Exercise-4-problem-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "72f8389ed65d7b12b1160a2d3cb19897",
     "grade": false,
     "grade_id": "cell-dc7532d3649929fe",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "# Exercise 4\n",
    "\n",
    "This week we will practice how to do **data classification and aggregation in Geopandas**. We continue from the last week's exerise with rather similar idea. In this exercise we use data from the [Helsinki Region Travel Time Matrix](https://blogs.helsinki.fi/accessibility/helsinki-region-travel-time-matrix/) as input.\n",
    "\n",
    "\n",
    "## Problem 1: Join accessibility datasets into a grid and visualize them by using a classifier (10 points)\n",
    "\n",
    "\n",
    "![P1 example result](img/traveltimes_example.png)\n",
    "\n",
    "*Desired output from problem 1: Travel times by public transport (PT) and car to Itis and Myyrmanni*\n",
    "\n",
    "**Data:**\n",
    "\n",
    "[data/](data/) directory contains all the required input data for this exercise:\n",
    " \n",
    "- Travel time data: `travel_times_to_[XXXXXXX]_[NAME-OF-THE-CENTER].txt` (Travel times and distances in the Helsinki Region to different shopping centers  as text files)\n",
    "- Reference grid: `MetropAccess_YKR_grid_EurefFIN.shp`. \n",
    "\n",
    "Column `from_id` in the travel time data sets corresponds to the `YKR_ID` column in the grid shapefile.\n",
    "     \n",
    "**Steps:**\n",
    "\n",
    "In problem 1 we will focus on travel times to **Itis** and **Myyrmanni**. Our goal is to visualize travely times by public transport and car to both shopping centers. Before plotting the maps we need to classify the data. Your task is to **first develop the processing steps using Itis as input**, and then repeat the steps for Myyrmanni."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "9b046616aa0c41114316e2e56ad69d16",
     "grade": false,
     "grade_id": "cell-fc5d70b88edd7ad6",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "- Read the Polygon grid `MetropAccess_YKR_grid_EurefFIN.shp` into a GeoDataFrame called `grid` using geopandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "d0ec6d4c9eb95f7e88fa5a05a48d05b0",
     "grade": false,
     "grade_id": "read_grid",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>YKR_ID</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>381875.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785640</td>\n",
       "      <td>POLYGON ((382000.000 6697750.000, 381750.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>382125.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785641</td>\n",
       "      <td>POLYGON ((382250.000 6697750.000, 382000.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>382375.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785642</td>\n",
       "      <td>POLYGON ((382500.000 6697750.000, 382250.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>382625.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785643</td>\n",
       "      <td>POLYGON ((382750.000 6697750.000, 382500.000 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>381125.0</td>\n",
       "      <td>6697630.0</td>\n",
       "      <td>5787544</td>\n",
       "      <td>POLYGON ((381250.000 6697500.000, 381000.000 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x          y   YKR_ID  \\\n",
       "0  381875.0  6697880.0  5785640   \n",
       "1  382125.0  6697880.0  5785641   \n",
       "2  382375.0  6697880.0  5785642   \n",
       "3  382625.0  6697880.0  5785643   \n",
       "4  381125.0  6697630.0  5787544   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((382000.000 6697750.000, 381750.000 6...  \n",
       "1  POLYGON ((382250.000 6697750.000, 382000.000 6...  \n",
       "2  POLYGON ((382500.000 6697750.000, 382250.000 6...  \n",
       "3  POLYGON ((382750.000 6697750.000, 382500.000 6...  \n",
       "4  POLYGON ((381250.000 6697500.000, 381000.000 6...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# REPLACE THE ERROR BELOW WITH YOUR OWN CODE\n",
    "import geopandas as gpd\n",
