01-create-spatial-buffers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapping the places affected by the city's proposal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read raw data from various sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawfiles = {\n",
    "    'homeless': 'la_homeless_facilities.geojson',\n",
    "    'childcare': 'la_city_childcare.geojson',\n",
    "    'parks': 'la_city_county_parks.geojson',\n",
    "    'public_schools': 'lausd_schools_boundaries_clipped.geojson',\n",
    "    'private_schools': 'la_county_private_school_parcels.geojson',\n",
    "    'special_venues': 'la_city_special_venues.geojson'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readprocess(fpath, category):\n",
    "    gdf = gpd.read_file(f'input/polygons/{fpath}')[['geometry']]\n",
    "    gdf['category'] = category\n",
    "    # use a projection in meters for accurate area calculations\n",
    "    gdf.to_crs({'init': 'epsg:3310'}, inplace=True)\n",
    "    return gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "processedshapes = {r: readprocess(rawfiles[r], r) for r in rawfiles}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "alldata = pd.concat(processedshapes.values(), sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:3310'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conversion factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "SQMETERS_TO_SQMI = 2590000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "alldata['area'] = alldata.geometry.area / SQMETERS_TO_SQMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>geometry</th>\n",
       "      <th>category</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((162235.1707873342 -439706.7449111249...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((157825.9805167973 -445239.4206681503...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((159349.7365253346 -449897.639234968,...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((159349.7365253346 -449897.639234968,...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((159601.9190465482 -437824.3984980457...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  category      area\n",
       "0  POLYGON ((162235.1707873342 -439706.7449111249...  homeless  0.000647\n",
       "1  POLYGON ((157825.9805167973 -445239.4206681503...  homeless  0.000216\n",
       "2  POLYGON ((159349.7365253346 -449897.639234968,...  homeless  0.000179\n",
       "3  POLYGON ((159349.7365253346 -449897.639234968,...  homeless  0.000179\n",
       "4  POLYGON ((159601.9190465482 -437824.3984980457...  homeless  0.000238"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c1343e160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alldata.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many of each shape did we bring in and how much area (in square miles) does that category add up to?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>childcare</td>\n",
       "      <td>1161</td>\n",
       "      <td>7.349280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>homeless</td>\n",
       "      <td>36</td>\n",
       "      <td>0.091682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>parks</td>\n",
       "      <td>836</td>\n",
       "      <td>70.899342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>private_schools</td>\n",
       "      <td>842</td>\n",
       "      <td>1.940742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>public_schools</td>\n",
       "      <td>640</td>\n",
       "      <td>8.146147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>special_venues</td>\n",
       "      <td>9</td>\n",
       "      <td>0.691073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count        sum\n",
       "category                         \n",
       "childcare         1161   7.349280\n",
       "homeless            36   0.091682\n",
       "parks              836  70.899342\n",
       "private_schools    842   1.940742\n",
       "public_schools     640   8.146147\n",
       "special_venues       9   0.691073"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.groupby('category')['area'].aggregate(['count','sum'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine the polygons into one shape to account for overlaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adddummyfield(row):\n",
    "    row['category'] = 'x'\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dissolvedshapes = alldata.apply(adddummyfield, axis=1).dissolve('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dissolvedshapes.crs = {'init':'epsg:3310'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>geometry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>x</td>\n",
       "      <td>(POLYGON Z ((162629.5480530739 -451456.3778770...</td>\n",
       "      <td>0.000647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   geometry      area\n",
       "category                                                             \n",
       "x         (POLYGON Z ((162629.5480530739 -451456.3778770...  0.000647"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dissolvedshapes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c133462b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dissolvedshapes.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw the 500-foot buffer around each of these properties (152.4 meters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "alldata['withbuffer'] = alldata.buffer(152.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>geometry</th>\n",
       "      <th>category</th>\n",
       "      <th>area</th>\n",
