyc9JZ7eXkxfvx4goODcXZ2BrRNgnZ2dgbTA4UQQgghhBCZ9zqNIIkuXLhAvXr16NOnDz/88EOqfZL27t3Lli1bePr0qS5zbO7cufj5+bFkyRKGDRuWhWeXMZkKpPr37w9gcN6ojIzaN3z4cBo3bkzhwoWJiIhgxYoV7Nu3j507d+q2uXbtGgcOHDCY5+jt7Y2HhwddunRhypQpPHz4kB9++IEvv/zSYIuTEEIIIYQQIutkpBEE4Pz589StW5du3boxfvz4NPef2F9JrdaftUmtVqPRaF6j5K8vU4FUVhU6ODiYrl27EhQUhL29PRUqVGDnzp00aNBAt80ff/xBoUKF8Pb2TvZ6ExMTtmzZQr9+/fDy8sLGxoZu3brx448/Zkn5hBBCCCGEEFqv2why7tw56tatS8OGDRk4cKBucLikscWxY8fo2rUre/bsoWDBgnh5eZE7d266devGqFGjsLKy4rfffuPmzZs0bdpU77jPnj3j4cOHREVFERgYCICHh4fRpkRK94S8xrBw4UJu3bpFTEwMwcHB7N69Wy+IApgwYQJ37txJFoUmKlKkCNu2bSMyMpLHjx8zbdo0TE0zFR8KIYQQQoi32OtOGAuZm9h1wYIF1KlTBzs7O1QqFaGhocm2CQkJoVOnTtjZ2eHg4EDPnj159uxZVpz2G5PYCFKmTBnq1avH8ePHM9QIsnbtWh4/fsyyZcvInz+/7pF0VO7IyEguX76sS/dzdHRkx44dPHv2jLp16+Lp6cmhQ4fYtGkTFStW1L2uV69eVK5cmfnz53PlyhUqV65M5cqVefDgQbJyZJVMB1L79+/Hx8eHkiVLUrJkSZo3b87BgwezsmxCCCGEEEKkW+KEsSdPnuTEiRPUrVuXFi1acP78eVxdXQkKCtJ7jB07Fltb22Sjzn3++ed06NAh3ceNjIykUaNGfP/99ylu06lTJ86fP4+fnx9btmzhwIEDuslo3xav2wgyZswYFEVJ9rh69apumzp16qAoit6gdp6enuzcuZP//vuP8PBwAgICkv3N9u3bZ3DfWTE4Xkoy1XSzbNkyevToQevWrfnqq68A8Pf3p169eixevJjPPvssSwsphBBCCCFEWnx8fPSejx8/Hl9fX44cOULZsmWTjeq8YcMG2rdvj62trW7Zr7/+CsDjx485c+ZMuo77zTffANof84ZcvHiRHTt2cPz4cTw9PQGYNWsWTZo0Ydq0aRQoUCBdxxE5S6YCqfHjxzNlyhS+/fZb3bKvvvqKn3/+mZ9++kkCKSGEEEIIka0SEhJYs2YNz58/x8vLK9n6kydPEhgYyJw5c4xeloCAABwcHHRBFED9+vVRq9UcPXqUVq1aGb0MIutlKrXvxo0bySJ+0A57fvPmzdculBBCCCGEEJlx9uxZbG1tsbCw4IsvvmDDhg0Gp8VZuHAh7u7uev1zjOXhw4e6qXoSmZqakidPHt2AC+Ltk6lAytXVlT179iRbvnv3blxdXV+7UEIIIYQQQmRGmTJlCAwM5OjRo/Tr149u3bpx4cIFvW2ioqJYsWIFPXv2zKZSindBplL7vvvuO7766isCAwN1Uby/vz+LFy9m5syZWVpAIYQQQggh0isrJozNai4uLgQHB+sti4+PJyQkJFm/LfH2yFQg1a9fP1xcXJg+fTqrV68GwN3dnVWrVtGiRYssLaAQQggh/t/encdFVa9/AP/MsG/DvsqmqCAoqJgK5S6IGmlZdk3FzDK9djX7ZcatVDTN1LravYXetNKM6qpZaSpS7rkvKO4biLKK7Pswc35/IEcnQJmR47B83q8XL51zzpzznIdxnGe+GxHpStsFY6UQEhKC/Px8nDhxAsHBwQCAXbt2Qa1Wo1evXo8lBmp8WhVSX3/9NQYOHAgvLy88++yzHBhHRERERE3Goy4YW7P/rwu7KpVKcV2jtLQ0DBo0COvWrUPPnj0BVI+ByszMxNWrVwFUj9OysrKCp6cn7Ozs0KlTJ0REROC1117DypUroVQq8cYbb+Bvf/sbZ+xrxrQqpP7+97+jsrISXl5eGDBgAAYOHIgBAwbwBUBEREREelezYGxGRgasra0RGBio1YKxQPXCrnv37hUfd+vWDQDEroFKpRKXLl1CaWmpeMzKlSsRExMjPu7bty+A6kaIl19+GQDw3Xff4Y033sCgQYMgl8sxatQocap1ap60KqTy8/Nx8OBB7N27F7t370ZcXBwqKyvRvn17DBgwAAMGDED//v3h7OwsVbxERERERHVas2bNQ49ZtGgRFi1aVO/+utaCUiqVYguWt7c3BEHQ2D9v3jzMmzfvgde1s7NDXFzcQ+Oj5kOrQsrExEQsmObNm4fy8nIcOnQIu3fvxp49e7B27VoolUpUVVVJFS8REREREZHe6TT9ufhkuRxyuRwymQwymQyCIMDT07OxYiMiIiIiImqStGqRqqysxOHDh7Fnzx7s2rULR44cgZeXF/r27YvXXnsN69ev5zpSRERERETU4mlVSFlbW8PJyQmRkZGYNm0afvjhB859T0RERERErY5WhVRQUBBOnTqFffv2id36+vfvD3t7e6niIyIiIiIianK0GiN1+PBh3LlzB0uWLIGZmRmWLFkCV1dXdO7cGW+88QY2bNhQa9VmIiIiIiKilkarFikAsLS0REREBCIiIgAARUVF2L9/PxISEvDaa6+huLiYs/YREREREVGLpnUhVUOtVuPYsWPYs2cPdu/ejT///BMlJSXw8vJqzPiIiIiIiIiaHK0KqaNHj2LPnj3Ys2cPDhw4gOLiYri7u6N///747LPPMGDAAHh7e0sUKhERERERUdOgVSHVu3dvuLi4YMCAAfj0008xYMAA+Pj4SBUbERERERFRk6RVIXXhwgX4+vpKFQsREREREVGzoNWsfSyiiIiIiIiItCykiIiIiIiIiIUUERERERGR1lhIERERERERaemRCylBECAIQmPEQkRERERE1CzoXEitW7cOXbp0gZmZGczMzBAYGIhvv/22MWMjIiIiIiJqkrSa/rzGp59+ig8++ABvvPEGnnzySQDAgQMHMGXKFOTk5GDmzJmNGiQREREREVFTolMh9e9//xuxsbGIiooStz3zzDMICAjAvHnzWEgREREREVGLplPXvoyMDISGhtbaHhoaioyMjEcOioiIiIiIqCnTqZBq3749/ve//9Xa/uOPP6JDhw6PHBQREREREVFTplPXvpiYGLz44ovYt2+fOEbqzz//xB9//FFngUVERERERNSS6NQiNWrUKBw5cgQODg74+eef8fPPP8PBwQFHjx7Fs88+29gxEhERERERNSk6tUgBQHBwMNavX9+YsRARERERETULOrVIGRgYIDs7u9b2O3fuwMDA4JGDIiIiIiIiasp0KqQEQahze0VFBYyNjR8pICIiIiIioqZOq659n332GQBAJpNh9erVsLS0FPepVCrs27cPfn5+jRshERERERFRE6NVIfWvf/0LQHWL1MqVKzW68RkbG8Pb2xsrV65s3AiJiIiIiIiaGK0KqeTkZADAgAED8NNPP8HW1laSoIiIiIiIiJoynWbt2717d2PHQURERERE1GzoNNkEERERERFRa8ZCioiIiIiISEsspIiIiIiIiLSk10IqNjYWgYGBUCgUUCgUCAkJwfbt2zWOOXToEAYOHAgLCwsoFAr07dsXZWVl4v7c3FyMHTsWCoUCNjY2mDRpEoqLix/3rRARERERUSuicyG1f/9+jBs3DiEhIUhLSwMAfPvttzhw4ECDz+Hu7o7FixfjxIkTOH78OAYOHIgRI0bg3LlzAKqLqIiICISHh+Po0aM4duwY3njjDcjl98IeO3Yszp07h4SEBGzduhX79u3D5MmTdb0tIiIiIiKih9KpkNq0aROGDBkCMzMznDp1ChUVFQCAgoICLFq0qMHniYyMxLBhw9ChQwd07NgRCxcuhKWlJQ4fPgwAmDlzJqZPn453330XAQEB8PX1xejRo2FiYgIAuHDhAnbs2IHVq1ejV69eeOqpp/Dvf/8bP/zwA9LT03W5NSIiIiIioofSafrzDz/8ECtXrkRUVBR++OEHcfuTTz6JDz/8UKdAVCoVNmzYgJKSEoSEhCA7OxtHjhzB2LFjERoaimvXrsHPzw8LFy7EU089BaC6xcrGxgY9evQQzzN48GDI5XIcOXIEzz77bJ3XqqioEIs/ACgsLAQAKJVKKJVKneJvLDXX13ccLRXzKy3mV1rMr7SYX2kxv9JifqXF/EqrqeW3oXHoVEhdunQJffv2rbXd2toa+fn5Wp0rKSkJISEhKC8vh6WlJTZv3gx/f3+xVWrevHlYtmwZunbtinXr1mHQoEE4e/YsOnTogMzMTDg5OWnekKEh7OzskJmZWe81P/roI8TExNTavnPnTpibm2sVv1QSEhL0HUKLxvxKi/mVFvMrLeZXWsyvtJhfaTG/0moq+S0tLW3QcToVUi4uLrh69Sq8vb01th84cADt2rXT6ly+vr5ITExEQUEBNm7ciAkTJmDv3r1Qq9UAgNdffx0TJ04EAHTr1g1//PEHvvrqK3z00Ue6hA4AiI6OxltvvSU+LiwshIeHB8LDw6FQKHQ+b2NQKpVISEhAWFgYjIyM9BpLS8T8Sov5lRbzKy3mV1rMr7SYX2kxv9Jqavmt6a32MDoVUq+99hpmzJiBr776CjKZDOnp6Th06BDefvttfPDBB1qdy9jYGO3btwcABAcH49ixY1ixYgXeffddAIC/v7/G8Z06dUJqaiqA6oIuOztbY39VVRVyc3Ph4uJS7zVNTEzEcVb3MzIyahK/PKBpxdISMb/SYn6lxfxKi/mVFvMrLeZXWsyvtJpKfhsag06F1Lvvvgu1Wo1BgwahtLQUffv2hYmJCd5++2384x//0OWUIrVajYqKCnh7e8PNzQ2XLl3S2H/58mUMHToUABASEoL8/HycOHECwcHBAIBdu3ZBrVajV69ejxQHERERERFRfXQqpGQyGd577z3MmjULV69eRXFxMfz9/WFpaanVeaKjozF06FB4enqiqKgIcXFx2LNnD+Lj4yGTyTBr1izMnTsXQUFB6Nq1K9auXYuLFy9i48aNAKpbpyIiIvDaa69h5cqVUCqVeOONN/C3v/0Nbm5uutwaERERERHRQ+lUSNUwNjau1fVOG9nZ2YiKikJGRgasra0RGBiI+Ph4hIWFAQDefPNNlJeXY+bMmcjNzUVQUBASEhLg4+MjnuO7777DG2+8gUGDBkEul2PUqFH47LPPHuW2iIiIiIiIHkinQqq8vBz//ve/sXv3bmRnZ4sTQ9Q4efJkg86zZs2ahx7z7rvviuOl6mJnZ4e4uLgGXY+IiIiIiKgx6FRITZo0CTt37sTzzz+Pnj17QiaTNXZcRERERERETZZOhdTWrVuxbds2PPnkk40dDxERERERUZMn1+VJbdq0gZWVVWPHQkRERERE1CzoVEh98sknmD17Nm7cuNHY8RARERERETV5OnXt69GjB8rLy9GuXTuYm5vXWrQqNze3UYIjIiIiIiJqinQqpMaMGYO0tDQsWrQIzs7OnGyCiIiIiIhaFZ0KqYMHD+LQoUMICgpq7HiIiIiIiIiaPJ3GSPn5+aGsrKyxYyEiIiIiImoWdCqkFi9ejP/7v//Dnj17cOfOHRQWFmr8EBERERERtWQ6de2LiIgAAAwaNEhjuyAIkMlkUKlUjx4ZERERERFRE6VTIbV79+7GjoOIiIiIiKjZ0KmQ6tevX2PHQURERERE1Gw0uJA6c+YMOnfuDLlcjjNnzjzw2MDAwEcOjIiIiIiIqKlqcCHVtWtXZGZmwsnJCV27doVMJoMgCLWO4xgpIiIiIiJq6RpcSCUnJ8PR0VH8OxERERERUWvV4ELKy8sLBgYGyMjIgJeXl5QxERERERERNWlarSNVV1c+IiIiIiKi1kanBXmJiIiIiIhaM62nP1+9ejUsLS0feMz06dN1DoiIiIiIiKip07qQWrlyJQwMDOrdL5PJWEgREREREVGLpnUhdfz4cTg5OUkRCxERERERUbOg1RgpmUwmVRxERERERETNBmftIyIiIiIi0pJWhdTcuXMfOtEEERERERFRS6fVGKm5c+dKFQcREREREVGzwXWkiIiIiIiItMRCioiIiIiISEsspIiIiIiIiLTEQoqIiIiIiEhLDZ5solu3bg1eR+rkyZM6B0RERERERNTUNbiQGjlypIRhEBERERERNR8NLqQ49TkREREREVE1ncdI5efnY/Xq1YiOjkZubi6A6i59aWlpjRYcERERERFRU6TVgrw1zpw5g8GDB8Pa2hopKSl47bXXYGdnh59++gmpqalYt25dY8dJRERERETUZOjUIvXWW2/h5ZdfxpUrV2BqaipuHzZsGPbt29dowRERERERETVFOhVSx44dw+uvv15re5s2bZCZmfnIQRERERERETVlOhVSJiYmKCwsrLX98uXLcHR0fOSgiIiIiIiImjKdCqlnnnkG8+fPh1KpBADIZDKkpqZi9uzZGDVqVKMGSERERERE1NToVEh98sknKC4uhpOTE8rKytCvXz+0b98eVlZWWLhwYWPHSERERERE1KToNGuftbU1EhIScODAAZw5cwbFxcXo3r07Bg8e3NjxERERERERNTk6FVI3b96Eh4cHnnrqKTz11FONHRMREREREVGTplPXPm9vb/Tr1w9ffvkl8vLyGjsmIiIiIiKiJk2nQur48ePo2bMn5s+fD1dXV4wcORIbN25ERUWFVueJjY1FYGAgFAoFFAoFQkJCsH37dnF///79IZPJNH6mTJmicY7U1FQMHz4c5ubmcHJywqxZs1BVVaXLbRERERERETWIToVUt27dsHTpUqSmpmL79u1wdHTE5MmT4ezsjFdeeaXB53F3d8fixYtx4sQJHD9+HAMHDsSIESNw7tw58ZjXXnsNGRkZ4s+SJUvEfSqVCsOHD0dlZSUOHjyItWvX4ptvvsGcOXN0uS0iIiIiIqIG0amQqiGTyTBgwAB8+eWX+P3339G2bVusXbu2wc+PjIzEsGHD0KFDB3Ts2BELFy6EpaUlDh8+LB5jbm4OFxcX8UehUIj7du7cifPnz2P9+vXo2rUrhg4digULFuDzzz9HZWXlo9waERERERFRvXSabKLGrVu3EBcXh7i4OJw9exYhISH4/PPPdTqXSqXChg0bUFJSgpCQEHH7d999h/Xr18PFxQWRkZH44IMPYG5uDgA4dOgQunTpAmdnZ/H4IUOGYOrUqTh37hy6detW57UqKio0uiHWLC6sVCrFtbH0peb6+o6jpWJ+pcX8Sov5lRbzKy3mV1rMr7SYX2k1tfw2NA6ZIAiCtidftWoV4uLi8Oeff8LPzw9jx47FSy+9BC8vL60DTUpKQkhICMrLy2FpaYm4uDgMGzYMAPDf//4XXl5ecHNzw5kzZzB79mz07NkTP/30EwBg8uTJuHHjBuLj48XzlZaWwsLCAtu2bcPQoUPrvOa8efMQExNTa3tcXJxYpBERERERUetTWlqKl156CQUFBRq94f5Kp0LKw8MDY8aMwdixYxEUFPRIgVZWViI1NRUFBQXYuHEjVq9ejb1798Lf37/Wsbt27cKgQYNw9epV+Pj46FxI1dUi5eHhgZycnAcm63FQKpVISEhAWFgYjIyM9BpLS8T8Sov5lRbzKy3mV1rMr7SYX2kxv9JqavktLCyEg4PDQwspnbr2paamQiaT6Rzc/YyNjdG+fXsAQHBwMI4dO4YVK1Zg1apVtY7t1asXAIiFlIuLC44ePapxTFZWFgDAxcWl3muamJjAxMSk1nYjI6Mm8csDmlYsLRHzKy3mV1rMr7SYX2kxv9JifqXF/EqrqeS3oTHoVEjVFFGlpaVITU2tNbFDYGCgLqcFAKjV6nqnUU9MTAQAuLq6AgBCQkKwcOFCZGdnw8nJCQCQkJAAhUJRZ4sWERERERFRY9CpkLp9+zZefvll7Nixo879KpWqQeeJjo7G0KFD4enpiaKiIsTFxWHPnj2Ij4/HtWvXxPFS9vb2OHPmDGbOnIm+ffuKhVp4eDj8/f0xfvx4LFmyBJmZmXj//fcxbdq0OluciIiIiIiIGoNO05+/+eabKCgowJEjR2BmZoYdO3Zg7dq16NChA3799dcGnyc7OxtRUVHw9fXFoEGDcOzYMcTHxyMsLAzGxsb4/fffER4eDj8/P/zf//0fRo0ahS1btojPNzAwwNatW2FgYICQkBCMGzcOUVFRmD9/vi63RURERERE1CA6tUjt2rULv/zyC3r06AG5XA4vLy+EhYVBoVDgo48+wvDhwxt0njVr1tS7z8PDA3v37n3oOby8vLBt27YGx05ERERERPSodGqRKikpEcck2dra4vbt2wCALl264OTJk40XHRERERERUROkUyHl6+uLS5cuAQCCgoKwatUqpKWlYeXKleJEEERERERERC2VTl37ZsyYgYyMDADA3LlzERERge+++w7Gxsb45ptvGjM+IiIiIiKiJkenQmrcuHHi34ODg3Hjxg1cvHgRnp6ecHBwaLTgiIiIiIiImiKtu/YplUr4+PjgwoUL4jZzc3N0796dRRQREREREbUKWhdSRkZGKC8vlyIWIiIiIiKiZkGnySamTZuGjz/+GFVVVY0dDxERERERUZOn1Rip1NRUuLu749ixY/jjjz+wc+dOdOnSBRYWFhrH/fTTT40aJBERERERUVOiVSHVtm1bZGRkwMbGBqNGjZIqJiIiIiIioiZNq0JKEAQAwNdffy1JMERERERERM2B1mOkZDKZFHEQERERERE1G1qvI/XBBx/A3Nz8gcd8+umnOgdERERERETU1GldSCUlJcHY2Lje/WyxIiIiIiKilk7rQmrz5s1wcnKSIhYiIiIiIqJmQasxUmxtIiIiIiIi0rKQqpm1j4iIiIiIqDXTqpD6+uuvYW1tLVUsREREREREzYJWhdSECRNgYmIiVSxERERELVpsbCwCAwOhUCigUCgQEhKC7du31zpOEAQMHToUMpkMP//8s8a+6dOnIzg4GCYmJujatWuDrlteXo5p06bB3t4elpaWGDVqFLKysuo89s6dO3B3d4dMJkN+fr6Wd0jUemi9jhQRERER6cbd3R2LFy/GiRMncPz4cQwcOBAjRozAuXPnNI5bvnz5A8emv/LKK3jxxRcbfN2ZM2diy5Yt2LBhA/bu3Yv09HQ899xzdR47adIkBAYGNvjcRK2V1rP2EREREZFuIiMjNR4vXLgQsbGxOHz4MAICAgAAiYmJ+OSTT3D8+HG4urrWOsdnn30GALh9+zbOnDnz0GsWFBRgzZo1iIuLw8CBAwFUD9fo1KkTDh8+jN69e4vHxsbGIj8/H3PmzKmzpYyI7mGLFBEREZEeqFQq/PDDDygpKUFISAgAoLS0FC+99BI+//xzuLi4NMp1Tpw4AaVSicGDB4vb/Pz84OnpiUOHDonbzp8/j/nz52PdunWQy/kRkehh2CJFRERE9BglJSUhJCQE5eXlsLS0xObNm+Hv7w+gugteaGgoRowY0WjXy8zMhLGxMWxsbDS2Ozs7IzMzEwBQUVGBMWPGYOnSpfD09MT169cb7fpELVWDCylbW9sGryOVm5urc0BERERELZmvry8SExNRUFCAjRs3YsKECdi7dy+uXr2KXbt24dSpU489pujoaHTq1Anjxo177Ncmaq4aXEgtX75cwjCIiIiIWgdjY2O0b98eABAcHIxjx45hxYoVMDMzw7Vr12q1HI0aNQp9+vTBnj17dLqei4sLKisrkZ+fr3HurKwssfvgrl27kJSUhI0bNwK4t3aog4MD3nvvPcTExOh0baKWrMGF1IQJE6SMg4iIiKhVUqvVqKioQExMDF599VWNfV26dMG//vWvWpNUaCM4OBhGRkb4448/MGrUKADApUuXkJqaKo7N2rRpE8rKysTnHDt2DK+88gr2798PHx8fna9N1JI98kjC8vJyFBYWavwQERERUW3R0dHYt28fUlJSkJSUhOjoaOzZswdjx46Fi4sLOnfurPEDAJ6enmjbtq14jqtXryIxMRGZmZkoKytDYmIiEhMToVQqAQBpaWnw8/PD0aNHAQDW1taYNGkS3nrrLezevRsnTpzAxIkTERISIs7Y5+Pjo3Hdmut16tQJTk5OjzNF1IQ9bB20119/HT4+PjAzM4OjoyNGjBiBixcvapzjjz/+QGhoKKysrODi4oLZs2ejqqrqgdft378/ZDKZxs+UKVNqHffNN98gMDAQpqamcHJywrRp0xrnxuuh02QTJSUlmD17Nv73v//hzp07tfarVKpHDoyIiIiopcnOzkZUVBQyMjJgbW2NwMBAxMfHIywsrMHnePXVV7F3717xcbdu3QAAq1atAgAolUpcunQJpaWl4jH/+te/IJfLMWrUKFRUVGDIkCH44osvGumuqLWoWQetQ4cOEAQBa9euxYgRI3Dq1CkEBAQgODgYY8eOhaenJ3JzczFv3jyEh4cjOTkZBgYGOH36NIYNG4b33nsP69atQ1paGqZMmQKlUol+/fo98NqvvfYa5s+fLz42NzfX2P/pp5/ik08+wdKlS9GrVy+UlJQgJSVFijSIdCqk3nnnHezevRuxsbEYP348Pv/8c6SlpWHVqlVYvHhxY8dIRERE1CKsWbNGq+Nrxirdr66xUkqlEtu2bQMAeHt713qeqakpPv/8c3z++ecNum7//v3rvDa1bg9bB23y5MniPm9vb3z44YcICgpCSkoKfHx88OOPPyIwMBBz5swBALRv3x5LlizB6NGj0bNnzwde29zcvN4lAfLy8vD+++9jy5YtGDRokLhd6oWlderat2XLFnzxxRcYNWoUDA0N0adPH7z//vtYtGgRvvvuu8aOkYiIiIiImpC61kG7X0lJCb7++mu0bdsWHh4eAKqn2Tc1NdU4zszMDOXl5bh69eoDr/fdd9/BwcEBnTt3RnR0tEaLa0JCAtRqNdLS0tCpUye4u7tj9OjRuHnzZiPcaf10KqRyc3PRrl07AIBCoRCnO3/qqaewb9++xouOiIiIHqvGGANx7NgxDBo0CDY2NrC1tcWQIUNw+vTpeq+ZkpJSa/xDzc+GDRtqHX/nzh24u7tDJpMhPz+/0e6diB4uKSkJlpaWMDExwZQpUzTWQQOAL774ApaWlrC0tMT27duRkJAAY2NjAMCQIUNw8OBBfP/991CpVEhLSxO76+Xl5dV7zZdeegnr16/H7t27ER0djW+//VZjqv7r169DrVZj0aJFWL58OTZu3Ijc3FyEhYWhsrJSokzoWEi1a9cOycnJAKpXxv7f//4HoLql6q9TdhIREVHzUTMG4sSJEzh+/DgGDhyIESNG4Ny5cwCqZ4D7+uuvceHCBcTHx0MQBISHh4vjo4uLixEREQFPT08cOXIEBw4cgJWVFZ5++ul6B5R7eHggIyND4ycmJgaWlpYYOnRoreMnTZokeZcdIqpbzTpoR44cwdSpUzFhwgScP39e3D927FicOnUKe/fuRceOHTF69GiUl5cDAMLDw7F06VJMmTIFJiYm6NixI4YNGwYAkMvrL0smT56MIUOGoEuXLhg7dizWrVuHzZs349q1awCqZ75UKpX47LPPMGTIEPTu3Rvff/89rly5gt27d0uWC50KqYkTJ4rfLL377rv4/PPPYWpqipkzZ2LWrFmNGiARERE9PpGRkRg2bBg6dOiAjh07YuHChbC0tMThw4cBVH+g6du3L7y9vdG9e3d8+OGHuHnzpjio++LFi8jNzcX8+fPh6+uLgIAAzJ07F1lZWbh9+3ad1zQwMICLi4vGz+bNmzF69GhYWlpqHBsbG4v8/Hy8/fbbkuaBiOpWsw5acHAwPvroIwQFBWHFihXifmtra3To0AF9+/bFxo0bcfHiRWzevFnc/9ZbbyE/Px+pqanIycnBiBEjAADOzs4NjqFXr14AIHYHdHV1BQCNljFHR0c4ODggNTVV95t9CJ0mm5g5c6b498GDB+PixYs4ceIE2rdvz2+IiIiIWgiVSoUNGzZoNQbC19cX9vb2WLNmDf75z39CpVJhzZo18PPza/A02idOnEBiYmKtiRHOnz+P+fPn48iRI7h+/fqj3yARPbKaddDqIggCBEGotV8mk8HNzQ0A8P3338PDw0McNtQQiYmJAO4VUE8++SSA6vXR3N3dAVQPRcrJyYGXl5dW96ONR15HCgC8vLzw3HPPsYgiIiJqAR5lDISVlRX27NmD9evXw8zMDJaWltixYwe2bNkCAwODBl1/zZo16NSpE0JDQ8VtFRUVGDNmDJYuXQpPT8/GvWEiapAHrYN2/fp1fPTRRzhx4gRSU1Nx8OBBvPDCCzAzMxO77wHA0qVLkZSUhHPnzmHBggVYvHgxPv30U/H94a/roF27dg0LFizAiRMnkJKSgl9//RVRUVHo27evWHt07NgRI0aMwIwZM3Dw4EGcPXsWEyZMgJ+fHwYMGCBZPnRqkbp/Dve61ExpSERERM1PzRiIgoICbNy4ERMmTMDevXvFYmrs2LEICwtDRkYGli1bhtGjR+PPP/+EqakpysrKMGnSJDz55JPigPJly5ZhxIgRmDt37kOvXVZWhri4OHzwwQca26Ojo9GpUyeNAeZE9Hg9aB209PR07N+/H8uXL0deXh6cnZ3Rt29fHDx4UKM1evv27Vi4cCEqKioQFBSEX375BYMHDxan7//rOmjGxsb4/fffsXz5cpSUlMDDwwOjRo3C+++/rxHbunXrMHPmTAwfPhxyuRz9+vXDjh07YGRkJFk+dCqk7u/nCFTfcHJyMgwNDeHj48NCioiIqBmrGQMBVE8ucezYMaxYsUJc8NXa2locB9G7d2/Y2tpi8+bNGDNmDOLi4pCSkoJDhw6Jg8fj4uJga2uLo0eP4tlnn33gtTdu3IjS0lJERUVpbN+1axeSkpKwceNGAPfWV3JwcMB7772HmJiYRs0BEdX2oHXQ3NzcxGLoQXbt2lVrm1KpFP/+13XQPDw8NBagro9CocCaNWu0XqvtUehUSJ06darWtsLCQrz88ssPfYMkIiKi5kWbMRClpaWQy+WQyWTiMTWP1Wr1Q6+1Zs0aPPPMM3B0dNTYvmnTJpSVlYmPjx07hldeeQX79++Hj4+PLrfVYqjUAo4k5+JEjgz2ybkIae8EA7ns4U8kokfSKGOkgOoqMCYmplZTPBERETUfjzoGIiwsDHl5eZg2bRouXLiAc+fOYeLEiTA0NESXLl0A1B4DUePq1avYt28fXn311Vpx+fj4oHPnzuJP27ZtAQCdOnVq8CQWLdGOsxl46uNdGPfVcay7YoBxXx3HUx/vwo6zGfoOTS8edR20b775ps71zIyNjR+4ZtkzzzwDT09PmJqawtXVFePHj0d6errGMWfOnEGfPn1gamoKDw8PLFmypNHvnx6vRiukAKCgoAAFBQWNeUoiIiJ6jGrGQPj6+mLQoEE4duyYOAbC1NQU+/fvx7Bhw9C+fXu8+OKLsLKy0hgD4efnhy1btuDMmTMICQlBnz59kJ6ejq1bt8LOzg5A7TEQNb766iu4u7sjPDz8sd93c7TjbAamrj+JjIJyje2ZBeWYuv5kqyymHnUdtBdffLHWmmZDhgxB3759H7hW6oABA/C///0Ply5dwqZNm3Dt2jU8//zz4v7CwkKEh4fDy8sLJ06cwNKlSzFv3jz897//lTQfJC2ZcH8nxAb67LPPNB4LgoCMjAx8++236NevH+Li4hotwMehsLAQ1tbWKCgogEKh0GssSqUS27Ztw7BhwyQdHNdaMb/SYn6lxfxKi/mVFvPbuFRqAU99vKtWEVVDBsDF2hQHZg9s9d387OzssHTpUkyaNKnWvjNnziAoKAhXr16ts4vo7du30aZNG6xatQp2dnYNfv