Barnsley Location Data Review.ipynb

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    "> > > > *Gavin Chait, B.Sc., B.Sc.(Eng), data scientist at Whythawk*\n",
    "\n",
    "---\n",
    "\n",
    "> **Abstract**\n",
    ">\n",
    "> In February 2021, Whythawk, an open data research service, was asked to provide a standard quarterly-aggregated extract of commercial location data for local authorities in England and Wales. These data are derived from our Sqwyre commercial location data longitudinal study which integrates data from the master database of commercial hereditaments maintained by the Valuations Office Agency (VOA), and regularly-updated commercial ratepayers registers published by local authorities. During subsequent review, a potential anomoly was discovered in the quarterly aggregation for Barnsley Metropolitan Borough Council.\n",
    ">\n",
    "> This research report was initiated to investigate these data transitions and answer the following questions:\n",
    ">\n",
    "> - What aggregate VOA changes occurred during updates between April to December 2019 in terms of hereditaments added, withdrawn, or recategorised?\n",
    "> - Were there any meaningful changes during the 2019 calendar year which may have influenced estimates for hereditament vacancy?\n",
    "> - Within each series, what proportion of hereditaments can be unambiguously classified in terms of occupation status, and how does ambiguity arise?\n",
    "> - What proportion of the total count of the hereditament base, as defined by the VOA, are represented in ratepayer data updates?\n",
    "> - What proportion of Barnsley data, in terms of reference numbers and matching quality, can be merged into the VOA master database?\n",
    "> - How are matches, and misses, reflected in vacancy tallies in general, temporally, and for each of the main categories of use (retail, office, industrial and leisure) considered in the initial study?\n",
    ">\n",
    "> The objective for the Sqwyre commercial location database is to produce a well-structured, standardised dataset for research and analysis. The specific defaults used to aggregate the historical record for a quarterly report for each hereditament are designed to ensure that restatements and corrections provided via regular updates from the Valuations Office Agency and local authorities take precedence over previous updates. Each successive update takes precedence over the ones which came before. This ensures corrections happen, but can also leave long-term errors in place if no subsequent updates, or corrections, are released.\n",
    ">\n",
    "> Sources of ambiguity which prevent integration of these datasets include:\n",
    ">\n",
    "> - Unclear, or missing, hereditament reference codes,\n",
    "> - Duplicate rows with conflicting dates and occupation status,\n",
    "> - Provided data not covering the complete list of VOA hereditaments,\n",
    "> - Provided data for hereditaments not yet registered with VOA.\n",
    "> \n",
    "> After a thorough review process, this research was able to replicate the results produced for the original quarterly-aggregated extract, and answer the research questions.\n",
    ">\n",
    "> The core deflators of Barnsley's stated vacancy rate in their October 2019 commercial ratepayer data release are:\n",
    "> \n",
    "> - Understating the total number of hereditaments (declaring 8,765 vs 9,630 in the VOA register),\n",
    "> - Ambiguous or irreconcilable hereditaments (138 out of 767 are ambiguous),\n",
    "> - Including non-retail, office, industrial or leisure hereditaments in the total (55 hereditaments classified as `other` in the total).\n",
    ">\n",
    "> While overall vacancy in the earliest two Barnsley updates were 7.2% and 6.5% respectively, the fall in occupation seems to be precipitated by an enormous rise in the number of hereditaments as 2,000 new records are introduced. This increased the total hereditament count from 7,000 to 9,000, deflating the vacancy rate even as the absolute number of voids remained relatively consistent.\n",
    ">\n",
    "> A subsequent update, listed as \"October 2020\" not available at the time of the initial generation of the aggregation data but brought to our attention for inclusion in this review, increased overall vacancy rates to 7.6%. However, a period of 18 months passed between updates published by Barnsley, and this lack of data access meant that changes were not reflected as expected.\n",
    ">\n",
    "> Our recommendations for limiting the impact of such challenges are that local authorities must publish their register of commercial ratepayers quarterly and continuously, and that ambiguity can be limited by adoption of universal definitions and data structure standards through a defined schema.\n",
    "\n",
    "---\n",
    "\n",
    "# Background to this review\n",
    "\n",
    "In February 2021, [Whythawk](https://whythawk.com), an open data research service, was asked to provide [a standard quarterly-aggregated extract of commercial location data](https://whythawk.com/index.php/research-reports/24-sqwyre-commercial-location-data-aggregations-for-england-and-wales-as-of-july-2020) for local authorities in England and Wales. These data are derived from our [Sqwyre](https://sqwyre.com) commercial location data longitudinal study. During subsequent review, a potential anomoly was discovered in the quarterly aggregation for Barnsley Metropolitan Borough Council.\n",
    "\n",
    "![Barnsley Voids & Occupation Overview](data/barnsley-voids-overview.png)\n",
    "\n",
    "*Figure 1: Barnsley Voids & Occupation Overview, source: Sqwyre.com - blue: office | green: industrial | red: retail | yellow: leisure*\n",
    "\n",
    "Our foundation data are that produced by the [Valuations Office Agency (VOA)](https://voaratinglists.blob.core.windows.net/html/rlidata.htm), enriched with ratepayer occupation and rates-paid data provided by contributing local authorities. Where local authority data are missing for any reason, we do not predict occupation or vacancy for individual hereditaments, but set them as `occupied`.\n",
    "\n",
    "The timeline of the large deviations in Barnsley's aggregate void data are:\n",
    "\n",
    "- Barnsley first cross-referenced data release in Q2 2017, six months after the start of our data series, leading to an initial 0% vacancy as a result of our default approach,\n",
    "- Transition from 2010 to 2017 rates valuation in April 2017 appears to coincide with a dramatic change in categorisation of hereditaments from office to leisure. This is unexplained,\n",
    "- In October 2019 we received the last update from Barnsley for what would be about 18 months. That update resulted in a dramatic change in voids. This is unexplained.\n",
    "\n",
    "As can be seen in *Figure 2*, greater than 90% of Barnsley's published data match that of the VOA, meaning that the source of these unexplained transitions may not lie directly (unless all 3-4% of unmatched data are also voids).\n",
    "\n",
    "![Barnsley Update History](data/barnsley-update-history.png)\n",
    "\n",
    "*Figure 2: Barnsley Update History, source: Sqwyre.com*\n",
    "\n",
    "Further, this is unlikely to be a generalised problem inherent in the Sqwyre software or methodology as Barnsley is an outlier. While such phase transitions have occassionally been noted in other authorities, they are often explained by poor quality source data. These events do not seem to synchronise, as can be seen in *Figure 3* where the total England and Wales aggregate data do not exhibit these massive deviations.\n",
    "\n",
    "![England & Wales Vacancy Rates](data/england-wales-vacancy-rates.png)\n",
    "\n",
    "*Figure 3: England & Wales Vacancy Rates, source: Sqwyre.com - blue: office | green: industrial | red: retail | yellow: leisure*\n",
    "\n",
    "This research report was initiated to investigate these data transitions.\n",
    "\n",
    "# Research questions\n",
    "\n",
    "It is unclear whether any assumed data anomolies occur in the VOA data, the local authority releases, Sqwyre methodology and assumptions, or some combination. As such, each will be tested individually, and then together. To ensure complete transparency and replication, all code, source data, and output are presented in this Jupyter Notebook.\n",
    "\n",
    "## Questions concerning Valuations Office Agency data\n",
    "\n",
    "The VOA has a fortnightly update cycle, releasing changes to the 2017 register directly to their [repository](https://whythawk.com/index.php/research-reports/24-sqwyre-commercial-location-data-aggregations-for-england-and-wales-as-of-july-2020). There is a mostly-complete [schema definition](https://voaratinglists.blob.core.windows.net/html/documents/2017%20Compiled%20List%20and%20SMV%20Data%20Specification.pdf) published which explains the structure of the data. Whythawk has engaged with the VOA regularly to gain insight into database decisions, explore missing categorisation definitions, or understand incomplete methodology.\n",
    "\n",
    "- What aggregate VOA changes occurred during updates between April to December 2019 in terms of hereditaments added, withdrawn, or recategorised?\n",
    "- Were there any meaningful changes during the 2019 calendar year which may have influenced estimates for hereditament vacancy?\n",
    "\n",
    "## Questions concerning Barnsley Metropolitan Borough Council data\n",
    "\n",
    "Barnsley historically had a relatively consistent commercial ratepayer data release schedule, with updates to the end of April and October each year. Initially, they did not provide the unique reference numbers necessary to import and merge their data, and so their first captured release is for April 2017. Updates were continuous till October 2019, when they stopped, resuming again only in 2021. No schema or metadata are provided. Their data is [found here](https://www.barnsley.gov.uk/services/business-rates/requests-for-information-about-business-rates/).\n",
    "\n",
    "The datasets included in this study are those with release dates from April 2017 to October 2019, and October 2020 included (but did not form part of the data under review, since this series was not available at the time).\n",
    "\n",
    "- Within each series, what proportion of hereditaments can be unambiguously classified in terms of occupation status, and how does ambiguity arise?\n",
    "- What proportion of the total count of the hereditament base, as defined by the VOA, are represented in ratepayer data updates?\n",
    "\n",
    "## Questions concerning merged VOA and Barnsley data\n",
    "\n",
    "- What proportion of Barnsley data, in terms of reference numbers and matching quality, can be merged into the VOA master database?\n",
    "- How are matches, and misses, reflected in vacancy tallies in general, temporally, and for each of the main categories of use (retail, office, etc) considered in the initial study?\n",
    "\n",
    "## Scope of work and study limitations\n",
    "\n",
    "The period for the research will be limited to the 2017 VOA rates valuation data series, and for the period 1 April 2017 to 31 December 2019. We are only interested in reasons relating to void classification. Floor area or rates valuations changes, and hereditament durations of occupation or vacancy, do not form part of this review.\n",
    "\n",
    "To ensure replication and review, data of concern have been extracted from the Sqwyre database as CSV files, and available with this release. This automatically simplifies the range of queries possible, since the data are no longer part of a larger, more enriched, database. \n",
    "\n",
    "VOA data releases are continuous, and ordinary practice is to reconcile all data released to date (at the time of a merge and aggregation) to generate reports. Given that subsequent releases can restate the past, and that the dates of concern are 18 months in arrears of updates, what follows can only be a proxy for the original analysis."
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    "# Valuations Office Agency data review\n",
    "\n",
    "Commercial rates are a legal requirement and must be paid on all hereditaments considered to be in a lettable / habitable state. The official definition of hereditament is somewhat self-referential: \n",
    "\n",
    "> *\"Hereditament\" means property which is or may become liable to a rate, being a unit of such property which is, or would fall to be, shown as a separate item in the valuation list.*\n",
    "\n",
    "In practical terms, these are individual, unique commercial units at specific addresses.\n",
    "\n",
    "For England and Wales, these data are developed and maintained by the VOA. Their rates valuations are closest to the true (rather than quoted) rentals paid by each tenant. They release updates to their 2010 and 2017 ratings lists fortnightly, issuing adjustments to valuations of existing hereditaments, or removing and adding hereditaments to the list. Each is assigned a category of use (SCAT) from a list of 457 types. The next revaluation will be for 2023.\n",
    "\n",
    "Hereditaments themselves may include multiple distinct components (store rooms, offices, mezzenine floors, etc.), each rated according to their classification, but being summed to a total floor area and rental valuation for each hereditament. For the sake of simplicity, we do not consider these subcomponents in our analysis but use only the total hereditament data.\n",
    "\n",
    "Some 10% of the VOA's data are incorrectly coded, or contain missing data or errors. The process of gap-filling and error-correction is performed at the geospatial level. The Office of National Statistics (ONS) publish quarterly updates to their postcode database. We match hereditaments to postcodes, to code each with their appropriate latitude and longitude.\n",
    "\n",
    "We run two processes to ameliorate these data issues, the first to add or correct postcodes, the second to interpolate for missing floor areas. This is run every two weeks, since VOA fortnightly updates introduce new errors from their processes.\n",
    "\n",
    "1. *Postcodes:* All VOA addresses with postcodes are checked against the master ONS list. Each item with malformed or missing postcodes is checked against nearby addresses with known postcodes from the master list (starting in the same street, then the same town) to find the closest approximate address. This is performed using an implementation of the [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distancehttps://en.wikipedia.org/wiki/Levenshtein_distance), a natural language processing mechanism for estimating similarity between text. Our objective is to situate an hereditament as close as possible to its accurate position.\n",
    "2. *Floor area:* This is a geospatial algorithmic interpolation step. Each hereditament with a missing floor area is added to a list. For each, filter by distance, taking the nearest addresses with the same categorisation and with a known floor area. For the filtered items with known floor area, divide the rateable value by floor area to get per-m2 values. For the hereditament being assessed, /m2/rateable value = m2 for that site.\n",
    "\n",
    "Each hereditament is assigned Standard Industry Codes (SICs) for businesses likely to occupy those sites based on the SCATs assigned by the VOA. For example, a `Warehouse` could be used for `Construction`, `Transport`, but also `Wholesale Trade`. These SIC categorisations must hold, subject to deliberate change of use, irrespective of future or previous occupants.  Between them, the SCATs and SICs define building type, and its long-term potential for use.\n",
    "\n",
    "Finally, updates may be active retrospectively. A floor area, categorisation or valuation change may be effective any time in arrears of the VOA update. These are consolidated into the overall database.\n",
    "\n",
    "These location data form our foundation list of all commercial locations in England and Wales for further enrichment with ratepayer, occupation, employment and econometric data. The data extract provided as source are extracted from this Sqwyre database and include all updates available till 31 March 2021.\n",
    "\n",
    "What follows is `Python` code to develop our research questions further."
   ]
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    "################################################################################\n",
    "# Imports and constants\n",
    "################################################################################\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import pytz\n",
    "from datetime import date, datetime, timedelta\n",
    "\n",
    "# Specify source\n",
    "directory = \"data/\""
   ]
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    "################################################################################\n",
    "# 1. Valuations Office Agency data review\n",
    "################################################################################\n",
    "#  - What aggregate VOA changes occurred during updates between April to \n",
    "#    December 2019 in terms of hereditaments added, withdrawn, or recatogorised?\n",
    "#  - Were there any meaningful changes during the 2019 calendary year which may \n",
    "#    have influenced estimates for hereditament vacancy?\n",
    "################################################################################\n",
