01_download.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Downloads the raw data files necessary to analyze Houston buildings found within flood zones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Buildings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download a list of all buildings published by the Harris County Appraisal District, as well as their tax districts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  221M  100  221M    0     0  1163k      0  0:03:15  0:03:15 --:--:-- 2283k\n"
     ]
    }
   ],
   "source": [
    "!curl -o input/Real_building_land.zip http://pdata.hcad.org/download/2017/Real_building_land.zip\n",
    "!curl -o input/Real_jur_exempt.zip http://pdata.hcad.org/download/2017/Real_jur_exempt.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After unzipping, convert them to CSVs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.read_csv(\n",
    "    \"input/Real_building_land/building_res.txt\",\n",
    "    delimiter=\"\\t\",\n",
    "    dtype={\"ACCOUNT\": str},\n",
    "    names=[\n",
    "        'ACCOUNT',\n",
    "        'USE_CODE',\n",
    "        'BUILDING_NUMBER',\n",
    "        'IMPRV_TYPE',\n",
    "        'BUILDING_STYLE_GUIDE',\n",
    "        'CLASS_STRUCTURE',\n",
    "        'CLASS_STRUCTURE_DESRICPTION',\n",
    "        'DEPRECIATION_VALUE',\n",
    "        'CAMA_REPLACEMENT_COST',\n",
    "        'ACCRUED_DEPR_PCT',\n",
    "        'QUALITY',\n",
    "        'QUALITY_DESCRIPTION',\n",
    "        'DATE_ERECTED',\n",
    "        'EFFECTIVE_DATE',\n",
    "        'YR_REMODEL',\n",
    "        'YR_ROLL',\n",
    "        'APPRAISED_BY',\n",
    "        'APPRAISED_DATE',\n",
    "        'NOTE',\n",
    "        'IMPR_SQ_FT',\n",
    "        'ACTUAL_AREA',\n",
    "        'HEAT_AREA',\n",
    "        'GROSS_AREA',\n",
    "        'EFFECTIVE_AREA',\n",
    "        'BASE_AREA',\n",
    "        'PERIMETER',\n",
    "        'PERCENT_COMPLETE',\n",
    "        'NBDH_FACTOR',\n",
    "        'RCNLD',\n",
    "        'SIZE_INDEX',\n",
    "        'LUMP_SUM_ADJ'\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "jur = pd.read_csv(\n",
    "    \"input/Real_jur_exempt/jur_value.txt\",\n",
    "    delimiter=\"\\t\",\n",
    "    dtype={\"ACCOUNT\": str, 'TAX_DISTRICT': str},\n",
    "    names=[\n",
    "        \"ACCOUNT\",\n",
    "        \"TAX_DISTRICT\",\n",
    "        \"TYPE\",\n",
    "        \"PERCENT_IN_DISTRICT\",\n",
    "        \"APPRAISED_VALUE\",\n",
    "        \"TAXABLE_VALUE\"\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "res['ACCOUNT'] = res.ACCOUNT.str.strip()\n",
    "jur['ACCOUNT'] = jur.ACCOUNT.str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "res.to_csv(\"input/building_res.csv\", index=False)\n",
    "jur.to_csv(\"input/jur_value.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parcels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download a map of the parcels where the buildings sit from the same source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  345M  100  345M    0     0  1240k      0  0:04:44  0:04:44 --:--:-- 1281k   3 10.6M    0     0  1292k      0  0:04:33  0:00:08  0:04:25 1505k   0  1372k      0  0:04:17  0:03:08  0:01:09 2216k\n"
     ]
    }
   ],
   "source": [
    "!curl -o input/Parcels.exe http://pdata.hcad.org/GIS/Parcels.exe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flood zones "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the flood zones from FEMA [here](https://msc.fema.gov/portal/advanceSearch#searchresultsanchor)"
   ]
  }
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