3++uuvGDlyJCoqKmBkZITY2Fi89957yMzMFGe4fPfdd/Hzzz9rtIi1Vk3t/aGhtYFOY6T+9a9/aTyWy+VwdHTEhAkTEB0drcspiYiIiKgOpZVVuHGn9O5PCVLulCLpVn69RRQACAAyCsqx7lAKxvT0hKlRw6aeb0l0WQftr9atWwdzc3OMGjUKu3fvbtB1c3Nz8d133yE0NFQsCg4dOoS+ffuKRRQADBkyBB9//DHy8vJga2urwx2SvulUSCUnJzd2HEREREStVkGpEjdyq4uk1LvF0o07JbhxpxTZRXVP9NEQMVvOY9G2C/B3s0YPL1sE3/1xVpg2YvRNS1JSEkJCQlBeXg5LS8s610F75513UFJSAl9fX4110P5qzZo1eOmll2BmZvbQ686ePRv/+c9/UFpait69e2Pr1q3ivszMTHFcXw1nZ2dxHwup5kmnQqqxxMbGIjY2FikpKQCAgIAAzJkzB0OHDtU4ThAEDBs2DDt27MDmzZsxcuRIcV9qaiqmTp2K3bt3w9LSEhMmTMBHH30EQ0O93hoRERGRSBAE5BRXisVRTcvSjdzqv+eXKh/4fBtzI3jZmcPL3gLe9uaoVAlYuffaQ69rbWaEgjIlTt/Mx+mb+VhzoPrL8DY2ZmJRFexlCz8XKxgaNOrQeb15lHXQ7nfo0CFcuHAB3377bYOuO2vWLEyaNAk3btxATEwMoqKisHXrVo0ZLKll0anaKCkpweLFi/HHH38gOzu71nSm169fb9B5agYEdujQAYIgYO3atRgxYgROnTqFgIAA8bjly5fX+SJUqVQYPnw4XFxccPDgQWRkZCAqKgpGRkZYtGiRLrdGRERN3MO+hHv99dfx+++/Iz09HZaWlggNDcXHH38MPz8/jfN88803+PTTT3H58mUoFAqMGjUKERERdV4zNzcXc+fOxc6dO5GamgpHR0eMHDkSCxYsgLW1tXjc9OnT8eeff+Ls2bPo1KkTEhMTJckBNU1qtYCMwvL7iqV7BVPqnRKUVKoe+HxHKxN421cXS1525vByuPunvTlszDVbTFRqAb8kpiGzoBx1DXavGSO1/50ByCgox8nUPJy4Uf1zIaMQafllSMsvw6+nq2eWMzMyQFcPG7Gw6uZpU+uazcWjrIN2v9WrV6Nr164IDg7WWOeoPg4ODnBwcEDHjh3RqVMneHh44PDhwwgJCYGLiwuysrI0jq957OLi0hi3TXqgUyH16quvYu/evRg/fjxcXV11rrQjIyM1Hi9cuBCxsbE4fPiwWEglJibik08+wfHjx+Hq6qpx/M6dO3H+/Hn8/vvvcHZ2RteuXbFgwQLMnj0b8+bNq7eZloiImq+HfQkXHByMsWPHwtPTE7m5uZg3bx7Cw8ORnJwMA4PqcSKffvopPvnkEyxduhS9evVCSUkJrl69Wu8109PTkZ6ejmXLlsHf3x83btzAlClTkJ6eLi4QW+OVV17BkSNHcObMGUnzQPqhVKmRlleGlDslSM0tRUrO3S54uaVIzS1FZVX9a2XJZICbtRm87O+1LNX83dPOHBYmDf9YZiCXYW6kP6auPwkZoFFM1XwqmxvpD0MDOTzszOFhZ44RXdsAAIorqnDmZj5O3MjD8Rt5OJmah6LyKhy6fgeHrt8Rz9PeyRLBntWFVXcvW7RzsIC8GU5coc06aDWKi4vxv//9Dx999JHO1wQgnjckJATvvfcelEqlOG4qISEBvr6+rb5bX3NeB02nQmr79u347bff8OSTTzZaIHUNCCwtLcVLL72Ezz//vM5q/dChQ+jSpYvYxxSoHrg3depUnDt3Dt26davzWhUVFRr/YAoLCwFUzxjSkG8cpFRzfX3H0VIxv9JifqXF/Fb7a6vRvHnzEBsbiz///BMdO3bExIkTxX1t2rTB3Llz0aNHD1y5cgU+Pj7Iy8vD+++/j82bN2PgwIHise3bt0dCQkKd+fX19cUPP/wgPvb09ERMTAxefvlllJWVid3JP/nkEwDVYx5Onz7d6n9X92tOr99ypQo3c8uQmlt6t0Aqu9sFrxTpBeVQqeuf8NhQLoO7rRm87MzhaWcGT3vzu383h7utGUwM6+s+J2idm0G+Dvj334Lw4baLyCy897nGxdoE7w31wyBfhzrPaSIHnvCyxhNe1gC8oVYLuHa7BCdv5uNkaj5OpeYj+U4prmYX42p2MX48fhMAYGNmhK4e1ujuaYPunjbo0kYBc+OmNZTivffeQ0REBDw8PFBUVIQffvgBe/bswW+//YZLly5hw4YNCAsLg4ODA9LS0rBkyRKYmZkhLCxMI1dxcXGoqqrCiy++qPH5UKlU4tixY5g4cSLi4+PRpk0bHD16FMePH0doaChsbW1x/fp1zJs3Dz4+PujRoweUSiVeeOEFxMTEYOLEiXj77bdx7tw5rFixAsuWLWsW/yakEn8u677XrwHWXTkOF4UJ3h/mhyEBzg99vlQa+jvR6dVva2srrgXxqB40IHDmzJkIDQ3FiBEj6nxuZmamRhEFaA7cq89HH32EmJiYWtt37twJc3NzXW+lUSUkJOg7hBaN+ZUW8yst5vcelUqFgwcPoqioCJWVldi2bZvG/vLycnz33XdwdnbG2bNncenSJRw4cABVVVXYvn07Xn31VZSVlcHX1xcTJ06Eo6Njg/NbM6Zi586dtfZduXIFhYWFteKhpvP6La8CciqA2+Uy5JQDOeWyuz9AfuWDvw03kgtwMAEcTAU4mN7709FUgI0JYCBTAqj+khZ5QFkecOkacEmie5ntD1wrlKFQCSiMAB9FCVQ3TmDbDe3OYwWgnynQryNQrARSimRIvvuTWgzklymx53IO9lzOAQDIIaCNBdDWSkBbKwHeVgJsjatb3vTl5MmT+Oabb5CXlwcLCwt4eXlh7ty5qKysxMGDB7F582YsW7YMJSUlsLa2RkBAABYsWIDjx49rnOfTTz9Fz549cfDgQY3tCQkJSEpKwuXLl7Fz5044OzsjJSUFa9aswQcffIDy8nLY2tqie/fuePXVV/HHH3+Iz42OjsaqVavQs2dPKBQKPP/883Bzc2u17xOn78jw1eWaLxbuvWgyC8vxxg+JeKWjGkH2Wq/S1Cj+usZdfXRaR2r9+vX45ZdfsHbt2kcuPCorK5GamioOCFy9ejX27t2Lq1ev4v/+7/9w6tQpWFpaVgcrk2lMNjF58mTcuHED8fHx4vlKS0thYWGBbdu21Zq0okZdLVIeHh7IyclpEutIJSQkICwsrEnMo9/SML/SYn6lxfzek5SUhL59+4pfwq1bt07jPX/lypWIjo5GSUkJOnbsiF9++UVcI2bJkiWIiYlB27Zt8emnn8La2hpz587FrVu3sHDhwgatY5KTk4PevXtjzJgxWLBgQa398+fPx6+//lrrw1lr9rhfv4IgIK9UKbYopd4pFbvf3cgtRW7Jg79xtjQxhJd9dcuS192ucV72ZvC0M4eTpUmT6+ImdX6VKjUuZBTh5M3qFquTqfkarWA1nBUm6O5hg253W606uVjBuN5WuOaD77+NS6UW0P+TfXW+hoCaMX4m2P1WX7108yssLISDg4M060h98sknuHbtGpydneHt7V3rBXXy5MkGn6u+AYFmZma4du1arVWkR40ahT59+mDPnj1wcXHB0aNHNfY3ZOCeiYkJTExMam03MjJqMv84mlIsLRHzKy3mV1rML9C5c2eNWbkmTZqkMStXVFQUIiIixFm5xo4dK7YgyWQyKJVK/Pvf/0Z4eDgA4IcffoCLiwvOnj2LESNGPDC/hYWFGDlyJPz9/bFgwYI6jzUwMIBMJmv1v6caKrWAkzVjIG4VNdoYCLVaQHZRxb3JHXI1pw0vKq964PPtLYzFMUpe9ubwtreA590/bc2NmuVsa1K9PxgZAcFtTRDc1kHclp5fJk5gcTI1D+fSC5FVWIHt57Kw/Vz15zETQzmC3G3Q/e4kFt09bWBvWfszWHPB91/tVFSpcCvv7hcZNWMJ75TiQmZhvUUUULMOWgVO3SpCiI/94wv4rob+jnUqpO6ffryx1QwIjImJwauvvqqxr0uXLvjXv/4lTlIREhKChQsXIjs7G05OTgCqm1wVCoXGegFERNSyPMqsXDUTF93//4SjoyMcHBxw+/btB163qKgIERERsLKywubNm/mBqgF2nM1AzJbzdxePrR4D4WptirmR/ojo7PrQ51ep1MgoKEfKX6YNT71bOJUr65/cAQBcrU3haadZJHndneTBypS/v0fhZmMGNxszRAa5AaheOPjMrYLqwupGHk6k5iG/VImjKbk4mpIrPq+tgwW6e96ber2Dk2WTa+GjhisqV+LGnbutvfd9kZGaW4r0gjJo3/ftnuyi+hedbgp0KqTmzp3bKBePjo7G0KFD4enpiaKiIsTFxWHPnj2Ij4+Hi4tLna1Knp6e4oJm4eHh8Pf3x/jx47FkyRJkZmbi/fffx7Rp0+pscSIiopZJm1m5aiZKunTpEtzd3QFUT2+ek5MDR0fHeq9RWFiIIUOGwMTEBL/++mutNWeoth1nMzB1/cla03NnFpRj6vqTiB3XHRGdXVFRVTO5QwlScqo/gNUUTrfySqFU1f9JzEAuQxsbM7E4qi6UqoslTztzmBoZSHuTJDI3NkTvdvbo3a66BUEQBFzPKblXWN3Iw5XsYiTnlCA5pwSbTt4CAFiZGqKbp604Q2BXTxtYajGDIUlLEATcLqoQJ1xJvduyVFMs5ZZUPvD5ZkYG4r9HL3tzeNpboLSiCh9tv/jQaztZNe332Ud6lVZWVta5jpSnp2eDnp+dnY2oqChkZGTA2toagYGBiI+PR1hYWIOeb2BggK1bt2Lq1KkICQmBhYUFJkyYgPnz52t9L0RE1Dw86Eu469ev48cff0R4eDgcHR1x69YtLF68GGZmZhg2bBgAoGPHjhgxYgRmzJiB//73v1AoFIiOjoavry+6dOkCAEhLS8OgQYOwbt069OzZE4WFhQgPD0dpaSnWr1+PwsJCccZXR0dHcVr1q1evori4GJmZmSgrKxPXkfL39291S3Ko1AJitpyvc42jmm3Tv0+Eg+V5ZBSWP/Bba2MDuTj7nZe9BbwdzMVWpja2ZjBqIQvJtjQymQw+jpbwcbTE6B4eAICCUiVO3rxXWCXezEdReRX2Xb6NfZerW4TlMsDXRYFgr7vrWnnawcPOrFl2tWwulCo10vPL7naRvVss3blXLJUpH7wGmr2F8b0ZKu3vrX/maW8OR0uTWr87lVrANwdTHroOWs+2jTO5nVR0KqQuX76MSZMm1ZrJRBAEyGQyqFQPTnaNNWvWaHXduubF8PLyarWznRARtUYP+hIuPT0d+/fvx/Lly5GXlwdnZ2f07dsXBw8eFLuAA8C6deswc+ZMDB8+HHK5HP369cPWrVuRlJQEoHpg+aVLl8SZm06ePIkjR44AgNilsEZycjK8vb0B3FtnsUbNMhz3H9OSCYKAjIJyXL9dgt8vZN3tzle/SpUa6XePMTc2ENdW0uyCZwEXhWmzWVeGHsza3AgDfJ0wwLf632OVSo2LmUUaCwbfyivDhYxCXMgoxPrDqQAAB0uTe4WVly0C3KzZ2qil0sqq+4qjEo3ueGn5ZQ+c1l8uA1yt77X8etpZaLQyadtNtqHroDX1f/c6FVITJ06EoaEhtm7d+kgL8hIREWnrQV/CNXQqYYVCgTVr1micS6lUioWUt7e3xpd3/fv3r/PLvL/as2fPQ49pCUorq3D9dgmu3S7G9dsluJ5Tgmt3u2w97Jvrv3pzUAeM7e0FB0tjfp5ohQwN5Ojcxhqd21gjKsQbAJBVWC62WJ1IzcPZtALkFFcg/lwW4u9OYmFsIEfnNgqxsOruZdvku4FJTRAE5JZUihM61EzAUlM85RTXP7kDUD0xiNj9rqZQutvK5G5r3uizL0Z0dkXsuO73jaGs5qLFGEp906mQSkxMxIkTJ+Dn59fY8RAREVEToFYLSC8oqy6UbhdXF0t3C6cHtTQZymXwtDeHjZkxTqbmPfQ6vdrZw9GK45rpHmeFKYZ2ccXQLtUfpMuVKpxNK9CYITCnuBIn707D/uX+ZACAh52ZOM6qu5ctfJ2tYNjCun2q1ALS88vuTeyQWyIWTam5pSiuePBMldZmRhotSV529yZhcbJ6/NP6R3R2RZi/Cw5dzcbO/UcQ3qdXo83q+TjoVEj5+/sjJyensWMhIiKix6y4ogrJYutSMa7llOD67RIk5xQ/cEY8OwtjtHOwgI+jJdo5WqDd3T897cxhZCCHSi3gqY93NfsxEKR/pkYG6OFthx7e1a8VQRCQmlsqFlYnbuThUlYRbuaW4WZuGX5OTAcAWBgboKunDYI9qwurbp62sDZr+jM1litVuCkWSpqz4D1s8hXg3kyVNV1j7y+arM2b3v0byGXo1dYOdy4I6NXWrtkUUYCOhdTHH3+Md955B4sWLUKXLl1qTf+q70VtiYiI6B61WkBaftl9XfGKcS27+s+sB6zlYmQgg5e9Bdo53CuUqicPsICN+YMnz2gpYyCo6ZHJZHdnZrTAc92rZ94sKlci8Wa+WFglpuajqKIKf169gz+v3rn7PKCDk+Xd9ayqW67aOlg8tEupSi3gSM06aMm5jdJikl9aWXtih7td8jILHzy20MhABg9bc40JWGrGLrnbcqbKx0mnQmrw4MEAgEGDBmls13ayCSKilig2NhaxsbFISUkBAAQEBGDOnDkYOnQoAOD111/H77//jvT0dFhaWiI0NBQff/yx2F36zp07GDt2LM6cOYM7d+7AyckJI0aMQExMzEOv/dtvv2H+/Pk4c+YMTE1N0a9fP/z888/i/ro+MHz//ff429/+9ug3TnpXVK4UC6X7xzAl55Sgoqr+1iUHS2O0c7hXKNW0MHnYmj1S16iWMAaCmgcrUyP06eCIPh2qlzBQqQVcyS661x3wRh5S7pTiclYxLmcV4/ujNwEAtuZGYlfAYE9bBLrbwMz4XiGi6zpoarWArKLyu9OFa45VunGnBIUPWSzaysSwulC6b7xS9Yx45nC1NuMXEE2EToXU7t27GzsOIqIWw93dHYsXL0aHDh0gCALWrl2LESNG4NSpUwgICEBwcDDGjh0LT09P5ObmYt68eQgPD0dycjIMDAwgl8sxYsQIfPjhh3B0dMTVq1cxbdo05OTkYMyYMfVed9OmTXjttdewaNEiDBw4EFVVVTh79myt477++mtERESIj21sbKRIA0lEpRaQllfdunTt7til67eLce12CW4X1d+6ZGwgh5e9ea2ueD4OlpJ292nuYyCoeTKQy+DnooCfiwJje3kBAHKKK8SFgk/eyMPpWwXIK1Xi9wvZ+P1CNoDqMX4Bbgp097KFgVyG1XfHX92vZh20z8Z0hb+b9d0xSiX3JnnILcXN3NIHfnkBAI5WJrValDzvPrY1N+LkK82AToVUv3796t1X13/aREStSWRkpMbjhQsXIjY2FocPH0ZAQAAmT54s7vP29saHH36IoKAgpKSkwMfHB7a2tpg6dap4jJeXF/7+979j6dKl9RZSVVVVmDFjBpYuXYpJkyaJ2/39/Wsda2NjU+eC59S0FJQpqyd5+EtXvJQ7pah8wAc0RysTsSuez30tTO625norXprzGAhqORwsTRAe4ILwgOr3v8oqNc6lF4gTWBxPyUN2UQVO3yrA6VsF9Z6nppvqP75PfOD1DOQyuNua1ZrYoaZgMjfmosPNXaP8BouKivD9999j9erVOHHiBLv2ERHdpVKpsGHDBpSUlCAkJKTW/pKSEnz99ddo27YtPDw86jxHeno6fvrpJ/Tp06fe65w8eRJpaWmQy+Xo1q0bMjMz0bVrVyxduhSdO3fWOHbatGl49dVX0a5dO0yZMgUTJ07kN596UqVS41ZemUZXvGu3qyd7eNBUxcaGcrS1t6jVFa+dowUUWq7nQtRaGRvK0c2zehIKoHqISlp+GU7cyMO2MxmIP5/18HMYyNHO0aLOiR3cbExb3KyBpOmRCql9+/ZhzZo12LRpE9zc3PDcc8/h888/b6zYiIiaraSkJISEhKC8vByWlpbYvHmzRuvQF198gXfeeQclJSXw9fVFQkICjI01B++PGTMGv/zyC8rKyhAZGYlVq1Zh165ddV7v+vXrAIB58+bh008/hbe3Nz755BP0798fly9fhp1d9WxX8+fPx8CBA2Fubo6dO3fi73//O4qLizF9+nSJMtE8SDGY/H4FpUpcvTsrXk1XvOu3S5Byp+SBM3A5WZnUKpTaO1rCzYZjJIgam0wmg7tt9YQNABpUSC19PhAjurWROjRqorQupDIzM/HNN99gzZo1KCwsxOjRo1FRUYGff/65zi4kREStka+vLxITE1FQUICNGzdiwoQJ2Lt3r/g+OXbsWISFhSEjIwPLli3D6NGj8eeff8LU9N6Ckv/6178wd+5cXL58GdHR0Zg1a5Y4YcVfqdXVXb3ee+89jBo1CkD1WCh3d3ds2LABr7/+OgDggw8+EJ/TrVs3lJSUYOnSpa26kNJ1MPlfVanUSM0trXOyhzsllfU+z8RQjrb3TSNe82dbBwtYsXWJSC8aurivk6J1LwLc2mlVSEVGRmLfvn0YPnw4li9fjoiICBgYGGDlypVSxUdE1CwZGxujffv2AIDg4GAcO3YMK1aswKpVqwAA1tbWsLa2RocOHdC7d2/Y2tpi8+bNGmOgXFxc4OLiAj8/P9jZ2aFPnz7o1atXnddzda3+wH//F1omJiZo164dUlNT642zV69eWLBgASoqKmBi0voWRd1xNgNT15+stc5RzWDy2HHdaxVTeSWV1WOW7iuUrt8uRmrug9d3cVGY1u6K52CBNjZmj30RTCJ6sJ5t7eBqbcp10OiBtCqktm/fjunTp2Pq1Kno0KGDVDEREbU4arUaFRV1j3kRBAGCINS7v+b5AKBUKuvcHxwcDBMTE1y6dAlPPfWUeGxKSgq8vLzqPW9iYiJsbW1bZRGlUguI2XK+zg9JNduif0rC9ZwSpNxdpPba7WLkldb9OwAAUyO5OI34/ZM9tHWwgIUJB5YTNRdcB40aQqt39QMHDmDNmjUIDg5Gp06dMH78eK49QkT0F9HR0Rg6dCg8PT1RVFSEuLg47NmzB/Hx8bh+/Tp+/PFHhIeHw9HREbdu3cLixYthZmaGYcOGAQC2bduGrKwsPPHEE7C0tMS5c+cwa9YshIaGwtnZGQBw9OhRREVF4Y8//kCbNm2gUCgwZcoUzJ07Fx4eHvDy8sLSpUsBAC+88AIAYMuWLcjKykLv3r1hamqKhIQELFq0CG+//bZ+EqVnR5NzNdY2qkteqRJLdlyqtd3N2lRjgdqawslVYcrWJaIWguug0cNoVUj17t0bvXv3xvLly/Hjjz/iq6++wltvvQW1Wo2EhAR4eHjAyspKqliJqJE86oKxADB9+nT8+eefOHv2LDp16oTExMSHXvfatWt4++23ceDAAVRUVCAiIgL//ve/xeIAAJ555hkkJiYiOzsbtra2GDx4MD7++GO4ubk1ag6klJ2djaioKGRkZMDa2hqBgYGIj49HWFgY0tPTsX//fixfvhx5eXlwdnZG3759cfDgQTg5OQEAzMzM8OWXX2LmzJmoqKiAh4cHnnvuOfzf//0fDh48CAAoLS3FpUuXNFqoli5dCkNDQ4wfPx5lZWXo1asXdu3aBVvb6hmpjIyM8Pnnn2PmzJkQBAHt27fHp59+itdee+3xJ+kxun/skrj20u0SXMgobNDzu3vaoG9HR7GFqa2DBactJmoluA4aPYhO/xNYWFjglVdewSuvvIJLly5hzZo1WLx4Md59912EhYXh119/bew4iagRPeqCsTVeeeUVHDlyBGfOnHnoNUtKShAeHo6goCBx5rkPPvgAkZGROHz4MOTy6iliBwwYgH/+859wdXVFWloa3n77bTz//PNiAdEcrFmzpt59bm5u2LZt2wOfP2DAgDrv9/6iqX///hAEzU5pRkZGWLZsGZYtW1bneSMiIjQW4m1pCkqVuJZTjGvZ1TPjXcuuLpoeNnbpYWYN8UOIj30jRkpEzQnXQaP6PPJXar6+vliyZAk++ugjbNmyBV999VVjxEVEEnrUBWMB4LPPPgMA3L59u0GF1J9//omUlBScOnUKCoUCALB27VrY2tpi165dGDx4MABg5syZ4nO8vLzw7rvvYuTIkVAqlTAy4gxmrZ1KLeBWXqnYqnRv3aVi5BTXPzOemZFB9cx4TtWtSu0cLeFtb47X1h1HdmEFB5MTEZHWGq1vgoGBAUaOHImRI0c21imJ6DFojAVjG6KiogIymUxjUgNTU1PI5XIcOHBALKTul5ubi++++w6hoaEsolqZwnKlOBve/UVTSk4pKlXqep/nojCFj5MF2jncnejByfKBY5dingngYHIiItIJO3kTtVKNsWCsNnr37g0LCwvMnj0bixYtgiAIePfdd6FSqZCRkaFx7OzZs/Gf//wHpaWl6N27N7Zu3arzdanpUqsFpOWXabQq1RRN2UX1z2B4/7pLYrHkYIm2jhaw1HJmPA4mJyIiXbGQImqlGmPBWG04Ojpiw4YNmDp1Kj777DPI5XKMGTMG3bt3F8dH1Zg1axYmTZqEGzduICYmBlFRUdi6dStkMrYMNEclFVXiIrXV45aqW5eSc0pQUVV/65KTlcl9s+Ldm0q8sddd4mByIiLSBQspolaqMRaM1VZ4eDiuXbuGnJwcGBoawsbGBi4uLmjXrp3GcQ4ODnBwcEDHjh3RqVMneHh44PDhw3V2PaSmQa0WkFFYXt2qVDPZw+1iXMsuQWZh/VOMGxvI4e1gLk4hXlM0tXO0gML08XXn5GByIiLSFgspIgLw6AvGasPBwQEAsGvXLmRnZ+OZZ555YFwAGu3a9GjKKlXVLUtiV7zqP6/fLkGZUlXv8xwsjavHLTlZaBRN7rbmLFqIiKhZYiFF1Ao96oKxAHD16lUUFxcjMzMTZWVlSExMhFKpFKfoTktLw6BBg7Bu3Tr07NkTAPD111+jU6dOcHR0xKFDhzBjxgzMnDkTvr6+AIAjR47g2LFjeOqpp2Bra4tr167hgw8+gI+PD1ujHiNBEJBVWHF3vNK9rnjXb5cgLb+s3ucZymXwsjfX7IrnZAkfB0tYm3OyECIiallYSBG1Qo+6YCwAvPrqq9i7d6/4uFu3bgAgdg1UKpW4dOkSSktLxWMuXbqE6Oho5ObmwtvbG++9957GdOfm5ub46aefMHfuXJSUlMDV1RURERF4//33NWb7a41UagFHknNxIkcG++TcRhnDU65UIeVOCa5ll2gUTddvF6Oksv7WJVtzo1pd8XwcLeBhZw4jA3m9zyMiImpJWEgRtUKPumAsAOzZs6fWNqVSKT7X29u71oKxixcvxuLFi+s9Z5cuXcTFeumeHWcz7ptVzgDrrhyHawNnlRMEAbeLK+4rlu7+mVOMW3llEOpZp9ZALoOXnfl9xdK9osnOQvfZG4mIiFoKFlJERE3YjrMZmLr+ZK0FYzMLyjF1/UnEjuuOiM6uqKhS4cad0ntd8bKLcS2nBNezi1FUUVXv+RWmhncXqb1XLPk4WsDTzgLGhmxdIiIiqg8LKSKiJkqlFhCz5XytIgq4t3jsjB8S4ay4gFt5ZVDX07oklwEeduZoV7P2kpNl9d+dLGFvYcxp5YmIiHTAQoqIqAmorFIju6gcWYXlyCqsQGZBOU6l5mksEluXiio1UnOrJ4CwMjGs1RXPx8kSXvbmMDE0eBy3QURE1GqwkCIikpBaLSC3tPJugXSvSKp5nFlYgezCctwpqdT5Gm8M8EFUiDccrUzYukRERPSYsJAiItJRSUUVMgvLaxVJ2UXld4ulCmQXlUOpqqfP3V8YG8jhpDCBi8IUzgpTqAUB289mPvR5T7Z3hJPC9FFvh4iIiLTAQoqI6C+UKjVuF1XcazUqKEdWUQWyCsqRdbdIyi6seOAkDveTyQB7CxM43y2SnBSmd4slEzhbm4qFk625kUaLkkot4KmPdyGzoLzOcVIyAC7WpujZ1q5xbpyIiIgajIUUEbUagiAgv1Sp0YqUWVCBrKLy+4qkCtwpqah3WvC/sjQxFFuR7hVJJnBWmMLZurpAcrIy0Wl9JQO5DHMj/TF1/UnIAI1iqqbcmhvp/8jrSREREZH2WEgRUaOQYsFYbZRVqu6OOfpLV7vCe0VSVmEFKqvUDTqfoVxWXQTd19XOWWEKF2sTOFvdK5IsTaR9G43o7IrYcd3vW0eqmksD15EiIiIiabCQIqJH9igLxj5MlUqNOyWVyCyoLpKyxWJJs+tdYXnDutkBgJ2F8d3CSLOrnYu1CZysTOFibQo7c2PIm0hLT0RnV4T5u+DQ1Wzs3H8E4X16PfZClYiIiDSxkCKiR9LQBWP/ShAEFJZViWOO7s1ip1kk3S6qqHd9pL8yMzKAi/XdsUcaXe3ubXNSmDTLqcAN5DL0amuHOxcE9GprxyKKiIhIz1hIEZHOGrJg7Ls/JSE1txS3iyqQeV+BlFVYjnJlw7rZGchlcLIy0Rx/VNPV7r5JG6xMDDn9NxERET0WLKSISGdHk3MfumBsfqkSi7ZdrHe/jbmROOaoviLJ3tKELTBERETUpLCQIiKdJecUN+i47p42CPayrVUkOSlMYGrU/LrZEREREbGQIiKtnU8vxNqDKfjp5K0GHT9riB9CfOwljoqIiIjo8WEhRUQNolSpsfNcFtYeTMHRlFxxu6Fchqp6ZoPggrFERETUUrGQIqIHyimuwA9HU7H+cCoyC6vHQxnKZYjo7IKXQ71xu6gCf//uJAAuGEtEREStBwspIqrT6Zv5WHswBVvPZKBSVT27noOlMV7q6Ymxvb3grDAVj+WCsURERNTasJAiIlFllRrbz2bgm4MpOJWaL24P8rDBy6FeGNbFtc41mLhgLBEREbU2cn1ePDY2FoGBgVAoFFAoFAgJCcH27dvF/a+//jp8fHxgZmYGR0dHjBgxAhcvak6jnJqaiuHDh8Pc3BxOTk6YNWsWqqqqHvetEDVr2YXl+DThMkIX78KMHxJxKjUfRgYyPNutDX6e9iR+mfYknu3m/sCFbGsWjA124IKxRERE1PLptUXK3d0dixcvRocOHSAIAtauXYsRI0bg1KlTCAgIQHBwMMaOHQtPT0/k5uZi3rx5CA8PR3JyMgwMDKBSqTB8+HC4uLjg4MGDyMjIQFRUFIyMjLBo0SJ93hpRkycIAk6m5uGbgzewPSlDnDDCWWGCsb28MKanJxytTPQcJREREVHTpNdCKjIyUuPxwoULERsbi8OHDyMgIACTJ08W93l7e+PDDz9EUFAQUlJS4OPjg507d+L8+fP4/fff4ezsjK5du2LBggWYPXs25s2bB2Nj48d9S0RNXrlShS2n07H2UArOphWK25/wtsWEUG8MCXCBkYFeG6uJiIiImrwmM0ZKpVJhw4YNKCkpQUhISK39JSUl+Prrr9G2bVt4eHgAAA4dOoQuXbrA2dlZPG7IkCGYOnUqzp07h27dutV5rYqKClRUVIiPCwurP0wqlUoolcrGvC2t1Vxf33G0VK05vxkF5Yg7ehM/Hr+FvNLq+zc2lCMy0AXje3kiwE1RfaBaBaVapdM1WnN+HwfmV1rMr7SYX2kxv9JifqXV1PLb0DhkgiDUvQDMY5KUlISQkBCUl5fD0tIScXFxGDZsmLj/iy++wDvvvIOSkhL4+vrit99+g4+PDwBg8uTJuHHjBuLj48XjS0tLYWFhgW3btmHo0KF1XnPevHmIiYmptT0uLg7m5uaNfIdE+iMIwLVCYF+mHEm5MqjvTkpuayzgKRc1ejsJsDTSc5BERERETUhpaSleeuklFBQUQKFQ1Huc3gupyspKpKamoqCgABs3bsTq1auxd+9e+Pv7AwAKCgqQnZ2NjIwMLFu2DGlpafjzzz9hamqqcyFVV4uUh4cHcnJyHpisx0GpVCIhIQFhYWEwMuIn3MbWWvJbVqnCr2cysP5wKi5mFYvbe7e1xfjenhjo6whDCbrvtZb86gvzKy3mV1rMr7SYX2kxv9JqavktLCyEg4PDQwspvXftMzY2Rvv27QEAwcHBOHbsGFasWIFVq1YBAKytrWFtbY0OHTqgd+/esLW1xebNmzFmzBi4uLjg6NGjGufLysoCALi4uNR7TRMTE5iY1B5Eb2Rk1CR+eUDTiqUlaqn5vZlbim8P38CPx26ioKy6WdrMyADPdm+DCSHe8HWxeixxtNT8NhXMr7SYX2kxv9JifqXF/EqrqeS3oTHovZD6K7VardFadD9BECAIgrg/JCQECxcuRHZ2NpycnAAACQkJUCgUYosWUUsnCAL+vHoH3xxMwR8Xs1DTxuxpZ46oEC+8EOwBa3P9vykRERERtSR6nZorOjoa+/btQ0pKCpKSkhAdHY09e/Zg7NixuH79Oj766COcOHECqampOHjwIF544QWYmZmJY6jCw8Ph7++P8ePH4/Tp04iPj8f777+PadOm1dniRM3Hg9YYy83NxT/+8Q/4+vrCzMwMnp6emD59OgoKCuo81507d+Du7g6ZTIb8/PwHXvfy5csYMWIEHBwcoFAo8NRTT2H37t0ax/zxxx8IDQ2FlZUVXFxcMHv2bL2sXVZSUYVvD6Ug7F/7MG7NEfx+obqI6tPBAWsm9MDut/vj1T7tWEQRERERSUCvLVLZ2dmIiopCRkYGrK2tERgYiPj4eISFhSE9PR379+/H8uXLkZeXB2dnZ/Tt2xcHDx4UW58MDAywdetWTJ06FSEhIbCwsMCECRMwf/58fd4WNYIHrTEmCALS09OxbNky+Pv748aNG5gyZQrS09OxcePGWueaNGkSAgMDkZaW9tDrPv300+jQoQN27doFMzMzLF++HE8//TSuXbsGFxcXnD59GsOGDcN7772HdevWIS0tDVOmTIFKpcKyZcukSEUtyTklWHcoBRuP30JRRXUBZ2FsgOeD3TE+xBvtnSwfSxxERERErZleC6k1a9bUu8/NzQ3btm176Dm8vLwadBw1Lw9aY2zSpEnYtGmTuM/HxwcLFy7EuHHjUFVVBUPDey/r2NhY5OfnY86cOWKLVn1ycnJw5coVrFmzBoGBgQCAxYsX44svvsDZs2fh4uKCH3/8EYGBgZgzZw4AoH379liyZAlGjx6NuXPnwspKmjFIarWAvVduY+3BFOy5dFvc3s7BAlEhXhgV7A4rU7Y8ERERET0uTW6MFNFfPWyNMQDirCr3F1Hnz5/H/PnzceTIEVy/fv2h17G3t4evry/WrVuH7t27w8TEBKtWrYKTkxOCg4MBVM/4aGpqqvE8MzMzlJeX48SJE+jfv7/uN1qHwnIlNh6/hW8P30ByTgkAQCYDBvg6YUKoN/q0d4BcLmvUaxIRERHRw7GQoibrr2uMbd68uc5JRHJycrBgwQJMnjxZ3FZRUYExY8Zg6dKl8PT0bFAhJZPJ8Pvvv2PkyJGwsrKCXC6Hk5MTduzYAVtbWwDVCz4vX74c33//PUaPHo3MzEyxK2lGRkYj3TlwNbsIaw/ewKaTt1BaWb04rpWpIUb38MD43l7wdrBotGsRERERkfZYSFGT5evri8TERHGNsQkTJmisMQZUz/M/fPhw+Pv7Y968eeL26OhodOrUCePGjWvw9QRBwLRp0+Dk5IT9+/fDzMwMq1evRmRkJI4dOwZXV1eEh4dj6dKlmDJlCsaPHw8TExN88MEH2L9/P+TyR5u7RaUWsOtiNtYeTMGBqzni9g5OlpgQ6o1nu7WBhQn/yRIRERE1BfxURk3Ww9YYKyoqQkREBKysrLB582aNOf937dqFpKQkcfKJmnWnXV1d8fzzz4szP95v165d2Lp1K/Ly8sTF17744gskJCRg7dq1ePfddwEAb731FmbOnImMjAzY2toiJSUF0dHRaNeunU73mV9aif8dv4l1h27gVl4ZAEAuAwZ3csbLod4I8bGHTMbue0RERERNCQspajbuX2OssLAQQ4YMgYmJCX799dda45Y2bdqEsrIy8fGxY8fwyiuvYPfu3fV28ystLQWAWi1LcrkcarVaY5tMJoObmxsA4Pvvv4eHhwe6d++u1f1czCzE2oMp2HwqDeXK6vPbmBvhxSc8MK6XFzzszLU6HxERERE9PiykqEmKjo7G0KFD4enpiaKiIsTFxWHPnj2Ij49HYWEhwsPDUVpaivXr16OwsBCFhYUAAEdHRxgYGMDHx0fjfDk51V3l/Pz8kJubCwA4evQooqKi8Mcff6BNmzYICQmBra0tJkyYgDlz5sDMzAxffvklkpOTMXz4cPFcS5cuRUREBORyOX766ScsXrwY//vf/2BgYPDQ+6pSqZFwPgvfHEzBkeRccXsnVwVeDvXCiK5tYGr08PMQERERkX6xkKIm6UFrjO3ZswdHjhwBALHrX43k5GR4e3s36BqlpaW4dOkSlEolAMDBwQE7duzAe++9h4EDB0KpVCIgIAC//PILgoKCxOdt374dCxcuREVFBYKCgvDLL79g6NChD7zWneIK/HDsJtYfvoGMgnIAgIFchogAF0wI9cYT3rbsvkdERETUjLCQoibpQWuM9e/fXxzz1FA1z6kpmuo7T48ePRAfH//Ac+3atavB1z2bVoBvDqbg19PpqKyq7r5nb2GMMT09Mba3J1ytzbS4CyIiIiJqKlhIETWyyio1tp/NwLpDN3DiRp64PdDdGhNCvDE80JXd94iIiIiaORZSRI0ku6gccUdSEXckFdlF1ZNiGBnIMKyLKyaEeqObhw277xERERG1ECykiB7RqdQ8fHMwBduSMqBUVXcVdLQywdhenniplyecrEwfcgYiIiIiam5YSBHpoKJKha2nM7DuUApO3yoQt3f3tMGEUG8M7ewKY8NHW6CXiIiIiJouFlJEWsgsKMf6wzfw/dFU3CmpBAAYG8oRGeiGl0O90cXdWs8REhEREdHjwEKK6CEEQcCxlDysPZiCHecyoVJXd99ztTbFuN5e+NsTHrC3NNFzlERERET0OLGQIqpHuVKFXxLT8M3BG7iQUShu79nWDhNDvRHm7wxDA3bfIyIiImqNWEgR/cWtvFJ8e/gGfjx2E/ml1etOmRrJMbJrG0wI9UYnV4WeIyQiIiIifWMhRa2GSi3gSHIuTuTIYJ+ci5D2TjCQV09HLggCDl27g28OpuD3C1m423sP7rZmiArxwugeHrAxN9Zj9ERERETUlLCQolZhx9kMxGw5j4yCcgAGWHflOFytTfHuUD8UlVdh3aEUXM4qFo9/qr0DJoR6Y6DfvWKLiIiIiKgGCylq8XaczcDU9Sch/GV7RkE5ZvyQKD42NzbAqO7umBDqhfZOVo81RiIiIiJqXlhIUYumUguI2XK+VhF1PwO5DNFD/TD6CQ8oTI0eW2xERERE1HxxyjFq0Q5fv3O3O1/9VGoBAW7WLKKIiIiIqMHYIkUtjiAIOJmajy2n07Hp5K0GPSe76MHFFhERERHR/VhIUYsgCALOpRdiy5l0bD2dgbT8Mq2e72RlKlFkRERERNQSsZCiZu1KVhG2nE7H1jMZuJ5TIm63MDZAeIALhndxwfs/n0NWYXmd46RkAFysTdGzrd1ji5mIiIiImj8WUtTs3LhTgq1nMrDldDouZhaJ200M5RjcyRlPB7pigJ8TTI0MAABVagFT15+EDNAopmomNZ8b6c8pzomIiIhIKyykqFlIzy/Db2cysPVMOk7fKhC3GxnI0K+jIyKD3DCokzMsTWq/pCM6uyJ2XPf71pGq5mJtirmR/ojo7PpY7oGIiIiIWg4WUtRk3S6qwPaz1S1Px1LyxO0GchlCfewRGeiGIQEusDZ/+Gx7EZ1dEebvgkNXs7Fz/xGE9+mFkPZcbJeIiIiIdMPpz3UUGxuLwMBAKBQKKBQKhISEYPv27eL+//73v+jfvz8UCgVkMhny8/NrnePkyZMICwuDjY0N7O3tMXnyZBQXF9d7TaVSidmzZ6NLly6wsLCAm5sboqKikJ6ernHcwoULERoaCnNzc9jY2DTWLT8W+aWV+PFYKsatPoJei37HnF/OiUVUz7Z2WDCyM478cxC+ndQLo5/waFARVcNALkOvtnYIdhDQq60diygiIiIi0hlbpHTk7u6OxYsXo0OHDhAEAWvXrsWIESNw6tQpBAQEoLS0FBEREYiIiEB0dHSt56enp2Pw4MF48cUX8Z///AeFhYV48803MWnSJEyYMKHOa5aWluLkyZP44IMPEBQUhLy8PMyYMQPPPPMMjh8/Lh5XWVmJF154ASEhIVizZo1kOWgsxRVVSDifia2nM7Dvym0oVfdGMgV52CAy0BXDA13ham2mxyiJiIiIiO5hIaWjyMhIjccLFy5EbGwsDh8+jICAALz55psAgD179tT5/K1bt8LIyAiff/455PLqhsGVK1ciMDAQ4eHhdT7H2toaCQkJGtv+85//oGfPnkhNTYWnpycAICYmBgDwzTff6Hh30itXqrDrYja2nE7HrovZqKhSi/v8XKwQGeSGyEA3eNqb6zFKIiIiIqK6sZBqBCqVChs2bEBJSQlCQkIa9JyKigoYGxuLRRQAmJlVt7icP3++wdcuKCiATCZrFl34KqvU2H/lNracTkfC+SyUVKrEfe0cLKqLpyBXtHey0mOUREREREQPx0LqESQlJSEkJATl5eWwtLTE5s2b4e/v36DnDhw4EG+99RaWLl2KGTNmoKSkBO+++y4AIC8v7yHPrlZeXo7Zs2djzJgxUCgUOt+HlKpUahy6fgdbTqdjx9lMFJZXifva2JiJxZO/a/VYMiIiIiKi5oCF1CPw9fVFYmIiCgoKsHHjRkyYMAF79+5tUDEVEBCAtWvX4q233kJ0dDQMDAwwffp0ODs7a7RS1UepVGL06NEQBAGxsbGNcTuNRq0WcPxGHracTse2pAzcKakU9zlZmeDpQDc8HeSKbh42LJ6IiIiIqFliIfUIjI2N0b59ewBAcHAwjh07hhUrVmDVqlUNev5LL72El156CVlZWbCwsIBMJsOnn34KZ2fnBz6vpoi6ceMGdu3a1SRaowRBwOlbBdhyOh2/nclAZuG99ZpszY0wrIsrIoPc8IQ3Z8sjIiIiouaPhVQjUqvVqKio0Pp5NYXTV199BVNTUwQFBdV7bE0RdeXKFezevRv29vY6x/uoBEHAxcwibDmdji1n0nEzt0zcZ2ViiCGdXRAZ5IZQH3sYGXCmfSIiIiJqOVhI6Sg6OhpDhw6Fp6cnioqKEBcXhz179iA+Ph4AkJmZiczMTFy9ehVA9XgqKysreHp6ws7ODkD1jHuhoaGwtLREQkICZs2ahYULF8LS0lK8jp+fHz766CM8++yzUCqVeP7553Hy5Els3boVKpUKmZmZAAA7OzsYGxsDAFJTU5Gbm4vU1FSoVCokJiYCANq3b69xbl1du12MraczsOVMOq5m31v3yszIAGH+zng60BX9fB1hYmjwyNciIiIiImqKWEjpKDs7G1FRUcjIyIC1tTUCAwMRHx+PsLAwANVTmddMQw4Affv2BQB8/fXXePnllwEAR48exdy5c1FcXAw/Pz+sWrUKf/vb37Bt2zbxeZcuXUJBQQEAIC0tDb/++isAoGvXrhrx7N69G/379wcAzJkzB2vXrhX3devWrdYx2rqZW4qtZzKw9Uw6zqUXituNDeUY4OuIyCA3DPRzgrkxX1JERERE1PLxU6+OHrbQ7bx58zBv3rwHHrNu3bpa25RKpcZjQbi3OK23t7fG4/p88803jbKGVFZhOX47U93ydCo1X9xuKJehTwcHPB3ohrAAZyhMjR75WkREREREzQkLKdJwp7gC289mYuuZdBxJzkVN3SaTASHt7BEZ5IaIABfYWhjrN1AiIiIiIj1iIUUoKFNi57lMbDmTgT+v5kClvtfqFexli8hAVwzr4gonhakeoyQiIiIiajpYSLVSpZVV+P1CNracTsfeS7dRqVKL+7q0scbTga4YHugKd1tzPUZJRERERNQ0sZBqRcqVKuy5dBtbz6TjjwvZKFOqxH0dnCzxTJAbng5yQ1sHCz1GSURERETU9LGQauGUKjUOXM3BltPpSDiXhaKKKnGfl705IgPdEBnkBl8XKz1GSURERETUvOh1ldTY2FgEBgZCoVBAoVAgJCQE27dvBwDk5ubiH//4B3x9fWFmZgZPT09Mnz5dnAq8RmpqKoYPHw5zc3M4OTlh1qxZqKqqqutyrYZKLeDgtRxE/5SEJxb+jolfH8NPJ9NQVFEFN2tTTO7bDlveeAp73u6Pt4f4sogiIiIiItKSXluk3N3dsXjxYnTo0AGCIGDt2rUYMWIETp06BUEQkJ6ejmXLlsHf3x83btzAlClTkJ6ejo0bNwIAVCoVhg8fDhcXFxw8eBAZGRmIioqCkZERFi1apM9be+zUagGnbuZhy+kM/JaUgdtFFeI+B0tjDO/iisggN3T3tIVcLtNjpEREREREzZ9eC6nIyEiNxwsXLkRsbCwOHz6MSZMmYdOmTeI+Hx8fLFy4EOPGjUNVVRUMDQ2xc+dOnD9/Hr///jucnZ3RtWtXLFiwALNnz8a8efNgbNyyp+gWBAHn0gux5XQ6tp7JQFp+mbjP2swIw7q44OlAN/RqawdDA702PhIRERERtShNZoyUSqXChg0bUFJSgpCQkDqPKSgogEKhgKFhddiHDh1Cly5d4OzsLB4zZMgQTJ06FefOnUO3bt3qPE9FRQUqKu612BQWFgKoXgz3rwviPk4qtYDD127jRI4M1ley0dvHEQZ1tB5dySrG1qRMbDubiZQ7peJ2CxMDhPk5YXigC0Lb2cPYsLp4EtQqKNWqWudpjWp+v/r8PbdkzK+0mF9pMb/SYn6lxfxKi/mVVlPLb0PjkAmCIDz8MOkkJSUhJCQE5eXlsLS0RFxcHIYNG1bruJycHAQHB2PcuHFYuHAhAGDy5Mm4ceMG4uPjxeNKS0thYWGBbdu2YejQoXVec968eYiJiam1PS4uDubm+pnu+/QdGX5KkSO/8l7hZGMs4DlvNYLsBdwuA07dkeFkjhwZZfeOMZILCLAV0N1eQCcbAcYG+oieiIiIiKhlKC0txUsvvSQ24tRH74VUZWUlUlNTUVBQgI0bN2L16tXYu3cv/P39xWMKCwsRFhYGOzs7/PrrrzAyMgKgeyFVV4uUh4cHcnJyHpgsqcSfy8I/fjiN+n4RnnZmSM29123PyECGvh0cMLyLCwb6OsLCpMk0LDZ5SqUSCQkJCAsLE19H1HiYX2kxv9JifqXF/EqL+ZUW8yutppbfwsJCODg4PLSQ0vsncGNjY7Rv3x4AEBwcjGPHjmHFihVYtWoVAKCoqAgRERGwsrLC5s2bNZLr4uKCo0ePapwvKytL3FcfExMTmJiY1NpuZGT02H95KrWAhdsv1VtEAUBqbhnkMuDJ9g6IDHLDEH8XWJvr/0XWnOnjd92aML/SYn6lxfxKi/mVFvMrLeZXWk0lvw2NocnNQKBWq8XWosLCQoSHh8PY2Bi//vorTE1NNY4NCQlBUlISsrOzxW0JCQlQKBQaLVpN2dHkXGQUlD/0uC/Gdse3k3phdA8PFlFERERERHqm1xap6OhoDB06FJ6enigqKkJcXBz27NmD+Ph4sYgqLS3F+vXrUVhYKE4K4ejoCAMDA4SHh8Pf3x/jx4/HkiVLkJmZiffffx/Tpk2rs8WpKcouengRBQAVVWqJIyEiIiIioobSayGVnZ2NqKgoZGRkwNraGoGBgYiPj0dYWBj27NmDI0eOAIDY9a9GcnIyvL29YWBggK1bt2Lq1KkICQmBhYUFJkyYgPnz5+vjdnTiZGX68IO0OI6IiIiIiKSn10JqzZo19e7r378/GjIPhpeXF7Zt29aYYT1WPdvawdXaFJkF5XWOk5IBcLE2Rc+2do87NCIiIiIiqkeTGyPV2hjIZZgbWT2e668rRtU8nhvpX+d6UkREREREpB8spJqAiM6uiB3XHS7Wmt33XKxNETuuOyI6u+opMiIiIiIiqovepz+nahGdXRHm74JDV7Oxc/8RhPfphZD2TmyJIiIiIiJqglhINSEGchl6tbXDnQsCerW1YxFFRERERNREsWsfERERERGRllhIERERERERaYmFFBERERERkZZYSBEREREREWmJhRQREREREZGWWEgRERERERFpiYUUERERERGRllhIERERERERaYmFFBERERERkZZYSBEREREREWmJhRQREREREZGWWEgRERERERFpiYUUERERERGRlgz1HUBTIAgCAKCwsFDPkQBKpRKlpaUoLCyEkZGRvsNpcZhfaTG/0mJ+pcX8Sov5lRbzKy3mV1pNLb81NUFNjVAfFlIAioqKAAAeHh56joSIiIiIiJqCoqIiWFtb17tfJjys1GoF1Go10tPTYWVlBZlMptdYCgsL4eHhgZs3b0KhUOg1lpaI+ZUW8yst5ldazK+0mF9pMb/SYn6l1dTyKwgCioqK4ObmBrm8/pFQbJECIJfL4e7uru8wNCgUiibxQmqpmF9pMb/SYn6lxfxKi/mVFvMrLeZXWk0pvw9qiarBySaIiIiIiIi0xEKKiIiIiIhISyykmhgTExPMnTsXJiYm+g6lRWJ+pcX8Sov5lRbzKy3mV1rMr7SYX2k11/xysgkiIiIiIiItsUWKiIiIiIhISyykiIiIiIiItMRCioiIiIiISEsspIiIiIiIiLTEQoqIiIiIiEhLLKSaGU6yKC2VSqXvEIh0olar9R1Ci1deXg6AuZYa/5+TDnNL1LhYSDUTlZWVAICKigoAfDNsbJmZmQAAAwMDFlMSuHLlChITE/UdRot17do1/Oc//8Ht27f1HUqLdf78efj5+eH06dOQy/lfZ2MrLCxEXl4eMjMzIZPJWKw2sqqqKgD3Pjswv43rr5/J+Bmt9eD/Bs3AxYsX8frrr2PIkCF4/fXXcfbsWchkMn2H1WJcu3YNbm5uGDZsGAAWU43t9OnT8PX1xaFDh/QdSot05swZ9OrVCzdu3EBOTg4AfkhqbImJiejTpw9SU1ORkJAAgDluTOfOncPTTz+NQYMGITAwEDt37mSx2oguXLiA6dOn44UXXsDMmTNx6NAh5rcRXbp0CXPnzsXLL7+M1atX4+LFi/wyoBFlZWXh8uXL+g6jXvyX1MQlJSUhNDQUJiYm8PHxQVZWFj799FMolUp+49FIsrOz4e7ujqtXryIiIgJAdTHFN8FHd/r0aYSGhuKdd97B1KlT9R1Oi5ORkYHnnnsOEyZMwCeffIJOnToBuNdyTY/u9OnTCAkJwZtvvokZM2Zg5cqVqKqqglwu53twI7h48SL69euH3r17Y9asWXj22WfxxhtvoLCwEAC/2X9U586dw5NPPglBEODo6IisrCz07dsXq1evRklJib7Da/bOnz+PXr164fz587hy5QpWr16NsLAw/PHHH3yPaAQXLlxAz5498cEHH+DcuXP6DqduAjVZ169fF3x8fIT33ntP3DZv3jzhlVdeEQRBEIqLiwVBEASVSqWX+FoCtVotHDp0SOjUqZMQFxcndOzYURg2bJi4Py0tTY/RNW8XLlwQDA0NhXfffVcQhOpcb9q0SVi0aJHw/fffC5cuXdJzhM3fjh07hNDQUEEQqt8H/vGPfwjDhw8XnnjiCWHdunVCWVmZniNs3k6dOiUYGhoK0dHRgiAIQnJysuDh4SEsWbJEz5G1DEqlUoiKihKioqLEbQkJCcJzzz0n5ObmCjdv3tRjdM1feXm5MGrUKOEf//iHuC09PV3w8/MTjI2NhU8++UQQhOr3ZtJeVVWVMG7cOGHs2LHitlOnTgmTJk0SDAwMhK1btwqCwM9oukpLSxNCQ0OFoKAgoWfPnsKkSZOEpKQkfYdVC1ukmrBjx47hySefxPTp08VthYWFOH78OHr16oWIiAjEx8fzW49HIJPJEBgYCH9/f/Tr1w8ff/wxLl++jOeeew6vvPIK/vvf/6K0tFTfYTZLe/fuhUqlwlNPPQW1Wo0BAwZg8eLFWLlyJRYtWoRhw4axu98junPnDgwNDQEA/fv3x5UrVxAUFIRevXphwoQJWLx4MQB+q6+LoqIivP/++3j77bexaNEiAIC9vT26du2K3bt36zm6lqGqqgrJyclo166duO3AgQPYvXs3+vbti86dOyMmJoYtrDpSKpW4cuUKAgICAFTn29XVFU8++SQGDx6Mt99+G7/99huHCuhIrVbj5s2b8PDwELd17doVH330ESZPnoznn38ehw8fZjdKHV28eBFWVlZYu3Yt/v73v+PUqVNYvnw5zp49q+/QNOm7kqP65eXlCRcvXhQfL1myRDA1NRWWL18urFy5Upg6dapgbGwsnDlzRo9RNn/l5eVCt27dxG+Pdu3aJdjY2AgymUzMrVKp1GeIzda8efMEAwMDwcfHRxg1apRw6dIloaqqSjh69KjwwgsvCD169BCysrL0HWaztX37dsHU1FRYu3at8Nxzz2nkct26dYJMJhMOHDigxwibt/tbTWu+VT5w4IAgk8mEjRs36iusFmX69OmClZWV8PnnnwvTpk0TzMzMhO+//144deqU8N133wkymUz46aef9B1ms1RZWSlERkYKkyZNEgoKCgRBEISUlBTBwcFB2Llzp/Dyyy8LTz75pFBSUqLnSJuvadOmCSEhIUJubq7G9tTUVGHUqFHCsGHDxNyTdsrKyoSDBw+Kj7/66iuhe/fuwqRJkzQ+9+q7RZVlchNUM9GBjY0NfH19AVTP2nflyhX8+uuvmDFjBl5//XXMmjULDg4OnA1NS/ePfRIEASYmJggMDIRSqQQAfPnll5DL5fDw8MCcOXMAQPzWnx7u/ok65s6di5iYGJibm+P9999Hx44dYWBggCeeeAKjR49GcnIysrKy9Bht83P/6zc8PBwjR47EvHnzcOHCBVhYWEClUkGtVmP8+PHo2rUrjh49qsdom6ea94KOHTuK22pa/gMDAxEZGYlffvkFZWVlHEupg/tzNmPGDERFReHQoUM4dOgQ5s+fj7/97W/o2rUrXnrpJYSGhmLnzp16jLb5qcmvkZERBg0aJE7m8c9//hMBAQF4/vnnERYWhiFDhiAtLU18vZP2+vbti/Lycnz99dcoKioSt3t4eCAyMhKJiYkoKCjQY4TNl6mpKXr37i0+njhxIqZPn45Tp05hxYoVYsvUggULcObMGX2FyckmmpL8/HwAtWeNEwQBxsbGWLlyJcLCwsQ3SZlMBldXV41mZapfTX7lcrlGDgEgICAAiYmJGDduHHbv3o1t27YhNjYW+/btw4svvqivkJuV+l6/7733HtatWwc/Pz8A9/6Td3Nzg6OjI8zNzR97rM1RXa9fuVyO5557DjY2NkhOTsa1a9dgYGAgHmNpaQlbW1s9Rt281OTYyMiozgJJJpPBysoKgwcPxk8//YS0tDR2rdbC/a/hmveIdu3a4T//+Q++/PJLGBoawsXFBUD1FzJVVVUwMTFB27Zt9RVys3J/fmumO58xYwamTp2Kdu3a4fLly1i4cCFiY2MBACYmJlAoFPoKt9lJT0/H1q1b8dNPP+H48eMAgNGjR6N379748ssvsX79euTm5orHP/HEEzA3N9cosKh+9+f3xIkTAKrfcwVBEN+PJ0yYoFFMvfjii4iJidHvl916bQ8j0fnz54W2bdsKH3zwgbjtrwMU/9p8+c9//lMICgoSMjIyHkuMzdnD8rt69WpBJpMJHTp0EE6cOCEIQnWXv99++024cuXKY4+3uakrv1VVVQ98zv/93/8JoaGhQl5ensTRNX915ff+7qbffvut4OvrKygUCuHnn38Wfv/9d+H9998X3N3dhevXr+sj5GZHm/dgtVothIaGCuPHjxcqKysfa5zNVUPeIyZNmiQMHz5cSE5OFnJycoS5c+cKbdq04XtwA9SV34qKCo1j/vpanTJlihAeHi6UlpY+lhibszNnzgjt2rUTevbsKTg4OAg9evQQvv/+e3H/yy+/LHTp0kV48803hatXrwq3b98W3nnnHaFjx45CTk6OHiNvHurK74YNGzSOuf/9eM2aNYKRkZFgbW0tnDp16jFHq4mFVBOQmpoqdO3aVejQoYPQuXNnISYmRtxX12wvFy9eFGbOnCnY2toKiYmJjzPUZulB+b3/P/LZs2cLx48f10eIzVpD81vjwoULwptvvinY2toKp0+ffpyhNksPyu/9H5T2798vTJgwQbC0tBT8/f2FwMBA4eTJk/oIudnR9j1YEAThtddeE3r16iXOnkr1a2h+169fL/Tr108wNjYWevfuLXh6evI13AAPyu/9X7jUfBHw559/CtOmTRMUCgXfgxvg6tWrgru7u/DOO+8I+fn5wvHjx4UJEyYIr7zyilBeXi4eFxMTI/Tp00eQyWRCcHCw4OLiwtdvAzwov1VVVRqNCGq1WqiqqhKmT58u2NraCmfPntVj5NVYSOmZWq0WPv74Y2HYsGHCzp07hblz5wp+fn71fhg9e/as8O677wpPPPEE3wAboCH55RTRutP29XvmzBlh5syZQpcuXfglQAM0JL9//db5ypUrQmZmpnDnzp3HHW6zpO1ruEZBQYFw7dq1xxlqs9SQ/N7fUpKUlCSsWbNG2LRpk3Djxg19hNysaPv6ValUwi+//CKEhITwPbgBKioqhLfeeksYPXq0xnvtmjVrBHt7+1qtTTk5OcL27duFAwcOcPr+BtA2v4IgCEePHhVkMplw7NixxxlqvTiCXs9kMhmioqLg7OyMsLAwBAUFAQC+//57CIKAuXPniovDyuVyBAQEYNy4cZg5cyacnJz0HH3T15D8mpqaQqVSwcDAQM/RNj/avn67dOmCqKgovPPOO+JYCKpfQ/JrbGyMqqoqsY+4j48PpzPWgravYaB6GmmFQsHxJQ3QkPwaGRlBqVTCyMgInTt3RufOnfUcdfOh7etXLpfjmWeewYABA2BlZaXn6Js+tVoNd3d3dOrUCcbGxhAEATKZDKGhobC0tBQn6qjJr729PSIiIvQcdfPR0Pze74knnkBubi5sbGwef8B10VsJR/VKT08Xv1WaN2+euH3Tpk16jKrlqC+/P//8MxfOawR8/UqLr1/pMcfSqi+/mzdvfujYSno45rdx3T/OtKabWUZGhtC+fXshNTVV3MdufLrRJb/6nvL8fmyR0oOMjAzcvHkTeXl5GDx4sNgSolarxZn4Jk+eDAD44YcfIAgCCgoKsGLFCty6dQtubm76DL/JY36lxfxKi/mVHnMsLeZXWsyvtGrym5ubi/DwcHHWyPt7rhQUFCAvL098zpw5c/Cf//wHV65cgZ2dHXsFPECLy68ei7hW6fTp04KXl5fQsWNHwdraWvDz8xPi4uLE8QwqlUqstNPT04U5c+YIMplMsLW15UQIDcD8Sov5lRbzKz3mWFrMr7SYX2k9LL81ub106ZLg6Ogo5ObmCgsWLBDMzMyY3wZoifllIfUYZWdnC35+fsI///lP4dq1a0JaWprw4osvCp06dRLmzp0rZGdnC4Kg2WQ5fvx4QaFQCOfOndNX2M0G8yst5ldazK/0mGNpMb/SYn6l1dD8CoIgZGVlCd26dRNefPFFwdjYuMl+yG9KWmp+WUg9RufOnRO8vb1rvSBmz54tdOnSRViyZIlQUlIibl+9erVgY2PDfrcNxPxKi/mVFvMrPeZYWsyvtJhfaWmT3/PnzwsymUwwMzPT+zpGzUVLzS8LqccoMTFRcHd3F/bt2ycIgqCxCN706dOFtm3bakxpnpmZycU0tcD8Sov5lRbzKz3mWFrMr7SYX2lpk9+MjAxh2rRpwoULF/QSa3PUUvMrEwRB0Pc4rdakZ8