mobility_creategraph_tobler.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mobility in Puerto Rico following Hurricane Maria\n",
    "\n",
    "This file includes steps for:\n",
    "* Creating driving network (graph)\n",
    "* Connecting points of origin and destination to road network\n",
    "* Assigning driving times for all road segments on the road network\n",
    "* Measuring walking distance and time from points of origin to the road network using Tobler's hiking function (Euclidean distance & elevation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "GISFolder = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\grace\\\\GIS\\\\puerto rico'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: gostnet.py and associated files are in the current working directory.\n",
    "\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import GOSTnet as gn # Python couldn't find the module. Moved it into C:\\Users\\grace\\Anaconda3\\envs\\access\\Lib\\site-packages\n",
    "import pandas as pd\n",
    "from geopandas import GeoDataFrame\n",
    "import shapely\n",
    "from shapely.geometry import Point, box\n",
    "import geopandas as gpd\n",
    "import osmnx as ox\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import rasterio as rt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Didn't use in this iteration:\n",
    "import fiona\n",
    "import peartree\n",
    "from osgeo import gdal\n",
    "import importlib\n",
    "import matplotlib.pyplot as plt\n",
    "import subprocess, glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\grace\\\\GIS\\\\puerto rico\\\\data\\\\working files\\\\gn'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pth = os.path.join(GISFolder, \"data\\working files\\gn\")\n",
    "pth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare and clean the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OSM road network is in WGS84. Projected each dataset to match.\n",
    "# Multi-point shapefiles don't read well. Re-created shapefile from csv with geopandas for each offending file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>ID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>ZIP4</th>\n",
       "      <th>TELEPHONE</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>...</th>\n",
       "      <th>WEBSITE</th>\n",
       "      <th>STATE_ID</th>\n",
       "      <th>ALT_NAME</th>\n",
       "      <th>ST_FIPS</th>\n",
       "      <th>OWNER</th>\n",
       "      <th>TTL_STAFF</th>\n",
       "      <th>BEDS</th>\n",
       "      <th>TRAUMA</th>\n",
       "      <th>HELIPAD</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7990</td>\n",
       "      <td>400631</td>\n",
       "      <td>HOSP GENERAL CASTANER INC</td>\n",
       "      <td>KM 64-2 ROUTE 135</td>\n",
       "      <td>CASTAÑER</td>\n",
       "      <td>PR</td>\n",
       "      <td>631</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>(787) 829-5010</td>\n",
       "      <td>GENERAL ACUTE CARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>115</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>72</td>\n",
       "      <td>NON-PROFIT</td>\n",
       "      <td>-999</td>\n",
       "      <td>24</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>POINT (-66.83318174999999 18.18101307)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7995</td>\n",
       "      <td>900605</td>\n",
       "      <td>HOSPITAL BUEN SAMARITANO</td>\n",
       "      <td>CARR #2 KM 141-1 AVE SEVERIANO CUEVAS #18 BO. ...</td>\n",
       "      <td>AGUADILLA</td>\n",
       "      <td>PR</td>\n",
       "      <td>605</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>(787) 658-0000</td>\n",
       "      <td>GENERAL ACUTE CARE</td>\n",
       "      <td>...</td>\n",
       "      <td>http://www.hbspr.org</td>\n",
       "      <td>8</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>72</td>\n",
       "      <td>NON-PROFIT</td>\n",
       "      <td>-999</td>\n",
       "      <td>154</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>POINT (-67.15088511 18.44280934)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7996</td>\n",
       "      <td>1000676</td>\n",
       "      <td>HOSP SAN CARLOS BORROMEO</td>\n",
       "      <td>550 CONCEPCION VERA AYALA</td>\n",
       "      <td>MOCA</td>\n",
       "      <td>PR</td>\n",
       "      <td>676</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>(787) 877-8000</td>\n",
       "      <td>GENERAL ACUTE CARE</td>\n",
       "      <td>...</td>\n",
       "      <td>http://www.hscbpr.org</td>\n",
