Accessbility_CRP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peartree version: 0.6.1 \n",
      "networkx version: 2.2 \n",
      "matplotlib version: 3.0.2 \n",
      "osmnx version: 0.9 \n"
     ]
    }
   ],
   "source": [
    "import importlib\n",
    "import sys,os,math,time\n",
    "\n",
    "import networkx as nx\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import osmnx as ox\n",
    "import pandas as pd\n",
    "import rasterio\n",
    "import pylab as plt\n",
    "import json\n",
    "import datetime\n",
    "import pycrs\n",
    "import seaborn as sns\n",
    "from glob import glob\n",
    "from rasterio.plot import show, show_hist\n",
    "from rasterio.mask import mask\n",
    "from shapely.geometry import Point\n",
    "from shapely.geometry import LineString\n",
    "sys.path.append('GOSTNets')\n",
    "\n",
    "import GOSTnet as gn\n",
    "from pandana.loaders import osm;\n",
    "\n",
    "from osgeo import gdal, gdalconst, gdal_array\n",
    "from rasterstats import point_query, zonal_stats\n",
    "from make_fishnet import make_fishnet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_amenities(string, tag):\n",
    "    \n",
    "    if tag == 'healthcare':\n",
    "        amenities = ['hospital', 'doctor', 'healthcare', 'clinic']\n",
    "    elif tag=='schools':\n",
    "        amenities = ['school', 'university', 'college']\n",
    "    else:\n",
    "        print('Invalid tag. Enter either healthcare or schools.')\n",
    "\n",
    "\n",
    "    osm_tags = '\"amenity\"~\"{}\"'.format('|'.join(amenities))\n",
    "\n",
    "    #pois = osm.node_query(coords[0],coords[1],coords[2],coords[3],tags=osm_tags) # required: lat_min, lng_min, lat_max, lng_max\n",
    "    \n",
    "    poly = get_polygon(string) ### minx\tminy\tmaxx\tmaxy\n",
    "    \n",
    "    extent = poly.bounds\n",
    "    try:\n",
    "        pois = osm.node_query(extent[1], extent[0], extent[3], extent[2], tags=osm_tags)\n",
    "\n",
    "        pois = pois[pois['amenity'].isin(amenities)]\n",
    "\n",
    "        try:\n",
    "            osm_data = pois[['amenity', 'lat', 'lon', 'name', 'name:en']]\n",
    "        except:\n",
    "            osm_data = pois[['amenity', 'lat', 'lon', 'name']]\n",
    "\n",
    "\n",
    "        osm_data['geometry'] = (list(zip(osm_data.lon,osm_data.lat)))\n",
    "        osm_data['geometry'] = osm_data.geometry.apply(lambda x: Point(x))\n",
    "        osm_data = gpd.GeoDataFrame(osm_data, geometry='geometry')\n",
    "\n",
    "        osm_data.crs = {'init':'epsg:4326'}\n",
    "        string = str(string)\n",
    "        print('Saving OSM data to shapefile at path data/{}'.format(string))\n",
    "    #osm_data.to_file('data/{}/{}_{}.shp'.format(string, string, tag))\n",
    "    #except HTTPError:\n",
    "    #    osm_data = gpd.read_file('data/{}/{}_{}.shp'.format(string, string, tag))\n",
    "    except:\n",
    "        print('Error occured while fetching OSM data')\n",
    "        osm_data = None\n",
    "        pass\n",
    "    \n",
    "    return osm_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_polygon(string):\n",
    "    \n",
    "    lis = glob('/nfshome/gb1877/gb1877/WorldBank/CRP/Betweenness_Centrality/data/AOI/*/*.shp')\n",
    "    \n",
    "    city = []\n",
    "    for i in lis:\n",
    "        x = i.split('/')[-2]\n",
    "        city.append(x.split('_')[0])\n",
    "        \n",
    "    if string in city:\n",
    "        l = glob('/nfshome/gb1877/gb1877/WorldBank/CRP/Betweenness_Centrality/data/AOI/{}_AOI/*.shp'.format(string))\n",
    "        adm = gpd.GeoDataFrame.from_file(l[0])\n",
    "        boundary_poly = adm.geometry.values[0]\n",
    "    else:\n",
    "        boundary_GDF = ox.gdf_from_place('{}'.format(string),which_result=1)\n",
    "        boundary_poly = boundary_GDF.loc[0,'geometry']\n",
    "        if boundary_poly.geom_type == 'Polygon':\n",
    "            boundary_poly = boundary_poly\n",
    "        else:\n",
    "            try:\n",
    "                boundary_GDF = ox.gdf_from_place('{}'.format(string),which_result=2)\n",
    "                boundary_poly = boundary_GDF.loc[0,'geometry']\n",
    "            except:\n",
    "                print('Polygon not available')\n",
    "                boundary_poly =  -1\n",
    "    \n",
    "    return boundary_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_graph(string, boundary_GDF, network_type, target_crs='epsg:3395'):\n",
    "    \n",
    "    print('Fetching graph data for {}'.format(string))\n",
    "\n",
    "    G = ox.graph_from_polygon(get_polygon(string), network_type = network_type)\n",
    "    #G = ox.project_graph(G, to_crs = {'init': '{}'.format(target_crs)})\n",
    "    #G_project = ox.project_graph(G_drive, to_crs = {'init':'epsg:28411'} )\n",
    "    node_dict = {}\n",
    "\n",
    "    G2 = G.copy()\n",
    "\n",
    "    for u, data in G2.nodes(data = True):\n",
    "        node_dict[u] = Point(data['x'],data['y'])\n",
    "\n",
    "    for u, v, data in G2.edges(data = True):\n",
    "\n",
    "        if 'Wkt' not in data.keys():\n",
    "            strt = node_dict[u]\n",
    "            fini = node_dict[v]\n",
    "            data['Wkt'] = LineString([strt, fini])\n",
    "\n",
    "        if 'geometry' in data.keys():\n",
    "            data['Wkt'] = data['geometry']\n",
    "            data.pop('geometry')\n",
    "            \n",
    "    importlib.reload(gn)\n",
    "    G3 = gn.salt_long_lines(G2,\n",
    "                           source = 'epsg:4326',\n",
    "                          target = target_crs,\n",
    "                          thresh = 50,\n",
    "                          factor = 1)\n",
    "    \n",
    "    \n",
    "    return G3\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_time(t1, t2):\n",
    "    \n",
    "    diff = t2 - t1\n",
    "    \n",