    "\n",
    "fp = r\"C:\\Users\\paul-\\Desktop\\AutoGIS\\Lesson4\\Exercise\\data\\MetropAccess_YKR_grid_EurefFIN.shp\"\n",
    "\n",
    "grid= gpd.read_file(fp)\n",
    "\n",
    "grid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "17e0eb06e58e031f5c3a4bf37eca1a55",
     "grade": true,
     "grade_id": "problem_1_grid_input_test",
     "locked": true,
     "points": 0.5,
     "schema_version": 3,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "YKR GRID: 13231 rows\n"
     ]
    }
   ],
   "source": [
    "# NON-EDITABLE TEST CELL\n",
    "# Check the data\n",
    "print(\"\\nYKR GRID:\", len(grid), \"rows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "619651f4ad9a06c75f4d422d95c13c86",
     "grade": false,
     "grade_id": "cell-9328a2a52f7e9a86",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "- Read the travel time data file for Itis using Pandas into a variable called `data`. Separator is `\";\"` in the input file.\n",
    "- Select only following columns from the file:\n",
    "    \n",
    "   - `pt_r_t`\n",
    "   - `car_r_t`\n",
    "   - `from_id`\n",
    "   - `to_id`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "151de7bc9173c1a268bd1a031699dab0",
     "grade": false,
     "grade_id": "read_data",
     "locked": false,
     "schema_version": 3,
     "solution": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>from_id</th>\n",
       "      <th>to_id</th>\n",
       "      <th>walk_t</th>\n",
       "      <th>walk_d</th>\n",
       "      <th>car_r_t</th>\n",
       "      <th>car_r_d</th>\n",
       "      <th>car_m_t</th>\n",
       "      <th>car_m_d</th>\n",
       "      <th>pt_r_t</th>\n",
       "      <th>pt_r_tt</th>\n",
       "      <th>pt_r_d</th>\n",
       "      <th>pt_m_t</th>\n",
       "      <th>pt_m_tt</th>\n",
       "      <th>pt_m_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5785640</td>\n",
       "      <td>5944003</td>\n",
       "      <td>494</td>\n",
       "      <td>34582</td>\n",
       "      <td>50</td>\n",
       "      <td>36229</td>\n",
       "      <td>44</td>\n",
       "      <td>36229</td>\n",
       "      <td>132</td>\n",
       "      <td>157</td>\n",
       "      <td>38465</td>\n",
       "      <td>142</td>\n",
       "      <td>173</td>\n",
       "      <td>38465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5785641</td>\n",
       "      <td>5944003</td>\n",
       "      <td>457</td>\n",
       "      <td>31966</td>\n",
       "      <td>51</td>\n",
       "      <td>36439</td>\n",
       "      <td>45</td>\n",
       "      <td>36439</td>\n",
       "      <td>135</td>\n",
       "      <td>154</td>\n",
       "      <td>38671</td>\n",
       "      <td>145</td>\n",
       "      <td>171</td>\n",
       "      <td>38671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5785642</td>\n",
       "      <td>5944003</td>\n",
       "      <td>458</td>\n",
       "      <td>32046</td>\n",
       "      <td>58</td>\n",
       "      <td>35841</td>\n",
       "      <td>52</td>\n",
       "      <td>34064</td>\n",
       "      <td>137</td>\n",
       "      <td>154</td>\n",
       "      <td>38789</td>\n",
       "      <td>147</td>\n",
       "      <td>172</td>\n",
       "      <td>38789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5785643</td>\n",
       "      <td>5944003</td>\n",
       "      <td>462</td>\n",
       "      <td>32338</td>\n",
       "      <td>57</td>\n",
       "      <td>37723</td>\n",
       "      <td>50</td>\n",
       "      <td>37935</td>\n",
       "      <td>141</td>\n",
       "      <td>163</td>\n",
       "      <td>39082</td>\n",
       "      <td>151</td>\n",
       "      <td>176</td>\n",
       "      <td>39082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5787544</td>\n",
       "      <td>5944003</td>\n",
       "      <td>487</td>\n",
       "      <td>34093</td>\n",
       "      <td>50</td>\n",
       "      <td>35803</td>\n",
       "      <td>44</td>\n",
       "      <td>35803</td>\n",
       "      <td>125</td>\n",
       "      <td>142</td>\n",
       "      <td>37976</td>\n",