+esLS0xK5duwAAFRUVMDExAVA9pWP79u3x/fffczpuHTG/0mJ+pcX8So85lhbzKy3mV1oNzS8AlJeXw9TUVG+xNkctMb9yfQfQkpWUlKCoqAiFhYXitlWrVuHcuXN46aWXAAAmJiaoqqoCAPTt2xclJSUAwDfABmB+pcX8Sov5lR5zLC3mV1rMr7QeJb8AmsWHfH1qLfllISWR8+fP47nnnkO/fv3QqVMnfPfddwCATp06YcWKFUhISMALL7wApVIpLvKYnZ0NCwsLVFVVgQ2FD8b8Sov5lRbzKz3mWFrMr7SYX2kxv9JqTfnlOlISOH/+PPr27YuoqCj06NEDJ06cwMSJE+Hv749u3brhmWeegYWFBf7+978jMDAQfn5+MDY2xm+//YbDhw/D0JC/lgdhfqXF/EqL+ZUecywt5ldazK+0mF9ptbb8coxUI8vNzcWYMWPg5+eHFStWiNsHDBiALl264LPPPhO3FRUV4cMPP0Rubi5MTU0xdepU+Pv76yPsZoP5lRbzKy3mV3rMsbSYX2kxv9JifqXVGvPbvMq+ZkCpVCI/Px/PP/88gOqVxuVyOdq2bYvc3FwAgFA9WyKs+fcq3AAABS1JREFUrKzw8ccfaxxHD8b8Sov5lRbzKz3mWFrMr7SYX2kxv9JqjfltnlE3Yc7Ozli/fj369OkDAFCpVACANm3aiC8SmUwGuVyuMQBPJpM9/mCbIeZXWsyvtJhf6THH0mJ+pcX8Sov5lVZrzC8LKQl06NABQHWFbWRkBKC6As/OzhaP+eijj7B69WpxtpLm/CJ63JhfaTG/0mJ+pcccS4v5lRbzKy3mV1qtLb/s2ichuVwOQRDEF0hNNT5nzhx8+OGHOHXqVLMbVNeUML/SYn6lxfxKjzmWFvMrLeZXWsyvtFpLftkiJbGauTwMDQ3h4eGBZcuWYcmSJTh+/DiCgoL0HF3zx/xKi/mVFvMrPeZYWsyvtJhfaTG/0moN+W3+pWATV1OBGxkZ4csvv4RCocCBAwfQvXt3PUfWMjC/0mJ+pcX8So85lhbzKy3mV1rMr7RaQ37ZIvWYDBkyBABw8OBB9OjRQ8/RtDzMr7SYX2kxv9JjjqXF/EqL+ZUW8yutlpxfriP1GJWUlMDCwkLfYbRYzK+0mF9pMb/SY46lxfxKi/mVFvMrrZaaXxZSREREREREWmLXPiIiIiIiIi2xkCIiIiIiItISCykiIiIiIiItsZAiIiIiIiLSEgspIiIiIiIiLbGQIiIiIiIi0hILKSIialEEQcDgwYPFRSDv98UXX8DGxga3bt3SQ2RERNSSsJAiIqIWRSaT4euvv8aRI0ewatUqcXtycjLeeecd/Pvf/4a7u3ujXlOpVDbq+YiIqOljIUVERC2Oh4cHVqxYgbfffhvJyckQBAGTJk1CeHg4unXrhqFDh8LS0hLOzs4YP348cnJyxOfu2LEDTz31FGxsbGBvb4+nn34a165dE/enpKRAJpPhxx9/RL9+/WBqaorvvvtOH7dJRER6JBMEQdB3EERERFIYOXIkCgoK8Nxzz2HBggU4d+4cAgIC8OqrryIqKgplZWWYPXs2qqqqsGvXLgDApk2bIJPJEBgYiOLiYsyZMwcpKSlITEyEXC5HSkoK2rZtC29vb3zyySfo1q0bTE1N4erqque7JSKix4mFFBERtVjZ2dkICAhAbm4uNm3ahLNnz2L//v2Ij48Xj7l16xY8PDxw6dIldOzYsdY5cnJy4OjoiKSkJHTu3FkspJYvX44ZM2Y8ztshIqImhF37iIioxXJycsLrr7+OTp06YeTIkTh9+jR2794NS0tL8cfPzw8AxO57V65cwZgxY9CuXTsoFAp4e3sDAFJTUzXO3aNHj8d6L0RE1LQY6jsAIiIiKRkaGsLQsPq/u+LiYkRGRuLjjz+udVxN17zIyEh4eXnhyy+/hJubG9RqNTp37ozKykqN4y0sLKQPnoiImiwWUkRE1Gp0794dmzZtgre3t1hc3e/OnTu4dOkSvvzyS/Tp0wcAcODAgccdJhERNQPs2kdERK3GtGnTkJubizFjxuDYsWO4du0a4uPjMXHiRKhUKtja2sLe3h7//e9/cfXqVezatQtvvfWWvsMmIqImiIUUERG1Gm5ubvjzzz+hUqkQHh6OLl264M0334SNjQ3kcjnkcjl++OEHnDhxAp07d8bMmTOxdOlSfYdNRERNEGftIyIiIiIi0hJbpIiIiIiIiLTEQoqIiIiIiEhLLKSIiIiIiIi0xEKKiIiIiIhISyykiIiIiIiItMRCioiIiIiISEsspIiIiIiIiLTEQoqIiIiIiEhLLKSIiIiIiIi0xEKKiIiIiIhISyykiIiIiIiItMRCioiIiIiISEv/D0c0cP8DlvkgAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.plot(years_rac1p1, values_rac1p1, label='white riders', marker='o')\n", + "plt.plot(years_rac1p2, values_rac1p2, label='black riders', marker='o')\n", + "\n", + "# Annotate data points with numerical values\n", + "for year, value in zip(years_rac1p1, values_rac1p1):\n", + " plt.annotate(f'{value:.2f}', (year, value), textcoords=\"offset points\", xytext=(0,10), ha='center')\n", + "\n", + "for year, value in zip(years_rac1p2, values_rac1p2):\n", + " plt.annotate(f'{value:.2f}', (year, value), textcoords=\"offset points\", xytext=(0,-15), ha='center')\n", + "\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Annual Travel Time to Work(Hours)')\n", + "plt.title('Annual Travel Time to Work for Black Riders and White Riders 2013-2021')\n", + "plt.legend()\n", + "plt.xticks(rotation=45)\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Drawing a bar chart for average JWMNP of RAC1P=1 (White) and RAC1P=2 (Black) from 2014 to 2021\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Setting the bar width\n", + "bar_width = 0.35\n", + "\n", + "# Creating an index for each set of bars\n", + "index = range(len(years_rac1p1))\n", + "\n", + "# Plotting bars for RAC1P=1 (White)\n", + "plt.bar(index, values_rac1p1, bar_width, label='RAC1P=1 (White)')\n", + "\n", + "# Plotting bars for RAC1P=2 (Black) in the next position\n", + "plt.bar([i + bar_width for i in index], values_rac1p2, bar_width, label='RAC1P=2 (Black)')\n", + "\n", + "# Adding details to the plot\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Average JWMNP')\n", + "plt.title('Average JWMNP by Year for RAC1P=1 (White) and RAC1P=2 (Black)')\n", + "plt.xticks([i + bar_width / 2 for i in index], years_rac1p1)\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Your provided data\n", + "data = {\n", + " 'Year': ['2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021'],\n", + " 'RAC1P=1': [38.99, 39.83, 40.04, 40.34, 40.65, 40.93, 40.93, 40.75],\n", + " 'RAC1P=2': [45.74, 46.02, 45.57, 45.96, 46.08, 45.69, 45.69, 44.74]\n", + "}\n", + "# Convert to DataFrame\n", + "df = pd.DataFrame(data)\n", + "\n", + "\n", + "# Create two axes for bar plots\n", + "fig, ax1 = plt.subplots(figsize=(10, 6))\n", + "ax2 = ax1.twinx()\n", + "\n", + "# Line plot for RAC1P=1 on the first axis\n", + "df.plot(x='Year', y='RAC1P=1', kind='line', marker='o', ax=ax1, color='blue', linewidth=2)\n", + "\n", + "# Line plot for RAC1P=2 on the second axis\n", + "df.plot(x='Year', y='RAC1P=2', kind='line', marker='^', ax=ax2, color='green', linewidth=2)\n", + "\n", + "# Bar plot for RAC1P=1\n", + "df.plot(x='Year', y='RAC1P=1', kind='bar', ax=ax1, color='lightblue', position=0, width=0.4, alpha=0.7)\n", + "\n", + "# Bar plot for RAC1P=2\n", + "df.plot(x='Year', y='RAC1P=2', kind='bar', ax=ax2, color='lightgreen', position=1, width=0.4, alpha=0.7)\n", + "\n", + "# Setting labels and titles\n", + "ax1.set_xlabel('Year')\n", + "ax1.set_ylabel('Average JWMNP for RAC1P=1 (White)', color='blue')\n", + "ax2.set_ylabel('Average JWMNP for RAC1P=2 (Black)', color='green')\n", + "ax1.set_title('Average JWMNP (2014-2021) for RAC1P=1 (White) and RAC1P=2 (Black)')\n", + "\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ds", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e404ea1ef7f2df89c944f6f921e11737f45932cb Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:10:04 -0500 Subject: [PATCH 14/39] Update README.md --- README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/README.md b/README.md index 7974de6..c3b7dac 100644 --- a/README.md +++ b/README.md @@ -7,3 +7,15 @@ Create a new branch from dev branch, add changes to your team folder (on the new Open a Pull Request to your dev. Add your PM and TPM as reviewers. ***Do not delete any team folders.*** At the end of the semester during project wrap up open a final Pull Request to main from dev. + +# Data usage + +Question1 + +Question2 + +Question3 +Data from https://www.census.gov/programs-surveys/acs/microdata/access.2020.html#list-tab-735824205. We are using the ACS 5-Year PUMS from 2011 to 2021. With Label RAC1P=1,2,3,4,6,7,9 and JWTRN for year 2019 to year 2021( JWTR from 2011 to 2018). +Also we use the HISP to count for the Latinx group. + +Question4 From caa9acca61459c3bb55843130b9693c4de113473 Mon Sep 17 00:00:00 2001 From: laowangbushilaowang <53445096+laowangbushilaowang@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:14:17 -0500 Subject: [PATCH 15/39] Update README.md --- README.md | 82 ++++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 78 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index c3b7dac..5a1aff5 100644 --- a/README.md +++ b/README.md @@ -10,12 +10,86 @@ At the end of the semester during project wrap up open a final Pull Request to < # Data usage -Question1 +## Question1 -Question2 +## Q2 (Bohan Wang) -Question3 +### q2-final.ipynb + +All data should be downloaded from: https://www2.census.gov/programs-surveys/acs/data/pums/ + +Because the data before 2016 do not have area information: PUMA, so we choose 5 years data in MA from 2016-2021. + +#### Description of variables: +2016-2018: https://data.census.gov/mdat/#/search?ds=ACSPUMS5Y2018 (Can be searched on this website) + +2019-2022: https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2022.pdf + +##### The variables I used: + +PUMA: + + Public use microdata area code (PUMA) based on 2020 Census definition + (areas with population of 100,000 or more, use with ST for unique code) + 00100..81003 .Public use microdata area codes + +JWTRNS: + + Means of transportation to work + bb .N/A (not a worker-not in the labor force, including persons + .under 16 years; unemployed; employed, with a job but not at + .work; Armed Forces, with a job but not at work) + 01 .Car, truck, or van + 02 .Bus + 03 .Subway or elevated rail + 04 .Long-distance train or commuter rail + 05 .Light rail, streetcar, or trolley + 06 .Ferryboat + 07 .Taxicab + 08 .Motorcycle + 09 .Bicycle + 10 .Walked + 11 .Worked from home + 12 .Other method + +JWTR: + + Same as JWTRNS but data before 2019 use this name. + +RAC1P: + + Recoded detailed race code + 1 .White alone + 2 .Black or African American alone + 3 .American Indian alone + 4 .Alaska Native alone + 5 .American Indian and Alaska Native tribes specified; or + .American Indian or Alaska Native, not specified and no other + .races + 6 .Asian alone + 7 .Native Hawaiian and Other Pacific Islander alone + 8 .Some Other Race alone + 9 .Two or More Races + +JWMNP: + + Travel time to work + bbb .N/A (not a worker or worker who worked at home) + 1..200 .1 to 200 minutes to get to work (Top-coded) + + +_Data should be rename to psam_p25_year.csv and put them in the same dirt._ + +### Data processes include: + +1. Handle missing values by dropping lines with missing JWMNP. +2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf, create two dicts to store the area name and code of PUMA. +3. Find the areas which in all years to analysis their average. Calculate the time difference between black and white of different areas across these years. Use (average * 2 * 5 * 50) / 60 to calculate the travel time difference between B&W each year. +4. To draw Geography graph, I processed the result. Change all the areas names to zip codes, and then draw a graph using tableau. +5. Draw ring diagrams. + +## Question3 Data from https://www.census.gov/programs-surveys/acs/microdata/access.2020.html#list-tab-735824205. We are using the ACS 5-Year PUMS from 2011 to 2021. With Label RAC1P=1,2,3,4,6,7,9 and JWTRN for year 2019 to year 2021( JWTR from 2011 to 2018). Also we use the HISP to count for the Latinx group. -Question4 +## Question4 From af5416f0e6504eb1a95b64657b208e8d3fcc943f Mon Sep 17 00:00:00 2001 From: laowangbushilaowang <53445096+laowangbushilaowang@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:14:39 -0500 Subject: [PATCH 16/39] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5a1aff5..6d0b9c0 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ At the end of the semester during project wrap up open a final Pull Request to < ## Question1 -## Q2 (Bohan Wang) +## Question2 (Bohan Wang) ### q2-final.ipynb From b5bf52596899d51e2937414f8363cd080644c59c Mon Sep 17 00:00:00 2001 From: laowangbushilaowang <53445096+laowangbushilaowang@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:15:13 -0500 Subject: [PATCH 17/39] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6d0b9c0..45729e2 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ Open a Pull Request to your dev. Add your PM and TPM as reviewers. ***Do not de At the end of the semester during project wrap up open a final Pull Request to main from dev. -# Data usage + ## Question1 From 8d3ec0a29c45b9a59016f746b7d4d2714e41e2d7 Mon Sep 17 00:00:00 2001 From: yuli0316 <144358568+yuli0316@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:21:38 -0500 Subject: [PATCH 18/39] Update README.md --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 45729e2..d059389 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,11 @@ At the end of the semester during project wrap up open a final Pull Request to < ## Question1 +Data: https://www.census.gov/programs-surveys/acs/microdata/access.2022.html#list-tab-735824205 +I use the ACS 5-Year PUMS from 2013 to 2021 with the label RAC1P=1 and 2, JWTRNS(2019-2021)/JWTR(2013-2018)=2 and JWMNP. + +Data preprocess: + ## Question2 (Bohan Wang) From 65909b351f84f4fe39c732a94e255015cc6ea208 Mon Sep 17 00:00:00 2001 From: laowangbushilaowang <53445096+laowangbushilaowang@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:22:22 -0500 Subject: [PATCH 19/39] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d059389..edba432 100644 --- a/README.md +++ b/README.md @@ -88,7 +88,7 @@ _Data should be rename to psam_p25_year.csv and put them in the same dirt._ ### Data processes include: 1. Handle missing values by dropping lines with missing JWMNP. -2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf, create two dicts to store the area name and code of PUMA. +2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on [https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf](https://www2.census.gov/geo/maps/DC2020/PUMA/st25_ma/Catalog_PUMAmaps_st25.pdf), create two dicts to store the area name and code of PUMA. 3. Find the areas which in all years to analysis their average. Calculate the time difference between black and white of different areas across these years. Use (average * 2 * 5 * 50) / 60 to calculate the travel time difference between B&W each year. 4. To draw Geography graph, I processed the result. Change all the areas names to zip codes, and then draw a graph using tableau. 