    "    c = round(diff.total_seconds() / 60, 2)\n",
    "    \n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_crs(string):\n",
    "    \n",
    "    crs_dict = {\n",
    "    'Coron' : 'epsg:25391', 'Puerto Princesa': 'epsg:25391', 'El Nido' : 'epsg:25391', 'Davao City' : 'epsg:25391', \n",
    "    'Tashkent' : 'epsg:28411', 'Hebron' : 'epsg:2039', 'Bethlehem' : 'epsg:2039', 'Lima' : 'epsg:24891', 'Vinh Long' : 'epsg:2045', \n",
    "    'Khanh Hoa' : 'epsg:2045', 'Osh' : 'epsg:7685', 'Monrovia': 'epsg:2137'\n",
    "    }\n",
    "    \n",
    "    return crs_dict['{}'.format(string)]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_accessibility(place, grid_size=100):\n",
    "        \n",
    "    t1= datetime.datetime.now()\n",
    "    string = place.split(',')[0]\n",
    "\n",
    "    poly = get_polygon(string)\n",
    "\n",
    "    if poly == -1:\n",
    "        boundary_GDF = ox.gdf_from_place('{}'.format(string),which_result=2)\n",
    "        xmin = boundary_GDF.bbox_west[0]\n",
    "        xmax = boundary_GDF.bbox_east[0]\n",
    "        ymin = boundary_GDF.bbox_south[0]\n",
    "        ymax = boundary_GDF.bbox_north[0]\n",
    "    else:\n",
    "        bounds = poly.bounds\n",
    "        xmin = bounds[0]\n",
    "        xmax = bounds[2]\n",
    "        ymin = bounds[1]\n",
    "        ymax = bounds[3]\n",
    "    \n",
    "    boundary_GDF = ox.gdf_from_place('{}'.format(string),which_result=1)\n",
    "    \n",
    "    osm_schools = get_amenities(string, 'schools')\n",
    "    \n",
    "    osm_hosp = get_amenities(string, 'healthcare')\n",
    "\n",
    "    t2 = datetime.datetime.now()\n",
    "    \n",
    "    print('{} minutes elapsed!'.format(get_time(t1, t2)))\n",
    "    #### For drive network\n",
    "    \n",
    "    crs = get_crs(string)\n",
    "\n",
    "    G_drive = get_graph(string,boundary_GDF, 'drive', target_crs= crs)\n",
    "    \n",
    "    t3 = datetime.datetime.now()\n",
    "    \n",
    "    print('{} minutes elapsed!'.format(get_time(t1, t3)))\n",
    "    print('Snapping OSM data to drive network')\n",
    "    \n",
    "    osm_schools.set_geometry(col='geometry', crs=crs, inplace=True)\n",
    "    osm_hosp.set_geometry(col='geometry', crs=crs, inplace=True)\n",
    "    \n",
    "    schools = gn.pandana_snap(G_drive, osm_schools, target_crs=crs, add_dist_to_node_col=True)\n",
    "    \n",
    "    health = gn.pandana_snap(G_drive, osm_hosp,target_crs=crs,  add_dist_to_node_col=True)\n",
    "    \n",
    "    #node_gdf_drive = ox.graph_to_gdfs(G_drive, nodes=True, edges=False)\n",
    "    #node_gdf_drive = gn.pandana_snap(G_drive, node_gdf_drive)\n",
    "    \n",
    "    t4 = datetime.datetime.now()\n",
    "    \n",
    "    print('{} minutes elapsed!'.format(get_time(t1, t4)))\n",
    "    \n",
    "    fail_value = 9999\n",
    "    \n",
    "    print('Creating fishnet')\n",
    "    make_fishnet('data/{a}/{b}_{c}_grid.shp'.format(a=string,b=string, c='fishnet'),xmin, xmax, ymin, ymax,grid_size,grid_size) \n",
    "    # takes xmin, xmax, ymin, ymax   \n",
    "    \n",
    "    \n",
    "    grid=gpd.read_file('data/{a}/{b}_{c}_grid.shp'.format(a=string,b=string, c='fishnet'))\n",
    "    \n",
    "    grid['centroid'] = grid.geometry.centroid\n",
    "    grid_graph = grid[['FID', 'centroid']]\n",
    "    grid_graph.rename(columns = {'centroid':'geometry'}, inplace=True)\n",
    "    grid_graph = gpd.GeoDataFrame(grid_graph)\n",
    "    grid_graph.set_geometry(col='geometry', crs=crs, inplace=True)\n",
    "    \n",
    "    grid_graph = gn.pandana_snap(G_drive, grid_graph,target_crs=crs,  add_dist_to_node_col=True)\n",
    "    \n",
    "    t5 = datetime.datetime.now()\n",
    "    \n",
    "    print('{} minutes elapsed!'.format(get_time(t1, t5)))\n",
    "    \n",
    "    \n",
    "    importlib.reload(gn)\n",
    "    print('Calculating OD matrix for drive network')\n",
    "    #schools_OD_drive = gn.calculate_OD(G=G_drive, origins=list(node_gdf_drive.NN), destinations = list(schools.NN), \n",
    "    #                             fail_value = fail_value, weight='time')\n",
    "    \n",
    "    #health_OD_drive = gn.calculate_OD(G=G_drive, origins=list(node_gdf_drive.NN), destinations = list(health.NN), \n",
    "    #                         fail_value = fail_value, weight='time')\n",
    "    \n",
    "    schools_OD_drive = gn.calculate_OD(G=G_drive, origins=list(grid_graph.NN), destinations = list(schools.NN), \n",
    "                                 fail_value = fail_value, weight='distance')\n",
    "    \n",
    "    health_OD_drive = gn.calculate_OD(G=G_drive, origins=list(grid_graph.NN), destinations = list(health.NN), \n",
    "                             fail_value = fail_value, weight='distance')\n",
    "    \n",
    "    #node_gdf_drive['distance_school'] = [schools_OD_drive[i].min() for i in range(schools_OD_drive.shape[0])]\n",
    "    #node_gdf_drive['distance_hospital'] = [health_OD_drive[i].min() for i in range(health_OD_drive.shape[0])]\n",
    "    \n",
    "    grid_graph['school_drive'] = [schools_OD_drive[i].min() for i in range(schools_OD_drive.shape[0])]\n",
    "    grid_graph['hosp_drive'] = [health_OD_drive[i].min() for i in range(health_OD_drive.shape[0])]\n",
    "    \n",
    "    grid['school_trans'] = grid_graph['school_drive']\n",
    "    grid['hosp_trans'] = grid_graph['hosp_drive']\n",
    "    grid['NN_dist'] = grid_graph.NN_dist\n",
    "    grid['total_school'] = grid_graph.school_drive + grid_graph.NN_dist\n",
    "    grid['total_hosp'] = grid_graph.hosp_drive + grid_graph.NN_dist\n",
    "    \n",
    "    \n",
    "    \n",
    "    grid.drop(columns=['centroid'], inplace=True)\n",
    "    #grid.crs = {'init':'epsg:4326'}\n",
    "    grid = grid[grid.total_school != 9999.0]\n",
    "    print('Writing output file!!')\n",
    "    grid.to_file('data/{}/Access_School_Hosp_{}_{}.shp'.format(string, string, grid_size))\n",