       "      <td>122</td>\n",
       "      <td>148</td>\n",
       "      <td>37786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   from_id    to_id  walk_t  walk_d  car_r_t  car_r_d  car_m_t  car_m_d  \\\n",
       "0  5785640  5944003     494   34582       50    36229       44    36229   \n",
       "1  5785641  5944003     457   31966       51    36439       45    36439   \n",
       "2  5785642  5944003     458   32046       58    35841       52    34064   \n",
       "3  5785643  5944003     462   32338       57    37723       50    37935   \n",
       "4  5787544  5944003     487   34093       50    35803       44    35803   \n",
       "\n",
       "   pt_r_t  pt_r_tt  pt_r_d  pt_m_t  pt_m_tt  pt_m_d  \n",
       "0     132      157   38465     142      173   38465  \n",
       "1     135      154   38671     145      171   38671  \n",
       "2     137      154   38789     147      172   38789  \n",
       "3     141      163   39082     151      176   39082  \n",
       "4     125      142   37976     122      148   37786  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# REPLACE THE ERROR BELOW WITH YOUR OWN CODE\n",
    "import pandas as pd\n",
    "\n",
    "fp = r\"C:\\Users\\paul-\\Desktop\\AutoGIS\\Lesson4\\Exercise\\data\\TravelTimes_to_5944003_Itis.txt\"\n",
    "\n",
    "data = pd.read_csv(fp, sep=';')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "968a7bffb9362c1788ce18fb64701bf8",
     "grade": true,
     "grade_id": "problem_1_data_input_test",
     "locked": true,
     "points": 0.5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ITIS: 13231 rows\n"
     ]
    }
   ],
   "source": [
    "# NON-EDITABLE TEST CELL\n",
    "# Check the data\n",
    "print(\"\\nITIS:\", len(data), \"rows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "0cd5890bb5ae2a1e651bd4f287746624",
     "grade": false,
     "grade_id": "cell-7466fe21ede7360b",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "- join attributes from `data` to `grid` using the [merge](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html) function: \n",
    "  - **`YKR_ID`** -column in `grid` corresponds to the **`from_id`** column in `data`\n",
    "  - store the join output into a variable called `data_geo` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "f7a3330d7c1b94c80fd77771e9ba1def",
     "grade": false,
     "grade_id": "table_join",
     "locked": false,
     "schema_version": 3,
     "solution": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>YKR_ID</th>\n",
       "      <th>geometry</th>\n",
       "      <th>from_id</th>\n",
       "      <th>to_id</th>\n",
       "      <th>walk_t</th>\n",
       "      <th>walk_d</th>\n",
       "      <th>car_r_t</th>\n",
       "      <th>car_r_d</th>\n",
       "      <th>car_m_t</th>\n",
       "      <th>car_m_d</th>\n",
       "      <th>pt_r_t</th>\n",
       "      <th>pt_r_tt</th>\n",
       "      <th>pt_r_d</th>\n",
       "      <th>pt_m_t</th>\n",
       "      <th>pt_m_tt</th>\n",
       "      <th>pt_m_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>381875.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785640</td>\n",
       "      <td>POLYGON ((382000.000 6697750.000, 381750.000 6...</td>\n",
       "      <td>5785640</td>\n",
       "      <td>5944003</td>\n",
       "      <td>494</td>\n",
       "      <td>34582</td>\n",
       "      <td>50</td>\n",
       "      <td>36229</td>\n",
       "      <td>44</td>\n",
       "      <td>36229</td>\n",
       "      <td>132</td>\n",
       "      <td>157</td>\n",
       "      <td>38465</td>\n",
       "      <td>142</td>\n",
       "      <td>173</td>\n",
       "      <td>38465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>382125.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785641</td>\n",
       "      <td>POLYGON ((382250.000 6697750.000, 382000.000 6...</td>\n",
       "      <td>5785641</td>\n",
       "      <td>5944003</td>\n",
       "      <td>457</td>\n",
       "      <td>31966</td>\n",
       "      <td>51</td>\n",
       "      <td>36439</td>\n",