       "      <td>148</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>72</td>\n",
       "      <td>NON-PROFIT</td>\n",
       "      <td>-999</td>\n",
       "      <td>106</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>POINT (-67.10996904 18.39019435)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8007</td>\n",
       "      <td>3400705</td>\n",
       "      <td>HOSP GENERAL MENONITA - AIBONITO</td>\n",
       "      <td>C/JOSE C VAZQUEZ - SALIDA BO CAONILLA</td>\n",
       "      <td>AIBONITO</td>\n",
       "      <td>PR</td>\n",
       "      <td>705</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>(787) 735-8001</td>\n",
       "      <td>GENERAL ACUTE CARE</td>\n",
       "      <td>...</td>\n",
       "      <td>http://www.hospitalmenonita.com/</td>\n",
       "      <td>11</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>72</td>\n",
       "      <td>PROPRIETARY</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>POINT (-66.26170737 18.14379446)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7988</td>\n",
       "      <td>100614</td>\n",
       "      <td>HOSP METROPOLITANO DR SUSONI</td>\n",
       "      <td>55 NICOMEDES RIVERA STREET</td>\n",
       "      <td>ARECIBO PUEBLO</td>\n",
       "      <td>PR</td>\n",
       "      <td>614</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>(787) 650-1030</td>\n",
       "      <td>GENERAL ACUTE CARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>19</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>72</td>\n",
       "      <td>PROPRIETARY</td>\n",
       "      <td>-999</td>\n",
       "      <td>138</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>POINT (-66.71691497 18.47328848)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID       ID                              NAME  \\\n",
       "0      7990   400631         HOSP GENERAL CASTANER INC   \n",
       "1      7995   900605          HOSPITAL BUEN SAMARITANO   \n",
       "2      7996  1000676          HOSP SAN CARLOS BORROMEO   \n",
       "3      8007  3400705  HOSP GENERAL MENONITA - AIBONITO   \n",
       "4      7988   100614      HOSP METROPOLITANO DR SUSONI   \n",
       "\n",
       "                                             ADDRESS            CITY STATE  \\\n",
       "0                                  KM 64-2 ROUTE 135        CASTAÑER    PR   \n",
       "1  CARR #2 KM 141-1 AVE SEVERIANO CUEVAS #18 BO. ...       AGUADILLA    PR   \n",
       "2                          550 CONCEPCION VERA AYALA            MOCA    PR   \n",
       "3              C/JOSE C VAZQUEZ - SALIDA BO CAONILLA        AIBONITO    PR   \n",
       "4                         55 NICOMEDES RIVERA STREET  ARECIBO PUEBLO    PR   \n",
       "\n",
       "   ZIP           ZIP4       TELEPHONE                TYPE  ...  \\\n",
       "0  631  NOT AVAILABLE  (787) 829-5010  GENERAL ACUTE CARE  ...   \n",
       "1  605  NOT AVAILABLE  (787) 658-0000  GENERAL ACUTE CARE  ...   \n",
       "2  676  NOT AVAILABLE  (787) 877-8000  GENERAL ACUTE CARE  ...   \n",
       "3  705  NOT AVAILABLE  (787) 735-8001  GENERAL ACUTE CARE  ...   \n",
       "4  614  NOT AVAILABLE  (787) 650-1030  GENERAL ACUTE CARE  ...   \n",
       "\n",
       "                            WEBSITE  STATE_ID       ALT_NAME  ST_FIPS  \\\n",
       "0                     NOT AVAILABLE       115  NOT AVAILABLE       72   \n",
       "1              http://www.hbspr.org         8  NOT AVAILABLE       72   \n",
       "2             http://www.hscbpr.org       148  NOT AVAILABLE       72   \n",
       "3  http://www.hospitalmenonita.com/        11  NOT AVAILABLE       72   \n",
       "4                     NOT AVAILABLE        19  NOT AVAILABLE       72   \n",
       "\n",
       "         OWNER  TTL_STAFF  BEDS         TRAUMA        HELIPAD  \\\n",
       "0   NON-PROFIT       -999    24  NOT AVAILABLE  NOT AVAILABLE   \n",
       "1   NON-PROFIT       -999   154  NOT AVAILABLE  NOT AVAILABLE   \n",
       "2   NON-PROFIT       -999   106  NOT AVAILABLE  NOT AVAILABLE   \n",
       "3  PROPRIETARY       -999  -999  NOT AVAILABLE  NOT AVAILABLE   \n",
       "4  PROPRIETARY       -999   138  NOT AVAILABLE  NOT AVAILABLE   \n",
       "\n",
       "                                 geometry  \n",
       "0  POINT (-66.83318174999999 18.18101307)  \n",
       "1        POINT (-67.15088511 18.44280934)  \n",
       "2        POINT (-67.10996904 18.39019435)  \n",
       "3        POINT (-66.26170737 18.14379446)  \n",