    "\n",
    "# Open Barnsely VOA data as a Pandas dataframe\n",
    "dtype_dict = {\n",
    "    \"location_code\": str,\n",
    "    \"rates_code\": str\n",
    "}\n",
    "parse_dates = [\"report_date\", \"created_date\", \"effective_date\", \n",
    "               \"start_date\", \"end_date\"]\n",
    "voa_barnsely_source = \"barnsley-2017-valuation-data.csv\"\n",
    "df = pd.read_csv(directory + voa_barnsely_source, \n",
    "                 dtype=dtype_dict, \n",
    "                 parse_dates=parse_dates)\n",
    "################################################################################\n",
    "# NOTE: a significant challenge is that VOA update cycles don't map nicely to\n",
    "#       frequency of release or a date. The below uses filter_date as a proxy\n",
    "#       and report_date for subsequent sorting and filtering.\n",
    "################################################################################\n",
    "df[\"filter_date\"] = df[\"start_date\"]\n",
    "df[\"filter_date\"].fillna(df.effective_date, inplace=True)\n",
    "df.sort_values(by=[\"update_cycle\", \"filter_date\"], inplace=True)\n",
    "\n",
    "aggregation = {\n",
    "    \"update_cycle\": [],\n",
    "    \"count_total\": [],\n",
    "    \"count_added\": [],\n",
    "    \"count_removed\": [],\n",
    "    \"count_changed\": []\n",
    "}\n",
    "\n",
    "previous_references = set()\n",
    "for update_cycle, group in df.groupby(\"update_cycle\"):\n",
    "    if previous_references:\n",
    "        # Sort the group by report date and take the latest\n",
    "        group = group.copy()\n",
    "        group.sort_values(by=[\"report_date\"], inplace=True)\n",
    "        group.dropna(subset=[\"report_date\"], inplace=True)\n",
    "        group.drop_duplicates(keep=\"last\", inplace=True)\n",
    "        # Get all unique rates codes in this update cycle\n",
    "        current_references = set(group[\"rates_code\"].tolist())\n",
    "        # Get all active rates codes as at last update cycle\n",
    "        # NOTE: VOA use 'floor_area' and 'rental_valuation' as \n",
    "        #       methods to store information about the activity \n",
    "        #       status of hereditaments,\n",
    "        #       None, 0 or 1 mean a hereditament is usually not active\n",
    "        active_references = set(df[(~df[\"floor_area\"].isin([None, 0, 1])) \n",
    "                                   &\n",
    "                                   (~df[\"rental_valuation\"].isin([None, 0, 1])) \n",
    "                                   & \n",
    "                                   (df[\"update_cycle\"] < update_cycle)\n",
    "                                  ][\"rates_code\"].unique())\n",
    "        # Get inactive references in latest release\n",
    "        current_inactive_references = set(group[(group[\"floor_area\"\n",
    "                                                      ].isin([None, 0, 1])) |\n",
    "                                                (group[\"rental_valuation\"\n",
    "                                                      ].isin([None, 0, 1]))\n",
    "                                                ][\"rates_code\"].unique())\n",
    "        # Start aggregation\n",
    "        aggregation[\"update_cycle\"].append(update_cycle)\n",
    "        count_added = len(current_references - previous_references)\n",
    "        count_removed = len(active_references & current_inactive_references)\n",
    "        count_changed = len(current_references) - count_added - count_removed\n",
    "        count_total = len(previous_references) + count_added\n",
    "        aggregation[\"count_added\"].append(count_added)\n",
    "        aggregation[\"count_removed\"].append(count_removed)\n",
    "        aggregation[\"count_changed\"].append(count_changed)\n",
    "        aggregation[\"count_total\"].append(count_total)\n",
    "    previous_references |= set(group[\"rates_code\"].tolist())"
   ]
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19PRoYGBAq1at0tVXX61du3apvb1d//Ef/6Fjjz22Aa8eAAAAMPuKnj+6k+ea+eKoUcAClWs6fi5OOFLYBCOITTG/N5KuUSbpRhdHremkFs3J6NhlHVrUntGCKCxaNCejBe0ZtWWSM/yKAThQTDdA2m6MWWSt3RaVqL0cLX9R0rKa9ZZGy17USMlbZfkD0fKlE6zflI466ih9+MMf1plnninXdXX88cfrlltu0WWXXaYbbrih2om2JF1++eVas2aNjj32WK1evVq5XG6P93/FFVdo9erVWrx48YSdaB977LE6/vjjdfjhh2vZsmU67bTTJEmZTEa33nqr3vzmNyubzeqMM87QwMCAJOmjH/2o3vWud+moo47SqaeeWm3ZdeSRR+r666/XeeedpyAIlEwm9bnPfU4nn3yyrrvuOp1yyinq6OjQcccd16BXDwAAAAiV/aA63Hm+6Gu45Ctf8qrToWI0FHrZj/rnGd9vT9m38qP+e8pBOB8uC/v7qayXL/qjwqK9DXIqjNGoIcon6gQ6lXDU3pKsGd7cTNgpdOXv00lHc1qS6simqqNwzYlG4HIZ9hzALDF7M+BZ1AfSt2tGYbtB0q6aTrS7rLXXGmPeLOkvFY7CtkrSZ6y1J0WdaK+XdEJ0l7+UdKK1tscY83NJV2mkE+1brLXf3dM2rVy50q5bt27Uso0bN+qII47Ym+eN/RTvIQAAQHMaLHp67IVePbGtXz35knYPlUeVYRU8X34QBjhBEIY+gQ2n/pjLdIZwD8usjJKOIzcqw6oui8qzEs7oUi3XMWpNJ8JLJqHWdFJtmUR1WS6dqF7PpV2lK6OF1YRECYcRuwDEizFmvbV25djle2yBZIz5isLWQ93GmC0KR1P7lKR7jTF/Kul5Se+IVv+uwvDoGUlDki6TpCgo+rikX0Tr/X2lQ21J71U40luLws6zm3IENgAAAOBAUPR8vdxf1MsDBT29fVC/eqFXj27u1VMvD6jyv+mEY9SRTWpOS9jR8qI5GWVSrhJOOGS6GwU7jglDHccZPW1JumpJJZRLuWpJucqlEsqmXGXT0TTlKptKqCXpVoMgQhwAmFl7Mwrbuya56dwJ1rWS/mKS+7lN0m0TLF8n6bV72g6MtmrVKhWLxVHLvvzlL+voo4+epS0CAADA/qK2Q+aSF5Vu2bDlj7UK562VtVaBVdgqyIa3Fcq+Xh4oant/oTrdUXO9d2h0351zWpI6blmH3nT0Qh23rEPHLO1QZzZJoAMAMVN3J9qYHY888shsbwIAAABeYYNFT794rkcP/26X1m3qUd9wecKOm8t+Y8abSbpG81rTmt+e0fK5Oa1aMVfz29Ja0J7RvPa0Du7KakV3jrAIAA4ABEgAAADAfshaq219Bf3mxT49urlXP/vdLv3mxT75gVXSNTp2aYcOW9g2utPmCTpkTkVDr4d99UiOqZR8jcw7RjImLC9LJhzNb0trfltandmUHDptBgCIAAkAAACYVSUv0Pb+grb3F7S1r6CnXhrQb17s0+Mv9mlXviQp7FPouGUduvLMQ3TKIXN1wkGdakm5s7zlAIADCQESAAAAME1BYLWtv6Df7RjUsy8PatOuoQmHgff8QIVyoELZ13DZV7EcKF/ytL2/qJ2Do/u1dB2jQ+e36uzD5+voJXP02iVzdOSidgIjAMCsIkACAAAAJA2VPO0YKKp/2NNAoaz+QjgdKHjRJZovhtOdgyU9t3NQhfJIYBSOGjb+FDvhGGWSjjJJN7o4WtCe0TFL52hBe0aL5mS0cE6LFrZndPDcrDJJwiIAwP6FAKmJ3HzzzbriiiuUzWZne1PqctZZZ+nGG2/UypUrZ3tTAADAfqgygthwyVe+5Gu45Clf9DVU8jVU8qrLSl4gP7DybdgSyLc2HE2sZt4PRkYf8wMpsCPL8kVPL/cXtX2goB39RQ0UvSm3qyXpqi2TiC5JLZqT0amHzNWr5uX0qu5WHTIvp3ltaTqUBgDEEgFSE7n55pv17ne/e68DJN/35br89woAAOwf+gtlbe0d1tbeYb3YW9CO/oJ2DJa0c7BYvfQOlTVc8uUFjRlFLOEYOU7YOXSls2jXCedbUq4WtGV0+MI2vf7QeZrfnta81rTmtCTVlkmqLZNQezRtzSSUdJ2GbBMAAM0otgHSp3/+aT3Z82RD7/PwrsP1/pPeP+U6d955p2688UYZY3TMMcfo4x//uN7znvdo586dmjdvnm6//XYddNBBuvTSS/WWt7xFF154oSSptbVVg4ODeuCBB3Tdddepu7tbjz/+uE488UTddddduuWWW7R161adffbZ6u7u1tq1ayd8/NbWVv3Zn/2ZfvjDH+pzn/ucNm3apM985jMqlUpatWqVPv/5z8t1XbW2turKK6/Ud7/7XS1atEj/8A//oGuvvVabN2/WzTffrPPPP1+FQkFXXnml1q1bp0QioZtuuklnn322Tj75ZH3xi1/UUUcdJWmkRdERRxyhv/qrv9Ljjz+ucrms6667TmvWrNHw8LAuu+wy/frXv9bhhx+u4eHhhr4vAABgZpT9QJt25rW9vygvCBRYK8+34TSoaeETjG7V0ztU1u6hsvqGS9qdL6snX9LW3uFxLXyMkbqyKXW3ptXdltIJB3WqM5tSLu0qm0oom3KjS2LUNJcOy8RSrhOGQcbIcRQFRKZmGS2BAABolNgGSLNhw4YNuv766/XTn/5U3d3d6unp0SWXXFK93Hbbbbrqqqv0zW9+c8r7efTRR7VhwwYtXrxYp512mh566CFdddVVuummm7R27Vp1d3dP+rf5fF6rVq3S//k//0cbN27Upz/9aT300ENKJpN673vfq7vvvlt/8id/onw+r3POOUc33HCDLrjgAn3kIx/R/fffryeeeEKXXHKJzj//fH3uc5+TMUa/+c1v9OSTT+q8887TU089pYsuukj33nuvPvaxj2nbtm3atm2bVq5cqQ996EM655xzdNttt6m3t1cnnXSSfu/3fk//+q//qmw2q40bN+qxxx7TCSec0OBXHgAA7InnB+odLqt3qKzeoZIGip6CwCqwkh9YWWtVDqxe6BnSb18a0FPbB/TsjkGV/X1vCZRwjDqySc1pSaozm9JBc7M65ZC5WtyR0eKOlvAyp0XdrSklaNUDAEBTiG2AtKeWQjPhxz/+sd7+9rdXA56uri49/PDD+vrXvy5Juvjii3Xttdfu8X5OOukkLV26VJJ03HHHadOmTTr99NP3ahtc19Uf/uEfSpJ+9KMfaf369Xrd614nSRoeHtb8+fMlSalUSqtXr5YkHX300Uqn00omkzr66KO1adMmSdJPfvIT/dVf/ZUk6fDDD9fBBx+sp556Su94xzt03nnn6WMf+5juvffeaiuqH/zgB7rvvvt04403SpIKhYI2b96sBx98UFdddZUk6ZhjjtExxxyzV88FAACEPD/QYDHsyLm/UNZgpVPnYjjfH10fLE7Q4XPBU/9weY/9+9Ra0tGiwxa26czD5umwBW1a0tGiRNTaJxG18km40TQqB6tcsilXrekE/QABABAzsQ2Q9neJREJBEI7YEQSBSqVS9bZ0Ol2dd11Xnrf3J3yZTKba75G1Vpdccok++clPjlsvmUxWT+wcx6k+puM4e3y8JUuWaO7cuXrsscf01a9+VV/4wheqj/ef//mfOuyww/Z6ewEAOFAVyr76C2X1D3vqGy6rvxC2DNraW4j6CAr7CtrWW9ir8Cfpmmq/Pa3psKPnpZ1ZtWcSam9JqiMbtgbqyCbVkU2pLZOohkHGjJR/LZqTUVsm+Qq8AgAAoJkQIDXQOeecowsuuEDve9/7NHfuXPX09OjUU0/VPffco4svvlh33323zjjjDEnS8uXLtX79er3jHe/Qfffdp3K5vMf7b2tr08DAwJQlbLXOPfdcrVmzRn/913+t+fPnq6enRwMDAzr44IP36u/POOMM3X333TrnnHP01FNPafPmzdVw6KKLLtI//uM/qq+vr9qi6I1vfKNuueUW3XLLLTLG6NFHH9Xxxx+v17/+9fr3f/93nXPOOXr88cf12GOP7dXjAwDQTPzA6uWBgrb1FfRSXzjd3h/O9w6X1R+FRP3DYSuikhdMel9zcykt7mjRiu6cTj2kW125VE0wlBw1ElglLEonHFr9AACAGUOA1EBHHXWUPvzhD+vMM8+U67o6/vjjdcstt+iyyy7TDTfcUO1EW5Iuv/xyrVmzRscee6xWr16tXC63x/u/4oortHr1ai1evHjSTrRrHXnkkbr++ut13nnnKQgCJZNJfe5zn9vrAOm9732vrrzySh199NFKJBL60pe+VG2pdOGFF+rqq6/W3/7t31bX/9u//Vtdc801OuaYYxQEgVasWKFvf/vbuvLKK3XZZZfpiCOO0BFHHKETTzxxrx4fAID9RRBYDZdHhpEfLHp6oWdIT28f1DM7BvX09kE9u2NQxTGhUDrhaEF7Rp3ZpNpbklrS2aL2TFLtLeHoXnNawuXVVkItSS2a06KWFKOoAgCA/YuxtjFDpL7SVq5cadetWzdq2caNG3XEEUfM0hahEXgPAQCvlKLnK1/0w/6Eor6EdgwW9ULPsLbsHtILu8PpS30FDZX8Se9nSUeLXj2/VYfOb9WKeTktmpPRwvYWLZqTUUc2SasgAADQVIwx6621K8cupwUSAADYbwWB1UAx7AS6L7r0DpU1UCir6AUqeYFKflCd7xsuqydfVE++VL3ki+PDn8ow9JPpzCa1rCurwxe26azXzFdrJqFcylU2nVA2GQ4tv6SzRYfMa1UuzekUAACIP854mtSqVatULBZHLfvyl7+so48+epa2CAAQJ9ZaDRbD0b2GS74K5colUKHsq+wH8gIrP7DRNLweVK/XTH0r346s4/vhbV4QyKvM+4HKvo36CCqrNwqL+ofLmiLnGSXlOmpvSagrl1JnNqXDFrapK5dSazqpsY2AjKRcOuxTqDWdUGsmobZ0Ql2tKS3tzKqVUAgAAGAUzo6a1COPPDLbmwAA2A9Ya1X0Ag2VfHn++E6Zi16grb3D2rI7vLywe0gv7h7WcHl8q5yyH1Q7eR4o7H1ws7cqw70nHCPHMUq6jhLR9YTrKBGNItaRTenguTnNiUYOm9My+lIZQSydcJSqXFw6kAYAAJhJBEgAAMwQP7AaKJS1K1/S7nxJu6KSqt6hcrUFj1edWhU8X0NFL+qoOeysuVAOFFirwIateayVfGs1XPI1XPKVL3l7HfQYIy1oy4QdObeMH6Y94RgdOr+1pmPncLSvlpSrTNJVSzKcZpKOkq6jpGvkOk41EKoNiMKpI8dRODUi4AEAAGhiBEgAAIxRKPva3DOkzbuGtCtfrA67XimpGix6KnqBiuVARc9X0QvLuopeEC0P56fqY6cibH0Thi2ZpKNsKqFsyo0uCXXlHDkmDGQcY2SM5DpGLUlXLSlXuVQimrpKJpwJ739xR4uWdma1uCOjdILRvQAAALDvCJAAAAe0l/sL+p+ndujnz/Vo0668nt81pJcHiuPWc4yqrXJa0wllko7SCVeduZTSiXA+nXCUTjrKJFylo9uzKVdzW1PqyqU1N5dSZy6lzmxSKdeR6xha5QAAAKApECABAGLHWqvhsq/Boidrx94mPbczr/95aof+56kd2ritX5I0N5fSIfNbdeZr5unguVkt68rqoK6s5rdnNKclqVzKJewBAADAAYsAqYncfPPNuuKKK5TNZvfp76677jq1trbqb/7mb2Zoy5pzWwDs/6y1KpQDDRTLGix4Gix6Gix42pkv6eX+gl4eKFanuwZLGiyGnT8PFvfcL1DCMVq5vFPvX324znzNPB2xqI2ACAAAAJgEAVITufnmm/Xud797nwMkAJiOSlgzllXY4XPJD4dgL/tBtUPosheoXB2SPRyW3QsClT2rchCEt/tWu2oDoIGCtvcXlS+Of6yw8+jJtzGVcDS/La0F7RkdPDertqjT58qw7LmUK9cZ3y/QvLa0TjlkLkO1AwAAAHsptmfOL/3DP6i48cmG3mf6iMO18EMfmnKdO++8UzfeeKOMMTrmmGP08Y9/XO95z3u0c+dOzZs3T7fffrsOOuggXXrppXrLW96iCy+8UJLU2tqqwcFBPfDAA7ruuuvU3d2txx9/XCeeeKLuuusu3XLLLdq6davOPvtsdXd3a+3atRM+/ve//3196EMfku/76u7u1o9+9CNJ0hNPPKGzzjpLmzdv1jXXXKOrrrpKkvTWt75VL7zwggqFgq6++mpdccUV1e25+uqr9e1vf1stLS361re+pQULFujZZ5/VH//xHyufz2vNmjW6+eabNTg4KEm64YYbdO+996pYLOqCCy7Qxz72MUnSJz7xCd1xxx2aP3++li1bphNPPLH+NwOApHCUrxd3D2tzz5CGSp5KfqCSF12i+aIXhjmV5RN17BxYq558aVSLnqHS+GHeGyXpGs1vy2heW1orunNatWKu2jIJjW0AZGSUi8KgtnQYDOXSCc1tTWlBW0btLQlaDQEAAACvgNgGSLNhw4YNuv766/XTn/5U3d3d6unp0SWXXFK93Hbbbbrqqqv0zW9+c8r7efTRR7VhwwYtXrxYp512mh566CFdddVVuummm7R27Vp1d3dP+Hc7duzQ5ZdfrgcffFArVqxQT09P9bYnn3xSa9eu1cDAgA477DBdeeWVSiaTuu2229TV1aXh4WG97nWv0x/+4R9q7ty5yufzOvnkk/WJT3xC1157rf7v//2/+shHPqKrr75aV199td71rnfpC1/4QvX+f/CDH+jpp5/Wz3/+c1lrdf755+vBBx9ULpfTPffco1/96lfyPE8nnHACARIwgaLnq2+orN7hsnbnS+odLqtvqKyiH8haqyCw8m1Y0rV7qKTf7cjr2R2D2rRrSCUv2KvHSDhGqUQ45PpEoUtnNqn57Rm9dskczW/LaEF7Wm2Z5LhQZ/R9hUO5J11HiWiajEYUq867I+tU1mtNJeQ4BD8AAABAs4htgLSnlkIz4cc//rHe/va3VwOerq4uPfzww/r6178uSbr44ot17bXX7vF+TjrpJC1dulSSdNxxx2nTpk06/fTT9/h3P/vZz/T6179eK1asqD5+xZvf/Gal02ml02nNnz9f27dv19KlS/WZz3xG3/jGNyRJL7zwgp5++mnNnTtXqVRKb3nLWyRJJ554ou6//35J0sMPP1wNwP7oj/6o2pfRD37wA/3gBz/Q8ccfL0kaHBzU008/rYGBAV1wwQXVsrvzzz9/j88DaDZFz9fufFmDxbIGavrpGSh42pkvaudASTsGi9o5UNTOwbBUq+SHLYNKUeugvRjtvcp1jA7uyupV83I667D5elV3TgfPzak1nVAq4Yxc3HCajuYJbAAAAABMV2wDpP1dIpFQEIStBoIgUKlUqt6WTqer867ryvPG9wuyrya6zwceeEA//OEP9fDDDyubzeqss85SoVCQJCWTyWoLhb3ZBmutPvjBD+rP/uzPRi2/+eab69524JVirdVg0VPvUFm9Q2X1F8rKFz0Nlfzo4ilf9LVjsKCX+gra1hdOd+VLU95vLuWquy2t7ta0XjUvp7ZMshrwpKOwJ5N0NaclqY5sUp3ZVHU+lXDkGCPXGDnGyHGkTNJV0h3frw8AAAAAzBQCpAY655xzdMEFF+h973uf5s6dq56eHp166qm65557dPHFF+vuu+/WGWecIUlavny51q9fr3e84x267777VC6X93j/bW1tGhgYmLSE7eSTT9Z73/tePffcc9USttpWSGP19fWps7NT2WxWTz75pH72s5/tcRtOPvlk/ed//qcuuugi3XPPPdXlb3zjG/W3f/u3+uM//mO1trbqxRdfVDKZ1Otf/3pdeuml+uAHPyjP8/Rf//Vf40ImNDfPD0b1u1Os6Xtn7PDpUthapydf0q58SbvzJfXkS9o9VJIXWFk70mlyYG1UujWyzFo75vbKbVFHy2Ou25p19/T3wyVfvUPlCfsHGqszm9SC9owWzcnomKUdWjQno+7W9Eg/PZVOnNMJdbem1ZJyG//CAwAAAMAriACpgY466ih9+MMf1plnninXdXX88cfrlltu0WWXXaYbbrih2om2JF1++eVas2aNjj32WK1evVq5XG6P93/FFVdo9erVWrx48YSdaM+bN0+33nqr3va2tykIAs2fP79aejaR1atX6wtf+IKOOOIIHXbYYTr55JP3uA2VkeA+8YlPaPXq1ZozZ44k6bzzztPGjRt1yimnSAo74b7rrrt0wgkn6KKLLtKxxx6r+fPn63Wve90eHwP1K/uBHnpmpwYK3qjOlEfN1y4b0+FyOO9Put50S68mkko46swmlXTDljaOkRxjZKLpqHmnclvNeqpZ3zFKOmbcOo7RmOu19x/e1pJy1dEStf6JWgG1Z8IOm7MpV7l0Qi0pV9mkqwStfwAAAAAcYIydqIlAE1i5cqVdt27dqGUbN27UEUccMUtbdGAYGhpSS0uLjDG655579JWvfEXf+ta3Gnb/vIeN8f3Ht+nP7/rllOskXVPtI2d0nzlu2G+OO74vnXHXJ1mn0ufORB01pxJGXbm05uZS6syllEu5jKIFAAAAAPsJY8x6a+3KsctpgYR9sn79ev3lX/6lrLXq6OjQbbfdNtubhAlU+uS554qTtWhOphrwJCtBDx0qAwAAAAD2AQFSk1q1apWKxeKoZV/+8pd19NFHz+jjnnHGGfr1r389o4+B+uWLYafnr10yR61pPuYAAAAAgPrE7peltfaAKId55JFHZnsTGq5Zyyn3R4NFX5KUTdJ5MwAAAACgfrHqCTaTyWjXrl0EEU3IWqtdu3Ypk8nM9qbEQr7oKZdyKVMDAAAAADRErFogLV26VFu2bNGOHTtme1MwDZlMRkuXLp3tzYiFfNFTjtI1AAAAAECDxOoXZjKZ1IoVK2Z7M4BZN1j06PsIAAAAANAwsSphAxAaKvm0QAIAAAAANAwBEhBDg0VP2RQdaAMAAAAAGoMACYihPCVsAAAAAIAGIkACYohOtAEAAAAAjUSABMTQYJE+kAAAAAAAjUOABMRQWMJGH0gAAAAAgMYgQAJixg+shsu0QAIAAAAANA4BEhAz+ZInSXSiDQAAAABoGAIkIGbyxTBAogUSAAAAAKBRCJCAmCFAAgAAAAA0GgESEDP5oi9JdKINAAAAAGgYAiQgZiotkLIpWiABAAAAABqDAAmImcEinWgDAAAAABqLAAmImcoobPSBBAAAAABoFAIkIGYGoz6QcvSBBAAAAABoEAIkIGbylLABAAAAABqMAAmImXzRk2OkliQtkAAAAAAAjUGABMTMYNFTLpWQMWa2NwUAAAAAEBMESEDM5IseHWgDAAAAABqKAAmImXzRpwNtAAAAAEBDESABMZMv0QIJAAAAANBYBEhAzOSjPpAAAAAAAGgUAiQgZgaLPi2QAAAAAAANRYAExEy+6KmVPpAAAAAAAA1EgATEDKOwAQAAAAAajQAJiJnBoqdWAiQAAAAAQAMRIAEx4vmBil5ACyQAAAAAQEMRIAExki/6kkSABAAAAABoKAIkIEYGS54k0Yk2AAAAAKChCJCAGMkXwwCJFkgAAAAAgEYiQAJipBogpQiQAAAAAACNQ4AExAh9IAEAAAAAZgIBEhAjg9USNvpAAgAAAAA0DgESECOVErZWWiABAAAAABqIAAmIkXyJTrQBAAAAAI1HgATEyCAtkAAAAAAAM4AACYiRfNGT6xilE3y0AQAAAACNw69MIEbyRV+5lCtjzGxvCgAAAAAgRgiQgBgZLHqUrwEAAAAAGo4ACYiRfNFTlgAJAAAAANBgBEhAjORLPiOwAQAAAAAajgAJiJF80VNr2p3tzQAAAAAAxAwBEhAj+aKnXIoWSAAAAACAxiJAAmKETrQBAAAAADOBAAmIkXzRow8kAAAAAEDDESABMZIv0ok2AAAAAKDxCJCAmCh5gUp+QCfaAAAAAICGI0ACYiJf9CSJFkgAAAAAgIYjQAJiYpAACQAAAAAwQwiQgJjIl6IAKUWABAAAAABoLAIkICbyRV+SlKMPJAAAAABAgxEgATFR6QOplRI2AAAAAECD1RUgGWP+2hizwRjzuDHmK8aYjDFmhTHmEWPMM8aYrxpjUtG66ej6M9Hty2vu54PR8t8aY95Y53MCDkh0og0AAAAAmCnTDpCMMUskXSVppbX2tZJcSe+U9GlJ/2StfbWk3ZL+NPqTP5W0O1r+T9F6MsYcGf3dUZJWS/q8MYYaHGAfDdICCQAAAAAwQ+otYUtIajHGJCRlJW2TdI6kr0W33yHprdH8mui6otvPNcaYaPk91tqitfY5Sc9IOqnO7QIOOLRAAgAAAADMlGkHSNbaFyXdKGmzwuCoT9J6Sb3WWi9abYukJdH8EkkvRH/rRevPrV0+wd+MYoy5whizzhizbseOHdPddCCW8iU60QYAAAAAzIx6Stg6FbYeWiFpsaScwhK0GWOtvdVau9Jau3LevHkz+VBA0xksekq6RukEARIAAAAAoLHqKWH7PUnPWWt3WGvLkr4u6TRJHVFJmyQtlfRiNP+ipGWSFN0+R9Ku2uUT/A2AvZQvepSvAQAAAABmRD0B0mZJJxtjslFfRudKekLSWkkXRutcIulb0fx90XVFt//YWmuj5e+MRmlbIelQST+vY7uAA9Jg0VMuRYAEAAAAAGi8af/atNY+Yoz5mqRfSvIkPSrpVknfkXSPMeb6aNkXoz/5oqQvG2OekdSjcOQ1WWs3GGPuVRg+eZL+wlrrT3e7gANV2AKJ8jUAAAAAQOPV1VzBWvtRSR8ds/h3mmAUNWttQdLbJ7mfT0j6RD3bAhzohko+JWwAAAAAgBlRTwkbgP3IYNFTKwESAAAAAGAGECABMZGnDyQAAAAAwAwhQAJiIl+khA0AAAAAMDMIkICYCEvY6EQbAAAAANB4BEhADFhro1HYaIEEAAAAAGg8AiQgBopeIC+wBEgAAAAAgBlBgATEQL7oSZJyKUrYAAAAAACNR4AExEC+6EsSLZAAAAAAADOCAAmIgXwpbIHUSoAEAAAAAJgBBEhADFRL2AiQAAAAAAAzgAAJiIFBAiQAAAAAwAwiQAJioNIHEiVsAAAAAICZQIAExMBICRujsAEAAAAAGo8ACYiBSgkbLZAAAAAAADOBAAmIATrRBgAAAADMJAIkIAYGS55SCUdJl480AAAAAKDx+LUJxEC+6CmXov8jAAAAAMDMIEACYiBf9ClfAwAAAADMGAIkIAbyRY8OtAEAAAAAM4YACYiBfMmjBRIAAAAAYMYQIAExMEgJGwAAAABgBhEgATEQlrDRiTYAAAAAYGYQIAExEI7CRgskAAAAAMDMIEACYmCwSB9IAAAAAICZQ4AENDlrLaOwAQAAAABmFAES0OQK5UCBlbL0gQQAAAAAmCEESECTGyx6kkQLJAAAAADAjCFAAppcPgqQ6EQbAAAAADBTCJCAJpcvRQESLZAAAAAAADOEAAlocvmiL4kSNgAAAADAzCFAAppctYSNTrQBAAAAADOEAAlocnSiDQAAAACYaQRIQJMbaYFEgAQAAAAAmBkESECTGyRAAgAAAADMMAIkoMlVOtHOpegDCQAAAAAwMwiQgCaXL3lKJxwlXD7OAAAAAICZwS9OoMkNFj060AYAAAAAzCgCJKDJ5Yse/R8BAAAAAGYUvzqBJuX5gXYPlbVzsEiABAAAAACYUfzqBF5hhbKvnnxp1GWw6KlQ9lX0AhXKvoZLvgqer0I5vB5eAg2Xfe3Ol9QzVFLvULl6n6e/unsWnxEAAAAAIO4IkIA98PxABS8IQ52yr6I3EuZUgp1C2ddw2Vex5np/oayefFk9+aJ68iXtype0O19SvuRP+XiOkTJJV5mkq5akq3TSUSbhKpN01JJydcTidnVlU+rKpTS3NaXObEorl3e+Qq8GAAAAAOBARICE2LDWale+pK29w9rWV1C+6EUhTxjoFKOQp9qqJwqFwkBofChUWd8L7LS2J51wNDeXUldrSl25tFZ059SVS2tuaxj+dOVSmptLqTOXUls6oXQUGCVdI2NMg18dAAAAAACmjwAJ+7WhkqcXdw/rhd1D2rJ7WL1DZQ2VfA2VvOq0b7isrb0Fbe0dVtELpry/lmTYkqfSwidTuZ5w1d2aUEvKVSbhKh0tb6ldJxnelkm5yiScUa2EKrena/4m6dJHPQAAAAAgHgiQ0DDWWuVLvnoGS+obLte05glb+xSq/fqMLvuqtPap9PkzXPKVj4KjXfnSuMdJuY6yaVfZpKtsOqG2TEJHLm7XG45coMVzMlrc0aLFHS1qTdcGQo7SCYeWPQAAAAAATAMBEmSt1WDRU+9QWb1DZe0eKmmgEJZ/DZd9DZc8DZeCkfmyr6GoP6Chkq++4XK1j5/SHloA1Uo4JurjZ6QFT6U1T0c2paMWt2tpZ1ZLO1u0tDOrZZ0t6sylaNkDAAAAAMArjADpAFLyAj27Y1Abt/XryZcGtHFbv57aPqBdg6W96ucnlXCUTYUhT0vSDVv3JF0taM/oiEXt1f58unIpdbQkq7dXQqF0YuRvMglHCYKgGTP8+AZtueqv9KpvfEPunDmzvTkAAAAAgCZHgLSfCwKrvuGwVdDuoZJ68tF8NJR731C5Ovx7ePFVLAfV4eCry7xAgwWvGhSlXEeHLmjV6a+ep4Vz0upoSakjm1RHNqXObFJtmaSylQAoCo1ch/KvZlH63bPytm6T9/LLBEgAAAAAgLoRIO0ntvcX9Ojm3Xr0hV499kKftg8UopKykiZrHJRyHc3JJsMysIRTHe49nXTU3pIMlyXClj/ppKO2TEKvWdCmIxe1a0V3jhZAMWY9P5z6/ixvCQAAAAAgDgiQZtGDT+3QPb/YrEc392pbX0GSlHSNjlzUriMWtqszl1RnNqXObFQWlk2qKxde78yllEu5dAqNCVnfC6ceARIAAAAAoH4ESLNoa++wfvNin163vEvHLevQcQd16MhF7cok3dneNDS7SsujKEgCAAAAAKAeBEiz6KLXLdM7TzpotjcDMUQJGwAAAACgkegEZxZRfoYZUy1howUSAAAAAKB+BEhADFX7PqIFEgAAAACgAQiQgBiqlK7RiTYAAAAAoBEIkIAYsl45nNKJNgAAAACgAQiQgDiihA0AAAAA0EAESEAMVUvYyrRAAgAAAADUjwAJiKPKKGyUsAEAAAAAGoAACYghRmEDAAAAADQSARIQQ4zCBgAAAABoJAIkII4oYQMAAAAANBABEhBDlLABAAAAABqJAAmIoUrLI0rYAAAAAACNQIAExFG1BRIlbAAAAACA+hEgATFkPVogAQAAAAAahwAJiKHqKGz0gQQAAAAAaAACJCCOKqVrlLABAAAAABqAAAmIoUrpGiVsAAAAAIBGIEACYmhkFDZaIAEAAAAA6keABMQRo7ABAAAAABqIAAmIoWon2pSwAQAAAAAagAAJiKFqCRujsAEAAAAAGoAACYgjStgAAAAAAA1EgATEECVsAAAAAIBGIkACYqgy+pqlBRIAAAAAoAEIkIA4igIk0QIJAAAAANAABEhADFVL2OhEGwAAAADQAARIQAyNjMJGCRsAAAAAoH4ESEAcVUrXKGEDAAAAADQAARIQQ5SwAQAAAAAaiQAJiKFqCZtXnuUtAQAAAADEAQESEEeUsAEAAAAAGogACYghStgAAAAAAI1EgATEkPUYhQ0AAAAA0DgESEAcRQESJWwAAAAAgEYgQAJiiBI2AAAAAEAjESABMWOtlaoBEiVsAAAAAID6ESABcVPb6ogSNgAAAABAAxAgATFTW7ZGCRsAAAAAoBHqCpCMMR3GmK8ZY540xmw0xpxijOkyxtxvjHk6mnZG6xpjzGeMMc8YYx4zxpxQcz+XROs/bYy5pN4nBRzQPG/ieQAAAAAApqneFkj/LOn71trDJR0raaOkD0j6kbX2UEk/iq5L0pskHRpdrpD0L5JkjOmS9FFJqySdJOmjldAJwL6jBRIAAAAAoNGmHSAZY+ZIer2kL0qStbZkre2VtEbSHdFqd0h6azS/RtKdNvQzSR3GmEWS3ijpfmttj7V2t6T7Ja2e7nYBBzoCJAAAAABAo9XTAmmFpB2SbjfGPGqM+TdjTE7SAmvttmidlyQtiOaXSHqh5u+3RMsmWz6OMeYKY8w6Y8y6HTt21LHpQIzVlK1ZrzyLGwIAAAAAiIt6AqSEpBMk/Yu19nhJeY2Uq0mSrLVWkq3jMUax1t5qrV1prV05b968Rt0tECs2CpBMMskobAAAAACAhqgnQNoiaYu19pHo+tcUBkrbo9I0RdOXo9tflLSs5u+XRssmWw5gGiplayadpoQNAAAAANAQ0w6QrLUvSXrBGHNYtOhcSU9Iuk9SZSS1SyR9K5q/T9KfRKOxnSypLyp1+29J5xljOqPOs8+LlgGYjkoLpHSaUdgAAAAAAA2RqPPv/0rS3caYlKTfSbpMYSh1rzHmTyU9L+kd0brflfT7kp6RNBStK2ttjzHm45J+Ea3399banjq3CzhgjbRASikYHJzlrQEAAAAAxEFdAZK19leSVk5w07kTrGsl/cUk93ObpNvq2RYAIRv1e+Sk0vIoYQMAAAAANEA9fSAB2B/5o0vYwuwWAAAAAIDpI0ACYqZawpZJhwuCYBa3BgAAAAAQBwRIQMzYqONsJ50Jr1PGBgAAAACoEwESEDfVTrSjFkiMxAYAAAAAqBMBEhAzlU60TToVXqcFEgAAAACgTgRIQMxUS9hS6VHXAQAAAACYLgIkIG5qR2GTKGEDAAAAANSNAAmImeoobJSwAQAAAAAahAAJiJmRUdgqJWwESAAAAACA+hAgAXFTaYEU9YFUKWkDAAAAAGC6CJCAmBkZhS1qgUQJGwAAAACgTgRIQMzYqMWRk2EUNgAAAABAYxAgAXEzroSNFkgAAAAAgPoQIAExM66EjU60AQAAAAB1IkACYmZsCRudaAMAAAAA6kWABMRN1OcRnWgDAAAAABqFAAmImWoJW4oSNgAAAABAYxAgATFTKWEz6VS4gBI2AAAAAECdCJCAuIlK1pxqJ9oESAAAAACA+hAgATEzUsKWGnUdAAAAAIDpIkACYsb6nuQ4MsnkyHUAAAAAAOpAgATEjefLJBKS64bXGYUNAAAAAFAnAiQgZqzvS4lEGCKJEjYAAAAAQP0IkIC48T0Z15WptkCihA0AAAAAUB8CJCBmrOeH4ZFLCyQAAAAAQGMQIAExYz0vKmELWyDRiTYAAAAAoF4ESEDM2EoJW9QHEp1oAwAAAADqRYAExE21hC1qgUQJGwAAAACgTgRIQMyMG4WNEjYAAAAAQJ0IkIC4GTcKGy2QAAAAAAD1IUACYsZ6ftiBdoJR2AAAAAAAjUGABMRMWMKWrLZAsl55lrcIAAAAANDsCJCAuPHCEjY50cebEjYAAAAAQJ0IkICYsX44CpsxRkokKGEDAAAAANSNAAmIGet71f6PjOsyChsAAAAAoG4ESEDclL1q/0fGdSVaIAEAAAAA6kSABMSM9aNR2KSwhI0+kAAAAAAAdSJAAmLG+r7kUsIGAAAAAGgcAiQgbryaErZEghI2AAAAAEDdCJCAmLG+L1HCBgAAAABoIAIkIGas78nUlLCJEjYAAAAAQJ0IkIC48fywdE1RH0iUsAEAAAAA6kSABMQMo7ABAAAAABqNAAmIGet7o0dh88qzvEUAAAAAgGZHgATEjedXR2FTwmUUNgAAAABA3QiQgJixnlcdhc24lLABAAAAAOpHgATEjPV9RmEDAAAAADQUARIQN543qoSNUdgAAAAAAPUiQAJixvo+JWwAAAAAgIYiQAJiZlwJm0cJGwAAAACgPgRIQNx4nkzUAklJWiABAAAAAOpHgATEiA0CyVopUWmBRIAEAAAAAKgfARIQJ1G5GiVsAAAAAIBGIkACYqTS2qhawpZwaYEEAAAAAKgbARIQI9WwyK0dhY0WSAAAAACA+hAgATFiy2VJY0rYygRIAAAAAID6ECABcUIJGwAAAABgBhAgATEycQkbARIAAAAAoD4ESECcjB2FLcEobAAAAACA+hEgATEybhQ2WiABAAAAABqAAAmIEetVSthGOtEmQAIAAAAA1IsACYgTPyphS0QBUjJBCRsAAAAAoG4ESECMjC9howUSAAAAAKB+BEhAjIyUsI2Mwibfl7V2FrcKAAAAANDsCJCAOPHKkmpK2CotkWiFBAAAAACoAwESECPVEjZ3ZBS22uUAAAAAAEwHARIQIxONwiaJjrQBAAAAAHUhQALipDoKmztqagmQAAAAAAB1IEACYoQSNgAAAADATCBAAmKk2tLIHd2JNi2QAAAAAAD1IEAC4qTSAikZBkhyGYUNAAAAAFA/AiQgRiqdaFdK2AwlbAAAAACABiBAAmLE+hOXsDEKGwAAAACgHgRIQJxUStiqo7DRAgkAAAAAUD8CJCBGbDlsaTRuFDaPAAkAAAAAMH0ESECMVEvYEmNK2HxK2AAAAAAA00eABMSJP7oT7coobJSwAQAAAADqQYAExEi1VG3sKGx0og0AAAAAqAMBEhAjlRI2M66EjRZIAAAAAIDpI0AC4mSyEjZaIAEAAAAA6kCABMRIpYRtpAUSo7ABAAAAAOpHgATEyLhR2FxGYQMAAAAA1I8ACYgTz5eMkXGij3alE236QAIAAAAA1IEACYgR6/vV1kfSSCfa9IEEAAAAAKgHARIQI9Yrj5StqbaEjRZIAAAAAIDpI0AC4sTzRwVIohNtAAAAAEADECABMTK+hK3SBxIlbAAAAACA6SNAAmLE+h4lbAAAAACAhqs7QDLGuMaYR40x346urzDGPGKMecYY81VjTCpano6uPxPdvrzmPj4YLf+tMeaN9W4TcMAaW8LmUsIGAAAAAKhfI1ogXS1pY831T0v6J2vtqyXtlvSn0fI/lbQ7Wv5P0Xoyxhwp6Z2SjpK0WtLnjTE1v4AB7K1JR2GjhA0AAAAAUIe6AiRjzFJJb5b0b9F1I+kcSV+LVrlD0luj+TXRdUW3nxutv0bSPdbaorX2OUnPSDqpnu0CDli+V+33SKopYaMFEgAAAACgDvW2QLpZ0rWSguj6XEm91tpKc4ctkpZE80skvSBJ0e190frV5RP8zSjGmCuMMeuMMet27NhR56YD8WMnHYWNFkgAAAAAgOmbdoBkjHmLpJettesbuD1Tstbeaq1daa1dOW/evFfqYYGmEZawje9EmxI2AAAAAEA9EnteZVKnSTrfGPP7kjKS2iX9s6QOY0wiamW0VNKL0fovSlomaYsxJiFpjqRdNcsrav8GwL7wPRl3ghI2RmEDAAAAANRh2i2QrLUftNYutdYuV9gJ9o+ttX8saa2kC6PVLpH0rWj+vui6ott/bK210fJ3RqO0rZB0qKSfT3e7gAOZLXuTlLARIAEAAAAApq+eFkiTeb+ke4wx10t6VNIXo+VflPRlY8wzknoUhk6y1m4wxtwr6QlJnqS/sNbyaxeYhnGjsDmOZAwlbAAAAACAujQkQLLWPiDpgWj+d5pgFDVrbUHS2yf5+09I+kQjtgU4oPljWiBJYaBECyQAAAAAQB3qHYUNwH5k3ChskkwiEbZMAgAAAABgmgiQgBgZW8ImRR1pU8IGAAAAAKgDARIQJ974EjbjunSiDQAAAACoCwESECPW92XGtEBSIkEn2gAAAACAuhAgATEyeQkbLZAAAAAAANNHgATEyQQlbEpQwgYAAAAAqA8BEhAjYQnb2D6QErIeJWwAAAAAgOkjQAJixHqe5DIKGwAAAACgsQiQgDihhA0AAAAAMAMIkIAYCTvRnqCEjU60AQAAAAB1IEACYsT6vsxEJWz0gQQAAAAAqAMBEhAnE5aw0QIJAAAAAFAfAiQgRiYsYUskZOlEGwAAAABQBwIkIEas78skkqOWhSVstEACAAAAAEwfARIQJ5ONwkYJGwAAAACgDgRIQIxMPgobJWwAAAAAgOkjQAJiwlorTToKGy2QAAAAAADTR4AExIUXtjIyCUZhAwAAAAA0FgESEBPVkMgdW8LmynrlWdgiAAAAAEBcECABMWGjMrVxJWwJStgAAAAAAPUhQALiwp+khM2lhA0AAAAAUB8CJCAmpixhYxQ2AAAAAEAdCJCAmLCVTrTHlLCJEjYAAAAAQJ0IkIC4iFogmeSYPpAoYQMAAAAA1IkACYiJSUvYEgnJo4QNAAAAADB9BEhAXExSwmYSLi2QAAAAAAB1IUACYqISEjEKGwAAAACg0QiQgJiodKI90ShslLABAAAAAOpBgATERHUUtsT4UdhogQQAAAAAqAcBEhAXlRK2cS2QElIQyAbBbGwVAAAAACAGCJCAmLBeZRS28Z1oS6KMDQAAAAAwbQRIQFz4lRK2sZ1oh9cpYwMAAAAATBcBEhATdqoSNhEgAQAAAACmjwAJiIlqCVuCEjYAAAAAQGMRIAFx4U8yChslbAAAAACAOhEgATExaQlbIhne7hEgAQAAAACmhwAJiAlbKVGbbBQ2nxI2AAAAAMD0ECABceExChsAAAAAYGYQIAExscdR2OhEGwAAAAAwTQRIQEzscRQ2WiABAAAAAKaJAAmIi8oobC4lbAAAAACAxiJAAmKi2gJp3ChslLABAAAAAOpDgATEhK20QBpbwuZSwgYAAAAAqA8BEhAXlU60xwRIohNtAAAAAECdCJCAmKiUsI0bhS3qRJsACQAAAAAwXQRIQExUStjGjcJGCRsAAAAAoE4ESEBceJONwlYpYSNAAgAAAABMDwESEBOTjsKWjAIknxI2AAAAAMD0ECABMWF9T3JdGWNGLaeEDQAAAABQLwIkIC58f3z5mkQJGwAAAACgbgRIQExYzx/XgbY0MgqbKGEDAAAAAEwTARIQE9b3JmyBVFlmKWEDAAAAAEwTARIQF94kJWwJStgAAAAAAPUhQAJiwvq+lJyghM2lhA0AAAAAUB8CJCAmwhK28QGSKiVstEACAAAAAEwTARIQF5OUsJlqCRstkAAAAAAA00OABMSE9TwpMXkn2pSwAQAAAACmiwAJiInJS9joRBsAAAAAUB8CJCAuJi1hi/pA8gmQAAAAAADTQ4AExIT1fSkxwShslWWUsAEAAAAApokACYiJsIRtfAskRmEDAAAAANSLAAmIi8lK2IyRXFeWFkgAAAAAgGkiQAJiYrISNikaiY0+kAAAAAAA00SABMSF5430dzRWIkEJGwAAAABg2giQgJiw/sQlbFLYAokSNgAAAADAdBEgATERlrBNHiCJFkgAAAAAgGkiQAJiwnplGXeKEjb6QAIAAAAATBMBEhAXni8zRQsk65Vf4Q0CAAAAAMQFARIQE9b3pUlaIFHCBgAAAACoBwESEBeeN2kn2pSwAQAAAADqQYAExIT191DCxihsAAAAAIBpIkACYmLKErZEghI2AAAAAMC0ESABcUEJGwAAAABghhAgATFhfV8mOXkn2pSwAQAAAACmiwAJiAlGYQMAAAAAzBQCJCAuKGEDAAAAAMwQAiQgJqznTTkKmzxK2AAAAAAA00OABMTE1KOwubRAAgAAAABMGwESEAPW2qlL2FxK2AAAAAAA00eABMRBEITTKUrYrFd+BTcIAAAAABAnBEhADFRaF5lJStiUYBQ2AAAAAMD0ESABcRB1kF3tRDu/U/rhdZIfLaeEDQAAAABQBwIkIAaq4VClD6SnfyD95J+knb+VxChsAAAAAID6ECABMWCrLZCS4YLi4KipSdICCQAAAAAwfQRIQBxU+kCqlLCVBkZPKWEDAAAAANSBAAmIgXElbGNbIFHCBgAAAACoAwESEAO2PNJZtiSpNDh6mnBpgQQAAAAAmDYCJCAO/DGjsFVaIJXy4XJK2AAAAAAAdSBAAmJgXAlbpe+jYjilhA0AAAAAUI9pB0jGmGXGmLXGmCeMMRuMMVdHy7uMMfcbY56Opp3RcmOM+Ywx5hljzGPGmBNq7uuSaP2njTGX1P+0gANLdRS2SglbkRI2AAAAAEDj1NMCyZP0/1lrj5R0sqS/MMYcKekDkn5krT1U0o+i65L0JkmHRpcrJP2LFAZOkj4qaZWkkyR9tBI6AdhL40ZhG9uJNiVsAAAAAIDpm3aAZK3dZq39ZTQ/IGmjpCWS1ki6I1rtDklvjebXSLrThn4mqcMYs0jSGyXdb63tsdbulnS/pNXT3S7gQGS9sSVs+WgaBUgJVyqXZa2dha0DAAAAADS7hvSBZIxZLul4SY9IWmCt3Rbd9JKkBdH8Ekkv1PzZlmjZZMsnepwrjDHrjDHrduzY0YhNB+Kh2ol2MrxeKWGL+kCqBktB8ApvGAAAAAAgDuoOkIwxrZL+U9I11tr+2tts2NyhYU0erLW3WmtXWmtXzps3r1F3CzQ9O66ELQqOSiMlbLXrAQAAAACwL+oKkIwxSYXh0d3W2q9Hi7dHpWmKpi9Hy1+UtKzmz5dGyyZbDmAvjSthK47pA6kSLDESGwAAAABgGuoZhc1I+qKkjdbam2puuk9SZSS1SyR9q2b5n0SjsZ0sqS8qdftvSecZYzqjzrPPi5YB2FvVEraE5BWloBwur47CRgskAAAAAMD0Jer429MkXSzpN8aYX0XLPiTpU5LuNcb8qaTnJb0juu27kn5f0jOShiRdJknW2h5jzMcl/SJa7++ttT11bBdwwLFRyyLjuiOtj6RRo7DVrgcAAAAAwL6YdoBkrf2JJDPJzedOsL6V9BeT3Ndtkm6b7rYAB7qRErbESP9HqbbqfLWEjRZIAAAAAIBpaMgobABml62WsNW0QGpbGM5bW+0biRI2AAAAAMB0ECABcVAZhc11R/o9alsoWV/yitUSNjrRBgAAAABMBwESEAOjStiqLZAWhdPSYLWEjRZIAAAAAIDpIEAC4qC2hK3SB1LbwnBaHAiDJdUETQAAAAAA7AMCJCAGKsGQSUzdAqkSNAEAAAAAsC8IkIAYqHSiHY7CVtMHkhQGSpVOtOkDCQAAAAAwDQRIQBxUOtEeOwqbFLZAooQNAAAAAFAHAiQgBqolbG7UB5Kbllo6wxuLA5SwAQAAAADqQoAExEC1NK3SB1K6VUrlwmWlmhI2RmEDAAAAAEwDARIQB5VR2Fw3DIxSreFFkoqDMomkJErYAAAAAADTQ4AExMCoErbioJRuCy8So7ABAAAAAOpGgATEQHUUtkQi7AMp1Sq5ybAvpOJAGCyJEjYAAAAAwPQQIAFx4PuSMTKOM9IHkhROS4NSdRQ2WiABAAAAAPYdARIQA9bzw9ZHUtQHUtSBdqo16gOpUsJGCyQAAAAAwL4jQAJiwPqeTCVAKg5Kqaj/o3SbVMqPjMJGJ9oAAAAAgGkgQALiwPOr/RypVFPClmqVSgPVcMnSiTYAAAAAYBoIkIAYsH4UIFkblbDV9IFUHBwJlyhhAwAAAABMAwESEAPW98I+kMrDkg3GtECq6US7TAskAAAAAMC+I0AC4sDzwlZGpcHw+tgWSFEn2pSwAQAAAACmgwAJiIFwFDZXKg6EC9JRJ9qpNqlECRsAAAAAoD4ESEAMWN+TcRPjWyClcqNL2BiFDQAAAAAwDQRIQBxURmErRgFSuqaEzQYytiSJEjYAAAAAwPQQIAExYP2ohK3aAqlSwhYGScYfDq9TwgYAAAAAmAYCJCAGqiVs1T6QKi2QoiDJL4TrUcIGAAAAAJgGAiQgDjxfJjFRH0hRCyRvSBIlbAAAAACA6SFAAmKgWsI2UR9IkuTloyktkAAAAAAA+44ACYiDSUdhC0vYTCkvJRJh0AQAAAAAwD4iQAJiwJa9aBS2ASmZlRw3vKHSAqk0GN5OCRsAAAAAYBoIkIAYGDUKW6X1kTQyHwVIdKINAAAAAJgOAiQgBkZGYRscaXUkjcwXB8MSNo8WSAAAAACAfUeABMSB54clantqgUQJGwAAAABgGgiQgBgIS9iiFki1AZLjhn0iFQfCEjdK2AAAAAAA00CABMSBH3WiXRoYXcImSalc2AIpkWQUNgAAAADAtBAgATFgPV8m4Y5vgSSF14uMwgYAAAAAmD4CJCAGqiVspcHxLZDSrYzCBgAAAACoCwESEAdezShsqbbRt6XaRkZho4QNAAAAADANBEhADFjfl3EdqZyfpAXSACVsAAAAAIBpI0ACYsB6nmRseGWSPpCUoIQNAAAAADA9BEhAHHiejIJwftI+kBKytEACAAAAAEwDARIQA9b3ZUwUIE3SB5JxXYkWSAAAAACAaSBAAmIg7Bw7CocmaoFUzkuuSyfaAAAAAIBpIUAC4sDzZCoB0kR9IEkyTtRXEgAAAAAA+4gACYgB6/uSjcKhiVogKQyQRIAEAAAAAJgGAiSgydkgkIJARlE4NFEfSJJkRAkbAAAAAGBaCJCAZheFQtUAaWwLpFQuvN2xBEgAAAAAgGkhQAKaXCUUMrYcLhjbB1KlhM1YStgAAAAAANNCgAQ0OetFrYpsWZKptjiqqgZKAS2QAAAAAADTQoAENDsvbHlkgnIYFhkz+vZ02AeSMYGsTwskAAAAAMC+I0ACmly1VZEtje//SKq2QDLyJY8WSAAAAACAfUeABDS5SgmbCUrj+z+SakIlStgAAAAAANNDgAQ0u6gszQTF8f0fSVIyGoVNHiVsAAAAAIBpIUACmly1VVFQrPZ3NIrjSKlWStgAAAAAANNGgAQ0OetFLZD84sQlbFK43JYpYQMAAAAATAsBEtDs/EofSMMTd6ItSelWGetLHiVsmNjQL34hf2BgtjcDAAAAwH6KAAloctVWRX5hyhZIxparrZWAWn5fn56/5FLtvvvu2d4UAAAAAPspAiSgyVVL2ILCFC2Q2ihhw6RKmzZJQaDis7+b7U0BAAAAsJ8iQAKaXbWErSSlJuhEW5JSORlbknxf1tpXcOPQDIrPPScpCpIAAAAAYAIESECTq5alOZq8BVKqNQyQpGrgBFRUgqPSpk0EjAAAAAAmRIAENLlqCZuxk/eBlG6VgjBAoowNY5U2PS9JCgYG5Pf0zPLWAAAAANgfESABza5SwmY0dQukoBjO05E2xiht2iSTyYTzzz8/y1sDAAAAYH9EgAQ0OetFLYocO3kfSOk2GVsO16cFEmrYIFDp+eeVO+UUSVLpuU2zu0EAAAAA9ksESECTs36lhE1TtkCSsdH6BEgY4b38suzwsHKnnSYlEnSkDQAAAGBCBEhAs6uUsDlT94FkKp92SthQoxIYpV99iFLLlhEgAQAAAJgQARLQ5KolbHtsgRStTwsk1KgERqnly5VavpwACQAAAMCECJCAZlcpYXM0dR9IlRI2jwAJI0rPbZJpaVFi/vwwQNq8WTYIZnuzAAAAAOxnCJCAJmero7DZqUdhq3zafUrYMKK0aZNSBx8s4zhKHXywbLEo76WXZnuzAAAAAOxnCJCAJjcyCpsjJTITr5Su6USbPpBQo7Rpk1LLl0tSdUoZGwAAAICxCJCAJme9siTJZHLRUGwTqGmBRAkbKmy5rNKWLUotP1iSlFqxXJJUJEACAAAAMAYBEtDsKiVs6dzk66RaR7IlStgQKW3ZIvl+teVRYv58mZYWWiABAAAAGIcACWhy1RZF6ezkK6VbJScqYWMUNkQqQVE6CpCMMYzEBgAAAGBCBEhAs6uMwpaZpANtSUpmZZywCRJ9IKGitOl5SSN9H0lS6uCDVXr++VnaIgAAAAD7KwIkoMlVWiBNGSAZI5NqCedpgYRIadMmuR0dcjs6qstSyw9WecuLsqXS7G0YAAAAgP0OARLQ5KolaZm2qVeMAiQ60UZF7QhsFanlyyXfV2nLi7OyTQAAAAD2TwRIQLOrlrBNHSCZdBQg0Yk2IhMFSJX+kOgHCQAAAEAtAiSgyVVbFLVMUcImUcKGUYJ8Xt727RO3QBIBEgAAAIDRCJCAJmd9TzJWJr2HErZolDZK2CBJpc2bJWlcgOR2dMidM4eOtAEAAACMQoAENLtyScaRlN5DC6R0ThIlbAhVWhilViwfd1tq+XJaIAEAAAAYhQAJaHK2MCRjrJTaQx9ImTBAooQNUk2AdNBB424jQAIAAAAwFgES0ORsuSAZ7bEFkiotkMq0QEIYICUWLZLT0jLuttSK5fK2b1eQz8/ClgEAAADYHxEgAc2uVJBxrJSihA17r7hpk1LLD57wtmpH2lE/SQAAAABAgAQ0OVvauxZIJhuVuJVLM79R2K9Za1V6btO4DrQrUgeHwRIdaQMAAACoIEACmpwtF2WM9tgHUiVgsgXKkg50fm+vgv5+pScLkKJ+kegHCQAAAEAFARLQ7MrFsIRtTy2QWsKAyRaHXomtwn6s9NwmSZq0BZKTyymxYEF1PQAAAAAgQAKanC2VwhK2PfWBlIlaKBEgHfCqI7BNEiBVbqMFEgAAAIAKAiSgyVmvFLVA2kMJW7Y9XL80/ApsFfZnpU2bpERCySVLJl2HAAkAAABALQIkoNmVS1EfSHtqgRQGSLRAQmnTJqWWLZNJJCZdJ3XwwfL7+uTt3v0KbhkAAACA/dXkvx7wirFBoPLmzSr89ikVf/tblZ5/Xm1v+D21vfGNMsbM9uZhP2e9suQYKZGacj2TnROuXyq8EpuF/Vhp0+QjsFVUbi8//7wSnZ0zv1HAJKy1krUyDv/zQnPw+/vl5HIyrjvbmwIAQEMRIM2i/u99T7u+9CUVn3padjgqK3IcuR0d6v/Od9T6e+dq4d/9nZLz58/uhmK/ZstlGXcvfli1UMKGMLAuPf+8cqedNuV6lQCpuGmTWo47buY3DJhA/qc/1fZPflLFZ56V09Ymt61NTnu73LY2JebN05y3XaDcqafyzxbsF0pbtmjHZz6j/v/6tlKvepXmXXWV2s57w4zsn9b3Nfg/D6r/+99T9sSV6vjDt03ZqhT7xlorSRxbAGCM/eabxhizWtI/S3Il/Zu19lOzvEmvCCedUcfbL1TmsMOUPuxwpV99iEwioZ477tCOz9yi3735LVrwgfdrztvexpcYJrS3AZJpmSPJSuXizG8U9lveSy/JFot7boG0dInkugdWP0ilISnZInGsnXWlLS/q5U9/WgP336/ksmWae8UVCvJ5BQP98vsH5A/0K//ww+r/zneUPvRQdV3yJ2r/gz+Qk07X9bheT4/k+3K7u6f1nUtrqQOTt3Ondn7hX7X7q1+VcRx1vuudyj/yc7149dXKHHWU5l1zjXKnn9aQ8zi/t1e9//l17f7KV1TeskUmm1X/ff+lnttv17xrrlHbG8/jfLEOXk+Pev/ja9p9zz2yQ0NqPetMtZ5zrnKnnSa3NTfbmwcAs85UEvZZ3QhjXElPSXqDpC2SfiHpXdbaJyb7m5UrV9p169a9Qls4g6yVBrZJO5+Wdj0t7XxG2v2c1NKpYrlbL92zXkNPbFJu1Uma+96/kKyVLRYUDBdkiwXZclluZ6cSc+fK7e5WYu5cOS0tkqSgVJK/u1d+X6/83l75PT0qb9um8ubfydv8O5W3blV5V6+Sc9uVOXS5Wo48QpnjVip97MkyLa2yQSC/t1fejp3yd+2U398vk0rLybbIaWmRk83KtGTl9/aqvOUFlTa/oPILL6j0wgsKhvLKHH6EMke/Vi3HHKP0IYeM+8+YDQIFAwMKhoelIJANrGSDaD4IT8AdR7KBTHG3lN8hOY5M6zwpN1cmnZOi5uHW86RyWXa4XzbfI5vfLZm0bLJV1kmHt/u+5LhyWnNyW1vltLZO2sTcWlvXCdhe/efKWskvS35JSqQlNzn5qqWS/MFB+bt3q/jUUypsfFKFjRtV2LhR/s6dyi1L6KD7fyNJCmygncM71Vvs1byWeepId4TbUS5o47HHae4bj9P8f7pnr57D/nwSaq0N9+mt2+Rtf0lOLqfk0qVKLlwok5z8tdzTfdqhofDz0tenYHhYTmtrtdWDk8vJGBM+dm+v/J075b20Rd5LW2SHh2Sy7TK5OXKyOZl0RiadkpPJyKTTctJpmWRSJiE5LTkpk63/9Q0CaWCrtOtZqX+rlOuW5iwNL5N0qj740EN64U//lw664w7lVp005d0/c955yhx2qJZ+5rP7HKrYIFAwNCQ7PCzTkpXTkhn3WbOeFx5jenrkb39RCjwllh2i5MKFcrLZvX8wvyyVBmVLBVmblFVCtlyWLZXCgDWbVaKjQyY1psyzPCw9/1PpmR9Jz/xQ2vlbKT1HmvsqqesQae4h4bRtoZTtklo6pZYuKbUP21a7mYOD4TFy8wvydu5UcslipVesUHLJkglbDlSOwcHQkNyOjur+Vxdrw2Np7wthWNZxkJSeuP806/vye3rk9fTIyWTCbWhrm3Y4Yq1VkM+Hn53dvbLlktw5c+R2dMhtb5dJJhUMD2vX//037friFyXHUfef/Zm6Lrt0wmAoKJXU/53vqueOO1R88km5XV3qfNe71HLM0eF3h3FkHCM5rkwyIbe9PWy91N4uk8lIksqbN2to/S81tH6dhtf/shqYul1dSr/6VcqsWKr08oVKze9Q4CXkF628fFl+b1/0PHbL79klv2envN275fcPSpKSXe1KLuhSctF8JRctUmrpErnzFsqdt1CJBeG8E23DpLyibKFfdmCXgp6XZZ2klG4PP9uJ8G9NMiG3tXX8vl15jQoFeTt3yd+5I3xenZ379D4GvS/Je+JnKv32lwoGB5VY9molVhytxIpj5bSHrVr9gQEVNm5U8cknVXhiowpPPilZq+SSJUouWaLU0nCamD9fctzwO688JJUGpdKwTCopp6VVJtcqJ9smk2uTaWmVSWYkJ7FXxx5r7chnvlSSgiD8vGQyk35mrLXhup4Xvn6JRLiuX5aGd8uahPxCIL+vP/wc7N4tlcsj+5YbnqMUHn9cu27/kmypqI7VZ6n7wtcr2eLLZuaqb91m7bztHpW3blV25Up1vPOd4f7emgvPQVpb5bS0yJZKCopF2aEhBQO7Zft3KujfKTvQE14f7FOQ71dx0zb1rX9BthwouzSlzqOktsV5De5eqB0/K6n40oAyR7xG8//m2mor02B4WH5fX/VYkpg3T8kFCybdZ6Z4kcNjpleQAi+8+OVwWugLv4P6t0r9L4bntUM94XdR5Tg69xCpc3l4vrMfGv7N49p9993q/+53ZUslZU85Wcn58zX4wP/I7+uTSSaVPeVk5VadrKBYCM+ve3rk7+6R19urREen0oceqvRrDlX61a9W6pBXv+KBkz84KO+ll1R+cbO8zb+T9/JWuW2tSiyYp+T8hUosnC+3s0vGTUpOUnIT4WfMSco6ibqCb+v7CoaGFPT3Kdi9U35fj4K+Hqk0KNcMybEDcvw+OV6PHBVk5q6Qul8jdR8qzX211NLRuBciRva3lnCV81/v5R1ysi3h7859OV+b3oNO+D2wv/9OiQNjzHpr7cpxy/eTAOkUSddZa98YXf+gJFlrPznZ38QhQHpk7d9p7RNfCb+AK5yElGmXvKJUyktWWr7B1ZEPJ5Us792HxEtJslKiPMntSauhNqvhVqtC1irbb9Sx01GyFN6/71qV01JqWHLsvn0wiy1W+XYr37Wj7tNLSP3zwy+m1LBVsmCVKkhm9nc/+dFvWmMlWcmJtskaK9+VAtcocBVdjIKEqU79hJF1jdyyVaIYKFG0ShatEiUrE0h+UvIT4SVwJT9hFd79RE/chAdIYyQZOYFRshCE9zfmvQwcq4Euqa87UO9cX8++tlNPH3G0tuW3afvQdnmBV10342a0MLdQi3KL9Ffve1ClFqPB7pSsYxQ44fYbKyWHvfDxKo9ZlIwka2ov4fP20kZe2lE5mnopI2vC7bKOVWCsrAlkZGSsIxMYOdbIBEauL7lFT4miL7ccKFEM5HrR65Q08lKO/LQrL+XKuuEDG2ur74/rWbX0e2rp8+V6419Ha6ThNldDnUmVsq7cciDXs+G0bOV4dtzLbyS55UDJ4UCuP/m+Yo1UToWfLSeYYqfaC+H+ZcJ9IxG+rlaq7ojV/cQY2Wi/qExlJGt9yfqVtcInEW1j+KSMrOvK9Y0S0T6ZKI3sS1+7Zp52tBnlFShvA+XlK2UD5fxAOa+k1nJBq78pLdjiqJBV9F4aGYX7ZzDqsxFOHd9G+1D4ORj7+fYSRn7KyE85ShR8pQqTHwBKaanQnlChLSETWDle+D46XiDHt+HFs3J8jVz2cLzykkalrKNSiys/EciUS9VtNE54Qm2Nle/4CoyvwHgKHMk6NccqG762xjgjL3z0QQkfvfoGVK8nC4Fyu32lJ6keDRwp3+locG4YfGYGfaUHfWUG7aj9zHelUtZRMeuonHXD45UXRK9D+PrIStY1Clwn/Iy7RtYxcjxfTtmXW339jALHyk9KXsrISyfkpxOScZQZ8JQe8JXOB9XjYYU1UikjlTPhZ7VyHAzccN66Ro5n5ZZt9LkLp4lCoNRwMOXnppwOX7Fk0WrLEQlteH1ChWwUsgdB9dhYe5wMAwZX3VuMDlnvaeGz3uQPMOY195NGyWL4BEsZo11LHPUsChQYX+07A7XvdNTeY+T64/erwFiVWoxKGamU9sNpi1UpbSUjtQwY5fqNsgOOMkMT75de0spLmnAfc8PXNnAlycotSclS5Viz5+9hLxG+J+W0kZcyShUCpfNWydLE61tjVUpL5XT4OoQXV0HSkZ90lB4sK7vbVyZvq3vyRI/ppR1l8iNvaiHnqG9hSoEj5XrLyvb6k56LTMXKjnx3RlPfNXKC8Nhb+cy7nmQCTXrcDky4jV463F+dwMotWSVKgdySHbV/W0mBa8NjmiMli3t/DvTiIZ42rvKU7xh/THOsq4Ofyuk1P/NHvVbT4SWsthzu6LkT0+pfnBv559NwrzSwQ8uelA7/eULZQUeFrFGyZOVO8JGwkoqtrobmuBruSMpLOeFSG33RStV/6Mn6UhB939jw/hJlI7ccThOeZEa9/jWfURtE91l7c83nuHL8NOH746XCfdFLh/uh4wVyy76cclD9DjfWRt85RtbRyDmZG53TVI5Hrgn3DU/RsSicOr5GP1cbfl+17/DlJaUXjnT13HFGA53hths56tpqtPB3VgufDdTaGz6fcloqtjgq5cLvlHTeV9sOT4ma17uQNdHxeOTzbR1F51+qHp9ttK1jj52OX3kval/DynztvmmUKI8/V5yI71p5yfBzZKJL5bjspaLjSMYJLy0JWcdIgY3Ow6wUSK5vlSj64blF0VaPVfvKGjvq/NK6Gjnvrr6vGnXx3fB4Ge04kkwYIthoXiPneJXzRlPzspnKa2lHf6+b6jYpOteK5h1Tuevq9fA7P3rtfBu9hiPfv35l+6Pn4Hi+3KKvRCmIzn3D9za8XdX1rWPC78uilCzZ6usrE/6e8JJGftLKS1oFTuX8x4TT6LjoV74Lou+DcsaRn3IkOdHn0omO6VFYWAmoqq/FmOsKz7Va+n1l+n219Hvjjrde0qjQ6qiYc+Wlzehd09bOVN4bW33/AyfcJyvHXmuiVrzRPhfOS8mSo1TRUbIopQpGqYKV41V+Z4XHjSCa2uruEB4jrKl9D82Y26L3e+x6jhnZDxyjypm5dapzkmx4XmxN9HkKp5njj9DpH/nyXnwC9n/7e4B0oaTV1tr/FV2/WNIqa+1fjlnvCklXSNJBBx104vPPP/+Kb2sj/ftP/l6f/d03JONKxgn/M1ebpFpFX76+5vT7WrbdVzkhlRJG5aRUSoQnOblhaU4+UPugVXs+vASONJiR8i1SvsUonzEayEm75rgazrjhY0X/RQsPgr7m9fhavs3Twdt8ZYet+nNSX86oP7rkW6wSnpQuS5mSVapslSlJQxmjlzuMdnY4KmQqB/TwID2/J9DyrYFWbLNa9nIg3wm3Z7BFymeM8i1SMWWiwEEKVPkw22r44NjwK8GxYdDhRAcVJ5CMDWQkeU50wHaM/Ohg7DuSXzk4mTDUcv1w2zNFqaVo1VKS0iUbBiPRwSOIDjxuICV8KeFJiSB87glfSvo1857kBlalhFEhLQ2npOF0OG+NlPSMUmUbTj2r5LgTuQmPspINtzufkYYzRkOZ8D0cykjb5jraOs/IS4y81rlUmxbPWV4NihblFmlOZo52Du3Utvw2bctv00v5l3Tyvb/Rsq2+3MDKDcLn6Prhtg5lpKF0+FhDGaPh6J+ElfCm8tonfamlGL6O4TS8VO7LseE0EYQn774TnRCYcOo5UjElFVJGpaRRISWVk0YJzypdktLlcJqJTu4r70flUnal3W1GPe3SrvZwurvdKFO06u6VuvusuvvCabYglaLPSjkRzpddo4l+E5SSlX3ThJ+ZlnD7MiWrbKFykVoKVoX0yOeiv9VRf85RMRU+h6Rno/c8ULJslfSNkp6U8sL9pXJJlW24L5WtUp6U8MMvImkkxDQ1Jz5G4ZfoyPVw/6lGOjY8ownfKxuFoVblRM1+mZKG09LODumJIx3lAqnVBsoFVtkgUNlNKp9Ma9B1lTdGS58r63W/LkbvgQ2n0ddmwh/5PLi+UdKXPLd2P5KG0lI5KaXK4XEjXbJKl8PrhZQ02GI0kHM0kA2nktQ5EKizP1Bnv1XngNWcvJXvSJ5rwh/J7si85xp5bhhMlaOp51bWCX9s+Sbcp3LDVq3DVrlhKTdslfKiHxvRSULghK+o64f7eMKz0We8JnA0lVOGkY/vqPBOGrVvVa4XUtLLnUY7O4xe7nS0o9NRf85obl+ghbsCLdoVaOEuqwU94ee+L2fU2yr1thn1thoVU1LrsNSWt2obsmodllqHrGz0uow83/D4lfDD46MbndS6QeU1M+H6CRP9GLfKlGz0voRTY6W+1vDxw6mjgZxRsizlhgO1Dku5QrgN6dLIa1TZH9wg+qwlpHLCVD9/w2mNOu4PtoTbnCuE32GV+02XpYePcvTUwe7IC2tGjnXj0t/qOUz4PnX1BWofstH3xcj3RtIPP7vh5zhQthB+f22ZJz29zNFLc42s42jkx284b6w0f7fVvF6roeg5DLRYFdJRyFtZtzqN7qPmRDlRDjS3Lwjfv3ygtmGr1nygtqHw+brRD7LKMVlWKqSNCmmj4ZRRIWNUSIXfaaOer8LvomwxOjYVw+fXUgyPZf05E32HS/05RzLhvl/5DLQOh3+X8qyS0XEoVQ7fz8Fs+J2+a46jHZ2udnW6KqSM2gcDtQ964TQfKDdstb3LaPMCafM8o/7Wyi+ryll4+B5390lz8iOx6kiAEO43qXLluBkdG73wtalsVzL6/gyPBeE+5kX7/cj+H36/eNGPkHRZ1e+oyneW74THwVIy/FwWk1LgmGj/NUoEUtIPfwQMZRQem7LSQDY8pyonouOyNXKiY/RQWtrZlRi97xgTvleBXz2PS5Z8Leix1e/MTElqKRmlS9ExLBme35WSRqWko3JlmjAqpYxKKUfD6cp3/yQCX4mSp9f/sqSDtgfRZ65yHmhVTElzBqWufqmr32puv1VXf3h83rPwccPXzlSnxVR43K2GQeMCx5ofjXbUEbR6uxtE3xM15wDpchSaJ6RS0lTPf8PzM1s93rh+5fvIVs/bKst8J/reT4ycB3jVQ8vIdgZGeuzVjh462tFwJvphXTkfr/2dZMNjYCEV3U/tc5RkgvD8Y8kOacnO8LWtPR6PukQ/+ivnY9ZE739C1XP8cmLMj/GJXttI2Q3PhXa3OdrdHl76Wx3lhm31+7RzIFBXX6BU9Nr6zshFis6LC7Z6TMkWRp+HBdGPbd8Nj+mV84rwWBVN046G05VjWHjHlX29pShliuH3jrHh6xWe0wfhd1X0PiYr597R+XbSrz3nqA2MowBKI9+9leuKtrf6KtWsN9G6NV/z4e+Omum4+ej9CqLjje8o+g0RbtvI9ofzZTc87y0mjYrJ6HePU7Nu9DzdIDwmDadVPV8rpMINzJTC88hMqXKcDt/D2nMfzw0/P5Vz1WwxfA9T5dHPv/rq1TzvCc9nouueK/W2hufeu9uk3W1Sb84o7UnteUW/PaU5Q+F5weRG778jv7Ns9T12g7H7W7jPVc8hqr8hw+1K1ZxTpzwpWY7eI0UB36j3z456D43C5eHvyUne60n2h8r1yucncMLvn8CR+o/t0lv/5aEpXofmEYsAqVYcWiABAAAAAADsTyYLkPaXXh5flLSs5vrSaBkAAAAAAABm2f4SIP1C0qHGmBXGmJSkd0q6b5a3CQAAAAAAAJLGD/syC6y1njHmLyX9tyRX0m3W2g2zvFkAAAAAAADQfhIgSZK19ruSvjvb2wEAAAAAAIDR9pcSNgAAAAAAAOynCJAAAAAAAAAwJQIkAAAAAAAATIkACQAAAAAAAFMiQAIAAAAAAMCUCJAAAAAAAAAwJQIkAAAAAAAATIkACQAAAAAAAFMiQAIAAAAAAMCUCJAAAAAAAAAwJQIkAAAAAAAATIkACQAAAAAAAFMiQAIAAAAAAMCUCJAAAAAAAAAwJQIkAAAAAAAATIkACQAAAAAAAFMiQAIAAAAAAMCUCJAAAAAAAAAwJQIkAAAAAAAATMlYa2d7G6bFGLND0vOzvR0N0C1p52xvBPZL7BuYDPsGJsO+gcmwb2Ay7BuYDPsGJsO+EX8HW2vnjV3YtAFSXBhj1llrV872dmD/w76BybBvYDLsG5gM+wYmw76BybBvYDLsGwcuStgAAAAAAAAwJQIkAAAAAAAATIkAafbdOtsbgP0W+wYmw76BybBvYDLsG5gM+wYmw76BybBvHKDoAwkAAAAAAABTogUSAAAAAAAApkSABAAAAAAAgCnFPkAyxiwzxqw1xjxhjNlgjLk6Wt5ljLnfGPN0NO2Mlh9ujHnYGFM0xvxNzf0cZoz5Vc2l3xhzzSSPudoY81tjzDPGmA/ULF9hjHkkWv5VY0wqWp6Orj8T3b48Wj432vZBY8xn9/A8Pxj9/W+NMW+sY1teb4z5pTHGM8ZcuO+veHNo8v3iDcaY9caY30TTc6Z4nvu6X/xltMwaY7prlv/vmuf4uDHGN8Z07fsr3xyafP9IGmPuiPaPjcaYD07yeMYY85no7x8zxpxQc9sl0XN82hhzSc3yE6P7fSb6W1Nz218ZY56MXq9/nN4rv39rkv1i0mO4Meb7xpheY8y39/A89+n9n+L5r4n2rV8ZY9YZY07fx5e8aTT7vhHd3m6M2WKmON8wjTvXeF/0Wj1mjPmRMebgfXi5m0qz7xvGmH+MtnujGXPcH7Neo/aNP4+OM78yxvzEGHPkNF72ptAk+8akn1UzyXfFBI/ZqHNRYyY5b4mbZt43jDHHRduyIbrtoime576eb7w9ut/AGLOyZv2Tap7jr40xF0zzpcfesNbG+iJpkaQTovk2SU9JOlLSP0r6QLT8A5I+Hc3Pl/Q6SZ+Q9DeT3Kcr6SVJB09y27OSXiUpJenXko6MbrtX0juj+S9IujKaf6+kL0Tz75T01Wg+J+l0SX8u6bNTPMcjo8dJS1oRPb47zW1ZLukYSXdKunC23z/2iwn3i+MlLY7mXyvpxQbuF8dH+8AmSd2T3O8fSPrxbL+H7B+T7h9/JOmeaD4bvZfLJ3jM35f0PUlG0smSHomWd0n6XTTtjOY7o9t+Hq1ror99U7T8bEk/lJSuvB6z/R4ewPvFck1yDJd0bvT5/fYUz3E67/9kz79VI30tHiPpydl+D9k3Jv9+l/TPkv5dk5xvqLHnGmdLykbzVyo6fsXx0sz7hqRTJT1U8z4/LOmsGd432mvu93xJ35/t9/AA3zcm/Kxqiu+KBuwbE56LapLzljhemnzfeI2kQ6P5xZK2SeqY4DGnc75xhKTDJD0gaWXNfWUlJWpeu5cr17k0/hL7FkjW2m3W2l9G8wOSNkpaImmNpDui1e6Q9NZonZettb+QVJ7ibs+V9Ky19vkJbjtJ0jPW2t9Za0uS7pG0JkpOz5H0tbGPOWZbvibpXGOMsdbmrbU/kVTYw9Nco/AHY9Fa+5ykZ6Lt2OdtsdZustY+JinYw2M2tSbfLx611m6Nlm+Q1GKMSU/wmPu0X0TP81Fr7aYpnqMkvUvSV/awTlNr5v1DkpWUM8YkJLVIKknqn+Ax10i604Z+JqnDGLNI0hsl3W+t7bHW7pZ0v6TV0W3t1tqfWWutwh8alW25UtKnrLXFyusxxevQtJphv5jqGG6t/ZGkgT08zem8/5M9/8FoXSn8h0hlPnaafd8wxpwoaYGkH0yxPY0811hrrR2Klv9M0tIpHrepNfm+YSVlFP6gTEtKSto+wWM2ct+o/b7iuDH7+8Zkn9UJvysmeMxGnotOdt4SO828b1hrn7LWPh3Nb1UY5syb4DH3+XzDWrvRWvvbCV6vIWutF13NKMbHjf1B7AOkWiYs8The0iOSFlhrt0U3vaTwxGlvvVOT/4BeIumFmutbomVzJfXW7NyV5aP+Jrq9L1p/b032mNPZlgNOk+8Xfyjpl5Uf7nv5mJMt3yNjTFbhCcJ/7s36cdCE+8fXJOUV/sdns6QbrbU9+/CYUy3fMsFyKfxv0xlRE+f/Mca8bpLnGRv78X5Rr+m8/5M+f2PMBcaYJyV9R9J7GrSN+7Vm2zeMMY6k/yPpb6Zab4rHrHdb/lThf5hjr9n2DWvtw5LWKvw+2Sbpv621G/fhMae1LcaYvzDGPKuwtcVVU21jXDTJvlH7Wd3bc8lGnotO+/y1mTXhvlG77ScpDKCf3YfHnOp8Y1LGmFXGmA2SfiPpz2u2GQ12wARIxphWhT96rxnz3w1F6eZeJZVR3ef5kv6j4RuJV1wz7xfGmKMkfVrSn71Sj6mw/OWhSQKJ2GnS/eMkSb7CZsMrJP1/xphXzfBjJhQ2QT5Z0v+WdG+lXj2OmnS/eEWMff7W2m9Yaw9X+N/Dj8/Wdr1SmnTfeK+k71prt+xxzQYzxrxb0kpJN7zSj/1Ka8Z9wxjzaoUlI0sV/oA7xxhzxkw/rrX2c9baQyS9X9JHZvrxZlsz7BsH0md1f9LM+0bUkujLki6z1s54ZYu19hFr7VEKS/k+aIzJzPRjHqgOiADJGJNU+OG721r79Wjx9kqzx2i6tyUXb1LY4mN79LfLajrt+nNJL0paVrP+0mjZLoVNLRNjlqv2b6Lb50TrT/Z8Lqh5zJVTPOZ0tuWA0cz7hTFmqaRvSPoTa+2z0bJ694u9MdV/L2KlifePP1LYZ0TZhqVkD0laGf1Ht/KYi6d4zKmWL51guRT+d+jrUbPynyssg+hWDDXBfrGvz2dVzWOeP8VjTvX+7/H5W2sflPQqU9Mhatw08b5xiqS/NMZsknSjpD8xxnxqps81jDG/J+nDks6fpBVtbDTxvnGBpJ/ZsBx1UGELg1NewfPQezRSKhtLzbBvTPJZnfC+ZvhctJ7z16bTxPuGjDHtClsefzgqN2zU+cYeRa0kBxX2E4uZYPeDjphm8qKw8607Jd08ZvkNGt0J2T+Ouf06TdAJmcIvs8umeLyEwk7AVmikE7Kjotv+Q6M7IXtvNP8XGt0Z7r1j7vNSTd2J9lEa3UHd7xR2hrbP21Jzn19SvDvRbtr9QlJH9Pdv28Nz3Of9ouZvN2lMJ9oKA4oeSbnZfv/YP6bcP94v6fZoPifpCUnHTPCYb9bozih/Hi3vkvScwg4NO6P5rui2sZ0a/n60/M8l/X00/xqFTZLNbL+PB+J+UfO3X9LEHSWfpT13or2v7/+Ez1/Sqyv7gaQTFJ4Axm6/iMu+Ed12qSbvRLth5xoKyzGeVdTRapwvzbxvSLpI4QAJCYX9H/1I0h/M8L5xaM39/oGkdbP9Hh7I+8Zkn1VN8V1R775R87ebNLoT7QnPW+J4afJ9IxUdK67Zw3Pc5/ONmr99QKM70V6hkU60D5a0VZMMBsSlAfvnbG/AjD/BcBQzK+kxSb+KLr+vsKbzR5KeVvjlWNlhFyr8b3q/pN5ovj26LacwiZ2zh8f8fYW95T+rMHmtLH9V9IF4JvowVkYsykTXn4luf1XN32xS+KN9MNqWIyd5zA9Hj/dbRT3VT3NbXhc9Tj56rhtm+z1kvxi9Xyhszp2v2e5faZJRr6axX1wVPTdP4cH332puu1TR6F5xvzT5/tEaLd+gMDz635M8npH0uejxfqPRX8Tvie73GdWccChsovx49Def1Ug4kJJ0V3TbLyWdM9vv4QG8X0x6DJf0/yTtkDQcrfPGSR5zX9//yZ7/+6P98FcKR286fbbfQ/aNqb/fted/WDXqXOOHCjtjrrxW9832e8i+MX7fUPhD/18VduD7hKSbXoF94581ctxYqzGhQpwuTbJvTPpZ1STfFQ3YNyY8F9UU5y1xuzTzviHp3Qo78/5VzeW4SR5zX883LoieWzF67P+Oll+skePGLyW9dbbfwzhfKm8GAAAAAAAAMKEDog8kAAAAAAAATB8BEgAAAAAAAKZEgAQAAP7/du4l1Ko6iuP496eGGZaVaUT0GgQ9BkmK9JSykIImvRCyGhgINbCJgUVQNslZj1GWKEEWUWQFQRmCSVBpatdHBQVFjUxKhKjAbDU4/2unwz0n0/TG9fuBA3uv/zrrv9jDxX9vSZIkaSAHSJIkSZIkSRrIAZIkSZIkSZIGcoAkSZIkSZKkgRwgSZIkDZDk2yRn/EPOI8eqnz77/zya+0uSpLHPAZIkSdKRG9UBkiRJ0tHmAEmSJI1pSc5PsrPrfkmSx5NsSPJMks+S7Ewyu61PTbIuya4kK4F0/ffNJFva2qIWWw5ManXWtNjdSTa12Iok4wf0d1OSrUmGkqxPMi7JV0mmtfVxSb5OMi3JmUnWttyhJFeNUO+hJJuTbE+y7L96jpIk6fjmAEmSJB3PTqqqGcADwKoWewz4sKouBdYC53blL6yqmcAsYHGSqVW1FPi1qmZU1YIkFwPzgatb7QPAgpE2b0OiF4Dbq+oy4M6q+gN4qes/NwJDVbUHeBb4oOVeDuzqqTcPuBCYDcwAZiaZc3iPRpIk6S8TRrsBSZKkUfQKQFVtTHJKklOBOcBtLf5Okr1d+YuT3Nquz6EzrPmxp+YNwExgcxKAScAPffa/AthYVd+0/X5q8VXAW8DTwEJgdYvPBe5tuQeAfT315rXftnY/ufW4sd8DkCRJOhQOkCRJ0lj3O38/dX1i13X15PbeH5TkOjqnga6sql+SbOipdTAVeLGqHj6cZgGq6vsku5PMpXOaaMQTTH32frKqVhzu3pIkSSPxFTZJkjTW7Qamt28bTQRu6VqbD5DkGmBfVe2jc1rnrha/GTit5U4B9rbh0UV0Tg8N25/khHa9HrgjyfRW4/Qk5/Xp7WNgTpILhnO71lbSeZXttXbaaLj2/S13fJIpPfXeAxYmmdxyzh7uQ5Ik6Ug4QJIkSWNaVe0HngA2Ae8DX3Yt/5ZkG/AccF+LLaMz1NlF51W271r8XWBCki+A5XSGP8OeB7YnWVNVnwOPAuuSbG97ntWntz3AIuCNJEPAq13Lb9N5BW11V+xB4PokO4AtwCU99dYBLwMftZzXgZMHPB5JkqRDkqq+J7UlSZLGrPYK2pKq+nS0exlJklnAU1V17Wj3IkmS5DeQJEmS/meSLKXzqtqhfvtIkiTpqPIEkiRJ0jGQ5BNgYk/4nqraMRr9SJIk/RsOkCRJkiRJkjSQH9GWJEmSJEnSQA6QJEmSJEmSNJADJEmSJEmSJA3kAEmSJEmSJEkD/Qms60y01+EnmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Changes in each update - full series\n",
    "################################################################################\n",
    "df_aggregation = pd.DataFrame.from_dict(aggregation)\n",
    "df_aggregation.set_index(\"update_cycle\", inplace=True)\n",
    "df_aggregation.plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9b498ec-5626-42da-a3cc-80aabb3df0a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='update_cycle'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V7iMAgEe5QAIAeEGStaqm03geY2xf7AMAcIcACQAAAICWEm0AAAAAWgIkAAAAAFoCJAAAAABaAiQAAAAAWjuvtuCgemM1RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Cumulative changes in each update - full series\n",
    "################################################################################\n",
    "df_aggregation[[\"count_added\", \"count_removed\", \"count_changed\"]\n",
    "              ].cumsum().plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74c56d0-dbc6-4895-9c5e-bffcbe219c97",
   "metadata": {},
   "source": [
    "One of the large deviations in Barnsley's data during mid-2017 was an unusual swap of classification between office and leisure use. This appears to be explained by a dramatic set of changes in the 2017-0006 VOA update epoch, and then a smaller counter change in the 2017-0009 epoch. As can be seen below, these changes bracket a roughly 10 month period in late 2017 to 2018, and correspond with Barnsley data releases preceding their October 2017 and October 2018 releases. The initial update, added over 2,000 hereditaments, removed 13, and changed 7,000 - updating all of the hereditaments in Barnsley at that time - with the subsequent update restating about 750 hereditaments. \n",
    "\n",
    "Since these updates would have historical effective dates, they would result in the deviation seen Q2 and Q3 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "efc8622e-d382-47e6-93e7-364d15fc4ac3",
   "metadata": {},
   "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>count_total</th>\n",
       "      <th>count_added</th>\n",
       "      <th>count_removed</th>\n",
       "      <th>count_changed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>update_cycle</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-0006-0000</th>\n",
       "      <td>9271</td>\n",
       "      <td>2198</td>\n",
       "      <td>13</td>\n",
       "      <td>6946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0006-0002</th>\n",
       "      <td>9274</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0006-0003</th>\n",
       "      <td>9275</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0006-0004</th>\n",
       "      <td>9282</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0006-0005</th>\n",
       "      <td>9283</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0006-0006</th>\n",
       "      <td>9284</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0006-0007</th>\n",
       "      <td>9289</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0006-0008</th>\n",
       "      <td>9289</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0006-0009</th>\n",
       "      <td>9298</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0007-0001</th>\n",
       "      <td>9299</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0007-0002</th>\n",
       "      <td>9299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0007-0003</th>\n",
       "      <td>9301</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0007-0004</th>\n",
       "      <td>9301</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0007-0005</th>\n",
       "      <td>9302</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0007-0006</th>\n",
       "      <td>9303</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0007-0007</th>\n",
       "      <td>9311</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0007-0008</th>\n",
       "      <td>9316</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0008-0001</th>\n",
       "      <td>9328</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0008-0002</th>\n",
       "      <td>9334</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0008-0003</th>\n",
       "      <td>9344</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0008-0004</th>\n",
       "      <td>9347</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0008-0005</th>\n",
       "      <td>9355</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0008-0006</th>\n",
       "      <td>9356</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0009-0000</th>\n",
       "      <td>9379</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0022-0008</th>\n",
       "      <td>10104</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count_total  count_added  count_removed  count_changed\n",
       "update_cycle                                                          \n",
       "2017-0006-0000         9271         2198             13           6946\n",
       "2017-0006-0002         9274            3              0              4\n",
       "2017-0006-0003         9275            1              0              2\n",
       "2017-0006-0004         9282            7              0              9\n",
       "2017-0006-0005         9283            1              0              8\n",
       "2017-0006-0006         9284            1              0              2\n",
       "2017-0006-0007         9289            5              0              0\n",
       "2017-0006-0008         9289            0              0              2\n",
       "2017-0006-0009         9298            9              0              7\n",
       "2017-0007-0001         9299            1              0              2\n",
       "2017-0007-0002         9299            0              0              3\n",
       "2017-0007-0003         9301            2              0             16\n",
       "2017-0007-0004         9301            0              0              2\n",
       "2017-0007-0005         9302            1              0              6\n",
       "2017-0007-0006         9303            1              0              6\n",
       "2017-0007-0007         9311            8              0             14\n",
       "2017-0007-0008         9316            5              0              7\n",
       "2017-0008-0001         9328           12              0             17\n",
       "2017-0008-0002         9334            6              0             13\n",
       "2017-0008-0003         9344           10              0             10\n",
       "2017-0008-0004         9347            3              0              6\n",
       "2017-0008-0005         9355            8              0              6\n",
       "2017-0008-0006         9356            1              0              6\n",
       "2017-0009-0000         9379            0              6            756\n",
       "2017-0022-0008        10104            8              0              7"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Table of total hereditaments and update changes - 2017 Epochs 0002 to 0009\n",
    "################################################################################\n",
    "relevant_cycles = list(df[(df[\"filter_date\"] >= \"2017-11-01\") & \n",
    "                          (df[\"filter_date\"] < \"2018-09-01\")\n",
    "                         ][\"update_cycle\"].unique())\n",
    "df_aggregation[(df_aggregation.index.isin(relevant_cycles))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ac1f1b-f3e1-4d97-b725-d1222f918eac",
   "metadata": {},
   "source": [
    "The following charts focus on the 2019 calendar year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c186bd68-ce8e-4769-9fdb-a58bfd4343e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='update_cycle'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Changes in each update - 2019 only\n",
    "################################################################################\n",
    "relevant_cycles = list(df[(df[\"filter_date\"] >= \"2019-01-01\") & \n",
    "                          (df[\"filter_date\"] < \"2020-01-01\")\n",
    "                         ][\"update_cycle\"].unique())\n",
    "df_aggregation[(df_aggregation.index.isin(relevant_cycles))\n",
    "              ][[\"count_added\", \"count_removed\", \"count_changed\"]\n",
    "              ].plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f7b8279-a3e8-4d0b-b01b-013c97cda34f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='update_cycle'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Cumulative changes in each update - 2019 only\n",
    "################################################################################\n",
    "df_aggregation[(df_aggregation.index.isin(relevant_cycles))\n",
    "              ][[\"count_added\", \"count_removed\", \"count_changed\"]\n",
    "              ].cumsum().plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f24b6ee-7d25-4bf2-ad93-2230718b86e4",
   "metadata": {},
   "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>count_total</th>\n",
       "      <th>count_added</th>\n",
       "      <th>count_removed</th>\n",
       "      <th>count_changed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>update_cycle</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-0013-0001</th>\n",
       "      <td>9578</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0013-0002</th>\n",
       "      <td>9592</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0013-0003</th>\n",
       "      <td>9597</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0013-0004</th>\n",
       "      <td>9615</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0013-0005</th>\n",
       "      <td>9627</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0013-0006</th>\n",
       "      <td>9634</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0013-0007</th>\n",
       "      <td>9641</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0013-0008</th>\n",
       "      <td>9654</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0013-0009</th>\n",
       "      <td>9660</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0014-0001</th>\n",
       "      <td>9664</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0014-0002</th>\n",
       "      <td>9673</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0014-0003</th>\n",
       "      <td>9681</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0014-0004</th>\n",
       "      <td>9682</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0014-0005</th>\n",
       "      <td>9701</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0014-0006</th>\n",
       "      <td>9703</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0014-0007</th>\n",
       "      <td>9704</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0014-0008</th>\n",
       "      <td>9712</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0016-0001</th>\n",
       "      <td>9725</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0016-0002</th>\n",
       "      <td>9735</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0016-0003</th>\n",
       "      <td>9741</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0016-0004</th>\n",
       "      <td>9745</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0017-0003</th>\n",