       "      <td>45</td>\n",
       "      <td>36439</td>\n",
       "      <td>135</td>\n",
       "      <td>154</td>\n",
       "      <td>38671</td>\n",
       "      <td>145</td>\n",
       "      <td>171</td>\n",
       "      <td>38671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>382375.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785642</td>\n",
       "      <td>POLYGON ((382500.000 6697750.000, 382250.000 6...</td>\n",
       "      <td>5785642</td>\n",
       "      <td>5944003</td>\n",
       "      <td>458</td>\n",
       "      <td>32046</td>\n",
       "      <td>58</td>\n",
       "      <td>35841</td>\n",
       "      <td>52</td>\n",
       "      <td>34064</td>\n",
       "      <td>137</td>\n",
       "      <td>154</td>\n",
       "      <td>38789</td>\n",
       "      <td>147</td>\n",
       "      <td>172</td>\n",
       "      <td>38789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>382625.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785643</td>\n",
       "      <td>POLYGON ((382750.000 6697750.000, 382500.000 6...</td>\n",
       "      <td>5785643</td>\n",
       "      <td>5944003</td>\n",
       "      <td>462</td>\n",
       "      <td>32338</td>\n",
       "      <td>57</td>\n",
       "      <td>37723</td>\n",
       "      <td>50</td>\n",
       "      <td>37935</td>\n",
       "      <td>141</td>\n",
       "      <td>163</td>\n",
       "      <td>39082</td>\n",
       "      <td>151</td>\n",
       "      <td>176</td>\n",
       "      <td>39082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>381125.0</td>\n",
       "      <td>6697630.0</td>\n",
       "      <td>5787544</td>\n",
       "      <td>POLYGON ((381250.000 6697500.000, 381000.000 6...</td>\n",
       "      <td>5787544</td>\n",
       "      <td>5944003</td>\n",
       "      <td>487</td>\n",
       "      <td>34093</td>\n",
       "      <td>50</td>\n",
       "      <td>35803</td>\n",
       "      <td>44</td>\n",
       "      <td>35803</td>\n",
       "      <td>125</td>\n",
       "      <td>142</td>\n",
       "      <td>37976</td>\n",
       "      <td>122</td>\n",
       "      <td>148</td>\n",
       "      <td>37786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x          y   YKR_ID  \\\n",
       "0  381875.0  6697880.0  5785640   \n",
       "1  382125.0  6697880.0  5785641   \n",
       "2  382375.0  6697880.0  5785642   \n",
       "3  382625.0  6697880.0  5785643   \n",
       "4  381125.0  6697630.0  5787544   \n",
       "\n",
       "                                            geometry  from_id    to_id  \\\n",
       "0  POLYGON ((382000.000 6697750.000, 381750.000 6...  5785640  5944003   \n",
       "1  POLYGON ((382250.000 6697750.000, 382000.000 6...  5785641  5944003   \n",
       "2  POLYGON ((382500.000 6697750.000, 382250.000 6...  5785642  5944003   \n",
       "3  POLYGON ((382750.000 6697750.000, 382500.000 6...  5785643  5944003   \n",
       "4  POLYGON ((381250.000 6697500.000, 381000.000 6...  5787544  5944003   \n",
       "\n",
       "   walk_t  walk_d  car_r_t  car_r_d  car_m_t  car_m_d  pt_r_t  pt_r_tt  \\\n",
       "0     494   34582       50    36229       44    36229     132      157   \n",
       "1     457   31966       51    36439       45    36439     135      154   \n",
       "2     458   32046       58    35841       52    34064     137      154   \n",
       "3     462   32338       57    37723       50    37935     141      163   \n",
       "4     487   34093       50    35803       44    35803     125      142   \n",
       "\n",
       "   pt_r_d  pt_m_t  pt_m_tt  pt_m_d  \n",
       "0   38465     142      173   38465  \n",
       "1   38671     145      171   38671  \n",
       "2   38789     147      172   38789  \n",
       "3   39082     151      176   39082  \n",
       "4   37976     122      148   37786  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# REPLACE THE ERROR BELOW WITH YOUR OWN CODE\n",
    "data_geo = grid.merge(data,how='left', left_on='YKR_ID', right_on='from_id')\n",
    "\n",