5. Draw ring diagrams. From 78aa24f81e47486e264981ff41cd4bf167c9ff85 Mon Sep 17 00:00:00 2001 From: yuli0316 <144358568+yuli0316@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:26:45 -0500 Subject: [PATCH 20/39] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index edba432..95b29aa 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,9 @@ Data: https://www.census.gov/programs-surveys/acs/microdata/access.2022.html#lis I use the ACS 5-Year PUMS from 2013 to 2021 with the label RAC1P=1 and 2, JWTRNS(2019-2021)/JWTR(2013-2018)=2 and JWMNP. Data preprocess: - +1. Through observation, I found a lot of missing values in JWTRNS. I preprocessed the data by removing the row with missing values. +2. Calculate the average commute time data of black and white people from 2013 to 2021. I also calculate the time difference between black and white from 2013 to 2021. I also calculate the difference between blacks and whites for each year using (mean * 2 * 5 * 50)/ 60. +3. I use t-test to test the commuting differences between blacks and whites before and after the covid to test whether there was statistical significance and whether the covid had an impact on the commuting differences between blacks and whites. ## Question2 (Bohan Wang) From f7779b4f49ca23c262b8aad8dc605565383eec3e Mon Sep 17 00:00:00 2001 From: yuli0316 <144358568+yuli0316@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:27:32 -0500 Subject: [PATCH 21/39] Update README.md From 444af8cafd1b1f4deb6e2629cdda485123ebb1a5 Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:30:11 -0500 Subject: [PATCH 22/39] Update README.md --- fa23-team/README.md | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/fa23-team/README.md b/fa23-team/README.md index c0937d3..b91583b 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -72,4 +72,24 @@ _Data should be rename to psam_p25_year.csv and put them in the same dirt._ 2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf, create two dicts to store the area name and code of PUMA. 3. Find the areas which in all years to analysis their average. Calculate the time difference between black and white of different areas across these years. Use (average * 2 * 5 * 50) / 60 to calculate the travel time difference between B&W each year. 4. To draw Geography graph, I processed the result. Change all the areas names to zip codes, and then draw a graph using tableau. -5. Draw ring diagrams. \ No newline at end of file +5. Draw ring diagrams. + +6. ## Q1 and Q4 + +### Overview +This dataset is derived from the `American Community Survey (ACS) Public Use Microdata Sample (PUMS)` and contains information on the means of transportation to work for different racial groups over the time span of 2014 to 2021. + +### Dataset Source +The data can be accessed from the U.S. Census Bureau at the following link: [ACS PUMS Data](https://www2.census.gov/programs-surveys/acs/data/pums/). + +### Time Span +The dataset covers the period from 2014 to 2021. + +### Variables Description +The following key variables are included in the dataset: + +- `JWTR/JWTRNS = 02`: This variable indicates that the means of transportation to work is by bus. +- `RAC1P = 1`: This variable represents individuals who identify as White alone. +- `RAC1P = 2`: This variable represents individuals who identify as Black or African American alone. + +### Data Preprocess From 2875b5323a01bd2a9f86ed14af7476dd5f4b6dc9 Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:36:03 -0500 Subject: [PATCH 23/39] Update README.md --- fa23-team/README.md | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/fa23-team/README.md b/fa23-team/README.md index b91583b..dd63d3c 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -74,7 +74,7 @@ _Data should be rename to psam_p25_year.csv and put them in the same dirt._ 4. To draw Geography graph, I processed the result. Change all the areas names to zip codes, and then draw a graph using tableau. 5. Draw ring diagrams. -6. ## Q1 and Q4 +## Q1 and Q4 ### Overview This dataset is derived from the `American Community Survey (ACS) Public Use Microdata Sample (PUMS)` and contains information on the means of transportation to work for different racial groups over the time span of 2014 to 2021. @@ -93,3 +93,16 @@ The following key variables are included in the dataset: - `RAC1P = 2`: This variable represents individuals who identify as Black or African American alone. ### Data Preprocess + +## Q3 ## + +Data from https://www.census.gov/programs-surveys/acs/microdata/access.2020.html#list-tab-735824205. +We are using the ACS 5-Year PUMS from 2011 to 2021. +With Label `RAC1P=1,2,3,4,6,7,9` + +JWTRN for year 2019 to year 2021 and JWTR from 2011 to 2018. + +Also, we use the `HISP=1` which identifies the Latinx or not to count for the Latinx group. + +For visualization, we only use the matplotlib package in python. + From 57fe2e0155a0cef8991fc7e647a7955b4932c45f Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:38:14 -0500 Subject: [PATCH 24/39] Update README.md --- fa23-team/README.md | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/fa23-team/README.md b/fa23-team/README.md index dd63d3c..4276ffd 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -92,7 +92,7 @@ The following key variables are included in the dataset: - `RAC1P = 1`: This variable represents individuals who identify as White alone. - `RAC1P = 2`: This variable represents individuals who identify as Black or African American alone. -### Data Preprocess + ## Q3 ## @@ -104,5 +104,13 @@ JWTRN for year 2019 to year 2021 and JWTR from 2011 to 2018. Also, we use the `HISP=1` which identifies the Latinx or not to count for the Latinx group. -For visualization, we only use the matplotlib package in python. +For visualization, we only use the matplotlib package in Python. + +## Data processing ## + +1. Handle missing values by dropping lines with missing JWMNP. +2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf, create two dicts to store the area name and code of PUMA. +3. Find the areas which in all years to analyze their average. Calculate the time difference between Latinx and other different race groups of different areas across these years. Use (average * 2 * 5 * 50) / 60 to calculate the travel time difference between B&W each year. +4. Convert all the non-numerical value to int or float +5. Draw ring diagrams. From de734c4b4c467c5f5f616fb6e9243a3f5b1f0e1a Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:38:48 -0500 Subject: [PATCH 25/39] Update README.md --- README.md | 89 ------------------------------------------------------- 1 file changed, 89 deletions(-) diff --git a/README.md b/README.md index 95b29aa..0deea27 100644 --- a/README.md +++ b/README.md @@ -10,93 +10,4 @@ At the end of the semester during project wrap up open a final Pull Request to < -## Question1 -Data: https://www.census.gov/programs-surveys/acs/microdata/access.2022.html#list-tab-735824205 -I use the ACS 5-Year PUMS from 2013 to 2021 with the label RAC1P=1 and 2, JWTRNS(2019-2021)/JWTR(2013-2018)=2 and JWMNP. -Data preprocess: -1. Through observation, I found a lot of missing values in JWTRNS. I preprocessed the data by removing the row with missing values. -2. Calculate the average commute time data of black and white people from 2013 to 2021. I also calculate the time difference between black and white from 2013 to 2021. I also calculate the difference between blacks and whites for each year using (mean * 2 * 5 * 50)/ 60. -3. I use t-test to test the commuting differences between blacks and whites before and after the covid to test whether there was statistical significance and whether the covid had an impact on the commuting differences between blacks and whites. - -## Question2 (Bohan Wang) - -### q2-final.ipynb - -All data should be downloaded from: https://www2.census.gov/programs-surveys/acs/data/pums/ - -Because the data before 2016 do not have area information: PUMA, so we choose 5 years data in MA from 2016-2021. - -#### Description of variables: -2016-2018: https://data.census.gov/mdat/#/search?ds=ACSPUMS5Y2018 (Can be searched on this website) - -2019-2022: https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2022.pdf - -##### The variables I used: - -PUMA: - - Public use microdata area code (PUMA) based on 2020 Census definition - (areas with population of 100,000 or more, use with ST for unique code) - 00100..81003 .Public use microdata area codes - -JWTRNS: - - Means of transportation to work - bb .N/A (not a worker-not in the labor force, including persons - .under 16 years; unemployed; employed, with a job but not at - .work; Armed Forces, with a job but not at work) - 01 .Car, truck, or van - 02 .Bus - 03 .Subway or elevated rail - 04 .Long-distance train or commuter rail - 05 .Light rail, streetcar, or trolley - 06 .Ferryboat - 07 .Taxicab - 08 .Motorcycle - 09 .Bicycle - 10 .Walked - 11 .Worked from home - 12 .Other method - -JWTR: - - Same as JWTRNS but data before 2019 use this name. - -RAC1P: - - Recoded detailed race code - 1 .White alone - 2 .Black or African American alone - 3 .American Indian alone - 4 .Alaska Native alone - 5 .American Indian and Alaska Native tribes specified; or - .American Indian or Alaska Native, not specified and no other - .races - 6 .Asian alone - 7 .Native Hawaiian and Other Pacific Islander alone - 8 .Some Other Race alone - 9 .Two or More Races - -JWMNP: - - Travel time to work - bbb .N/A (not a worker or worker who worked at home) - 1..200 .1 to 200 minutes to get to work (Top-coded) - - -_Data should be rename to psam_p25_year.csv and put them in the same dirt._ - -### Data processes include: - -1. Handle missing values by dropping lines with missing JWMNP. -2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on [https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf](https://www2.census.gov/geo/maps/DC2020/PUMA/st25_ma/Catalog_PUMAmaps_st25.pdf), create two dicts to store the area name and code of PUMA. -3. Find the areas which in all years to analysis their average. Calculate the time difference between black and white of different areas across these years. Use (average * 2 * 5 * 50) / 60 to calculate the travel time difference between B&W each year. -4. To draw Geography graph, I processed the result. Change all the areas names to zip codes, and then draw a graph using tableau. -5. Draw ring diagrams. - -## Question3 -Data from https://www.census.gov/programs-surveys/acs/microdata/access.2020.html#list-tab-735824205. We are using the ACS 5-Year PUMS from 2011 to 2021. With Label RAC1P=1,2,3,4,6,7,9 and JWTRN for year 2019 to year 2021( JWTR from 2011 to 2018). -Also we use the HISP to count for the Latinx group. - -## Question4 From ef6be2693004294005e02d4d119df8f1bff7a371 Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:40:57 -0500 Subject: [PATCH 26/39] Update README.md --- fa23-team/README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/fa23-team/README.md b/fa23-team/README.md index 4276ffd..4294ff5 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -92,6 +92,15 @@ The following key variables are included in the dataset: - `RAC1P = 1`: This variable represents individuals who identify as White alone. - `RAC1P = 2`: This variable represents individuals who identify as Black or African American alone. + ## Data processing ## + +1. Handle missing values by dropping lines with missing JWMNP. +2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf, create two dicts to store the area name and code of PUMA. +3. Find the areas which in all years to analyze their average. Calculate the time difference between Latinx and other different race groups of different areas across these years. Use (average * 2 * 5 * 50) / 60 to calculate the travel time difference between B&W each year. +4. Convert all the non-numerical value to int or float +5. Draw ring diagrams. + + ## Q3 ## From a4f0b4e33841b7fbfdd2614d28744d37556b035e Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:41:38 -0500 Subject: [PATCH 27/39] Update README.md --- fa23-team/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fa23-team/README.md b/fa23-team/README.md index 4294ff5..7cd8780 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -92,7 +92,7 @@ The following key variables are included in the dataset: - `RAC1P = 1`: This variable represents individuals who identify as White alone. - `RAC1P = 2`: This variable represents individuals who identify as Black or African American alone. - ## Data processing ## +## Data processing ## 1. Handle missing values by dropping lines with missing JWMNP. 2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf, create two dicts to store the area name and code of PUMA. 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z1qlMYA&U)-0GvKh;}9gde`o|4Nw#jl`T)rytv?713PzF3kWeUyd@KwKaJ8hdK@eal z@?bAOApmgxXAS^8B$&Kj5J))qK(9i8Qwqq&0=~eZ+N ziL@s7Wl-{70BoQ@q`3$5kU)53HlQ#NB$~vVH@S3=}2Vd?+vyPQIT4 zG9;X=PJun~ANI-p2}$z;XhF&21!TZk2jqH){TCjRIso>`19bo#g@Hnmtp~9Gfe(yD zfZ+Rwf9zlT`>B(Isr7Ahhy8Ovw5{FE_a8lyS~#2&a4H1wtOQ;ZmF+EXJiqQ`WT+jT YOdXtlJ)|Hg1Q;l8Y5@U^h9dQU0A}7G^Z)<= literal 0 HcmV?d00001 From ac89e292bb065e212b27815f1de6dd8c0492389a Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:49:25 -0500 Subject: [PATCH 29/39] Update README.md --- fa23-team/README.md | 26 +++++++++++--------------- 1 file changed, 11 insertions(+), 15 deletions(-) diff --git a/fa23-team/README.md b/fa23-team/README.md index 7cd8780..786398d 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -74,36 +74,32 @@ _Data should be rename to psam_p25_year.csv and put them in the same dirt._ 4. To draw Geography graph, I processed the result. Change all the areas names to zip codes, and then draw a graph using tableau. 