       "4        POINT (-66.71691497 18.47328848)  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfH = os.path.join(GISFolder, \"data\\working files\\gn\", \"hospitals1.csv\")\n",
    "dfH = pd.read_csv(dfH)\n",
    "geometry = [Point(xy) for xy in zip(dfH.X, dfH.Y)]\n",
    "crs = {'init': 'epsg:4326'} \n",
    "inH = GeoDataFrame(dfH, crs=crs, geometry=geometry)\n",
    "inH.to_file(driver='ESRI Shapefile', filename='hospitals2.shp') \n",
    "# 75 observations.\n",
    "inH.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Provider Number</th>\n",
       "      <th>Facility Name</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>Address Line 1</th>\n",
       "      <th>City</th>\n",
       "      <th>Zip</th>\n",
       "      <th>County</th>\n",
       "      <th>Chain Organization</th>\n",
       "      <th># of Dialysis Stations</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>402513</td>\n",
       "      <td>FMC AGUADILLA DIALYSIS CENTER</td>\n",
       "      <td>18.468786</td>\n",
       "      <td>-67.118260</td>\n",
       "      <td>CARR 459 KM 0.7 BO CAMASEYES</td>\n",
       "      <td>AGUADILLA</td>\n",
       "      <td>603</td>\n",
       "      <td>AGUADILLA</td>\n",
       "      <td>FRESENIUS MEDICAL CARE</td>\n",
       "      <td>24</td>\n",
       "      <td>POINT (-67.1182603 18.4687862)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>402519</td>\n",
       "      <td>ATLANTIS HEALTH CARE GROUP (PUERTO RICO), INC.</td>\n",
       "      <td>18.391264</td>\n",
       "      <td>-67.117501</td>\n",
       "      <td>18 AVE SEVERIANO CUEVAS</td>\n",
       "      <td>AGUADILLA</td>\n",
       "      <td>605</td>\n",
       "      <td>AGUADILLA</td>\n",
       "      <td>ATLANTIS HEALTHCARE GROUP</td>\n",
       "      <td>25</td>\n",
       "      <td>POINT (-67.11750090000001 18.3912641)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>402508</td>\n",
       "      <td>FMC ARECIBO DIALYSIS CENTER</td>\n",
       "      <td>18.423322</td>\n",
       "      <td>-66.739066</td>\n",
       "      <td>1072 AVENUE MIRAMAR</td>\n",
       "      <td>ARECIBO</td>\n",
       "      <td>612</td>\n",
       "      <td>ARECIBO</td>\n",
       "      <td>FRESENIUS MEDICAL CARE</td>\n",
       "      <td>28</td>\n",
       "      <td>POINT (-66.7390661 18.4233218)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>402529</td>\n",
       "      <td>FMC ARECIBO NORTE DIALYSIS CENTER</td>\n",
       "      <td>18.388935</td>\n",
       "      <td>-66.734479</td>\n",
       "      <td>HOSPITAL PAVIA ARECIBO</td>\n",
       "      <td>ARECIBO</td>\n",
       "      <td>612</td>\n",
       "      <td>ARECIBO</td>\n",
       "      <td>FRESENIUS MEDICAL CARE</td>\n",
       "      <td>19</td>\n",
       "      <td>POINT (-66.7344786 18.3889353)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>402543</td>\n",
       "      <td>FMC SANTA JUANITA</td>\n",
       "      <td>18.368058</td>\n",
       "      <td>-66.159749</td>\n",
       "      <td>URB SANTA JUANITA</td>\n",
       "      <td>BAYAMON</td>\n",
       "      <td>956</td>\n",
       "      <td>BAYAMON</td>\n",
       "      <td>FRESENIUS MEDICAL CARE</td>\n",
       "      <td>33</td>\n",
       "      <td>POINT (-66.15974900000001 18.3680575)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Provider Number                                   Facility Name        lat  \\\n",
       "0           402513                   FMC AGUADILLA DIALYSIS CENTER  18.468786   \n",
       "1           402519  ATLANTIS HEALTH CARE GROUP (PUERTO RICO), INC.  18.391264   \n",
       "2           402508                     FMC ARECIBO DIALYSIS CENTER  18.423322   \n",
       "3           402529               FMC ARECIBO NORTE DIALYSIS CENTER  18.388935   \n",
       "4           402543                               FMC SANTA JUANITA  18.368058   \n",
       "\n",
       "        long                Address Line 1       City  Zip     County  \\\n",
       "0 -67.118260  CARR 459 KM 0.7 BO CAMASEYES  AGUADILLA  603  AGUADILLA   \n",
       "1 -67.117501       18 AVE SEVERIANO CUEVAS  AGUADILLA  605  AGUADILLA   \n",
       "2 -66.739066           1072 AVENUE MIRAMAR    ARECIBO  612    ARECIBO   \n",
       "3 -66.734479        HOSPITAL PAVIA ARECIBO    ARECIBO  612    ARECIBO   \n",
       "4 -66.159749             URB SANTA JUANITA    BAYAMON  956    BAYAMON   \n",
       "\n",
       "          Chain Organization  # of Dialysis Stations  \\\n",
       "0     FRESENIUS MEDICAL CARE                      24   \n",
       "1  ATLANTIS HEALTHCARE GROUP                      25   \n",