    "data_geo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "7b8d678d12a200d170cb06233ecf80cc",
     "grade": true,
     "grade_id": "problem_1_table_join_dtype_test",
     "locked": true,
     "points": 0.5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# NON-EDITABLE TEST CELL\n",
    "#Check data type\n",
    "assert type(data_geo)  == gpd.geodataframe.GeoDataFrame, \"output should be a geodataframe\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "498a84b345a5b0b581467c66952edcfd",
     "grade": true,
     "grade_id": "problem_1_table_join_columns_test",
     "locked": true,
     "points": 0.5,
     "schema_version": 3,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# NON-EDITABLE TEST CELL\n",
    "# Check that the merged output have (at least) the necessary columns\n",
    "required_columns = ['YKR_ID', 'pt_r_t', 'car_r_t', 'geometry']\n",
    "\n",
    "assert all(x in data_geo.columns for x in required_columns), \"Couldn't find all required columns\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "73c702d3a657f9f9ffc89778836ab366",
     "grade": false,
     "grade_id": "cell-c9758c259e0c8abd",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "- Remove all rows containing no-data values (-1) from `data_geo`. You can for example [replace](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) the no-data values with numpy's `np.nan`, and then drop rows containing no-data values using the [DataFrame.dropna](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html) function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "77edb0755e62f533924e50affba9f862",
     "grade": false,
     "grade_id": "replace_values",
     "locked": false,
     "schema_version": 3,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "# REPLACE THE ERROR BELOW WITH YOUR OWN CODE\n",
    "import numpy\n",
    "\n",
    "data_geo = data_geo.replace(-1, numpy.nan)\n",
    "\n",
    "data_geo = data_geo.dropna(axis=0, how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "0e17f161592415d4b3453e16cd29b363",
     "grade": true,
     "grade_id": "problem_1_replace_values_test",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# NON-EDITABLE TEST CELL\n",
    "# Check that -1 values are not present in the columns\n",
    "assert -1 not in data_geo[\"pt_r_t\"], \"No data values should be removed from the data!\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "980fd5cde16964493f0889eb8dcff69e",
     "grade": false,
     "grade_id": "cell-a1ae2db99f7de17b",
     "locked": true,
     "schema_version": 3,
     "solution": false
    }
   },
   "source": [
    "- Classify the travel times for both travel modes (public transport and private car) into five-minute intervals \n",
    "    - Create a `UserDefined` classifier using the [pysal/mapclassify](https://github.com/pysal/mapclassify#userdefined) library using these class breaks: `5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60`\n",
    "    - Apply the classifier to `pt_r_t` and `car_r_t` columns and store the results to `pt_r_t_cl` and `car_r_t_cl` columns, respectively\n",
    "    - Notice that these 12 class breaks will produce 13 classes (as 1 class break divides the data into two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "202eca52c16a72ab3b8d6521ce364b20",
     "grade": false,
     "grade_id": "classify_values",
     "locked": false,
     "schema_version": 3,
     "solution": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>YKR_ID</th>\n",
       "      <th>geometry</th>\n",
       "      <th>from_id</th>\n",
       "      <th>to_id</th>\n",
       "      <th>walk_t</th>\n",
       "      <th>walk_d</th>\n",
       "      <th>car_r_t</th>\n",
       "      <th>car_r_d</th>\n",
       "      <th>car_m_t</th>\n",
       "      <th>car_m_d</th>\n",
       "      <th>pt_r_t</th>\n",
       "      <th>pt_r_tt</th>\n",
       "      <th>pt_r_d</th>\n",
       "      <th>pt_m_t</th>\n",
       "      <th>pt_m_tt</th>\n",
       "      <th>pt_m_d</th>\n",
       "      <th>pt_r_t_cl</th>\n",
       "      <th>car_r_t_cl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>381875.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785640</td>\n",