    "    print('Done!!!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/ipykernel_launcher.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving OSM data to shapefile at path data/Tashkent\n",
      "Saving OSM data to shapefile at path data/Tashkent\n",
      "0.36 minutes elapsed!\n",
      "Fetching graph data for Tashkent\n",
      "peartree version: 0.6.1 \n",
      "networkx version: 2.2 \n",
      "matplotlib version: 3.0.2 \n",
      "osmnx version: 0.9 \n",
      "WARNING: \"factor behavior has changed! now divides rather than multiplies. This change brings gn.salt_long_lines into line with gn.convert_network_to_time\" \n",
      "Identified 17107 unique edge(s) longer than 50. \n",
      "Beginning new node creation...\n",
      "137912 new edges added and 29840 removed to bring total edges to 145276\n",
      "51849 new nodes added to bring total nodes to 66628\n",
      "1.82 minutes elapsed!\n",
      "Snapping OSM data to drive network\n",
      "2.17 minutes elapsed!\n",
      "Creating fishnet\n",
      "3.14 minutes elapsed!\n",
      "peartree version: 0.6.1 \n",
      "networkx version: 2.2 \n",
      "matplotlib version: 3.0.2 \n",
      "osmnx version: 0.9 \n",
      "Calculating OD matrix for drive network\n",
      "print origins type\n",
      "<class 'list'>\n",
      "weighted_origins equals false\n",
      "print origins type\n",
      "<class 'list'>\n",
      "weighted_origins equals false\n",
      "Writing output file!!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!!!\n"
     ]
    }
   ],
   "source": [
    "get_accessibility('Tashkent, Uzbekistan', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "priority_list = [\n",
    "    'Coron, Philippines', 'Puerto Princesa, Philippines', 'El Nido, Philippines', 'Davao City, Philippines', \n",
    "    'Tashkent, Uzbekistan' 'Hebron, West Bank', 'Bethlehem, West Bank', 'Lima, Peru', 'Vinh Long, Vietnam', \n",
    "    'Khanh Hoa, Vietnam', 'Osh, Kyrgyzstan', 'Monrovia, Liberia'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/ipykernel_launcher.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving OSM data to shapefile at path data/Bethlehem\n",
      "Saving OSM data to shapefile at path data/Bethlehem\n",
      "0.06 minutes elapsed!\n",
      "Fetching graph data for Bethlehem\n",
      "peartree version: 0.6.1 \n",
      "networkx version: 2.2 \n",
      "matplotlib version: 3.0.2 \n",
      "osmnx version: 0.9 \n",
      "WARNING: \"factor behavior has changed! now divides rather than multiplies. This change brings gn.salt_long_lines into line with gn.convert_network_to_time\" \n",
      "Identified 3242 unique edge(s) longer than 50. \n",
      "Beginning new node creation...\n",
      "32482 new edges added and 6181 removed to bring total edges to 34614\n",
      "12999 new nodes added to bring total nodes to 16652\n",
      "0.45 minutes elapsed!\n",
      "Snapping OSM data to drive network\n",
      "0.54 minutes elapsed!\n",
      "Creating fishnet\n",
      "0.77 minutes elapsed!\n",
      "peartree version: 0.6.1 \n",
      "networkx version: 2.2 \n",
      "matplotlib version: 3.0.2 \n",
      "osmnx version: 0.9 \n",
      "Calculating OD matrix for drive network\n",
      "print origins type\n",
      "<class 'list'>\n",
      "weighted_origins equals false\n",
      "print origins type\n",
      "<class 'list'>\n",
      "weighted_origins equals false\n",
      "Writing output file!!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!!!\n"
     ]
    }
   ],
   "source": [
    "get_accessibility('Bethlehem, West Bank', 250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "shp2 = gpd.GeoDataFrame.from_file('data/Bethlehem/Access_School_Hosp_Bethlehem_250.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "shp_school = gpd.GeoDataFrame.from_file('data/Bethlehem/Bethlehem_schools.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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       "      <th>FID</th>\n",
       "      <th>school_tra</th>\n",
       "      <th>hosp_trans</th>\n",
       "      <th>NN_dist</th>\n",
       "      <th>total_scho</th>\n",
       "      <th>total_hosp</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4502.123484</td>\n",
       "      <td>4542.123484</td>\n",
       "      <td>4578.123484</td>\n",
       "      <td>POLYGON ((35.08135986328136 31.74173355102539,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>4277.701598</td>\n",
       "      <td>4319.701598</td>\n",
       "      <td>4348.701598</td>\n",
       "      <td>POLYGON ((35.08135986328136 31.73910067059424,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>48.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4040.891024</td>\n",
       "      <td>4088.891024</td>\n",
       "      <td>4117.891024</td>\n",
       "      <td>POLYGON ((35.08135986328136 31.73646779016309,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>3802.793069</td>\n",
       "      <td>3850.793069</td>\n",
       "      <td>3879.793069</td>\n",
       "      <td>POLYGON ((35.08135986328136 31.73383490973194,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>3572.692517</td>\n",
       "      <td>3620.692517</td>\n",
       "      <td>3649.692517</td>\n",
       "      <td>POLYGON ((35.08135986328136 31.73120202930079,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FID  school_tra  hosp_trans      NN_dist   total_scho   total_hosp  \\\n",
       "0    0        40.0        76.0  4502.123484  4542.123484  4578.123484   \n",
       "1    1        42.0        71.0  4277.701598  4319.701598  4348.701598   \n",
       "2    2        48.0        77.0  4040.891024  4088.891024  4117.891024   \n",
       "3    3        48.0        77.0  3802.793069  3850.793069  3879.793069   \n",
       "4    4        48.0        77.0  3572.692517  3620.692517  3649.692517   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((35.08135986328136 31.74173355102539,...  \n",