       "      <td>9790</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-0019-0002</th>\n",
       "      <td>9927</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count_total  count_added  count_removed  count_changed\n",
       "update_cycle                                                          \n",
       "2017-0013-0001         9578            6              0             33\n",
       "2017-0013-0002         9592           14              0             13\n",
       "2017-0013-0003         9597            5              0             22\n",
       "2017-0013-0004         9615           18              0             30\n",
       "2017-0013-0005         9627           12              0             16\n",
       "2017-0013-0006         9634            7              0              7\n",
       "2017-0013-0007         9641            7              0              5\n",
       "2017-0013-0008         9654           13              0             11\n",
       "2017-0013-0009         9660            6              0             11\n",
       "2017-0014-0001         9664            4              0              4\n",
       "2017-0014-0002         9673            9              0             14\n",
       "2017-0014-0003         9681            8              0              6\n",
       "2017-0014-0004         9682            1              0              2\n",
       "2017-0014-0005         9701           19              0             16\n",
       "2017-0014-0006         9703            2              0              6\n",
       "2017-0014-0007         9704            1              0             27\n",
       "2017-0014-0008         9712            8              0             13\n",
       "2017-0016-0001         9725           13              0             13\n",
       "2017-0016-0002         9735           10              0              8\n",
       "2017-0016-0003         9741            6              0              5\n",
       "2017-0016-0004         9745            4              0              8\n",
       "2017-0017-0003         9790           17              0             74\n",
       "2017-0019-0002         9927            6              0              5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Table of total hereditaments and update changes - June to December 2019 only\n",
    "################################################################################\n",
    "relevant_cycles = list(df[(df[\"filter_date\"] >= \"2019-06-01\") & \n",
    "                          (df[\"filter_date\"] < \"2019-12-31\")\n",
    "                         ][\"update_cycle\"].unique())\n",
    "df_aggregation[(df_aggregation.index.isin(relevant_cycles))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cdb8bff-02fe-47bf-ad78-193178a9286c",
   "metadata": {},
   "source": [
    "While the early period of the 2017 rates revaluation demonstrated some significant change, the 2019 period is consistent. No hereditaments were withdrawn over the period, there were 626 changes, and 291 hereditaments were added to the register. The total hereditaments increased almost 3% to 9,927 by the end of 2019."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772922e5-4904-4440-912c-667edc7b4bcd",
   "metadata": {},
   "source": [
    "# Barnsley Metropolitan Borough Council data review\n",
    "\n",
    "Each of the 348 English and Welsh local authorities require complete knowledge of ratepayers for each commercial hereditament. Most routinely publish these data to their websites; some annually, monthly, or anything in between. There is no standard data format, which are released using multiple definitions, data structures, and file types. \n",
    "\n",
    "The absence of standards has resulted in a diverse and incompatible range of definitions on everything from business activity, to building use, to multiple *unique* identity and geographic systems. Adding to the complexity are scarce data skills leading to publication of incompatible data formats, or even resistance to publication.\n",
    "\n",
    "## Sqwyre study methodology\n",
    "\n",
    "Where an observational study is weighted by the physical process of sending fieldworkers out to conduct surveys, our main analytical task is data-wrangling; to find, acquire, import, restructure and validate all data published by public sources. During 2018, two years after the start of the Sqwyre study, we became concerned at the gradual deletion of the source data which inform our database by local authorities. That means, often, Sqwyre is the only record of an historical location-based event. To that end, we developed an open source data wrangling toolkit called [whyqd](https://whyqd.readthedocs.io/). This was launched in mid-2019, and Barnsley's October 2019 dataset was the first of their releases to be imported using this standardised and auditable approach.\n",
    "\n",
    "No matter how local authorities choose to publish, we receive a list of each ratepayer change to all hereditaments for the period between updates. The data we import are:\n",
    "\n",
    "- Billing authority hereditament reference code (linking the hereditament to the VOA database reference)\n",
    "- Firm's trading name (i.e. ratepayer)\n",
    "- Full hereditament address (number, street, postal code, town)\n",
    "- Occupied / Vacant status\n",
    "- Date of occupation / vacancy\n",
    "- Actual annual rates charged (in Pounds)\n",
    "- List of reliefs / exemptions received by the ratepayer\n",
    "\n",
    "Not all of these are provided, and sometimes not for all data released in the same update series. Our extract-transform-load process restructures these messy data into a single schema. These transformed data files are subjected to additional analysis and matching against the existing current VOA data. A matching quality score is offered for each data row, and passing data are imported into the main database. \n",
    "\n",
    "Matching is performed according to the Levenshtein Distance of the VOA Unique Address Reference Numbers (UARN) and address for each hereditament, and producing a probably of matching score. We pick the highest probability match for each address.\n",
    "\n",
    "## Sources of error, and sensible defaults\n",
    "\n",
    "The objective for the Sqwyre commercial location database is to produce a well-structured, standardised dataset for research and analysis. \n",
    "\n",
    "Sources of ambiguity which prevent matching include:\n",
    "\n",
    "- Unclear, or missing, hereditament reference codes,\n",
    "- Duplicate rows with conflicting dates and occupation status,\n",
    "- Provided data not covering the complete list of VOA hereditaments,\n",
    "- Provided data for hereditaments not yet registered with VOA.\n",
    "\n",
    "Hereditaments are continually being added, removed and updated in the register. Ordinarily, such updates are the result of engagement between the local authority and VOA. However, there is a physical delay for information to be processed - either to be added by the VOA, or by the local authority - and so there will always be some small number of hereditaments which remain stranded. Over a long enough series of updates, all activity should be captured.\n",
    "\n",
    "Where hereditament activity is unclear, we resolve to do no forecasting or estimation of activity but to adopt sensible defaults:\n",
    "\n",
    "- All hereditaments of unknown occupation status are regarded as `occupied`; we prefer to underestimate than overestimate vacancy,\n",
    "- Hereditaments with missing floor areas but with a rateable value receive an estimated floor area based on nearby hereditaments of the same category of use, since VOA estimates such valuations using standardised multipliers,\n",
    "\n",
    "We do no other changes.\n",
    "\n",
    "This establishes a history for each commercial hereditament, with the date of ratepayer change, giving us the period of occupation or vacancy, the name of the ratepayer (if a company), and any rates reliefs or exemptions.\n",
    "\n",
    "## History of Barnsley data imports\n",
    "\n",
    "Sqwyre began curating local authority data in March 2016. At the time, while Barnsley published ratepayer data, this did not include the unique hereditament numbers necessary to reconcile them with the VOA master database.\n",
    "\n",
    "We filed a [Freedom of Information request (FOI)](https://www.whatdotheyknow.com/request/complete_non_residential_busines_215https://www.whatdotheyknow.com/request/complete_non_residential_busines_215) via What Do They Know for the missing references to be included. On 14 April 2016, Barnsley undertook to do this. The first data we were able to include with these additions was in April 2017, 12 months later.\n",
    "\n",
    "Subsequently, Barnsley's data were released twice annually, with updates for April and October of each year. In 2020, there was no April release. We filed an [FOI request](https://www.whatdotheyknow.com/request/complete_non_residential_busines_1891https://www.whatdotheyknow.com/request/complete_non_residential_busines_1891) on 9 July 2020 requesting publication. On 11 August 2020, Barnsley replied stating they wouldn't be releasing data in response to the FOI request, since they intended to publish at some future date.\n",
    "\n",
    "By the end of our March 2021 update cycle, we had not yet discovered any Barnsley updates, although their October 2020 release was brought to our attention in April 2021.\n",
    "\n",
    "This explains the 18 month gap, from October 2019 to the present, when approximately three potential update releases were not available for inclusion in the Sqwyre data series. We have, however, included their October 2020 release in the analysis that follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "473a4afd-7cc1-4b8a-8dc2-4db4f73fbd00",
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# 2. Barnsley Metropolitan Borough Council data review\n",
    "#  - Within each series, what proportion of hereditaments can be unambiguously \n",
    "#    classified in terms of occupation status, and how does ambiguity arise?\n",
    "#  - What proportion of the total count of the hereditament base, as defined by \n",
    "#    the VOA, are represented in ratepayer data updates?\n",
    "################################################################################\n",
    "\n",
    "# Specify column import types\n",
    "dtype_dict = {\n",
    "    \"ba_ref\": str,\n",
    "    \"rates_code\": str,\n",
    "    \"occupation_state\": bool\n",
    "}\n",
    "parse_dates = [\"occupation_state_date\"]\n",
    "# Get all source data\n",
    "chunks = []\n",
    "for file in os.listdir(directory):\n",
    "    if file.startswith(\"E08000016_\"):\n",
    "        chunk = pd.read_csv(directory + file, \n",
    "                            dtype = dtype_dict, \n",
    "                            parse_dates = parse_dates)\n",
    "        chunk[\"update_cycle\"] = file.split(\".\")[0].split(\"_\")[-1]\n",
    "        chunks.append(chunk)\n",
    "\n",
    "df = pd.concat(chunks)\n",
    "\n",
    "# Do analysis based on ba_ref (which Barnsley provide) and rates_code \n",
    "# (which Sqwyre software matches)\n",
    "\n",
    "aggregation = {\n",
    "    \"update_cycle\": [],\n",
    "    \"ba_ref_duplicates\": [],\n",
    "    \"ba_ref_added\": [],\n",
    "    \"ba_ref_removed\": [],\n",
    "    \"ba_ref_void\": []\n",
    "}\n",
    "df.sort_values(by=[\"update_cycle\", \"ba_ref\", \"occupation_state_date\"], \n",
    "               inplace=True)\n",
    "\n",
    "previous_references = set()\n",
    "\n",
    "for update_cycle, group in df.groupby(\"update_cycle\"):\n",
    "    if previous_references:\n",
    "        # Remove duplicates and take the last\n",
    "        total_count = len(group)\n",
    "        group = group.copy()\n",
    "        group.drop_duplicates(subset=[\"ba_ref\"], keep=\"last\", inplace=True)\n",
    "        unique_count = len(group)\n",
    "        # Get all unique rates codes in this update cycle\n",
    "        current_references = set(group[\"ba_ref\"].tolist())\n",
    "        # Get all void rates codes\n",
    "        void_references = set(group[group[\"occupation_state\"] == False\n",
    "                                   ][\"ba_ref\"].unique())\n",
    "        # Start aggregation\n",
    "        aggregation[\"update_cycle\"].append(update_cycle)\n",
    "        ba_ref_duplicates = total_count - unique_count\n",
    "        ba_ref_added = len(current_references - previous_references)\n",
    "        ba_ref_removed = len(previous_references - current_references)\n",
    "        ba_ref_void = len(void_references)\n",
    "        aggregation[\"ba_ref_duplicates\"].append(ba_ref_duplicates)\n",
    "        aggregation[\"ba_ref_added\"].append(ba_ref_added)\n",
    "        aggregation[\"ba_ref_removed\"].append(ba_ref_removed)\n",
    "        aggregation[\"ba_ref_void\"].append(ba_ref_void)\n",
    "    previous_references = set(group[\"ba_ref\"].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f078be6b-b1d2-42bd-a499-123e80e94cb7",
   "metadata": {},
   "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>ba_ref_duplicates</th>\n",
       "      <th>ba_ref_added</th>\n",
       "      <th>ba_ref_removed</th>\n",
       "      <th>ba_ref_void</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>update_cycle</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>005</th>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>101</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>007</th>\n",
       "      <td>0</td>\n",
       "      <td>2044</td>\n",
       "      <td>129</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>009</th>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>95</td>\n",
       "      <td>767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011</th>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>82</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>013</th>\n",
       "      <td>740</td>\n",
       "      <td>164</td>\n",
       "      <td>217</td>\n",
       "      <td>767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>019</th>\n",
       "      <td>712</td>\n",
       "      <td>361</td>\n",
       "      <td>368</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ba_ref_duplicates  ba_ref_added  ba_ref_removed  ba_ref_void\n",
       "update_cycle                                                              \n",
       "005                           0           155             101          702\n",
       "007                           0          2044             129          716\n",
       "009                           0           159              95          767\n",
       "011                           0           155              82          803\n",
       "013                         740           164             217          767\n",
       "019                         712           361             368          731"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Table of changes in each update - full series\n",
    "################################################################################\n",
    "df_aggregation = pd.DataFrame.from_dict(aggregation)\n",
    "df_aggregation.set_index(\"update_cycle\", inplace=True)\n",
    "df_aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "365e656a-cfa0-4391-88ef-0570808e4104",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='update_cycle'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Changes in each update - full series\n",
    "################################################################################\n",
    "df_aggregation.plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf88eeb-d38c-4d37-9ddb-f64211e12d93",
   "metadata": {},
   "source": [
    "Focusing on the 2019 period, and considering the consistency and quality of the data yields the following reports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e59b34c0-58da-4a63-b11a-5a729c0388cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Duplicates start in the area of concern, therefore need to interogate further\n",
    "df.sort_values(by=[\"update_cycle\", \"ba_ref\", \"occupation_state_date\", \n",
    "                   \"occupation_state\"], \n",
    "               inplace=True)\n",
    "\n",
    "aggregation = {\n",
    "    \"update_cycle\": [],\n",
    "    \"void_state\": [],\n",
    "    \"occupied_state\": [],\n",
    "    \"unclear_state\": [],\n",
    "    \"date_clear\": [],\n",
    "    \"date_clear_occupied\": [],\n",
    "    \"date_unclear\": [],\n",
    "    \"not_matched_total\": [],\n",
    "    \"not_matched_void\": [],\n",
    "}\n",
    "\n",
    "for update_cycle, group in df.groupby(\"update_cycle\"):\n",
    "    # Get the headline void and occupation totals\n",
    "    group_tots = group.copy()\n",
    "    group_tots.dropna(subset=[\"rates_code\"], inplace=True)\n",
    "    group_tots.sort_values(by=[\"rates_code\", \"occupation_state_date\", \n",
    "                               \"occupation_state\"], \n",
    "                           inplace=True)\n",
    "    group_tots.drop_duplicates(subset=[\"rates_code\"], \n",
    "                               keep=\"last\", \n",
    "                               inplace=True)\n",
    "    void_state = len(group_tots[group_tots[\"occupation_state\"] == False])\n",
    "    occupied_state = len(group_tots[group_tots[\"occupation_state\"] == True])\n",