       "      <td>POLYGON ((382000.000 6697750.000, 381750.000 6...</td>\n",
       "      <td>5785640</td>\n",
       "      <td>5944003.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>34582.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>36229.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36229.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>38465.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>38465.0</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>382125.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785641</td>\n",
       "      <td>POLYGON ((382250.000 6697750.000, 382000.000 6...</td>\n",
       "      <td>5785641</td>\n",
       "      <td>5944003.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>31966.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>36439.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>36439.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>38671.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>38671.0</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>382375.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785642</td>\n",
       "      <td>POLYGON ((382500.000 6697750.000, 382250.000 6...</td>\n",
       "      <td>5785642</td>\n",
       "      <td>5944003.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>32046.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>35841.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>34064.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>38789.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>38789.0</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>382625.0</td>\n",
       "      <td>6697880.0</td>\n",
       "      <td>5785643</td>\n",
       "      <td>POLYGON ((382750.000 6697750.000, 382500.000 6...</td>\n",
       "      <td>5785643</td>\n",
       "      <td>5944003.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>32338.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>37723.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>37935.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>39082.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>39082.0</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>381125.0</td>\n",
       "      <td>6697630.0</td>\n",
       "      <td>5787544</td>\n",
       "      <td>POLYGON ((381250.000 6697500.000, 381000.000 6...</td>\n",
       "      <td>5787544</td>\n",
       "      <td>5944003.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>34093.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>35803.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>35803.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>37976.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>37786.0</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x          y   YKR_ID  \\\n",
       "0  381875.0  6697880.0  5785640   \n",
       "1  382125.0  6697880.0  5785641   \n",
       "2  382375.0  6697880.0  5785642   \n",
       "3  382625.0  6697880.0  5785643   \n",
       "4  381125.0  6697630.0  5787544   \n",
       "\n",
       "                                            geometry  from_id      to_id  \\\n",
       "0  POLYGON ((382000.000 6697750.000, 381750.000 6...  5785640  5944003.0   \n",
       "1  POLYGON ((382250.000 6697750.000, 382000.000 6...  5785641  5944003.0   \n",
       "2  POLYGON ((382500.000 6697750.000, 382250.000 6...  5785642  5944003.0   \n",
       "3  POLYGON ((382750.000 6697750.000, 382500.000 6...  5785643  5944003.0   \n",
       "4  POLYGON ((381250.000 6697500.000, 381000.000 6...  5787544  5944003.0   \n",
       "\n",
       "   walk_t   walk_d  car_r_t  car_r_d  car_m_t  car_m_d  pt_r_t  pt_r_tt  \\\n",
       "0   494.0  34582.0     50.0  36229.0     44.0  36229.0   132.0    157.0   \n",
       "1   457.0  31966.0     51.0  36439.0     45.0  36439.0   135.0    154.0   \n",
       "2   458.0  32046.0     58.0  35841.0     52.0  34064.0   137.0    154.0   \n",
       "3   462.0  32338.0     57.0  37723.0     50.0  37935.0   141.0    163.0   \n",