       "1  POLYGON ((35.08135986328136 31.73910067059424,...  \n",
       "2  POLYGON ((35.08135986328136 31.73646779016309,...  \n",
       "3  POLYGON ((35.08135986328136 31.73383490973194,...  \n",
       "4  POLYGON ((35.08135986328136 31.73120202930079,...  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'shp_school' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-1143d65d0efb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshp2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'total_scho'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'viridis'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mshp_school\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'shp_school' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = shp2.plot(column='total_scho', cmap='viridis',figsize=[10,10] )\n",
    "shp_school.plot(color='r', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving OSM data to shapefile at path data/Coron\n",
      "Saving OSM data to shapefile at path data/Coron\n",
      "0.04 minutes elapsed!\n",
      "Fetching graph data for Coron\n",
      "peartree version: 0.6.1 \n",
      "networkx version: 2.2 \n",
      "matplotlib version: 3.0.2 \n",
      "osmnx version: 0.9 \n",
      "WARNING: \"factor behavior has changed! now divides rather than multiplies. This change brings gn.salt_long_lines into line with gn.convert_network_to_time\" \n",
      "Identified 101 unique edge(s) longer than 50. \n",
      "Beginning new node creation...\n",
      "800 new edges added and 199 removed to bring total edges to 823\n",
      "299 new nodes added to bring total nodes to 392\n",
      "0.08 minutes elapsed!\n",
      "Snapping OSM data to drive network\n",
      "0.09 minutes elapsed!\n",
      "Creating fishnet\n",
      "0.1 minutes elapsed!\n",
      "peartree version: 0.6.1 \n",
      "networkx version: 2.2 \n",
      "matplotlib version: 3.0.2 \n",
      "osmnx version: 0.9 \n",
      "Calculating OD matrix for drive network\n",
      "print origins type\n",
      "<class 'list'>\n",
      "weighted_origins equals false\n",
      "print origins type\n",
      "<class 'list'>\n",
      "weighted_origins equals false\n",
      "Writing output file!!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!!!\n"
     ]
    }
   ],
   "source": [
    "get_accessibility('Coron, Philippines', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "shp1 = gpd.GeoDataFrame.from_file('data/Coron/Access_School_Hosp_Coron_100.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = shp1.plot(column='total_scho', cmap='viridis',figsize=[10,10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.single_source_dijkstra_path_length?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "string = 'Bethlehem'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/ipykernel_launcher.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving OSM data to shapefile at path data/Bethlehem\n",
      "Saving OSM data to shapefile at path data/Bethlehem\n",
      "Fetching graph data for Bethlehem\n",
      "peartree version: 0.6.1 \n",
      "networkx version: 2.2 \n",
      "matplotlib version: 3.0.2 \n",
      "osmnx version: 0.9 \n",
      "WARNING: \"factor behavior has changed! now divides rather than multiplies. This change brings gn.salt_long_lines into line with gn.convert_network_to_time\" \n",
      "Identified 3242 unique edge(s) longer than 50. \n",
      "Beginning new node creation...\n",
      "32482 new edges added and 6181 removed to bring total edges to 34614\n",
      "12999 new nodes added to bring total nodes to 16652\n"
     ]
    }
   ],
   "source": [
    "poly = get_polygon(string)\n",
    "\n",
    "if poly == -1:\n",
    "    boundary_GDF = ox.gdf_from_place('{}'.format(string),which_result=2)\n",
    "    xmin = boundary_GDF.bbox_west[0]\n",
    "    xmax = boundary_GDF.bbox_east[0]\n",
    "    ymin = boundary_GDF.bbox_south[0]\n",
    "    ymax = boundary_GDF.bbox_north[0]\n",
    "else:\n",
    "    bounds = poly.bounds\n",
    "    xmin = bounds[0]\n",
    "    xmax = bounds[2]\n",
    "    ymin = bounds[1]\n",
    "    ymax = bounds[3]\n",
    "\n",
    "boundary_GDF = ox.gdf_from_place('{}'.format(string),which_result=1)\n",
    "\n",
    "osm_schools = get_amenities(string, 'schools')\n",
    "osm_schools.to_crs({'init': 'epsg:3857'})\n",
    "osm_hosp = get_amenities(string, 'healthcare')\n",
    "osm_hosp.to_crs({'init': 'epsg:3857'})\n",
    "\n",
    "crs = get_crs(string)\n",
    "\n",
    "G_drive = get_graph(string,boundary_GDF, 'drive', target_crs= crs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 835.298x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 835.298x432 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7f33134c4d68>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ox.plot_graph(G_drive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_ = ox.project_graph(G_drive, to_crs = {'init':'epsg:3857'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = ox.graph_to_gdfs(G_drive, nodes=True, edges=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:4326'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating fishnet\n"
     ]
    }
   ],
   "source": [
    "crs = 'epsg:2039'\n",
    "\n",
    "schools = gn.pandana_snap(G_drive, osm_schools,target_crs=crs, add_dist_to_node_col=True)\n",
    "\n",
    "health = gn.pandana_snap(G_drive, osm_hosp,target_crs=crs, add_dist_to_node_col=True)\n",
    "\n",
    "\n",
    "fail_value = 9999\n",
    "\n",
    "print('Creating fishnet')\n",
    "\n",
    "\n",
    "grid=gpd.read_file('data/{a}/{b}_{c}_grid.shp'.format(a=string,b=string, c='fishnet'))\n",
    "\n",
    "grid['centroid'] = grid.geometry.centroid\n",
    "grid_graph = grid[['FID', 'centroid']]\n",
    "grid_graph.rename(columns = {'centroid':'geometry'}, inplace=True)\n",