    "    # Remove duplicates and take the last\n",
    "    group = group.copy()\n",
    "    dup_group = group[group.duplicated(subset=[\"ba_ref\"], keep=False)\n",
    "                     ].sort_values(by=[\"ba_ref\", \"occupation_state_date\"])\n",
    "    # Get all mis-matches and void mis-matches only\n",
    "    not_matched_total = (len(group[\"ba_ref\"].unique()) - \n",
    "                         len(group[group[\"rates_code\"].notnull()\n",
    "                                  ][\"rates_code\"].unique()))\n",
    "    ba_ref_voids = len((group[(group[\"occupation_state\"\n",
    "                                        ] == False) &\n",
    "                                  (group[\"ba_ref\"].notnull())\n",
    "                                 ][\"ba_ref\"].unique()))\n",
    "    rates_code_voids = len((group[(group[\"occupation_state\"\n",
    "                                        ] == False) &\n",
    "                                  (group[\"rates_code\"].notnull())\n",
    "                                 ][\"rates_code\"].unique()))\n",
    "    not_matched_void = ba_ref_voids - rates_code_voids\n",
    "    # Aggregations\n",
    "    unclear_state = 0\n",
    "    date_clear = 0\n",
    "    date_clear_occupied = 0\n",
    "    date_unclear = 0\n",
    "    for ba_ref, ref_group in dup_group.groupby(\"ba_ref\"):\n",
    "        # Unclear status, check if dates are clear\n",
    "        unclear_state += 1\n",
    "        if (ref_group[\"occupation_state_date\"].isna().any() or \n",
    "            len(ref_group[\"occupation_state_date\"].unique()) == 1):\n",
    "            date_unclear += 1\n",
    "        else:\n",
    "            date_clear += 1\n",
    "            if ref_group.iloc[-1][\"occupation_state\"]:\n",
    "                # i.e. the last row/latest date of ambiguous data\n",
    "                date_clear_occupied += 1\n",
    "    # Start aggregation\n",
    "    aggregation[\"update_cycle\"].append(update_cycle)\n",
    "    aggregation[\"void_state\"].append(void_state)\n",
    "    aggregation[\"occupied_state\"].append(occupied_state)\n",
    "    aggregation[\"unclear_state\"].append(unclear_state)\n",
    "    aggregation[\"date_clear\"].append(date_clear)\n",
    "    aggregation[\"date_clear_occupied\"].append(date_clear_occupied)\n",
    "    aggregation[\"date_unclear\"].append(date_unclear)\n",
    "    aggregation[\"not_matched_total\"].append(not_matched_total)\n",
    "    aggregation[\"not_matched_void\"].append(not_matched_void)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2f8d9fa-54d6-4c54-8e59-96057d2fccfe",
   "metadata": {},
   "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>void_state</th>\n",
       "      <th>occupied_state</th>\n",
       "      <th>unclear_state</th>\n",
       "      <th>date_clear</th>\n",
       "      <th>date_clear_occupied</th>\n",
       "      <th>date_unclear</th>\n",
       "      <th>not_matched_total</th>\n",
       "      <th>not_matched_void</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>update_cycle</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>003</th>\n",
       "      <td>713</td>\n",
       "      <td>6036</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>005</th>\n",
       "      <td>690</td>\n",
       "      <td>6108</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>007</th>\n",
       "      <td>712</td>\n",
       "      <td>8003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>009</th>\n",
       "      <td>761</td>\n",
       "      <td>8011</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011</th>\n",
       "      <td>799</td>\n",
       "      <td>8046</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>013</th>\n",
       "      <td>629</td>\n",
       "      <td>8136</td>\n",
       "      <td>740</td>\n",
       "      <td>616</td>\n",
       "      <td>5</td>\n",
       "      <td>124</td>\n",
       "      <td>68</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>019</th>\n",
       "      <td>594</td>\n",
       "      <td>8085</td>\n",
       "      <td>712</td>\n",
       "      <td>587</td>\n",
       "      <td>2</td>\n",
       "      <td>125</td>\n",
       "      <td>147</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              void_state  occupied_state  unclear_state  date_clear  \\\n",
       "update_cycle                                                          \n",
       "003                  713            6036              0           0   \n",
       "005                  690            6108              0           0   \n",
       "007                  712            8003              0           0   \n",
       "009                  761            8011              0           0   \n",
       "011                  799            8046              0           0   \n",
       "013                  629            8136            740         616   \n",
       "019                  594            8085            712         587   \n",
       "\n",
       "              date_clear_occupied  date_unclear  not_matched_total  \\\n",
       "update_cycle                                                         \n",
       "003                             0             0                 31   \n",
       "005                             0             0                 36   \n",
       "007                             0             0                 34   \n",
       "009                             0             0                 41   \n",
       "011                             0             0                 41   \n",
       "013                             5           124                 68   \n",
       "019                             2           125                147   \n",
       "\n",
       "              not_matched_void  \n",
       "update_cycle                    \n",
       "003                          4  \n",
       "005                         12  \n",
       "007                          4  \n",
       "009                          6  \n",
       "011                          4  \n",
       "013                         14  \n",
       "019                         16  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Table of changes in each update - full series\n",
    "################################################################################\n",
    "df_aggregation = pd.DataFrame.from_dict(aggregation)\n",
    "df_aggregation.set_index(\"update_cycle\", inplace=True)\n",
    "df_aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4ae7bc5-7b9d-49a7-a690-75b7018552ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='update_cycle'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Changes in each update - full series\n",
    "################################################################################\n",
    "df_aggregation.loc[ : , df_aggregation.columns != \"occupied_state\"\n",
    "                  ].plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4c1cd7-7e78-4671-afb1-05a5b5147694",
   "metadata": {},
   "source": [
    "Prior to October 2019 (cycle 011), Sqwyre audit systems were entirely manual, with manual processing and restructuring of data. That left structural decisions in the hands of the data wrangler with inevitable inconsistency. The last chart (above) may give the impression that Barnsley data \"suddenly\" became more unreliable but the reality is more likely that we simply missed earlier ambiguity which was subsequently resolved by the new system updates in mid-2019.\n",
    "\n",
    "Even so, the October 2019 release does indicate a far greater proportion of ambiguous and non-matching data, as well an increasing gap between the total hereditaments listed by the VOA, and those published by Barnsley:\n",
    "\n",
    "- VOA total hereditaments = 9,927\n",
    "- Barnsley released hereditaments = 8,765 (1,162 gap)\n",
    "- Barnsley total voids = 767 (7.6% of total hereditaments, 8.8% of Barnsley release)\n",
    "- Barnsley void non-match to VOA data = 124 ambiguous + 5 resolved as occupied + 14 unmatched voids (18.6% of Barnsley voids)\n",
    "- Barnsley matched voids = 629 (6.3% of total hereditaments)\n",
    "\n",
    "At this stage, even before considering the Barnsley data intersection with that of the VOA, the total auditable void records is reduced by 18.6% and needs to be considered against a ratings list about 12% larger. Looking ahead to the October 2020 update, the rate of mismatching appears to be growing.\n",
    "\n",
    "We now need to consider what those voids are in terms of the classifications for retail, office, industrial and leisure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8b862e-9bcc-407a-831c-c7a0c977577f",
   "metadata": {},
   "source": [
    "# Merged VOA and Barnsley data review\n",
    "\n",
    "There are two forms of aggregation in the Sqwyre system. The first is performed at the end of each quarter, with the data current at that time, and these reports are released online at Sqwyre.com. They are not revised or updated following subsequent data corrections, presenting what was known at a fixed moment in time.\n",
    "\n",
    "The second are aggregations performed in response to a specific research question. These aggregations, or queries, are generated using all the source data available - including any subsequent updates, corrections or restatements released by local authorities and the VOA - and customised to that particular research query.\n",
    "\n",
    "The second approach leads to quarterly aggregations that may change from ones produced previously, depending on the date in which they were generated, since they include subsequent restatements.\n",
    "\n",
    "Neither approach is better than the other, but they do have the potential to result in quite different values.\n",
    "\n",
    "The review that follows is closer to the second approach since, as careful as a query may be structured, historical restatements now exist in the source data. It is no longer strictly possible - using the source data we have - to generate the exact aggregation report as generated on 31 December 2019. Given that the total number of vacant hereditament under review is 767 out of 9,927 at the time (7.6%), differences of a few hundred can have a dramatic effect on the vacancy rate even though they only constitute a percent of the total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7dd7ac7d-77b0-47f7-933c-b0e611fcbc08",
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# 3. Merged VOA and Barnsley data review\n",
    "#  - What changes occurred between Barnsley data in terms of reference numbers, \n",
    "#    matching quality and matching to live versus historic hereditaments?\n",
    "#  - How do these changes interact over time?\n",
    "#  - How do these changes impact on vacancy rates in general, temporally, and \n",
    "#    in each of the main segmentations (retail, office, etc)\n",
    "################################################################################\n",
    "\n",
    "################################################################################\n",
    "# Merge will be performed as it would be on Sqwyre servers, using only VOA\n",
    "# updates to the time of the local authority data release. Future corrections\n",
    "# are excluded as best as possible as they wouldn't be known. Some artifacts\n",
    "# may remain.\n",
    "# Below is production code, modified slightly since this Notebook will use CSV\n",
    "# and not database queries as source data.\n",
    "# NOTE: a significant difference is that VOA update cycles don't map nicely to\n",
    "#       frequency of release or a date. The below uses filter_date as a proxy\n",
    "#       and status_date for subsequent sorting and filtering.\n",
    "################################################################################\n",
    "\n",
    "def parse_dates(x):\n",
    "    \"\"\"\n",
    "    This is the hard-won 'trust nobody' date parser.\n",
    "    \"\"\"\n",
    "    if pd.isnull(x): return pd.NaT\n",
    "    # Check if to_datetime can handle things\n",
    "    if not pd.isnull(pd.to_datetime(x, errors=\"coerce\", dayfirst=True)):\n",
    "        x = date.isoformat(pd.to_datetime(x, errors=\"coerce\", dayfirst=True))\n",
    "        return pd.to_datetime(x).tz_localize(\"UTC\")\n",
    "    # Manually see if coersion will work\n",
    "    x = str(x).strip()[:10]\n",
    "    x = re.sub(r\"[\\\\/,\\.]\",\"-\", x)\n",
    "    try:\n",
    "        y, m, d = x.split(\"-\")\n",
    "    except ValueError:\n",
    "        return pd.NaT\n",
    "    if len(y) < 4:\n",
    "        # Swap the day and year positions\n",
    "        # Ignore US dates\n",
    "        d, m, y = x.split(\"-\")\n",
    "    # Fat finger on 1999 ... not going to check for other date errors\n",
    "    if y[0] == \"9\": y = \"1\" + y[1:]\n",
    "    x = \"{}-{}-{}\".format(y, m, d)\n",
    "    try:\n",
    "        x = datetime.strptime(x,\"%Y-%m-%d\")\n",
    "    except ValueError:\n",
    "        return pd.NaT\n",
    "    x = date.isoformat(x)\n",
    "    try:\n",
    "        return pd.to_datetime(x).tz_localize(\"UTC\")\n",
    "    except pd.errors.OutOfBoundsDatetime:\n",
    "        return pd.NaT\n",
    "\n",
    "def merge_voa_and_la_reports(df_voa, df_la, cycle_date):\n",
    "    \"\"\"\n",
    "    Merge VOA and local authority dataframes to create a single merged report.\n",
    "\n",
    "    Args:\n",
    "        df_voa: Pandas Dataframe\n",
    "            VOA report current to this update cycle\n",
    "        df_la: Pandas Dataframe\n",
    "            Report from local authority\n",
    "\n",
    "    Returns:\n",
    "        Pandas DataFrame.\n",
    "    \"\"\"\n",
    "    # Sort & filter df_voa to exclude hereditaments with None, 0, 1 floor area\n",
    "    df_voa = df_voa.copy()\n",
    "    df_voa.sort_values(by=[\"rates_code\", \"report_date\"], inplace=True)\n",
    "    df_voa.drop_duplicates(subset=[\"rates_code\"], keep=\"last\", inplace=True)\n",
    "    # Sort and filter df_la to exclude duplicates and unmatched rates_codes\n",
    "    df_la = df_la.copy()\n",
    "    df_la.dropna(subset=[\"rates_code\"], inplace=True)\n",
    "    # Challenge for missing dates is to find a sensible default given there \n",
    "    # could be duplicates ... setting a future date could cause problems\n",
    "    # 1. Where dupes, drop na\n",
    "    dup_la = df_la[df_la.duplicated(subset=[\"rates_code\"], keep=False)].copy()\n",
    "    dup_la.dropna(subset=[\"status_date\"], inplace=True)\n",
    "    # 2. Drop all dupes, then fillna with cycle data\n",
    "    df_la.drop_duplicates(subset=[\"rates_code\"], keep=False, inplace=True)\n",
    "    # 3. Concatenate to get everything back\n",
    "    df_la = pd.concat([df_la, dup_la])\n",
    "    df_la.sort_values(by=[\"rates_code\", \"update_cycle\", \n",
    "                          \"status_date\", \"status\"], \n",
    "                      inplace=True)\n",
    "    df_la.drop_duplicates(subset=[\"rates_code\"], keep=\"last\", inplace=True)\n",
    "    # Perform the merge\n",
    "    df_merge = df_voa.merge(df_la, how=\"left\", on=\"rates_code\")\n",
    "    # Fix columns to ensure sensible defaults\n",
    "    df_merge[\"status\"].fillna(True, inplace=True)    \n",
    "    set_bool = {\"True\": True, \"False\": False}\n",
    "    df_merge[\"status\"].map(set_bool)\n",
    "    # After the merge, non-matching rows won't have a status date\n",
    "    df_merge[\"status_date\"].fillna(cycle_date, inplace=True) \n",
    "    df_merge.sort_values(by=[\"rates_code\", \"status_date\"], inplace=True)\n",
    "    df_merge.drop_duplicates(subset=[\"rates_code\"], keep=\"last\", inplace=True)\n",
    "    return df_merge.copy()\n",
    "\n",
    "def get_quarter_from_cycle(cycle):\n",
    "    \"\"\"\n",
    "    For a known cycle, but unkown VOA status start date, set the quarter \n",
    "    to the last day of the quarter, counted from OCCUPIER_BASE_QUARTER. \n",
    "    Legacy of the way we do quarterly update cycles for Sqwyre. These \n",
    "    start at 001 for the specific date of 1 July 2016. \n",
    "\n",
    "    Args:\n",
    "        cycle: String for current location update cycle.\n",
    "\n",
    "    Returns:\n",
    "        Datetime (UTC timezone)\n",
    "    \"\"\"\n",
    "    # Q3 2016, i.e. we make a request in one Q, for the previous Q\n",
    "    OCCUPIER_BASE_QUARTER = \"2016-07-01\"\n",
    "    TIME_ZONE = \"Europe/London\"\n",
    "    # Base is 1 (i.e. first cycle), and three months per quarter\n",
    "    # Then, get months to year change from base quarter\n",
    "    y, m, d = [int(x) for x in OCCUPIER_BASE_QUARTER.split(\"-\")]\n",
    "    # Get number of months to year change from base quarter\n",
    "    mths_to_1st_new_year = 12 - m + 1\n",
    "    months = (int(cycle) + 1) * 3\n",
    "    if months >= mths_to_1st_new_year:\n",
    "        y = y + 1 + (months - mths_to_1st_new_year)//12\n",
    "    m = (months - mths_to_1st_new_year)%12 + 1\n",
    "    quarter = date.isoformat(datetime(y, m, 1))\n",
    "    quarter = pd.to_datetime(quarter, format=\"%Y-%m-%d\") \\\n",
    "                .tz_localize(\"UTC\")\n",
    "    # Get last date of the cycle\n",
    "    quarter = (quarter - timedelta(days=1)).date()\n",
    "    quarter = datetime.combine(quarter, datetime.min.time()\n",
    "                           ).replace(tzinfo=pytz.utc)\n",
    "    return pd.to_datetime(quarter)\n",