       "4   487.0  34093.0     50.0  35803.0     44.0  35803.0   125.0    142.0   \n",
       "\n",
       "    pt_r_d  pt_m_t  pt_m_tt   pt_m_d  pt_r_t_cl  car_r_t_cl  \n",
       "0  38465.0   142.0    173.0  38465.0         12           9  \n",
       "1  38671.0   145.0    171.0  38671.0         12          10  \n",
       "2  38789.0   147.0    172.0  38789.0         12          11  \n",
       "3  39082.0   151.0    176.0  39082.0         12          11  \n",
       "4  37976.0   122.0    148.0  37786.0         12           9  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# REPLACE THE ERROR BELOW WITH YOUR OWN CODE\n",
    "import mapclassify\n",
    "\n",
    "classifier = mapclassify.UserDefined(data_geo['pt_r_t'],bins=[5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60]) \n",
    "\n",
    "data_geo['pt_r_t_cl'] = data_geo[['pt_r_t']].apply(classifier)\n",
    "data_geo['car_r_t_cl'] = data_geo[['car_r_t']].apply(classifier)\n",
    "\n",
    "data_geo.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "1b537b13dac72a0ba1503c8386414e3d",
     "grade": true,
     "grade_id": "problem_1_itis_pt_classify_test",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "travel times by public transport:\n",
      "    YKR_ID  pt_r_t  pt_r_t_cl\n",
      "0  5785640   132.0         12\n",
      "1  5785641   135.0         12\n",
      "2  5785642   137.0         12\n",
      "3  5785643   141.0         12\n",
      "4  5787544   125.0         12\n"
     ]
    }
   ],
   "source": [
    "# NON-EDITABLE TEST CELL\n",
    "#Check the output\n",
    "print(\"travel times by public transport:\")\n",
    "print(data_geo[['YKR_ID', 'pt_r_t', 'pt_r_t_cl']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "23b6f61399ac42b57b421096bcef84a8",
     "grade": true,
     "grade_id": "problem_1_itis_car_classify_test",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Travel times by car:\n",
      "    YKR_ID  car_r_t  car_r_t_cl\n",
      "0  5785640     50.0           9\n",
      "1  5785641     51.0          10\n",
      "2  5785642     58.0          11\n",
      "3  5785643     57.0          11\n",
      "4  5787544     50.0           9\n"
     ]
    }
   ],
   "source": [
    "# NON-EDITABLE TEST CELL\n",
    "#Check the output\n",
    "print(\"Travel times by car:\")\n",
    "print(data_geo[['YKR_ID','car_r_t', 'car_r_t_cl']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the classified travel times to Itis**:\n",
    "\n",
    "![itis_example.png](img/itis_example.png)\n",
    "\n",
    "Create a 1x2 plot where the you plot travel times by public transport and travel times by car to Itis:\n",
    "- Create subplots using matplotlib having 1 row and 2 columns (use parameters `nrows` & `ncols`), and the figure size should be 10 inches by 5 inches (use parameter `figsize`)\n",
    "- Add titles for the subplots as seen in the example above\n",
    "- Remove the empty whitespace around the plots using function `plt.tight_layout()`\n",
    "- Save the figure into disk with filename `itis_accessibility.png`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "d5bc01d196b50fee36dc911f13e07417",
     "grade": false,
     "grade_id": "plot_itis",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define output filename for the figure in here and use it when saving the file:\n",
    "output_fig1 = r\"C:\\Users\\paul-\\Desktop\\AutoGIS\\Lesson4\\Exercise\\data\\itis_accessibility.png\"\n",
    "\n",
    "# REPLACE THE ERROR BELOW WITH YOUR OWN CODE\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
    "\n",
    "data_geo.plot(ax=ax1, column=\"pt_r_t\", scheme=\"Natural_Breaks\", k=9, cmap=\"RdYlBu\", linewidth=0)\n",
    "\n",
    "ax1.set_title(\"ITIS - Travel times by public transport\");\n",
    "\n",
    "data_geo.plot(ax=ax2, column=\"car_r_t\", scheme=\"Natural_Breaks\", k=9, cmap=\"RdYlBu\", linewidth=0)\n",
    "\n",
    "ax2.set_title(\"ITIS - Travel times by car\");\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "data_geo.to_file(output_fig1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "da41a4213247c27fe15786ffd8c9ec02",