    "grid_graph = gpd.GeoDataFrame(grid_graph)\n",
    "grid_graph.set_geometry(col='geometry', crs={'init':'epsg:3857'}, inplace=True)\n",
    "\n",
    "grid_graph = gn.pandana_snap(G_drive, grid_graph,target_crs=crs,  add_dist_to_node_col=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:4326'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>amenity</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>name</th>\n",
       "      <th>name:en</th>\n",
       "      <th>geometry</th>\n",
       "      <th>NN</th>\n",
       "      <th>NN_dist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>431623078</th>\n",
       "      <td>school</td>\n",
       "      <td>31.712851</td>\n",
       "      <td>35.210799</td>\n",
       "      <td>مدرسة الأخاء الأسلامية</td>\n",
       "      <td>Al Ekha'a Islamic School</td>\n",
       "      <td>POINT (35.2107993 31.7128506)</td>\n",
       "      <td>431630018_1024_4874</td>\n",
       "      <td>92.164319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431623270</th>\n",
       "      <td>school</td>\n",
       "      <td>31.711804</td>\n",
       "      <td>35.191837</td>\n",
       "      <td>مدرسة مار افرام السرياني</td>\n",
       "      <td>St. Afram School</td>\n",
       "      <td>POINT (35.1918374 31.7118041)</td>\n",
       "      <td>506889648</td>\n",
       "      <td>27.271029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431623819</th>\n",
       "      <td>school</td>\n",
       "      <td>31.706188</td>\n",
       "      <td>35.218765</td>\n",
       "      <td>مدرسة بيت ساحور للبنين</td>\n",
       "      <td>Beit Sahour School for Boys</td>\n",
       "      <td>POINT (35.2187651 31.7061879)</td>\n",
       "      <td>431623813_1978_9975</td>\n",
       "      <td>56.628116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431623961</th>\n",
       "      <td>school</td>\n",
       "      <td>31.702420</td>\n",
       "      <td>35.219939</td>\n",
       "      <td>المدرسة الإنجيلية اللوثرية</td>\n",
       "      <td>Evangelical Lutheran School</td>\n",
       "      <td>POINT (35.2199389 31.7024198)</td>\n",
       "      <td>1241878510_2126_10823</td>\n",
       "      <td>21.879470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431624343</th>\n",
       "      <td>school</td>\n",
       "      <td>31.713573</td>\n",
       "      <td>35.190709</td>\n",
       "      <td>Latin Patriarchate School (Fransees Building)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (35.190709 31.713573)</td>\n",
       "      <td>431624299_2153_10953</td>\n",
       "      <td>14.675063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          amenity        lat        lon  \\\n",
       "id                                        \n",
       "431623078  school  31.712851  35.210799   \n",
       "431623270  school  31.711804  35.191837   \n",
       "431623819  school  31.706188  35.218765   \n",
       "431623961  school  31.702420  35.219939   \n",
       "431624343  school  31.713573  35.190709   \n",
       "\n",
       "                                                    name  \\\n",
       "id                                                         \n",
       "431623078                         مدرسة الأخاء الأسلامية   \n",
       "431623270                       مدرسة مار افرام السرياني   \n",
       "431623819                         مدرسة بيت ساحور للبنين   \n",
       "431623961                     المدرسة الإنجيلية اللوثرية   \n",
       "431624343  Latin Patriarchate School (Fransees Building)   \n",
       "\n",
       "                               name:en                       geometry  \\\n",
       "id                                                                      \n",
       "431623078     Al Ekha'a Islamic School  POINT (35.2107993 31.7128506)   \n",
       "431623270             St. Afram School  POINT (35.1918374 31.7118041)   \n",
       "431623819  Beit Sahour School for Boys  POINT (35.2187651 31.7061879)   \n",
       "431623961  Evangelical Lutheran School  POINT (35.2199389 31.7024198)   \n",
       "431624343                          NaN    POINT (35.190709 31.713573)   \n",
       "\n",
       "                              NN    NN_dist  \n",
       "id                                           \n",
       "431623078    431630018_1024_4874  92.164319  \n",
       "431623270              506889648  27.271029  \n",
       "431623819    431623813_1978_9975  56.628116  \n",
       "431623961  1241878510_2126_10823  21.879470  \n",
       "431624343   431624299_2153_10953  14.675063  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3317892160>"
      ]
     },
     "execution_count": 36,
     "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": [
    "schools['NN_dist'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3316b03fd0>"
      ]
     },
     "execution_count": 38,
     "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": [
    "grid_graph['NN_dist'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "print origins type\n",
      "<class 'list'>\n",
      "weighted_origins equals false\n"
     ]
    }
   ],
   "source": [
    "schools_OD_drive = gn.calculate_OD(G=G_drive, origins=list(grid_graph.NN), destinations = list(schools.NN), \n",
    "                                 fail_value = fail_value, weight='distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([256., 203., 283., 282., 203., 289., 223., 222., 235., 257., 442.,\n",
       "       185., 283., 170., 180., 230., 230., 388., 418., 411., 383., 337.,\n",
       "       366., 144.,  56., 336., 402., 453., 334., 304., 208., 427., 431.,\n",
       "       487., 487., 429., 389., 140., 423., 453., 530., 509., 498., 166.,\n",
       "       162., 126., 124., 348., 318., 338., 334., 304., 224., 209., 174.,\n",
       "       284., 583., 583., 572., 588., 583., 583., 583., 556., 583., 582.,\n",
       "       583., 583., 583., 583., 583., 571., 583., 583., 583., 583., 583.,\n",