    "\n",
    "# Open Barnsely VOA data as a Pandas dataframe\n",
    "# Specify column import types\n",
    "dtype_dict = {\n",
    "    \"location_code\": str,\n",
    "    \"rates_code\": str\n",
    "}\n",
    "parse_dates_list = [\"report_date\", \"created_date\", \"effective_date\", \n",
    "               \"start_date\", \"end_date\"]\n",
    "voa_barnsely_source = \"barnsley-2017-valuation-data.csv\"\n",
    "df_voa = pd.read_csv(directory + voa_barnsely_source, \n",
    "                     dtype = dtype_dict, \n",
    "                     parse_dates = parse_dates_list)\n",
    "df_voa.rename(index=str,\n",
    "              columns={\n",
    "                  \"category__grouped_by__grouped_by__description\": \"category_group\",\n",
    "              },\n",
    "             inplace=True)\n",
    "# Add in a column for date filtering\n",
    "df_voa[\"filter_date\"] = df_voa[\"start_date\"]\n",
    "df_voa[\"filter_date\"].fillna(df_voa.effective_date, inplace=True)\n",
    "# Filter out all VOA-assigned signifiers of removed hereditaments\n",
    "df_voa = df_voa[(~df_voa[\"floor_area\"].isin([None, 0, 1])) &\n",
    "                (~df_voa[\"rental_valuation\"].isin([None, 0, 1]))]\n",
    "\n",
    "# Open Barnsley local authority data\n",
    "# Specify column import types\n",
    "dtype_dict = {\n",
    "    \"ba_ref\": str,\n",
    "    \"rates_code\": str,\n",
    "    \"occupation_state\": bool\n",
    "}\n",
    "parse_dates_list = [\"occupation_state_date\"]\n",
    "# Get all source data\n",
    "chunks = []\n",
    "for file in os.listdir(directory):\n",
    "    if file.startswith(\"E08000016_\"):\n",
    "        chunk = pd.read_csv(directory + file, \n",
    "                            dtype = dtype_dict, \n",
    "                            parse_dates = parse_dates_list)\n",
    "        cycle = file.split(\".\")[0].split(\"_\")[-1]\n",
    "        chunk[\"update_cycle\"] = cycle\n",
    "        chunk[\"occupation_state_date\"].fillna(get_quarter_from_cycle(cycle), \n",
    "                                              inplace=True)\n",
    "        # Ensure dates are \"proper\" dates\n",
    "        chunk[\"occupation_state_date\"] = chunk[\"occupation_state_date\"\n",
    "                                              ].apply(lambda x: parse_dates(x))        \n",
    "        chunks.append(chunk)\n",
    "df_la = pd.concat(chunks)\n",
    "df_la.rename(index=str,\n",
    "             columns={\"occupation_state\": \"status\",\n",
    "                      \"occupation_state_date\": \"status_date\"\n",
    "                     },\n",
    "             inplace=True)\n",
    "\n",
    "# Ratios of void by category aggregations\n",
    "aggregation = {\n",
    "    \"update_cycle\": [],\n",
    "    \"total_count\": [],\n",
    "    \"total_count_excl\": [],\n",
    "    \"void_retail\": [],\n",
    "    \"void_office\": [],\n",
    "    \"void_industrial\": [],\n",
    "    \"void_leisure\": [],\n",
    "    \"void_other\": [],\n",
    "}\n",
    "\n",
    "# Get Barnsley unique update cycles\n",
    "cycles = list(df_la[\"update_cycle\"].unique())#[:-1]\n",
    "cycles.sort()\n",
    "\n",
    "# loop through updates in sequence\n",
    "for update_cycle in cycles:\n",
    "    cycle_end_date = get_quarter_from_cycle(update_cycle)\n",
    "    # Filter Barnsley update\n",
    "    df_c_la = df_la[df_la[\"update_cycle\"] == update_cycle].copy()\n",
    "    # Filter VOA data on report created_date\n",
    "    df_c_voa = df_voa[df_voa[\"filter_date\"] <= cycle_end_date].copy()\n",
    "    # Merge\n",
    "    df_merge = merge_voa_and_la_reports(df_c_voa, df_c_la, cycle_end_date)\n",
    "    # Aggregate - total count EXCLUDES non-retail,industrial,leisure,office\n",
    "    total_count = len(df_merge)\n",
    "    total_count_excl = len(df_merge[df_merge[\"category_group\"].notnull()])\n",
    "    total_void = len(df_merge[df_merge[\"status\"] == False])\n",
    "    void_retail = len(df_merge[(df_merge[\"category_group\"] == \"Retail\") & \n",
    "                               (df_merge[\"status\"] == False)])\n",
    "    void_office = len(df_merge[(df_merge[\"category_group\"] == \"Office\") & \n",
    "                               (df_merge[\"status\"] == False)])\n",
    "    void_industrial = len(df_merge[(df_merge[\"category_group\"] == \"Industrial\") \n",
    "                                   & \n",
    "                                   (df_merge[\"status\"] == False)])\n",
    "    void_leisure = len(df_merge[(df_merge[\"category_group\"] == \"Leisure\") & \n",
    "                                (df_merge[\"status\"] == False)])\n",
    "    void_other = (total_void - void_retail - void_office -\n",
    "                  void_industrial - void_leisure)\n",
    "    aggregation[\"update_cycle\"].append(update_cycle)\n",
    "    aggregation[\"total_count\"].append(total_count)\n",
    "    aggregation[\"total_count_excl\"].append(total_count_excl)\n",
    "    aggregation[\"void_retail\"].append(void_retail)\n",
    "    aggregation[\"void_office\"].append(void_office)\n",
    "    aggregation[\"void_industrial\"].append(void_industrial)\n",
    "    aggregation[\"void_leisure\"].append(void_leisure)\n",
    "    aggregation[\"void_other\"].append(void_other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea6c8540-0513-447f-a4b9-f04520d939d6",
   "metadata": {},
   "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>total_count</th>\n",
       "      <th>total_count_excl</th>\n",
       "      <th>void_retail</th>\n",
       "      <th>void_office</th>\n",
       "      <th>void_industrial</th>\n",
       "      <th>void_leisure</th>\n",
       "      <th>void_other</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>update_cycle</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>003</th>\n",
       "      <td>6959</td>\n",
       "      <td>6103</td>\n",
       "      <td>207</td>\n",
       "      <td>263</td>\n",
       "      <td>175</td>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>005</th>\n",
       "      <td>7127</td>\n",
       "      <td>6255</td>\n",
       "      <td>202</td>\n",
       "      <td>125</td>\n",
       "      <td>165</td>\n",
       "      <td>32</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>007</th>\n",
       "      <td>9179</td>\n",
       "      <td>8268</td>\n",
       "      <td>200</td>\n",
       "      <td>282</td>\n",
       "      <td>172</td>\n",
       "      <td>12</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>009</th>\n",
       "      <td>9364</td>\n",
       "      <td>8320</td>\n",
       "      <td>225</td>\n",
       "      <td>289</td>\n",
       "      <td>179</td>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011</th>\n",
       "      <td>9509</td>\n",
       "      <td>8288</td>\n",
       "      <td>207</td>\n",
       "      <td>277</td>\n",
       "      <td>175</td>\n",
       "      <td>8</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>013</th>\n",
       "      <td>9630</td>\n",
       "      <td>8322</td>\n",
       "      <td>178</td>\n",
       "      <td>246</td>\n",
       "      <td>142</td>\n",
       "      <td>8</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>019</th>\n",
       "      <td>10065</td>\n",
       "      <td>8519</td>\n",
       "      <td>164</td>\n",
       "      <td>241</td>\n",
       "      <td>110</td>\n",
       "      <td>6</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              total_count  total_count_excl  void_retail  void_office  \\\n",
       "update_cycle                                                            \n",
       "003                  6959              6103          207          263   \n",
       "005                  7127              6255          202          125   \n",
       "007                  9179              8268          200          282   \n",
       "009                  9364              8320          225          289   \n",
       "011                  9509              8288          207          277   \n",
       "013                  9630              8322          178          246   \n",
       "019                 10065              8519          164          241   \n",
       "\n",
       "              void_industrial  void_leisure  void_other  \n",
       "update_cycle                                             \n",
       "003                       175            13          47  \n",
       "005                       165            32         151  \n",
       "007                       172            12          46  \n",
       "009                       179             8          60  \n",
       "011                       175             8         131  \n",
       "013                       142             8          55  \n",
       "019                       110             6          71  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Table of changes in each update - full series\n",
    "################################################################################\n",
    "df_aggregation = pd.DataFrame.from_dict(aggregation)\n",
    "df_aggregation.set_index(\"update_cycle\", inplace=True)\n",
    "df_aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9baa29e5-1bad-4a68-a9ea-f61879afc569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='update_cycle'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Changes in each update - full series\n",
    "################################################################################\n",
    "df_aggregation[[\"void_retail\",\"void_office\",\"void_industrial\",\n",
    "                \"void_leisure\",\"void_other\"]].plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117b1727-ef7c-4598-8a9d-71b4bb779593",
   "metadata": {},
   "source": [
    "Following the merge, excluding all withdrawn and inactive hereditaments, the total live hereditaments dropped slightly. For the October 2019 release (with the quarter ending 31 December 2019):\n",
    "\n",
    "- VOA total live hereditaments = 9,630 (8,322 when excluding non-retail, office, leisure and industrial hereditaments)\n",
    "- Barnsley released hereditaments = 8,765 (91% of total hereditaments)\n",
    "- Barnsley total voids = 767 (8% of total hereditaments, 8.8% of Barnsley release)\n",
    "- Barnsley void non-match to VOA data = 124 ambiguous + 5 resolved as occupied + 14 unmatched voids (18.6% of Barnsley voids)\n",
    "- Barnsley matched voids = 629 (6.5% of total hereditaments)\n",
    "- Barnsley voids in Retail, Office, Industrial, Leisure = 574 (6.9% of 8,322)\n",
    "\n",
    "At this point of the review, the initial 767 voids have been reduced to 574, or a decrease of about 25%, for a total vacancy of 6.9%. The average vacancy rate for England and Wales on 31 December 2019 was just under 7%, indicating Barnsley as experiencing average levels of vacancy.\n",
    "\n",
    "As described above, it is difficult to replicate the original analysis exactly, but there doesn't appear to be any grounds for considering Barnsley's vacancy levels to be significant or remarkable.\n",
    "\n",
    "The core deflators of Barnsley's stated vacancy rate are:\n",
    "\n",
    "- Understating the total number of hereditaments (declaring 8,765 vs 9,630 in the VOA register),\n",
    "- Ambiguous or irreconcilable hereditaments (138 out of 767 are ambiguous),\n",
    "- Including non-retail, office, industrial or leisure hereditaments in the total (55 hereditaments classified as `other` in the total).\n",
    "\n",
    "**This stated**, this is **not** how we aggregate quarterly data for Sqwyre. We treat historical updates as being congruent with the latest updates. A quarterly aggregation report is the extent of all previous activity. Given that Barnsley present a subset of the complete hereditament record (undercounting by over 1,000 records) this may have a dramatic effect when taken cumulatively.\n",
    "\n",
    "## Ensuring adherence to sensible defaults\n",
    "\n",
    "So far, the text, tabular and visualisation components of the report did not require coding knowledge, even though the code for the analysis is presented here. However, a short discussion of two lines of code is required:\n",
    "\n",
    "    df_la.sort_values(by=[\"rates_code\", \"update_cycle\", \"status_date\", \"status\"], \n",
    "                      inplace=True)\n",
    "    df_la.drop_duplicates(subset=[\"rates_code\"], keep=\"last\", inplace=True)\n",
    "                      \n",
    "These lines of code are found in rows `84` to `86` of section 3 above. Assume we have a table with the following data indicating a history, including restatements of that history, for an individual hereditament over four update cycles:\n",
    "\n",
    "| rates_code     | update_cycle | status_date | status |\n",
    "|----------------|--------------|-------------|--------|\n",
    "| 50101002002522 | 005          | 2017-05-09  | FALSE  |\n",
    "| 50101002002522 | 007          | 2016-10-30  | TRUE   |\n",
    "| 50101002002522 | 009          | 2017-06-21  | TRUE   |\n",
    "| 50101002002522 | 013          | 2018-07-08  | FALSE  |\n",
    "| 50101002002522 | 013          | 2018-07-08  | TRUE   |\n",
    "\n",
    "These are entirely fictitious, but the columns are defined as follows:\n",
    "\n",
    "- *rates_code*: the unique reference number for a hereditament,\n",
    "- *update_cycle*: the specific quarterly update cycle during which that data was imported to Sqwyre,\n",
    "- *status_date*: the date at which the changes reflected in this update became effective,\n",
    "- *status*: the occupation state of the hereditament, where `TRUE` is occupied, and `FALSE` is void (or vacant).\n",
    "\n",
    "Cycle 005 gives a `status_date` of vacant on 9 May 2018, but cycle 007 gives a `status_date` of occupied 30 October 2017. How should we interpret this? We treat this is a *historical restatement*. \n",
    "\n",
    "The core of the work performed by the VOA is managing, reviewing and implementing such restatements. As demonstrated above, publishing changes and updates accounts for the majority of the data in fortnightly updates. Such information includes revaluations, changes of category of use, and changes to floor area, including numerous other administrative changes which may have no impact on any of the aggregations Sqwyre produces.\n",
    "\n",
    "Managing such restatements, and maintaining a consistent and continuous historical record, is a critical part of performing data curation for a longitudinal study. \n",
    "\n",
    "An aggregation, however, is not a reflection of the history of an area, but of its particular status at a specific moment in time. That means all ambiguities in the timeline for each hereditament must be resolved.\n",
    "\n",
    "We sort each of the columns in the following order: 1 `rates_code`, 2 `update_cycle`, 3 `status_update`, 4 `status`. The meaning of this is that a restatement of a date takes precidence over dates from previous updates; and the status of a location takes precidence over previous dates in previous updates.\n",
    "\n",
    "For the above example, that means for an aggregation drawn at the end of update cycle `007`, the hereditament listed would be regarded as `occupied` and the specific record for vacant - even though the date is more recent - would be regarded as having been replaced (restated) by the more recent record.\n",
    "\n",
    "In cycle `013` we get a more obvious ambiguity, and here the choice for any such conflict where on one date a site is somehow both occupied and vacant, is to treat it as occupied.\n",
    "\n",
    "All these choices and processes are encapsulated in these two commands:\n",
    "\n",
    "    df_la.sort_values(by=[\"rates_code\", \"update_cycle\", \"status_date\", \"status\"], \n",
    "                      inplace=True)\n",
    "    df_la.drop_duplicates(subset=[\"rates_code\"], keep=\"last\", inplace=True)\n",
    "    \n",
    "These commands state: ***sort data values** by each column, in order, and **drop duplicates** for each unique hereditmaent code, keeping only the last row for each duplicated set of codes.*\n",
    "                      \n",
    "The aggregation is a snapshot, but the complete record continues to be maintained in our database, such as the record below:\n",
    "\n",
    "![History example](data/history-example.png)\n",
    "\n",
    "*Figure 4: Extract of history from one specific hereditament. Source: Sqwyre.com*\n",
    "\n",
    "Ordinarily, restatements are rare. A dispute with a landlord as to when, exactly, their hereditament became vacant, for example, may be updated. Or a floor area reviewed and modified. Sometimes, as with the periodic rates revaluations, an entire database may be updated simultaneously.\n",
    "\n",
    "The following code makes two simple changes to the program listed above:\n",
    "\n",
    "- When performing the merge, the previous data are concatenated to the latest data for that cycle, and the aggregation is drawn from that. \n",
    "- Instead of performing updates only with Barnsley data releases, the algorithm runs quarterly sequentially, exactly as Sqwyre aggregations are produced, with historical data used, even though updates are lagging.\n",
    "\n",
    "The results are dramatic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "69296a0a-763c-4bae-ab94-73779027ff88",
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# Running the merge again, but also keeping a running history of all updates\n",