       "      <th>withbuffer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((162235.1707873342 -439706.7449111249...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>POLYGON ((162338.1895894707 -439821.7115103276...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((157825.9805167973 -445239.4206681503...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>POLYGON ((157978.3442876927 -445236.0978201961...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((159349.7365253346 -449897.639234968,...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>POLYGON ((159353.2306388219 -450049.9991744893...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((159349.7365253346 -449897.639234968,...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>POLYGON ((159353.2306388219 -450049.9991744893...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((159601.9190465482 -437824.3984980457...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>POLYGON ((159671.0848443312 -437688.5977159528...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  category      area  \\\n",
       "0  POLYGON ((162235.1707873342 -439706.7449111249...  homeless  0.000647   \n",
       "1  POLYGON ((157825.9805167973 -445239.4206681503...  homeless  0.000216   \n",
       "2  POLYGON ((159349.7365253346 -449897.639234968,...  homeless  0.000179   \n",
       "3  POLYGON ((159349.7365253346 -449897.639234968,...  homeless  0.000179   \n",
       "4  POLYGON ((159601.9190465482 -437824.3984980457...  homeless  0.000238   \n",
       "\n",
       "                                          withbuffer  \n",
       "0  POLYGON ((162338.1895894707 -439821.7115103276...  \n",
       "1  POLYGON ((157978.3442876927 -445236.0978201961...  \n",
       "2  POLYGON ((159353.2306388219 -450049.9991744893...  \n",
       "3  POLYGON ((159353.2306388219 -450049.9991744893...  \n",
       "4  POLYGON ((159671.0848443312 -437688.5977159528...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c135c49e8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gpd.GeoSeries(alldata['withbuffer']).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine into one layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "allbuffers = gpd.GeoDataFrame(\n",
    "    alldata[['category','withbuffer']].rename(columns={'withbuffer':'geometry'})\n",
    ")\\\n",
    ".apply(adddummyfield,axis=1)\\\n",
    ".dissolve(by='category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "allbuffers.crs = {'init':'epsg:3310'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c13756208>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "allbuffers.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subtract the shape of the dissolved polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "allbuffers['difference'] = allbuffers.apply(lambda x: x['geometry'].difference(dissolvedshapes.geometry[0]), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clip to shape of L.A., to account for buffers that go outside city boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cityofla = gpd.read_file('input/neighborhoods/la-city-boundary.geojson').to_crs({'init': 'epsg:3310'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "allbuffers['clipped'] = allbuffers.apply(lambda x: x['difference'].intersection(cityofla.geometry[0]), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Track how the area changes as we applied each transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>geometry</th>\n",
       "      <th>difference</th>\n",
       "      <th>clipped</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>x</td>\n",
       "      <td>220.439807</td>\n",
       "      <td>136.171638</td>\n",
       "      <td>127.324442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            geometry  difference     clipped\n",
       "category                                    \n",
       "x         220.439807  136.171638  127.324442"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allbuffers.apply(lambda x: gpd.GeoSeries(x).area / SQMETERS_TO_SQMI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalbuffers = gpd.GeoDataFrame(allbuffers[['clipped']].reset_index(drop=True).rename(columns={'clipped':'geometry'}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>(POLYGON Z ((158699.2582505594 -475485.0419580...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry\n",
       "0  (POLYGON Z ((158699.2582505594 -475485.0419580..."
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalbuffers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c13609a58>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "finalbuffers.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read LA Times neighborhood file (with Skid Row delineated in downtown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "hoods = gpd.read_file('input/neighborhoods/custom-la-hoods-skid-row.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:4326'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hoods.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "hoods.to_crs({'init': 'epsg:3310'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Adams-Normandie</td>\n",
       "      <td>(POLYGON ((156139.6401248094 -440624.691527145...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Arleta</td>\n",
       "      <td>(POLYGON ((145361.3096072993 -420096.183241404...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arlington Heights</td>\n",
       "      <td>(POLYGON ((155472.5469032948 -438950.195603739...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Atwater Village</td>\n",
       "      <td>(POLYGON ((158739.6492951898 -427752.278756475...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Baldwin Hills/Crenshaw</td>\n",
       "      <td>(POLYGON ((151801.1076706515 -442096.112350080...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name                                           geometry\n",
       "0         Adams-Normandie  (POLYGON ((156139.6401248094 -440624.691527145...\n",
       "1                  Arleta  (POLYGON ((145361.3096072993 -420096.183241404...\n",
       "2       Arlington Heights  (POLYGON ((155472.5469032948 -438950.195603739...\n",
       "3         Atwater Village  (POLYGON ((158739.6492951898 -427752.278756475...\n",
       "4  Baldwin Hills/Crenshaw  (POLYGON ((151801.1076706515 -442096.112350080..."