       "       583., 583., 583., 567., 583., 583., 570., 230., 583., 588., 583.,\n",
       "       583., 583., 583., 583., 583., 583., 583., 583., 583., 583., 583.,\n",
       "       583., 583., 588., 583., 583., 583., 572., 583., 583., 583., 583.,\n",
       "       583., 583., 583., 583., 230., 583., 583., 583., 583., 260., 583.,\n",
       "       583., 583., 583., 583., 583., 583., 583., 583., 583., 583., 583.,\n",
       "       260., 583., 583., 583., 583., 583., 583., 583., 583., 588., 583.,\n",
       "       583., 571., 583., 583., 583., 583., 583., 588., 571., 230., 583.,\n",
       "       583., 583., 583., 583., 330., 451., 202., 381., 247., 276., 272.,\n",
       "       240., 245., 228., 583., 583., 214., 583., 583., 249., 276., 281.,\n",
       "       282., 293., 306., 583., 317., 200., 234., 264., 282., 294., 250.,\n",
       "       583., 583., 583., 583., 583., 583., 583., 583., 583., 152., 144.,\n",
       "       144., 583., 177., 213., 213., 250., 583., 250., 583., 583., 230.,\n",
       "       518., 583., 436., 279., 466., 570.,  80.,  68.,  54.,  53.,  63.,\n",
       "        42.,  42.,  40.,  40.,  40.,  62.,  69.,  71.,  73.,  91.,  85.,\n",
       "        65., 222., 226.,  88.,  68., 225., 216., 583., 583., 583., 583.,\n",
       "       583., 582., 583., 583., 583., 583., 583., 583., 583., 583., 249.,\n",
       "       230., 475., 583., 583., 583., 583., 583.])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools_OD_drive[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "origins=list(grid_graph.NN)\n",
    "destinations = list(schools.NN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(260, 20776)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(origins), len(destinations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "if len(origins) > len(destinations):\n",
    "    flip = 1\n",
    "    o_2 = destinations\n",
    "    destinations = origins\n",
    "    origins = o_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "OD = np.zeros((len(origins), len(destinations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OD[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dict = nx.single_source_dijkstra_path_length(G_, origins[0], cutoff = None, weight = 'distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'431630018_1024_4874' in G_.node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34614"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G_.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.single_source_dijkstra_path_length?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'431630018_1024_4866'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-56-47289d960167>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mG_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'431630018_1024_4874'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'431630018_1024_4866'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.conda/envs/osm2/lib/python3.6/site-packages/networkx/classes/reportviews.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m   1105\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m         \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1107\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_adjdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1109\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbunch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '431630018_1024_4866'"
     ]
    }
   ],
   "source": [
    "G_.edges['431630018_1024_4874', '431630018_1024_4866', 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge = ox.graph_to_gdfs(G_, nodes=False, edges=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        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>Wkt</th>\n",
       "      <th>geometry</th>\n",
       "      <th>highway</th>\n",
       "      <th>junction</th>\n",
       "      <th>key</th>\n",
       "      <th>lanes</th>\n",
       "      <th>length</th>\n",
       "      <th>maxspeed</th>\n",
       "      <th>name</th>\n",
       "      <th>oneway</th>\n",
       "      <th>osmid</th>\n",
       "      <th>ref</th>\n",
       "      <th>service</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LINESTRING (35.14552669750174 31.6593564985731...</td>\n",
       "      <td>LINESTRING (3912382.135905401 3718678.46617900...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <td>49.000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3859570691</td>\n",
       "      <td>3859570691_1_0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LINESTRING (35.10951429750111 31.7021213985731...</td>\n",
       "      <td>LINESTRING (3908373.253875158 3724272.63286204...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>726794247</td>\n",
       "      <td>726794247_2_4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LINESTRING (35.10951429750111 31.7021213985731...</td>\n",
       "      <td>LINESTRING (3908373.253875158 3724272.63286204...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>726794247</td>\n",
       "      <td>726794247_3_18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LINESTRING (35.10951429750111 31.7021213985731...</td>\n",
       "      <td>LINESTRING (3908373.253875158 3724272.63286204...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>726794247</td>\n",
       "      <td>726794247_4_23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LINESTRING (35.1880059 31.6983643, 35.1884836 ...</td>\n",
       "      <td>LINESTRING (3917110.898818707 3723781.05541410...</td>\n",
       "      <td>secondary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>כביש 60 הישן</td>\n",
       "      <td>False</td>\n",
       "      <td>384338945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>525795337</td>\n",
       "      <td>431626371</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Wkt  \\\n",