5. Draw ring diagrams. -## Q1 and Q4 +## Question 1 & 4（Yu Liang & Xinyu Zhang) ### Overview -This dataset is derived from the `American Community Survey (ACS) Public Use Microdata Sample (PUMS)` and contains information on the means of transportation to work for different racial groups over the time span of 2014 to 2021. +This dataset is derived from the `American Community Survey (ACS) Public Use Microdata Sample (PUMS)` and contains information on the means of transportation to work for different racial groups over the time span of 2013 to 2021. ### Dataset Source The data can be accessed from the U.S. Census Bureau at the following link: [ACS PUMS Data](https://www2.census.gov/programs-surveys/acs/data/pums/). ### Time Span -The dataset covers the period from 2014 to 2021. +The dataset covers the period from 2013 to 2021. ### Variables Description The following key variables are included in the dataset: -- `JWTR/JWTRNS = 02`: This variable indicates that the means of transportation to work is by bus. +- `JWTR(2013-2018)/JWTRNS(1029-2021) = 02`: This variable indicates that the means of transportation to work is by bus. - `RAC1P = 1`: This variable represents individuals who identify as White alone. - `RAC1P = 2`: This variable represents individuals who identify as Black or African American alone. -## Data processing ## - -1. Handle missing values by dropping lines with missing JWMNP. -2. According to the PUMA information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf and updated information on https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf, create two dicts to store the area name and code of PUMA. -3. Find the areas which in all years to analyze their average. Calculate the time difference between Latinx and other different race groups of different areas across these years. Use (average * 2 * 5 * 50) / 60 to calculate the travel time difference between B&W each year. -4. Convert all the non-numerical value to int or float -5. Draw ring diagrams. - - - +### Data Preprocess +1. Through observation, I found a lot of missing values in JWTRNS. I preprocessed the data by removing the row with missing values. +2. I calculate the average commute time data of black and white people from 2013 to 2021. + I also calculate the time difference between black and white from 2013 to 2021. + I also calculate the difference between blacks and whites for each year using (average commute time * 2 * 5 * 50)/ 60. +4. I use T-test to test the commuting differences between blacks and whites before and after the covid to test whether there was statistical significance and whether the covid had an impact on the commuting differences between blacks and whites. -## Q3 ## +## Q3-Xiang Li ## Data from https://www.census.gov/programs-surveys/acs/microdata/access.2020.html#list-tab-735824205. We are using the ACS 5-Year PUMS from 2011 to 2021. From 48ca2747b6dfd2c31ca7616800e7b37f727447b1 Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:51:29 -0500 Subject: [PATCH 30/39] Update README.md --- fa23-team/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/fa23-team/README.md b/fa23-team/README.md index 786398d..535d4ac 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -98,6 +98,7 @@ The following key variables are included in the dataset: I also calculate the time difference between black and white from 2013 to 2021. I also calculate the difference between blacks and whites for each year using (average commute time * 2 * 5 * 50)/ 60. 4. I use T-test to test the commuting differences between blacks and whites before and after the covid to test whether there was statistical significance and whether the covid had an impact on the commuting differences between blacks and whites. +5. I used linear regression models to predict the average travel time to work for both White and Black riders for the years 2020 and 2021. Then plot a graph visually compares these predictions against the actual data. ## Q3-Xiang Li ## From b5e96ce1c6cbada61b70455ee6d264f41473c8b8 Mon Sep 17 00:00:00 2001 From: yuli0316 <144358568+yuli0316@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:52:37 -0500 Subject: [PATCH 31/39] Update README.md --- fa23-team/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fa23-team/README.md b/fa23-team/README.md index 535d4ac..d88b1cd 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -88,7 +88,7 @@ The dataset covers the period from 2013 to 2021. ### Variables Description The following key variables are included in the dataset: -- `JWTR(2013-2018)/JWTRNS(1029-2021) = 02`: This variable indicates that the means of transportation to work is by bus. +- `JWTR(2013-2018)/JWTRNS(2019-2021) = 02`: This variable indicates that the means of transportation to work is by bus. - `RAC1P = 1`: This variable represents individuals who identify as White alone. - `RAC1P = 2`: This variable represents individuals who identify as Black or African American alone. From 39f7e3cfd753c42315c0d5504c42294dc1775487 Mon Sep 17 00:00:00 2001 From: yuli0316 <144358568+yuli0316@users.noreply.github.com> Date: Tue, 28 Nov 2023 23:54:12 -0500 Subject: [PATCH 32/39] Update README.md --- fa23-team/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fa23-team/README.md b/fa23-team/README.md index d88b1cd..4197139 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -97,7 +97,7 @@ The following key variables are included in the dataset: 2. I calculate the average commute time data of black and white people from 2013 to 2021. I also calculate the time difference between black and white from 2013 to 2021. I also calculate the difference between blacks and whites for each year using (average commute time * 2 * 5 * 50)/ 60. -4. I use T-test to test the commuting differences between blacks and whites before and after the covid to test whether there was statistical significance and whether the covid had an impact on the commuting differences between blacks and whites. +4. I used T-test to test the commuting differences between blacks and whites before and after the covid. I want to test whether there was statistical significance and whether the covid had an impact on the commuting differences between blacks and whites. 5. I used linear regression models to predict the average travel time to work for both White and Black riders for the years 2020 and 2021. Then plot a graph visually compares these predictions against the actual data. ## Q3-Xiang Li ## From 78a7d1b1593817f51f13360e07e4090ba085d49e Mon Sep 17 00:00:00 2001 From: laowangbushilaowang <53445096+laowangbushilaowang@users.noreply.github.com> Date: Tue, 12 Dec 2023 19:56:46 -0500 Subject: [PATCH 33/39] wbh update --- fa23-team/q2-final.ipynb | 58 +-- fa23-team/q2_ana.ipynb | 1015 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 1044 insertions(+), 29 deletions(-) create mode 100644 fa23-team/q2_ana.ipynb diff --git a/fa23-team/q2-final.ipynb b/fa23-team/q2-final.ipynb index 9be9c6b..158b5c5 100644 --- a/fa23-team/q2-final.ipynb +++ b/fa23-team/q2-final.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 60, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -18,20 +18,20 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_30912\\781117606.py:5: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_10600\\781117606.py:5: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_2018 = pd.read_csv('./psam_p25_2018.csv')\n", - "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_30912\\781117606.py:6: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_10600\\781117606.py:6: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_2019 = pd.read_csv('./psam_p25_2019.csv')\n", - "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_30912\\781117606.py:7: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_10600\\781117606.py:7: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_2020 = pd.read_csv('./psam_p25_2020.csv')\n", - "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_30912\\781117606.py:8: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_10600\\781117606.py:8: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_2021 = pd.read_csv('./psam_p25_2021.csv')\n" ] } @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -270,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -313,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -983,7 +983,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1113,7 +1113,7 @@ "[622 rows x 4 columns]" ] }, - "execution_count": 68, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1140,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1283,7 +1283,7 @@ "[622 rows x 4 columns]" ] }, - "execution_count": 69, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1312,7 +1312,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1455,7 +1455,7 @@ "[552 rows x 4 columns]" ] }, - "execution_count": 70, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1478,7 +1478,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1621,7 +1621,7 @@ "[360 rows x 4 columns]" ] }, - "execution_count": 71, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1640,7 +1640,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1857,7 +1857,7 @@ "29 Worcester County (West Central) -3.324074" ] }, - "execution_count": 72, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1889,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -1898,7 +1898,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1971,7 +1971,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -2001,7 +2001,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -2046,7 +2046,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -2077,7 +2077,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2123,7 +2123,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 20, "metadata": {}, "outputs": [ { diff --git a/fa23-team/q2_ana.ipynb b/fa23-team/q2_ana.ipynb new file mode 100644 index 0000000..62ae5b3 --- /dev/null +++ b/fa23-team/q2_ana.ipynb @@ -0,0 +1,1015 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data should be renamed as psam_p25_year.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_18436\\781117606.py:5: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2018 = pd.read_csv('./psam_p25_2018.csv')\n", + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_18436\\781117606.py:6: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2019 = pd.read_csv('./psam_p25_2019.csv')\n", + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_18436\\781117606.py:7: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2020 = pd.read_csv('./psam_p25_2020.csv')\n", + "C:\\Users\\85816\\AppData\\Local\\Temp\\ipykernel_18436\\781117606.py:8: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2021 = pd.read_csv('./psam_p25_2021.csv')\n" + ] + } + ], + "source": [ + "# df_2014 = pd.read_csv('./psam_p25_2014.csv')\n", + "# df_2015 = pd.read_csv('./psam_p25_2015.csv')\n", + "df_2016 = pd.read_csv('./psam_p25_2016.csv')\n", + "df_2017 = pd.read_csv('./psam_p25_2017.csv')\n", + "df_2018 = pd.read_csv('./psam_p25_2018.csv')\n", + "df_2019 = pd.read_csv('./psam_p25_2019.csv')\n", + "df_2020 = pd.read_csv('./psam_p25_2020.csv')\n", + "df_2021 = pd.read_csv('./psam_p25_2021.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RT object\n", + "SERIALNO object\n", + "DIVISION int64\n", + "SPORDER int64\n", + "PUMA int64\n", + " ... \n", + "PWGTP76 int64\n", + "PWGTP77 int64\n", + "PWGTP78 int64\n", + "PWGTP79 int64\n", + "PWGTP80 int64\n", + "Length: 287, dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2021.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete rows in each DataFrame where the JWMNP column has a NaN value\n", + "# df_2014 = df_2014.dropna(subset=['JWMNP'])\n", + "# df_2015 = df_2015.dropna(subset=['JWMNP'])\n", + "df_2016 = df_2016.dropna(subset=['JWMNP'])\n", + "df_2017 = df_2017.dropna(subset=['JWMNP'])\n", + "df_2018 = df_2018.dropna(subset=['JWMNP'])\n", + "df_2019 = df_2019.dropna(subset=['JWMNP'])\n", + "df_2020 = df_2020.dropna(subset=['JWMNP'])\n", + "df_2021 = df_2021.dropna(subset=['JWMNP'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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" + ], + "text/plain": [ + " RAC1P PUMA Year JWMNP AGEP SCHL WKW \\\n", + "0 1 100 2016 43.516129 32.741935 17.967742 2.612903 \n", + "1 1 100 2017 46.419355 33.870968 17.612903 2.612903 \n", + "2 1 100 2018 40.777778 33.333333 17.666667 2.666667 \n", + "3 1 100 2019 41.392857 36.714286 17.535714 2.857143 \n", + "4 1 100 2020 38.000000 38.555556 17.851852 2.851852 \n", + ".. ... ... ... ... ... ... ... \n", + "551 2 4800 2021 54.750000 30.250000 17.250000 3.250000 \n", + "552 2 4903 2016 37.500000 39.000000 17.000000 3.500000 \n", + "553 2 4903 2017 37.500000 39.000000 17.000000 3.500000 \n", + "554 2 4903 2020 20.000000 34.000000 17.000000 1.000000 \n", + "555 2 4903 2021 20.000000 34.000000 17.000000 1.000000 \n", + "\n", + " WAGP \n", + "0 14767.096774 \n", + "1 23122.580645 \n", + "2 21774.074074 \n", + "3 26380.357143 \n", + "4 27142.592593 \n", + ".. ... \n", + "551 19775.000000 \n", + "552 40250.000000 \n", + "553 40250.000000 \n", + "554 22600.000000 \n", + "555 22600.000000 \n", + "\n", + "[556 rows x 8 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import statsmodels.api as sm\n", + "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n", + "\n", + "# Initialize the StandardScaler\n", + "\n", + "\n", + "dataframes = {\n", + " '2016': df_2016,\n", + " '2017': df_2017,\n", + " '2018': df_2018,\n", + " '2019': df_2019,\n", + " '2020': df_2020,\n", + " '2021': df_2021\n", + "}\n", + "\n", + "# required_columns = ['JWMNP', 'PUMA', 'RAC1P', 'AGEP', 'SCHL', 'WKW', 'SEX', 'OCCP', 'INDP']\n", + "required_columns = ['JWMNP','PUMA', 'RAC1P', 'AGEP', 'SCHL', 'WKW',\"WAGP\"]\n", + "# Check that each dataset contains all required columns and filter rows with JWTR or JWTRNS equal to 2\n", + "for year, df in dataframes.items():\n", + " missing_columns = [col for col in required_columns if col not in df.columns]\n", + " if missing_columns:\n", + " raise ValueError(f\"数据集{year}年缺少以下列: {missing_columns}\")\n", + "\n", + " # If the JWTRNS column exists, it is used\n", + " if 'JWTRNS' in df.columns:\n", + " df['JWTR'] = df['JWTRNS']\n", + " df = df[df['JWTR'] == 2]\n", + " df=df[df['RAC1P'].isin([1,2])]\n", + "\n", + " # Filter out the rows that contain the required columns, and remove the rows that contain NaN from those columns\n", + " df = df[required_columns].dropna(axis=1)\n", + " df['Year'] = year\n", + "\n", + " dataframes[year] = df\n", + "\n", + "# Combine data for all years\n", + "all_data = pd.concat(dataframes.values())\n", + "all_data['SCHL']=all_data['SCHL']\n", + "\n", + "# Group by RAC1P, PUMA and Year, and calculate the average for each group\n", + "grouped_data = all_data.groupby(['RAC1P', 'PUMA', 'Year']).mean().reset_index()\n", + "\n", + "\n", + "grouped_data\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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D_JWMNPD_AGEPD_SCHLD_WKWD_WAGP