    "# provided by Barnsley for a full reconciliation of known data.\n",
    "################################################################################\n",
    "\n",
    "# Ratios of void by category aggregations\n",
    "aggregation = {\n",
    "    \"update_cycle\": [],\n",
    "    \"total_count\": [],\n",
    "    \"total_count_excl\": [],\n",
    "    \"void_retail\": [],\n",
    "    \"void_office\": [],\n",
    "    \"void_industrial\": [],\n",
    "    \"void_leisure\": [],\n",
    "    \"void_other\": [],\n",
    "    \"void_percent\": []\n",
    "}\n",
    "\n",
    "# Create list of update cycles\n",
    "cycles = [(\"0\" * (3 - len(str(x))) + str(x)) for x in range(3,20)]\n",
    "\n",
    "chunks = []\n",
    "# loop through updates in sequence\n",
    "for update_cycle in cycles:\n",
    "    cycle_end_date = get_quarter_from_cycle(update_cycle)\n",
    "    # Filter Barnsley update and check for NaT\n",
    "    df_c_la = df_la[df_la[\"status_date\"] <= cycle_end_date].copy()\n",
    "    # Filter VOA data on report created_date\n",
    "    df_c_voa = df_voa[df_voa[\"filter_date\"] <= cycle_end_date].copy()\n",
    "    # Merge and filter for latest unique\n",
    "    chunks.append(merge_voa_and_la_reports(df_c_voa, df_c_la, cycle_end_date))\n",
    "    df_merge = pd.concat(chunks).copy()\n",
    "    df_merge.sort_values(by=[\"rates_code\", \"status_date\", \"status\"\n",
    "                            ], inplace=True)\n",
    "    df_merge.drop_duplicates(subset=[\"rates_code\"], keep=\"last\", inplace=True)\n",
    "    # Aggregate - total count EXCLUDES non-retail,industrial,leisure,office\n",
    "    total_count = len(df_merge)\n",
    "    total_count_excl = len(df_merge[df_merge[\"category_group\"].notnull()])\n",
    "    total_void = len(df_merge[df_merge[\"status\"] == False])\n",
    "    void_retail = len(df_merge[(df_merge[\"category_group\"] == \"Retail\") & \n",
    "                               (df_merge[\"status\"] == False)])\n",
    "    void_office = len(df_merge[(df_merge[\"category_group\"] == \"Office\") & \n",
    "                               (df_merge[\"status\"] == False)])\n",
    "    void_industrial = len(df_merge[(df_merge[\"category_group\"] == \"Industrial\") \n",
    "                                   & \n",
    "                                   (df_merge[\"status\"] == False)])\n",
    "    void_leisure = len(df_merge[(df_merge[\"category_group\"] == \"Leisure\") & \n",
    "                                (df_merge[\"status\"] == False)])\n",
    "    void_other = (total_void - void_retail - void_office -\n",
    "                  void_industrial - void_leisure)\n",
    "    void_percent = f\"{(total_void - void_other) / total_count_excl * 100:.2f}\"\n",
    "    aggregation[\"update_cycle\"].append(update_cycle)\n",
    "    aggregation[\"total_count\"].append(total_count)\n",
    "    aggregation[\"total_count_excl\"].append(total_count_excl)\n",
    "    aggregation[\"void_percent\"].append(void_percent)\n",
    "    aggregation[\"void_retail\"].append(void_retail)\n",
    "    aggregation[\"void_office\"].append(void_office)\n",
    "    aggregation[\"void_industrial\"].append(void_industrial)\n",
    "    aggregation[\"void_leisure\"].append(void_leisure)\n",
    "    aggregation[\"void_other\"].append(void_other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16893132-4366-4fc7-9f6e-6eb617d0b679",
   "metadata": {},
   "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>total_count</th>\n",
       "      <th>total_count_excl</th>\n",
       "      <th>void_retail</th>\n",
       "      <th>void_office</th>\n",
       "      <th>void_industrial</th>\n",
       "      <th>void_leisure</th>\n",
       "      <th>void_other</th>\n",
       "      <th>void_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>update_cycle</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>003</th>\n",
       "      <td>6959</td>\n",
       "      <td>6103</td>\n",
       "      <td>161</td>\n",
       "      <td>147</td>\n",
       "      <td>116</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>7.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>004</th>\n",
       "      <td>7050</td>\n",
       "      <td>6179</td>\n",
       "      <td>157</td>\n",
       "      <td>127</td>\n",
       "      <td>106</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>6.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>005</th>\n",
       "      <td>7127</td>\n",
       "      <td>6255</td>\n",
       "      <td>61</td>\n",
       "      <td>42</td>\n",
       "      <td>47</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>006</th>\n",
       "      <td>9148</td>\n",
       "      <td>8274</td>\n",
       "      <td>55</td>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>007</th>\n",
       "      <td>9179</td>\n",
       "      <td>8268</td>\n",
       "      <td>126</td>\n",
       "      <td>114</td>\n",
       "      <td>82</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>008</th>\n",
       "      <td>9258</td>\n",
       "      <td>8261</td>\n",
       "      <td>116</td>\n",
       "      <td>104</td>\n",
       "      <td>77</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>009</th>\n",
       "      <td>9364</td>\n",
       "      <td>8320</td>\n",
       "      <td>88</td>\n",
       "      <td>81</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>010</th>\n",
       "      <td>9417</td>\n",
       "      <td>8331</td>\n",
       "      <td>72</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011</th>\n",
       "      <td>9509</td>\n",
       "      <td>8288</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>124</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>012</th>\n",
       "      <td>9596</td>\n",
       "      <td>8309</td>\n",
       "      <td>78</td>\n",
       "      <td>70</td>\n",
       "      <td>56</td>\n",
       "      <td>5</td>\n",
       "      <td>138</td>\n",
       "      <td>2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>013</th>\n",
       "      <td>9630</td>\n",
       "      <td>8322</td>\n",
       "      <td>70</td>\n",
       "      <td>59</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>142</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>014</th>\n",
       "      <td>9748</td>\n",
       "      <td>8379</td>\n",
       "      <td>61</td>\n",
       "      <td>39</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>015</th>\n",
       "      <td>9819</td>\n",
       "      <td>8430</td>\n",
       "      <td>218</td>\n",
       "      <td>276</td>\n",
       "      <td>137</td>\n",
       "      <td>11</td>\n",
       "      <td>202</td>\n",
       "      <td>7.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>016</th>\n",
       "      <td>9930</td>\n",
       "      <td>8459</td>\n",
       "      <td>205</td>\n",
       "      <td>268</td>\n",
       "      <td>132</td>\n",
       "      <td>11</td>\n",
       "      <td>209</td>\n",
       "      <td>7.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>017</th>\n",
       "      <td>10030</td>\n",
       "      <td>8513</td>\n",
       "      <td>199</td>\n",
       "      <td>265</td>\n",
       "      <td>131</td>\n",
       "      <td>10</td>\n",
       "      <td>215</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>018</th>\n",
       "      <td>10065</td>\n",
       "      <td>8519</td>\n",
       "      <td>197</td>\n",
       "      <td>256</td>\n",
       "      <td>130</td>\n",
       "      <td>10</td>\n",
       "      <td>227</td>\n",
       "      <td>6.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>019</th>\n",
       "      <td>10065</td>\n",
       "      <td>8519</td>\n",
       "      <td>197</td>\n",
       "      <td>256</td>\n",
       "      <td>130</td>\n",
       "      <td>10</td>\n",
       "      <td>227</td>\n",
       "      <td>6.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              total_count  total_count_excl  void_retail  void_office  \\\n",
       "update_cycle                                                            \n",
       "003                  6959              6103          161          147   \n",
       "004                  7050              6179          157          127   \n",
       "005                  7127              6255           61           42   \n",
       "006                  9148              8274           55           37   \n",
       "007                  9179              8268          126          114   \n",
       "008                  9258              8261          116          104   \n",
       "009                  9364              8320           88           81   \n",
       "010                  9417              8331           72           64   \n",
       "011                  9509              8288           95           93   \n",
       "012                  9596              8309           78           70   \n",
       "013                  9630              8322           70           59   \n",
       "014                  9748              8379           61           39   \n",
       "015                  9819              8430          218          276   \n",
       "016                  9930              8459          205          268   \n",
       "017                 10030              8513          199          265   \n",
       "018                 10065              8519          197          256   \n",
       "019                 10065              8519          197          256   \n",
       "\n",
       "              void_industrial  void_leisure  void_other void_percent  \n",
       "update_cycle                                                          \n",
       "003                       116            14          17         7.18  \n",
       "004                       106            13          18         6.52  \n",
       "005                        47             8          10         2.53  \n",
       "006                        41             8          10         1.70  \n",
       "007                        82             8          17         3.99  \n",
       "008                        77             6          30         3.67  \n",
       "009                        54             3          37         2.72  \n",
       "010                        40             3          42         2.15  \n",
       "011                        70             5         124         3.17  \n",
       "012                        56             5         138         2.52  \n",
       "013                        42             5         142         2.11  \n",
       "014                        34             4         151         1.65  \n",
       "015                       137            11         202         7.62  \n",
       "016                       132            11         209         7.28  \n",
       "017                       131            10         215         7.11  \n",
       "018                       130            10         227         6.96  \n",
       "019                       130            10         227         6.96  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################################################################################\n",
    "# Table of changes in each update - full series\n",
    "################################################################################\n",
    "df_aggregation = pd.DataFrame.from_dict(aggregation)\n",
    "df_aggregation.set_index(\"update_cycle\", inplace=True)\n",
    "df_aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2c75550a-7bf5-40ae-81d5-332bc802992f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='update_cycle'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
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   "source": [
    "################################################################################\n",
    "# Changes in each update - full series\n",
    "################################################################################\n",
    "df_aggregation[[\"void_retail\",\"void_office\",\"void_industrial\",\n",
    "                \"void_leisure\",\"void_other\"]].plot(figsize=(20,10))"
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    "Following the continuous build, the following emerges for October 2019:\n",
    "\n",
    "- Non-continuous discrete method: voids in Retail, Office, Industrial, Leisure = 574 (6.9% of 8,322)\n",
    "- Continuous method: voids in Retail, Office, Industrial, Leisure = 176 (2.1% of 8,322)\n",
    "\n",
    "What is remarkable here is not that the voids are so low in comparison to the source data, but that they were high in previous aggregations:\n",
    "\n",
    "![Barnsley Voids & Occupation Overview](data/barnsley-voids-overview.png)\n",
    "\n",
    "This is, most likely, as a result of continuous VOA restatements. Overall vacancy in updates `003` and `005` were 7.2% and 6.5% respectively, exactly as it appears in the chart. Thereafter, it begins to fall rapidly. This fall seems to be precipitated by an enormous rise in the number of hereditaments as 2,000 new records are introduced (increasing the total hereditament count from 7,000 to 9,000 and instantly deflating the vacancy rate although the absolute number of voids did not change that much).\n",
    "\n",
    "From April 2019, another major change as the number of vacant hereditaments classified as \"Other\" rises and becomes dominant. Then, in June 2020 another enormous shift as overall vacancy levels rise to 7.6%.\n",
    "\n",
    "What information triggered this vacancy increase in June 2020? That would be the October 2020 update, which was only received in April 2021.\n",
    "\n",
    "# Conclusions and recommendations\n",
    "\n",
    "The objective of this review was to ensure that any result is derived from a transparent and replicable process. Reasonable people can debate what constitutes a sensible default, or how to resolve contradictions and ambiguity in source data. All of that depends on transparency in the first place, both in public disclosure of source data, and in the code and algorithms which curate that source data.\n",
    "\n",
    "The specific defaults used to aggregate the historical record for a quarterly report for each hereditament are designed to ensure that restatements and corrections take precedence over previous updates. Each successive update takes precedence over the ones which came before. This ensures corrections happen, but can also leave long-term errors in place if no subsequent updates, or corrections, are released.\n",
    "\n",
    "Much of the challenge of curating and merging commercial location data derives from the lack of consistent definitions, data schemas, and regular updates by local authorities. A concern about aggregate data in April 2021 would never have arisen if Barnsley had simply published their April 2020 and October 2020 updates on time. Instead, a period of 18 months passed before new data was released, and an error arising from some ambiguity in their own previous releases was never corrected.\n",
    "\n",
    "What can be stated is that, at the time that the aggregation generated in February 2021 was released, it was an accurate reflection of the data curated in the Sqwyre database. No bias or error appears to have caused the observed anomally which could not be replicated in the source data, and each of the large deviations can now be explained.\n",
    "\n",
    "- Barnsley first cross-referenced data release in Q2 2017, six months after the start of our data series, leading to an initial 0% vacancy as a result of our default approach,\n",
    "- Transition from 2010 to 2017 rates valuation in April 2017 coincides with a dramatic change in categorisation of hereditaments from office to leisure, revealed by a large change effecting 756 hereditaments,\n",
    "- In October 2019 we received the last update from Barnsley for what would be about 18 months. That update resulted in a dramatic change in voids. This appears to be a result of further restatements in both Barnsley and VOA source data which precipitated a correction.\n",
    "\n",
    "Clearly, such dramatic restatements are in no-ones interest, and cause challenges for researchers and policy-makers alike. They can also be avoided - or, at least, significantly mitigated - by adopting the following recommendations:\n",
    "\n",
    "- Local authorities must publish their register of commercial ratepayers quarterly and continuously, and - at a minimum - include the following fields:\n",
    "    - Billing authority hereditament reference code (linking the hereditament to the VOA database reference)\n",
    "    - Firm's trading name (i.e. ratepayer)\n",
    "    - Full hereditament address (number, street, postal code, town)\n",
    "    - Occupied / Vacant status\n",
    "    - Date of occupation / vacancy\n",
    "    - Actual annual rates charged (in Pounds)\n",
    "    - List of reliefs / exemptions received by the ratepayer\n",
    "- Standardise definitions of what constitutes occupation status, categories of reliefs / exemptions, dates for occupation status, and - critically - consistent and high-quality use of hereditament reference numbers for each record.\n",
    "\n",
    "Consistent and regular updates will not prevent future errors. We are human, and mistakes are inevitable. What it will do is ensure that corrections are swiftly brought into the custodial record."
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