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hoods.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hoods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How much area is in the city of L.A.?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "475.6773404706595"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totalcityarea = hoods.geometry.area.sum() / SQMETERS_TO_SQMI\n",
    "totalcityarea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How much total area is in these combined buffers?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127.3244417323887"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totalbufferarea = allbuffers.clipped[0].area / SQMETERS_TO_SQMI\n",
    "totalbufferarea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What percentage of the city is inacessible for sleeping under this proposal?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2676697645643734"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totalbufferarea / totalcityarea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the intersection of each hood with the buffers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = finalbuffers.geometry[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_intersection(row):\n",
    "    row['intersection'] = row['geometry'].intersection(b)\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "hoods = hoods.apply(calculate_intersection, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>intersection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Adams-Normandie</td>\n",
       "      <td>(POLYGON ((156139.6401248094 -440624.691527145...</td>\n",
       "      <td>(POLYGON Z ((156139.6401248094 -440624.6915271...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Arleta</td>\n",
       "      <td>(POLYGON ((145361.3096072993 -420096.183241404...</td>\n",
       "      <td>(POLYGON Z ((144763.5458591036 -420290.2059632...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arlington Heights</td>\n",
       "      <td>(POLYGON ((155472.5469032948 -438950.195603739...</td>\n",
       "      <td>(POLYGON Z ((155472.5469032948 -438950.1956037...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Atwater Village</td>\n",
       "      <td>(POLYGON ((158739.6492951898 -427752.278756475...</td>\n",
       "      <td>(POLYGON Z ((159169.0786837959 -428119.5991611...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Baldwin Hills/Crenshaw</td>\n",
       "      <td>(POLYGON ((151801.1076706515 -442096.112350080...</td>\n",
       "      <td>(POLYGON Z ((151801.1076706515 -442096.1123500...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name                                           geometry  \\\n",
       "0         Adams-Normandie  (POLYGON ((156139.6401248094 -440624.691527145...   \n",
       "1                  Arleta  (POLYGON ((145361.3096072993 -420096.183241404...   \n",
       "2       Arlington Heights  (POLYGON ((155472.5469032948 -438950.195603739...   \n",
       "3         Atwater Village  (POLYGON ((158739.6492951898 -427752.278756475...   \n",
       "4  Baldwin Hills/Crenshaw  (POLYGON ((151801.1076706515 -442096.112350080...   \n",
       "\n",
       "                                        intersection  \n",
       "0  (POLYGON Z ((156139.6401248094 -440624.6915271...  \n",
       "1  (POLYGON Z ((144763.5458591036 -420290.2059632...  \n",
       "2  (POLYGON Z ((155472.5469032948 -438950.1956037...  \n",
       "3  (POLYGON Z ((159169.0786837959 -428119.5991611...  \n",
       "4  (POLYGON Z ((151801.1076706515 -442096.1123500...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate area of neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "hoods['hood_area'] = hoods.geometry.area / SQMETERS_TO_SQMI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate how much area is prohibited in each neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "hoods['buffer_area'] = hoods['intersection'].apply(lambda x: x.area / SQMETERS_TO_SQMI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "hoods['pct_of_hood_in_buffer'] = hoods.buffer_area / hoods.hood_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "hoodsstats = hoods[['name','hood_area','buffer_area','pct_of_hood_in_buffer']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>name</th>\n",
       "      <th>hood_area</th>\n",
       "      <th>buffer_area</th>\n",
       "      <th>pct_of_hood_in_buffer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Cypress Park</td>\n",
       "      <td>0.726685</td>\n",
       "      <td>0.454308</td>\n",
       "      <td>0.625178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>Westlake</td>\n",
       "      <td>2.717338</td>\n",
       "      <td>1.692640</td>\n",
       "      <td>0.622904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>Watts</td>\n",
       "      <td>2.121397</td>\n",
       "      <td>1.248720</td>\n",
       "      <td>0.588631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>Koreatown</td>\n",
       "      <td>2.704202</td>\n",
       "      <td>1.562684</td>\n",
       "      <td>0.577872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>Pico-Union</td>\n",
       "      <td>1.670386</td>\n",