0-23.5161299.2580651.032258-1.6129035232.903226
112.650943-12.481132-0.320755-0.075472-16795.283019
21.742965-4.2451300.821429-0.073052-295.811688
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4-11.500000-27.333333-2.8333332.166667-20933.333333
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24312.750000-15.134615-2.4423081.865385-64201.923077
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" + ], + "text/plain": [ + " D_JWMNP D_AGEP D_SCHL D_WKW D_WAGP\n", + "0 -23.516129 9.258065 1.032258 -1.612903 5232.903226\n", + "1 12.650943 -12.481132 -0.320755 -0.075472 -16795.283019\n", + "2 1.742965 -4.245130 0.821429 -0.073052 -295.811688\n", + "3 9.550000 -15.200000 -2.300000 3.250000 -15527.000000\n", + "4 -11.500000 -27.333333 -2.833333 2.166667 -20933.333333\n", + ".. ... ... ... ... ...\n", + "240 -25.400000 -21.400000 -1.300000 0.700000 -38754.000000\n", + "241 -9.041667 -10.250000 -0.708333 -0.708333 -7512.500000\n", + "242 24.142857 -19.928571 2.000000 3.500000 -61128.571429\n", + "243 12.750000 -15.134615 -2.442308 1.865385 -64201.923077\n", + "244 -57.000000 -16.812500 -3.000000 -0.062500 -89475.000000\n", + "\n", + "[245 rows x 5 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create subsets of RAC1P == 1 and RAC1P == 2, respectively\n", + "data_rac1p_1 = all_data[all_data['RAC1P'] == 1]\n", + "data_rac1p_2 = all_data[all_data['RAC1P'] == 2]\n", + "\n", + "# Group by Year and PUMA, and calculate the average\n", + "grouped_rac1p_1 = data_rac1p_1.groupby(['Year', 'PUMA']).mean().reset_index()\n", + "grouped_rac1p_2 = data_rac1p_2.groupby(['Year', 'PUMA']).mean().reset_index()\n", + "\n", + "# Combine results\n", + "grouped_data = pd.merge(grouped_rac1p_1, grouped_rac1p_2, on=['Year', 'PUMA'], suffixes=('_1', '_2'))\n", + "\n", + "# Calculate the difference and traverse all columns\n", + "for col in grouped_rac1p_1.columns:\n", + " if col not in ['Year', 'PUMA'] and col+'_1' in grouped_data and col+'_2' in grouped_data:\n", + " grouped_data['D_' + col] = grouped_data[col + '_2'] - grouped_data[col + '_1']\n", + "\n", + "# Keep only the columns beginning with Year, PUMA, and Difference\n", + "columns_to_keep = [col for col in grouped_data.columns if col.startswith('D_')]\n", + "grouped_data = grouped_data[columns_to_keep]\n", + "grouped_data=grouped_data.drop(\"D_RAC1P\",axis=1)\n", + "\n", + "grouped_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.149\n", + "Model: OLS Adj. R-squared: 0.135\n", + "Method: Least Squares F-statistic: 10.54\n", + "Date: Tue, 12 Dec 2023 Prob (F-statistic): 6.97e-08\n", + "Time: 19:55:11 Log-Likelihood: -1067.1\n", + "No. Observations: 245 AIC: 2144.\n", + "Df Residuals: 240 BIC: 2162.\n", + "Df Model: 4 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const -75.9475 12.573 -6.040 0.000 -100.716 -51.179\n", + "D_AGEP 4.6794 7.761 0.603 0.547 -10.610 19.969\n", + "D_SCHL 59.2484 11.725 5.053 0.000 36.152 82.344\n", + "D_WKW 24.4000 8.505 2.869 0.004 7.645 41.155\n", + "D_WAGP 20.3719 11.787 1.728 0.085 -2.846 43.590\n", + "==============================================================================\n", + "Omnibus: 5.205 Durbin-Watson: 2.187\n", + "Prob(Omnibus): 0.074 Jarque-Bera (JB): 4.991\n", + "Skew: -0.280 Prob(JB): 0.0825\n", + "Kurtosis: 3.419 Cond. No. 21.5\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "\n", + "df_filtered =grouped_data\n", + "\n", + "X = df_filtered.drop('D_JWMNP', axis=1)\n", + "y = df_filtered['D_JWMNP'] \n", + "\n", + "\n", + "\n", + "#Use np.asarray to ensure that both X and y are appropriate arrays of numeric types\n", + "X_array = np.asarray(X, dtype=float)\n", + "y_array = np.asarray(y, dtype=float)\n", + "\n", + "scaler = MinMaxScaler()\n", + "\n", + "# Fit the scaler on the features, excluding the constant term and the dependent variable\n", + "X_array = scaler.fit_transform(X_array)\n", + "\n", + "\n", + "new_columns =list(X.columns) # Adds 'const' to the beginning of the column name list\n", + "X = pd.DataFrame(X_array, columns=new_columns)\n", + "# Add a constant term (intercept) to the model\n", + "\n", + "X_cons = sm.add_constant(X)\n", + "# Fit a linear regression model\n", + "model = sm.OLS(y_array, X_cons, missing='drop').fit()\n", + "\n", + "print(model.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected features: Index(['D_SCHL'], dtype='object')\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.117\n", + "Model: OLS Adj. R-squared: 0.114\n", + "Method: Least Squares F-statistic: 32.30\n", + "Date: Tue, 12 Dec 2023 Prob (F-statistic): 3.76e-08\n", + "Time: 19:55:11 Log-Likelihood: -1071.6\n", + "No. Observations: 245 AIC: 2147.\n", + "Df Residuals: 243 BIC: 2154.\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const -50.0922 7.947 -6.303 0.000 -65.747 -34.438\n", + "D_SCHL 58.5576 10.303 5.684 0.000 38.263 78.852\n", + "==============================================================================\n", + "Omnibus: 5.765 Durbin-Watson: 2.204\n", + "Prob(Omnibus): 0.056 Jarque-Bera (JB): 5.487\n", + "Skew: -0.317 Prob(JB): 0.0643\n", + "Kurtosis: 3.367 Cond. No. 13.3\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import statsmodels.api as sm\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.feature_selection import RFE\n", + "\n", + "\n", + "# Using sklearn's linear regression model\n", + "lin_reg_full = LinearRegression()\n", + "lin_reg_full.fit(X_cons, y_array)\n", + "\n", + "# Feature selection using RFE\n", + "rfe_full = RFE(lin_reg_full, n_features_to_select=1) # Adjust n_features_to_select as needed\n", + "rfe_full = rfe_full.fit(X_cons, y_array)\n", + "\n", + "# Print selected features\n", + "print(f\"Selected features: {X_cons.columns[rfe_full.support_]}\")\n", + "\n", + "# Filter columns using the boolean array 'rfe_full.support_'\n", + "X_selected_full = X_cons.loc[:, rfe_full.support_]\n", + "\n", + "# Fit the model using only the selected features\n", + "lin_reg_full.fit(X_selected_full, y_array)\n", + "\n", + "# For statsmodels, add a constant to the selected features\n", + "X_selected_full = sm.add_constant(X_selected_full)\n", + "\n", + "# Fit the model using statsmodels\n", + "model_full = sm.OLS(y_array, X_selected_full).fit()\n", + "\n", + "# Print the summary of the regression model\n", + "print(model_full.summary())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 6))\n", + "sns.scatterplot(x=X['D_SCHL'], y=y)\n", + "\n", + "# Adding a regression line\n", + "sns.regplot(x=X['D_SCHL'], y=y, ci=None, scatter_kws={'color':'red', 's':9}, line_kws={'color':'blue'})\n", + "\n", + "plt.title(f'Scatter Plot with Regression Line for D_SCHL')\n", + "plt.xlabel('D_SCHL')\n", + "plt.ylabel('D_JWMNP')\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearPUMAD_JWMNPD_SCHL
2520163306-0.0714290.406122
27201636021.6666671.400000
2920163900-40.857143-1.785714
65201733060.8470870.224919
672017360217.8151260.403361
6920173900-43.562500-1.375000
10720183306-1.4727270.449091
1102018360220.654902-0.121569
11220183900-48.509804-0.333333
149201933062.144521-0.603969
1522019360226.325490-0.619608
15420193900-34.333333-0.298246
190202033060.540881-0.669811
1932020360237.190476-1.095238
19520203900-35.880952-4.571429
231202133060.600810-1.261538
2342021360239.980519-1.383117
23620213900-26.421053-8.631579
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" + ], + "text/plain": [ + " Year PUMA D_JWMNP D_SCHL\n", + "25 2016 3306 -0.071429 0.406122\n", + "27 2016 3602 1.666667 1.400000\n", + "29 2016 3900 -40.857143 -1.785714\n", + "65 2017 3306 0.847087 0.224919\n", + "67 2017 3602 17.815126 0.403361\n", + "69 2017 3900 -43.562500 -1.375000\n", + "107 2018 3306 -1.472727 0.449091\n", + "110 2018 3602 20.654902 -0.121569\n", + "112 2018 3900 -48.509804 -0.333333\n", + "149 2019 3306 2.144521 -0.603969\n", + "152 2019 3602 26.325490 -0.619608\n", + "154 2019 3900 -34.333333 -0.298246\n", + "190 2020 3306 0.540881 -0.669811\n", + "193 2020 3602 37.190476 -1.095238\n", + "195 2020 3900 -35.880952 -4.571429\n", + "231 2021 3306 0.600810 -1.261538\n", + "234 2021 3602 39.980519 -1.383117\n", + "236 2021 3900 -26.421053 -8.631579" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "data_rac1p_1 = all_data[all_data['RAC1P'] == 1]\n", + "data_rac1p_2 = all_data[all_data['RAC1P'] == 2]\n", + "\n", + "\n", + "grouped_rac1p_1 = data_rac1p_1.groupby(['Year', 'PUMA']).mean().reset_index()\n", + "grouped_rac1p_2 = data_rac1p_2.groupby(['Year', 'PUMA']).mean().reset_index()\n", + "\n", + "\n", + "grouped_data = pd.merge(grouped_rac1p_1, grouped_rac1p_2, on=['Year', 'PUMA'], suffixes=('_1', '_2'))\n", + "\n", + "for col in grouped_rac1p_1.columns:\n", + " if col not in ['Year', 'PUMA'] and col+'_1' in grouped_data and col+'_2' in grouped_data:\n", + " grouped_data['D_' + col] = grouped_data[col + '_2'] - grouped_data[col + '_1']\n", + "\n", + "\n", + "columns_to_keep = ['Year', 'PUMA'] + [col for col in grouped_data.columns if col.startswith('D_')]\n", + "grouped_data = grouped_data[columns_to_keep]\n", + "grouped_data=grouped_data.drop(\"D_RAC1P\",axis=1)\n", + "\n", + "grouped_data=grouped_data[grouped_data['PUMA'].isin([3306,3900,3602])]\n", + "grouped_data=grouped_data[[ 'Year','PUMA','D_JWMNP','D_SCHL']]\n", + "grouped_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PUMAaverage_D_SCHLaverage_D_JWMNP
03306-0.2425310.431524
13602-0.23602823.938863
23900-2.832550-38.260798
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" + ], + "text/plain": [ + " PUMA average_D_SCHL average_D_JWMNP\n", + "0 3306 -0.242531 0.431524\n", + "1 3602 -0.236028 23.938863\n", + "2 3900 -2.832550 -38.260798" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "average_age_commute_by_puma_race = grouped_data.groupby(['PUMA']).agg(\n", + " # average_D_WKW=('D_WKW', 'mean'),\n", + " average_D_SCHL=('D_SCHL', 'mean'),\n", + " average_D_JWMNP=('D_JWMNP', 'mean')\n", + ").reset_index()\n", + "\n", + "# average_age_commute_by_puma_race_sorted = average_age_commute_by_puma_race.sort_values('average_age')\n", + "\n", + "average_age_commute_by_puma_race\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ds", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 8b8839a51b634128e817a43a46a1aaafc123ccca Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 12 Dec 2023 20:39:02 -0500 Subject: [PATCH 34/39] Update README.md --- fa23-team/README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/fa23-team/README.md b/fa23-team/README.md index 4197139..504b4b9 100644 --- a/fa23-team/README.md +++ b/fa23-team/README.md @@ -1,3 +1,11 @@ + +Each notebook contains our work of analisys the problem, the all the data used are in the data folder. + +Clone or download this branch to and run the notebook. + + + + ## Q2 (Bohan Wang) ### q2-final.ipynb From 6ff1610f882b0f2e08621be00f5433c95a13583d Mon Sep 17 00:00:00 2001 From: Xiang Li <102377374+GussLii@users.noreply.github.com> Date: Tue, 12 Dec 2023 20:41:19 -0500 Subject: [PATCH 35/39] Slide link inside The slide 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Subject: [PATCH 36/39] Analysis for Question 1 update_Yu --- fa23-team/Q1_Final_Update.ipynb | 873 ++++++++++++++++++++++++++++++++ 1 file changed, 873 insertions(+) create mode 100644 fa23-team/Q1_Final_Update.ipynb diff --git a/fa23-team/Q1_Final_Update.ipynb b/fa23-team/Q1_Final_Update.ipynb new file mode 100644 index 0000000..6972a88 --- /dev/null +++ b/fa23-team/Q1_Final_Update.ipynb @@ -0,0 +1,873 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_14579/26858416.py:1: DtypeWarning: Columns (109,110) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2013 = pd.read_csv('psam_p25_2013.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_14579/26858416.py:2: DtypeWarning: Columns (108,109) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2014 = pd.read_csv('psam_p25_2014.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_14579/26858416.py:3: DtypeWarning: Columns (108,109) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2015 = pd.read_csv('psam_p25_2015.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_14579/26858416.py:6: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2018 = pd.read_csv('psam_p25_2018.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_14579/26858416.py:7: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2019 = pd.read_csv('psam_p25_2019.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_14579/26858416.py:8: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2020 = pd.read_csv('psam_p25_2020.csv')\n", + "/var/folders/3q/g5yk9cxd5rdc0gry4j6129v40000gn/T/ipykernel_14579/26858416.py:9: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_2021 = pd.read_csv('psam_p25_2021.csv')\n" + ] + } + ], + "source": [ + "df_2013 = pd.read_csv('psam_p25_2013.csv')\n", + "df_2014 = pd.read_csv('psam_p25_2014.csv')\n", + "df_2015 = pd.read_csv('psam_p25_2015.csv')\n", + "df_2016 = pd.read_csv('psam_p25_2016.csv')\n", + "df_2017 = pd.read_csv('psam_p25_2017.csv')\n", + "df_2018 = pd.read_csv('psam_p25_2018.csv')\n", + "df_2019 = pd.read_csv('psam_p25_2019.csv')\n", + "df_2020 = pd.read_csv('psam_p25_2020.csv')\n", + "df_2021 = pd.read_csv('psam_p25_2021.