       "0  LINESTRING (35.14552669750174 31.6593564985731...   \n",
       "1  LINESTRING (35.10951429750111 31.7021213985731...   \n",
       "2  LINESTRING (35.10951429750111 31.7021213985731...   \n",
       "3  LINESTRING (35.10951429750111 31.7021213985731...   \n",
       "4  LINESTRING (35.1880059 31.6983643, 35.1884836 ...   \n",
       "\n",
       "                                            geometry    highway junction  key  \\\n",
       "0  LINESTRING (3912382.135905401 3718678.46617900...        NaN      NaN    0   \n",
       "1  LINESTRING (3908373.253875158 3724272.63286204...        NaN      NaN    0   \n",
       "2  LINESTRING (3908373.253875158 3724272.63286204...        NaN      NaN    0   \n",
       "3  LINESTRING (3908373.253875158 3724272.63286204...        NaN      NaN    0   \n",
       "4  LINESTRING (3917110.898818707 3723781.05541410...  secondary      NaN    0   \n",
       "\n",
       "  lanes  length maxspeed          name oneway      osmid  ref service  \\\n",
       "0   NaN  49.000      NaN           NaN    NaN        NaN  NaN     NaN   \n",
       "1   NaN  50.000      NaN           NaN    NaN        NaN  NaN     NaN   \n",
       "2   NaN  50.000      NaN           NaN    NaN        NaN  NaN     NaN   \n",
       "3   NaN  50.000      NaN           NaN    NaN        NaN  NaN     NaN   \n",
       "4   NaN  49.455      NaN  כביש 60 הישן  False  384338945  NaN     NaN   \n",
       "\n",
       "            u               v width  \n",
       "0  3859570691  3859570691_1_0   NaN  \n",
       "1   726794247   726794247_2_4   NaN  \n",
       "2   726794247  726794247_3_18   NaN  \n",
       "3   726794247  726794247_4_23   NaN  \n",
       "4   525795337       431626371   NaN  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "746.1519999999999"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge['length'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "G100 = ox.graph_from_place('Piedmont, California')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_gdf100 = ox.graph_to_gdfs(G100, nodes = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f88227373c8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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siYgnI+JQRLwQEXeX9lrrPS8ivhcRPyz1fra0XxERT5d6v14uXiAizi3rk2X7aD/HPx8RcU5EPBsRj5b1KmuNiCMRcSAinouIZ0rbknsfD2TQz7jNwk3Au4GPRcS7+zuqBfsycONZbduAvZm5Fthb1qFV99rymAAeWKQxdstpYGtmXgWsB+4qf3+11vtr4PrMfC9wNXBjRKwHPgfcV+o9AWwp/bcAJzLzSuC+0m/Q3A0cmrFec60bMvPqGZdRLr33cWYO3AP4IPCdGev3APf0e1xdqGsUODhj/UVgZVleCbxYlv8R+Nhs/QbxATxC695I1dcL/B7wA1rfFn8VWFba33hP07pi7YNleVnpF/0eewc1rqYVcNcDjwJRca1HgEvPalty7+OBnNEz+20WVvVpLL00kpnHAMrz5aW9mvrLj+rvA56m4nrLqYzngOPAHuAnwOuZebp0mVnTG/WW7SeBSxZ3xAvyBeAzwG/K+iXUW2sC342I/eXb/rAE38eDej/6mKVtmC4fqqL+iFgOfBP4VGb+PGK2slpdZ2kbqHoz8wxwdUSsAB4GrpqtW3ke2Hoj4iPA8czcHxHj082zdB34WovrMvNoRFwO7ImIH79F377VOqgz+rZus1CBVyJiJUB5Pl7aB77+iHg7rZD/amZ+qzRXW++0zHwdaND63cSKiJiebM2s6Y16y/YLgdcWd6Tzdh3w0Yg4QuvOtdfTmuHXWCuZebQ8H6f1AX4tS/B9PKhBPyy3WdgNbC7Lm2mdy55uv7P8Fn89cHL6R8VBEK2p+4PAocz8/IxNtdZ7WZnJExHnAx+i9YvKJ4HbSrez653+c7gNeCLLSd2lLjPvyczVmTlK69/lE5m5iQprjYgLIuKd08vAh4GDLMX3cb9/mbGAX4LcDPwHrXOdf93v8XShnq8Bx4D/pfXJv4XWucq9wOHyfHHpG7SuOvoJcAAY6/f4O6z1j2n9yPo88Fx53FxxvX8EPFvqPQj8TWl/F/A9YBL4V+Dc0n5eWZ8s29/V7xrmWfc48GittZaaflgeL0zn0FJ8H/vNWEmq3KCeupEktcmgl6TKGfSSVDmDXpIqZ9BLUuUMekmqnEEvSZUz6CWpcv8HdqWT3j0UjKcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "edge_gdf100['length'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/nfshome/gb1877/.conda/envs/osm2/lib/python3.6/site-packages/ipykernel_launcher.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving OSM data to shapefile at path data/Piedmont\n",
      "Saving OSM data to shapefile at path data/Piedmont\n"
     ]
    }
   ],
   "source": [
    "pschools = get_amenities('Piedmont', 'schools')\n",
    "#osm_schools.to_crs({'init': 'epsg:3857'})\n",
    "phosp = get_amenities('Piedmont', 'healthcare')\n",
    "#osm_hosp.to_crs({'init': 'epsg:3857'})\n",
    "pschools = pschools.to_crs({'init':'epsg:3310'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>amenity</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty GeoDataFrame\n",
       "Columns: [amenity, lat, lon, name, geometry]\n",
       "Index: []"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phosp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "gn.pandana_snap?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "pschools = gn.pandana_snap(G=G100, point_gdf = pschools,add_dist_to_node_col=True)\n",
    "\n",
    "#phealth = gn.pandana_snap(G = G100, point_gdf = phosp, add_dist_to_node_col=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>amenity</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>NN</th>\n",
       "      <th>NN_dist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>358819251</th>\n",
       "      <td>school</td>\n",
       "      <td>37.822905</td>\n",
       "      <td>-122.233678</td>\n",
       "      <td>Millennium Alternate High School</td>\n",
       "      <td>POINT (-196334.7716527503 -19193.45889316499)</td>\n",
       "      <td>53040794</td>\n",
       "      <td>197152.722893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358819659</th>\n",
       "      <td>school</td>\n",
       "      <td>37.813816</td>\n",
       "      <td>-122.214412</td>\n",
       "      <td>Corpus Christi School</td>\n",
       "      <td>POINT (-194665.2357171265 -20243.29319066927)</td>\n",
       "      <td>53040794</td>\n",
       "      <td>195597.283346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358819748</th>\n",
       "      <td>school</td>\n",
       "      <td>37.818619</td>\n",
       "      <td>-122.211076</td>\n",
       "      <td>Zion Lutheran School</td>\n",
       "      <td>POINT (-194359.5768201756 -19716.39283243939)</td>\n",
       "      <td>53040794</td>\n",
       "      <td>195239.259523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          amenity        lat         lon                              name  \\\n",