     "grade": true,
     "grade_id": "problem_1_itis_output_test",
     "locked": true,
     "points": 0.5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# NON-EDITABLE TEST CELL\n",
    "# Check that output figure file exists\n",
    "import os\n",
    "assert os.path.isfile(output_fig1), \"figure does not exists!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "64f40faa19751d350bf02ee0e4e5a7f7",
     "grade": false,
     "grade_id": "repeating-process",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "**Repeat the steps for Myyrmanni:**\n",
    "\n",
    "![myyrmanni example figure](img/myyrmanni_example.png)\n",
    "\n",
    "Next, your task is to repeat all the same steps for **Myyrmanni** (`data/TravelTimes_to_5902043_Myyrmanni.txt`):\n",
    "\n",
    "    # Read text file using pandas into a variable called data\n",
    "    # Select columns 'pt_r_t', 'car_r_t', 'from_id', 'to_id'\n",
    "    # Make table join between grid and travel time data. Store output in a variable called data_geo\n",
    "    # Remove no data values\n",
    "    # Classify the values in columns 'pt_r_t', 'car_r_t' into new columns 'pt_r_t_cl', 'car_r_t_cl'\n",
    "    # Plot travel times by public transport and travel times by car.\n",
    "    # Save output image to file (myyrmanni_accessibility.png)\n",
    "\n",
    "*Hint: You can copy paste the code from your earlier solutions in here, just change the input file path.*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "cc30b32e543db046535e6012638ccc8f",
     "grade": false,
     "grade_id": "cell-7a5388593168d78b",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Define output filename for the figure in here and use it when saving the file:\n",
    "output_fig2 = \"\"\n",
    "\n",
    "# REPLACE THE ERROR BELOW WITH YOUR OWN CODE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "7dcf3cca574a986249939ac9315ffe08",
     "grade": true,
     "grade_id": "problem_1_final_output_test",
     "locked": true,
     "points": 0.5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# NON-EDITABLE TEST CELL\n",
    "# Check that output figure file exists\n",
    "import os\n",
    "assert os.path.isfile(output_fig2), \"figure does not exists!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Optional:  4-panel figure** (+2 extra points available if you choose to do this)\n",
    "\n",
    "As an optional task, plot travel times to Itis and Myyrmanni all in one 4-panel figure (see example at the top of this page)!\n",
    "\n",
    "Hints:\n",
    "- create a for-loop where you repeat all the steps for Itis and Myyrmanni (you can iterate over a list of input filepaths)\n",
    "- Before the for-loop, create a figure with 4 subplots (2 rows and 2 columns) and use figsize=(10,10)\n",
    "- Inside the for-loop, plot each map into one of the subplots. \n",
    "- save the figure as `shopping_center_accessibility.png` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "b365e5801e120ac6e706ae42a938dc94",
     "grade": false,
     "grade_id": "for-loop",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# REPLACE THE ERROR BELOW WITH YOUR OWN CODE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finally**, check that you have provided adequate comments in your code. If you have any thoughts about this exercise, you can write them in here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "93ea07b57f7684eebcb15a4631ed5fbf",
     "grade": true,
     "grade_id": "cell-c75c11f6a94612ef",
     "locked": false,
     "points": 4,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "source": [
    "YOUR ANSWER HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Done!\n",
    "\n",
    "\n",
    "**Remember to commit the output figures** to your exercise repository! (One for Itis and one for Myyrmanni, or optionally a combined 4-panel figure for both shopping centers and both travel modes).\n",
    "\n",
    "That's it, once you have plotted the data and saved the output you are ready to continue with Problem 2."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}