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete NAN in JWMNP\n", + "df_2013 = df_2013.dropna(subset=['JWMNP'])\n", + "df_2014 = df_2014.dropna(subset=['JWMNP'])\n", + "df_2015 = df_2015.dropna(subset=['JWMNP'])\n", + "df_2016 = df_2016.dropna(subset=['JWMNP'])\n", + "df_2017 = df_2017.dropna(subset=['JWMNP'])\n", + "df_2018 = df_2018.dropna(subset=['JWMNP'])\n", + "df_2019 = df_2019.dropna(subset=['JWMNP'])\n", + "df_2020 = df_2020.dropna(subset=['JWMNP'])\n", + "df_2021 = df_2021.dropna(subset=['JWMNP'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "dataframes = {\n", + " '2013': df_2013,\n", + " '2014': df_2014,\n", + " '2015': df_2015,\n", + " '2016': df_2016,\n", + " '2017': df_2017,\n", + " '2018': df_2018,\n", + "}\n", + "\n", + "average_jwmnp_values = {}\n", + "\n", + "for year, df in dataframes.items():\n", + " # filter JWTRNS=02\n", + " df_filtered = df[df['JWTR'] == 2]\n", + "\n", + " for rac1p_value in [1, 2]:\n", + " condition = df_filtered['RAC1P'] == rac1p_value\n", + " average_jwmnp = df_filtered[condition]['JWMNP'].mean()\n", + " average_jwmnp_values[f\"{year}, RAC1P={rac1p_value}\"] = average_jwmnp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average JWMNP for 2013, RAC1P=1: 38.30573248407644\n", + "Average JWMNP for 2013, RAC1P=2: 47.769911504424776\n", + "Average JWMNP for 2014, RAC1P=1: 38.98692810457516\n", + "Average JWMNP for 2014, RAC1P=2: 45.735264735264735\n", + "Average JWMNP for 2015, RAC1P=1: 39.831717451523545\n", + "Average JWMNP for 2015, RAC1P=2: 46.01587301587302\n", + "Average JWMNP for 2016, RAC1P=1: 40.03766413268832\n", + "Average JWMNP for 2016, RAC1P=2: 45.56928508384819\n", + "Average JWMNP for 2017, RAC1P=1: 40.3446385748544\n", + "Average JWMNP for 2017, RAC1P=2: 45.95731153496821\n", + "Average JWMNP for 2018, RAC1P=1: 40.648849797023004\n", + "Average JWMNP for 2018, RAC1P=2: 46.08083560399637\n", + "Average JWMNP for 2019, RAC1P=1: 40.925135501355015\n", + "Average JWMNP for 2019, RAC1P=2: 45.685009487666036\n", + "Average JWMNP for 2020, RAC1P=1: 40.475467289719624\n", + "Average JWMNP for 2020, RAC1P=2: 44.53224043715847\n", + "Average JWMNP for 2021, RAC1P=1: 40.74730215827338\n", + "Average JWMNP for 2021, RAC1P=2: 44.737041719342606\n" + ] + } + ], + "source": [ + "dataframes = {\n", + " '2019': df_2019,\n", + " '2020': df_2020,\n", + " '2021': df_2021\n", + "}\n", + "\n", + "for year, df in dataframes.items():\n", + " # filter JWTRNS=02\n", + " df_filtered = df[df['JWTRNS'] == 2]\n", + "\n", + " for rac1p_value in [1, 2]:\n", + " condition = df_filtered['RAC1P'] == rac1p_value\n", + " average_jwmnp = df_filtered[condition]['JWMNP'].mean()\n", + " average_jwmnp_values[f\"{year}, RAC1P={rac1p_value}\"] = average_jwmnp\n", + " \n", + "for key, value in average_jwmnp_values.items():\n", + " print(f\"Average JWMNP for {key}: {value}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'2013, RAC1P=1': 319.2144373673036,\n", + " '2013, RAC1P=2': 398.0825958702065,\n", + " '2014, RAC1P=1': 324.8910675381264,\n", + " '2014, RAC1P=2': 381.1272061272061,\n", + " '2015, RAC1P=1': 331.9309787626962,\n", + " '2015, RAC1P=2': 383.46560846560845,\n", + " '2016, RAC1P=1': 333.647201105736,\n", + " '2016, RAC1P=2': 379.74404236540164,\n", + " '2017, RAC1P=1': 336.20532145711996,\n", + " '2017, RAC1P=2': 382.9775961247351,\n", + " '2018, RAC1P=1': 338.7404149751917,\n", + " '2018, RAC1P=2': 384.0069633666364,\n", + " '2019, RAC1P=1': 341.0427958446251,\n", + " '2019, RAC1P=2': 380.708412397217,\n", + " '2020, RAC1P=1': 337.29556074766356,\n", + " '2020, RAC1P=2': 371.10200364298726,\n", + " '2021, RAC1P=1': 339.5608513189448,\n", + " '2021, RAC1P=2': 372.80868099452175}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_travel_time = {}\n", + "for key, average_jwmnp in average_jwmnp_values.items():\n", + " # Using the formula (Average JWMNP * 2 * 5 * 50) / 60\n", + " yearly_travel_time = (average_jwmnp * 2 * 5 * 50) / 60\n", + " total_travel_time[key] = yearly_travel_time\n", + "total_travel_time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### T Test" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearWhiteBlack
02013319.214437398.082596
12014324.891068381.127206
22015331.930979383.465608
32016333.647201379.744042
42017336.205321382.977596
52018338.740415384.006963
62019341.042796380.708412
72020337.295561371.102004
82021339.560851372.808681
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" + ], + "text/plain": [ + " Year White Black\n", + "0 2013 319.214437 398.082596\n", + "1 2014 324.891068 381.127206\n", + "2 2015 331.930979 383.465608\n", + "3 2016 333.647201 379.744042\n", + "4 2017 336.205321 382.977596\n", + "5 2018 338.740415 384.006963\n", + "6 2019 341.042796 380.708412\n", + "7 2020 337.295561 371.102004\n", + "8 2021 339.560851 372.808681" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Organizing the data into a structured format for the table\n", + "years = sorted(set(key.split(\", \")[0] for key in total_travel_time.keys()))\n", + "table_data = {year: {'White': None, 'Black': None} for year in years}\n", + "\n", + "for key, value in total_travel_time.items():\n", + " year, race = key.split(\", \")\n", + " race = 'White' if race == 'RAC1P=1' else 'Black'\n", + " table_data[year][race] = value\n", + "\n", + "# Convert the dictionary to a pandas DataFrame\n", + "df = pd.DataFrame.from_dict(table_data, orient='index')\n", + "df.index.rename('Year', inplace=True)\n", + "df = df.reset_index()\n", + "\n", + "# Exporting to CSV\n", + "df.to_csv('T_test.csv')\n", + "df['Year']=df['Year'].astype(\"int\")\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "
" + ], + "text/plain": [ + " White Black\n", + "count 9.000000 9.000000\n", + "mean 333.614292 381.558123\n", + "std 7.274713 7.703815\n", + "min 319.214437 371.102004\n", + "25% 331.930979 379.744042\n", + "50% 336.205321 381.127206\n", + "75% 338.740415 383.465608\n", + "max 341.042796 398.082596" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats = df.describe()\n", + "stats = stats.drop(columns='Year')\n", + "stats" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(-4.331535857081992,\n", + " 0.00470223929757063,\n", + " -17.15605858491724,\n", + " -7.040666575220106)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import ttest_ind, norm\n", + "from scipy.stats import ttest_ind\n", + "\n", + "# Converting 'Year' column to integers\n", + "df['Year'] = df['Year'].astype(int)\n", + "\n", + "pre_pandemic_data = df[(df['Year'] >= 2014) & (df['Year'] <= 2020)]\n", + "during_pandemic_data = df[df['Year'] >= 2020]\n", + "\n", + "# Calculate the difference in commute times for pre-pandemic and during-pandemic\n", + "pre_pandemic_diff = pre_pandemic_data['White'] - pre_pandemic_data['Black']\n", + "during_pandemic_diff = during_pandemic_data['White'] - during_pandemic_data['Black']\n", + "\n", + "# Perform t-test\n", + "t_stat, p_value = ttest_ind(pre_pandemic_diff, during_pandemic_diff, equal_var=False)\n", + "\n", + "# Calculate the degrees of freedom to use in the t-distribution\n", + "# Welch-Satterthwaite equation\n", + "df = ((np.var(pre_pandemic_diff)/len(pre_pandemic_diff) + np.var(during_pandemic_diff)/len(during_pandemic_diff))**2 /\n", + " ((np.var(pre_pandemic_diff)/len(pre_pandemic_diff))**2 / (len(pre_pandemic_diff)-1) +\n", + " (np.var(during_pandemic_diff)/len(during_pandemic_diff))**2 / (len(during_pandemic_diff)-1)))\n", + "\n", + "# Calculate the critical t-value for a 95% confidence interval\n", + "t_crit = np.abs(norm.ppf((1-0.95)/2)) # two-tailed\n", + "\n", + "# Calculate the pooled standard deviation\n", + "pooled_std = np.sqrt(((len(pre_pandemic_diff) - 1)*np.var(pre_pandemic_diff) +\n", + " (len(during_pandemic_diff) - 1)*np.var(during_pandemic_diff)) /\n", + " (len(pre_pandemic_diff) + len(during_pandemic_diff) - 2))\n", + "\n", + "# Calculate the standard error of the difference in means\n", + "std_error_diff = np.sqrt(np.var(pre_pandemic_diff)/len(pre_pandemic_diff) +\n", + " np.var(during_pandemic_diff)/len(during_pandemic_diff))\n", + "\n", + "# The mean difference in commute times\n", + "mean_diff = np.mean(pre_pandemic_diff) - np.mean(during_pandemic_diff)\n", + "\n", + "# The margin of error for the 95% CI\n", + "margin_of_error = t_crit * std_error_diff\n", + "\n", + "# 95% CI lower and upper bounds\n", + "ci_lower = mean_diff - margin_of_error\n", + "ci_upper = mean_diff + margin_of_error\n", + "\n", + "# Plotting\n", + "fig, ax = plt.subplots()\n", + "\n", + "# Range of x values for plotting the t-distribution\n", + "x = np.linspace(mean_diff - 4*pooled_std, mean_diff + 4*pooled_std, 200)\n", + "\n", + "# Plot the t-distribution\n", + "ax.plot(x, norm.pdf(x, mean_diff, pooled_std), label='t-Distribution')\n", + "\n", + "# Fill the confidence interval\n", + "ax.fill_between(x, norm.pdf(x, mean_diff, pooled_std), where=(x > ci_lower) & (x < ci_upper), color='skyblue', alpha=0.5)\n", + "\n", + "# Add a line for the mean difference\n", + "ax.axvline(mean_diff, color='red', linestyle='--', label='Mean Difference')\n", + "\n", + "# Add lines for the confidence interval bounds\n", + "ax.axvline(ci_lower, color='black', linestyle='--', label='95% CI Lower')\n", + "ax.axvline(ci_upper, color='black', linestyle='--', label='95% CI Upper')\n", + "\n", + "# Labels and title\n", + "ax.set_xlabel('Difference in Commute Times')\n", + "ax.set_ylabel('Probability Density')\n", + "ax.set_title('95% Confidence Interval for Difference in Commute Times')\n", + "\n", + "# Add a legend\n", + "ax.legend()\n", + "\n", + "# Show plot\n", + "plt.show()\n", + "\n", + "# Return the t-statistic and p-value\n", + "t_stat, p_value, ci_lower, ci_upper\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualization 1" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2013: 78.86815850290287,\n", + " 2014: 56.2361385890797,\n", + " 2015: 51.53462970291224,\n", + " 2016: 46.096841259665666,\n", + " 2017: 46.772274667615136,\n", + " 2018: 45.2665483914447,\n", + " 2019: 39.66561655259187,\n", + " 2020: 33.8064428953237,\n", + " 2021: 33.24782967557695}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "# Calculating the difference in total travel time between RAC1P=2 and RAC1P=1 for each year\n", + "travel_time_difference = {}\n", + "\n", + "for year in range(2013, 2022): # Looping through the years 2017 to 2021\n", + " white = f\"{year}, RAC1P=1\"\n", + " black = f\"{year}, RAC1P=2\"\n", + "\n", + " # Calculate the difference if both keys exist in the dictionary\n", + " if white in total_travel_time and black in total_travel_time:\n", + " difference = total_travel_time[black] - total_travel_time[white]\n", + " travel_time_difference[year] = difference\n", + "travel_time_difference" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Extracting years and differences\n", + "years = list(travel_time_difference.keys())\n", + "differences = list(travel_time_difference.values())\n", + "\n", + "# Plotting the line graph\n", + "plt.plot(years, differences, marker='o', linestyle='-', color='orange')\n", + "plt.title('Annual Commute Time Disparity between Black and White Riders 2013-2021')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Travel Time Difference(Hours)')\n", + "\n", + "# Annotating each point with its numerical value\n", + "for year, difference in zip(years, differences):\n", + " plt.annotate(f'{difference:.2f}', (year, difference), textcoords=\"offset points\", xytext=(0, 5), ha='center')\n", + "\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate the time difference for balck and white riders(2017-2021)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "199" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "average_difference_2017_2021 = round(sum(travel_time_difference[year] for year in range(2017, 2022)))\n", + "average_difference_2017_2021" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bus Commute Disparity between Black and White Riders(2017-2021): 40 hours\n" + ] + } + ], + "source": [ + "# Calculating the average of the yearly differences from 2017 to 2021\n", + "average_difference_2017_2021 = round(sum(travel_time_difference[year] for year in range(2017, 2022)) / 5)\n", + "print(f'Bus Commute Disparity between Black and White Riders(2017-2021):', average_difference_2017_2021, f'hours')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualization 2" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "data = total_travel_time\n", + "\n", + "data_rac1p1 = {}\n", + "data_rac1p2 = {}\n", + "\n", + "for key, value in data.items():\n", + " year, rac1p = key.replace('Average JWMNP for ', '').split(', RAC1P=')\n", + " if rac1p == '1':\n", + " data_rac1p1[year] = value\n", + " elif rac1p == '2':\n", + " data_rac1p2[year] = value\n", + "\n", + "years_rac1p1 = list(data_rac1p1.keys())\n", + "values_rac1p1 = list(data_rac1p1.values())\n", + "\n", + "years_rac1p2 = list(data_rac1p2.keys())\n", + "values_rac1p2 = list(data_rac1p2.values())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.plot(years_rac1p1, values_rac1p1, label='white riders', marker='o')\n", + "plt.plot(years_rac1p2, values_rac1p2, label='black riders', marker='o')\n", + "\n", + "# Annotate data points with numerical values\n", + "for year, value in zip(years_rac1p1, values_rac1p1):\n", + " plt.annotate(f'{value:.2f}', (year, value), textcoords=\"offset points\", xytext=(0,10), ha='center')\n", + "\n", + "for year, value in zip(years_rac1p2, values_rac1p2):\n", + " plt.annotate(f'{value:.2f}', (year, value), textcoords=\"offset points\", xytext=(0,-15), ha='center')\n", + "\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Annual Commute Time to Work(Hours)')\n", + "plt.title('Annual Commute Time to Work for Black Riders and White Riders 2013-2021')\n", + "plt.legend()\n", + "plt.xticks(rotation=45)\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "bar_width = 0.35\n", + "\n", + "index = range(len(years_rac1p1))\n", + "\n", + "plt.bar(index, values_rac1p1, bar_width, label='RAC1P=1 (White)')\n", + "plt.bar([i + bar_width for i in index], values_rac1p2, bar_width, label='RAC1P=2 (Black)')\n", + "\n", + "# Adding details to the plot\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Average JWMNP')\n", + "plt.title('Average JWMNP by Year for RAC1P=1 (White) and RAC1P=2 (Black)')\n", + "plt.xticks([i + bar_width / 2 for i in index], years_rac1p1)\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ds", + "language": "python", + 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