       "id                                                                           \n",
       "358819251  school  37.822905 -122.233678  Millennium Alternate High School   \n",
       "358819659  school  37.813816 -122.214412             Corpus Christi School   \n",
       "358819748  school  37.818619 -122.211076              Zion Lutheran School   \n",
       "\n",
       "                                                geometry        NN  \\\n",
       "id                                                                   \n",
       "358819251  POINT (-196334.7716527503 -19193.45889316499)  53040794   \n",
       "358819659  POINT (-194665.2357171265 -20243.29319066927)  53040794   \n",
       "358819748  POINT (-194359.5768201756 -19716.39283243939)  53040794   \n",
       "\n",
       "                 NN_dist  \n",
       "id                        \n",
       "358819251  197152.722893  \n",
       "358819659  195597.283346  \n",
       "358819748  195239.259523  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pschools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 808.135x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'geometry'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-64-1949d7234849>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpschools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.conda/envs/osm2/lib/python3.6/site-packages/geopandas/geodataframe.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    532\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mthere\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m         \"\"\"\n\u001b[0;32m--> 534\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    536\u001b[0m     \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_dataframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osm2/lib/python3.6/site-packages/geopandas/plotting.py\u001b[0m in \u001b[0;36mplot_dataframe\u001b[0;34m(df, column, cmap, color, ax, categorical, legend, scheme, k, vmin, vmax, markersize, figsize, legend_kwds, **style_kwds)\u001b[0m\n\u001b[1;32m    412\u001b[0m         return plot_series(df.geometry, cmap=cmap, color=color, ax=ax,\n\u001b[1;32m    413\u001b[0m                            \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkersize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmarkersize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m                            **style_kwds)\n\u001b[0m\u001b[1;32m    415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m     \u001b[0;31m# To accept pd.Series and np.arrays as column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osm2/lib/python3.6/site-packages/geopandas/plotting.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(s, cmap, color, ax, figsize, **style_kwds)\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0mstyle_kwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'vmax'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstyle_kwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'vmax'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m     \u001b[0mgeom_types\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m     poly_idx = np.asarray((geom_types == 'Polygon')\n\u001b[1;32m    277\u001b[0m                           | (geom_types == 'MultiPolygon'))\n",
      "\u001b[0;32m~/.conda/envs/osm2/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5065\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5066\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5067\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5069\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'geometry'"
     ]
    }
   ],
   "source": [
    "ax = ox.plot_graph(G100)\n",
    "pschools.plot(ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amenity</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
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       "    <tr>\n",
       "      <th>358819251</th>\n",
       "      <td>school</td>\n",
       "      <td>37.822905</td>\n",
       "      <td>-122.233678</td>\n",
       "      <td>Millennium Alternate High School</td>\n",
       "      <td>POINT (-196334.7716527503 -19193.45889316499)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358819659</th>\n",
       "      <td>school</td>\n",
       "      <td>37.813816</td>\n",
       "      <td>-122.214412</td>\n",
       "      <td>Corpus Christi School</td>\n",
       "      <td>POINT (-194665.2357171265 -20243.29319066927)</td>\n",
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       "    <tr>\n",
       "      <th>358819748</th>\n",
       "      <td>school</td>\n",
       "      <td>37.818619</td>\n",
       "      <td>-122.211076</td>\n",
       "      <td>Zion Lutheran School</td>\n",
       "      <td>POINT (-194359.5768201756 -19716.39283243939)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          amenity        lat         lon                              name  \\\n",
       "id                                                                           \n",
       "358819251  school  37.822905 -122.233678  Millennium Alternate High School   \n",
       "358819659  school  37.813816 -122.214412             Corpus Christi School   \n",
       "358819748  school  37.818619 -122.211076              Zion Lutheran School   \n",
       "\n",
       "                                                geometry  \n",
       "id                                                        \n",
       "358819251  POINT (-196334.7716527503 -19193.45889316499)  \n",
       "358819659  POINT (-194665.2357171265 -20243.29319066927)  \n",
       "358819748  POINT (-194359.5768201756 -19716.39283243939)  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pschools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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