       "      <td>0.944300</td>\n",
       "      <td>0.565318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  hood_area  buffer_area  pct_of_hood_in_buffer\n",
       "21   Cypress Park   0.726685     0.454308               0.625178\n",
       "106      Westlake   2.717338     1.692640               0.622904\n",
       "102         Watts   2.121397     1.248720               0.588631\n",
       "51      Koreatown   2.704202     1.562684               0.577872\n",
       "73     Pico-Union   1.670386     0.944300               0.565318"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hoodsstats.sort_values('pct_of_hood_in_buffer', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>name</th>\n",
       "      <th>hood_area</th>\n",
       "      <th>buffer_area</th>\n",
       "      <th>pct_of_hood_in_buffer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Century City</td>\n",
       "      <td>0.701697</td>\n",
       "      <td>0.081154</td>\n",
       "      <td>0.115654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>Hansen Dam</td>\n",
       "      <td>1.965736</td>\n",
       "      <td>0.220110</td>\n",
       "      <td>0.111973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>West Los Angeles</td>\n",
       "      <td>1.050255</td>\n",
       "      <td>0.114821</td>\n",
       "      <td>0.109326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>Toluca Lake</td>\n",
       "      <td>1.216980</td>\n",
       "      <td>0.122684</td>\n",
       "      <td>0.100810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Griffith Park</td>\n",
       "      <td>5.835412</td>\n",
       "      <td>0.561094</td>\n",
       "      <td>0.096153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name  hood_area  buffer_area  pct_of_hood_in_buffer\n",
       "15       Century City   0.701697     0.081154               0.115654\n",
       "39         Hansen Dam   1.965736     0.220110               0.111973\n",
       "107  West Los Angeles   1.050255     0.114821               0.109326\n",
       "91        Toluca Lake   1.216980     0.122684               0.100810\n",
       "37      Griffith Park   5.835412     0.561094               0.096153"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hoodsstats.sort_values('pct_of_hood_in_buffer', ascending=False).tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a shapefile of the buffers split up by neighborhood, with all associated stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "intersection_gdf = gpd.GeoDataFrame(\n",
    "    hoods[['name','hood_area','buffer_area','pct_of_hood_in_buffer','intersection']],\n",
    "    geometry = 'intersection'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>hood_area</th>\n",
       "      <th>buffer_area</th>\n",
       "      <th>pct_of_hood_in_buffer</th>\n",
       "      <th>intersection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Adams-Normandie</td>\n",
       "      <td>0.804178</td>\n",
       "      <td>0.265064</td>\n",
       "      <td>0.329608</td>\n",
       "      <td>(POLYGON Z ((156139.6401248094 -440624.6915271...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Arleta</td>\n",
       "      <td>3.088396</td>\n",
       "      <td>0.577992</td>\n",
       "      <td>0.187150</td>\n",
       "      <td>(POLYGON Z ((144763.5458591036 -420290.2059632...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arlington Heights</td>\n",
       "      <td>1.029528</td>\n",
       "      <td>0.446194</td>\n",
       "      <td>0.433396</td>\n",
       "      <td>(POLYGON Z ((155472.5469032948 -438950.1956037...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Atwater Village</td>\n",
       "      <td>1.777260</td>\n",
       "      <td>0.510772</td>\n",
       "      <td>0.287393</td>\n",
       "      <td>(POLYGON Z ((159169.0786837959 -428119.5991611...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Baldwin Hills/Crenshaw</td>\n",
       "      <td>2.884810</td>\n",
       "      <td>0.944199</td>\n",
       "      <td>0.327300</td>\n",
       "      <td>(POLYGON Z ((151801.1076706515 -442096.1123500...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name  hood_area  buffer_area  pct_of_hood_in_buffer  \\\n",
       "0         Adams-Normandie   0.804178     0.265064               0.329608   \n",
       "1                  Arleta   3.088396     0.577992               0.187150   \n",
       "2       Arlington Heights   1.029528     0.446194               0.433396   \n",
       "3         Atwater Village   1.777260     0.510772               0.287393   \n",
       "4  Baldwin Hills/Crenshaw   2.884810     0.944199               0.327300   \n",
       "\n",
       "                                        intersection  \n",
       "0  (POLYGON Z ((156139.6401248094 -440624.6915271...  \n",
       "1  (POLYGON Z ((144763.5458591036 -420290.2059632...  \n",
       "2  (POLYGON Z ((155472.5469032948 -438950.1956037...  \n",
       "3  (POLYGON Z ((159169.0786837959 -428119.5991611...  \n",
       "4  (POLYGON Z ((151801.1076706515 -442096.1123500...  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersection_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reproject back to lat/long for output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "alldata.drop('withbuffer', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "intersection_gdf.crs = {'init': 'epsg:3310'}\n",
    "finalbuffers.crs = {'init': 'epsg:3310'}\n",
    "alldata.crs = {'init': 'epsg:3310'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "intersection_gdf.to_crs({'init': 'epsg:4326'}, inplace=True)\n",
    "finalbuffers.to_crs({'init': 'epsg:4326'}, inplace=True)\n",
    "alldata.to_crs({'init': 'epsg:4326'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>name</th>\n",
       "      <th>hood_area</th>\n",
       "      <th>buffer_area</th>\n",
       "      <th>pct_of_hood_in_buffer</th>\n",
       "      <th>intersection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Adams-Normandie</td>\n",
       "      <td>0.804178</td>\n",
       "      <td>0.265064</td>\n",
       "      <td>0.329608</td>\n",
       "      <td>(POLYGON Z ((-118.3090080000001 34.03741099912...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Arleta</td>\n",
       "      <td>3.088396</td>\n",
       "      <td>0.577992</td>\n",
       "      <td>0.187150</td>\n",
       "      <td>(POLYGON Z ((-118.428537804243 34.222443969608...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arlington Heights</td>\n",
       "      <td>1.029528</td>\n",
       "      <td>0.446194</td>\n",
       "      <td>0.433396</td>\n",
       "      <td>(POLYGON Z ((-118.315909 34.05261099912384 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Atwater Village</td>\n",
       "      <td>1.777260</td>\n",
       "      <td>0.510772</td>\n",
       "      <td>0.287393</td>\n",
       "      <td>(POLYGON Z ((-118.2737407388608 34.14962634824...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Baldwin Hills/Crenshaw</td>\n",
       "      <td>2.884810</td>\n",
       "      <td>0.944199</td>\n",
       "      <td>0.327300</td>\n",
       "      <td>(POLYGON Z ((-118.3562610000001 34.02483599912...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name  hood_area  buffer_area  pct_of_hood_in_buffer  \\\n",
       "0         Adams-Normandie   0.804178     0.265064               0.329608   \n",
       "1                  Arleta   3.088396     0.577992               0.187150   \n",
       "2       Arlington Heights   1.029528     0.446194               0.433396   \n",
       "3         Atwater Village   1.777260     0.510772               0.287393   \n",
       "4  Baldwin Hills/Crenshaw   2.884810     0.944199               0.327300   \n",
       "\n",
       "                                        intersection  \n",
       "0  (POLYGON Z ((-118.3090080000001 34.03741099912...  \n",
       "1  (POLYGON Z ((-118.428537804243 34.222443969608...  \n",
       "2  (POLYGON Z ((-118.315909 34.05261099912384 0, ...  \n",
       "3  (POLYGON Z ((-118.2737407388608 34.14962634824...  \n",
       "4  (POLYGON Z ((-118.3562610000001 34.02483599912...  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersection_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>(POLYGON Z ((-118.2881095246955 33.72272776229...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry\n",
       "0  (POLYGON Z ((-118.2881095246955 33.72272776229..."
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalbuffers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>geometry</th>\n",
       "      <th>category</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-118.2428240143944 34.04468561560458...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-118.2916493156461 33.99554268659588...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((-118.2760761217427 33.95330442315542...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-118.2760761217427 33.95330442315542...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((-118.2709667281731 34.06208810607586...</td>\n",
       "      <td>homeless</td>\n",
       "      <td>0.000238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  category      area\n",
       "0  POLYGON ((-118.2428240143944 34.04468561560458...  homeless  0.000647\n",
       "1  POLYGON ((-118.2916493156461 33.99554268659588...  homeless  0.000216\n",
       "2  POLYGON ((-118.2760761217427 33.95330442315542...  homeless  0.000179\n",
       "3  POLYGON ((-118.2760761217427 33.95330442315542...  homeless  0.000179\n",
       "4  POLYGON ((-118.2709667281731 34.06208810607586...  homeless  0.000238"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export spatial files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalbuffers.to_file('output/buffers/combined-buffers.geojson', driver='GeoJSON')\n",
    "intersection_gdf.to_file('output/buffers/buffers-by-hood.geojson', driver='GeoJSON')\n",
    "alldata.to_file('output/buffers/all-proposal-properties.geojson', driver='GeoJSON')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export neighborhood data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "hoodsstats\\\n",
    ".sort_values('pct_of_hood_in_buffer', ascending=False)\\\n",
    ".to_csv('output/buffers/buffers-by-hood-breakdown.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}