       "2     FRESENIUS MEDICAL CARE                      28   \n",
       "3     FRESENIUS MEDICAL CARE                      19   \n",
       "4     FRESENIUS MEDICAL CARE                      33   \n",
       "\n",
       "                                geometry  \n",
       "0         POINT (-67.1182603 18.4687862)  \n",
       "1  POINT (-67.11750090000001 18.3912641)  \n",
       "2         POINT (-66.7390661 18.4233218)  \n",
       "3         POINT (-66.7344786 18.3889353)  \n",
       "4  POINT (-66.15974900000001 18.3680575)  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfD = os.path.join(GISFolder, \"data\\working files\\gn\", \"dialysis2.csv\")\n",
    "dfD = pd.read_csv(dfD)\n",
    "geometry = [Point(xy) for xy in zip(dfD.long, dfD.lat)]\n",
    "crs = {'init': 'epsg:4326'} \n",
    "inD = GeoDataFrame(dfD, crs=crs, geometry=geometry)\n",
    "inD.to_file(driver='ESRI Shapefile', filename='dialysis2wgs84.shp') \n",
    "# Original dataset had 47 individual observations. Two of these were duplicates, making 45 total.\n",
    "inD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>FID</th>\n",
       "      <th>ID</th>\n",
       "      <th>SECCLASS</th>\n",
       "      <th>NAME</th>\n",
       "      <th>TELEPHONE</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>ADDRESS2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>...</th>\n",
       "      <th>HEALTH_95</th>\n",
       "      <th>PROVID_96</th>\n",
       "      <th>PROVID_97</th>\n",
       "      <th>HEALTH_99</th>\n",
       "      <th>PROVID_100</th>\n",
       "      <th>PROVID_101</th>\n",
       "      <th>HEALTH_103</th>\n",
       "      <th>PROVID_104</th>\n",
       "      <th>PROVID_105</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22001</td>\n",
       "      <td>11711758</td>\n",
       "      <td>UNCLASSIFIED</td>\n",
       "      <td>FARMACIA VILLA ESPERANZA</td>\n",
       "      <td>787-708-1300</td>\n",
       "      <td>AVENIDA EMILIANO POL 489</td>\n",
       "      <td>URBANIZACION LA CUMBRE</td>\n",
       "      <td>SAN JUAN</td>\n",
       "      <td>PR</td>\n",
       "      <td>926</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-66.074557 18.3609946)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22002</td>\n",
       "      <td>11645399</td>\n",
       "      <td>UNCLASSIFIED</td>\n",
       "      <td>FARMACIA YARIMAR</td>\n",
       "      <td>787-792-3196</td>\n",
       "      <td>CALLE 31 904 SUROESTE</td>\n",
       "      <td>BARRIO LAS LOMAS</td>\n",
       "      <td>SAN JUAN</td>\n",
       "      <td>PR</td>\n",
       "      <td>921</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-66.0949918 18.3904596)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22003</td>\n",
       "      <td>11656886</td>\n",
       "      <td>UNCLASSIFIED</td>\n",
       "      <td>KMART - 4490</td>\n",
       "      <td>787-792-7708</td>\n",
       "      <td>AVENIDA FRANKLIN D ROOSEVELT 1</td>\n",
       "      <td>URBANIZACION CAPARRA HEIGHTS SAN PATRICIO PLAZA</td>\n",
       "      <td>GUAYNABO</td>\n",
       "      <td>PR</td>\n",
       "      <td>968</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-66.1054762 18.409322)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22004</td>\n",
       "      <td>11652356</td>\n",
       "      <td>UNCLASSIFIED</td>\n",
       "      <td>ONE STOP PRESCRIPTION</td>\n",
       "      <td>787-977-2007</td>\n",
       "      <td>AVENIDA JOSE DE DIEGO 114</td>\n",
       "      <td>SUPERMERCADO EL MONTE</td>\n",
       "      <td>SANTURCE</td>\n",
       "      <td>PR</td>\n",
       "      <td>907</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-66.064924 18.4514286)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22005</td>\n",
       "      <td>11697024</td>\n",
       "      <td>UNCLASSIFIED</td>\n",
       "      <td>FARMACIA CARIDAD</td>\n",
       "      <td>787-253-7195</td>\n",
       "      <td>CALLE LUNA WD22</td>\n",
       "      <td>URBANIZACION LOS ANGELES</td>\n",
       "      <td>CAROLINA</td>\n",
       "      <td>PR</td>\n",
       "      <td>979</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-66.00932229999999 18.4296024)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FID        ID      SECCLASS                      NAME     TELEPHONE  \\\n",
       "0  22001  11711758  UNCLASSIFIED  FARMACIA VILLA ESPERANZA  787-708-1300   \n",
       "1  22002  11645399  UNCLASSIFIED          FARMACIA YARIMAR  787-792-3196   \n",
       "2  22003  11656886  UNCLASSIFIED              KMART - 4490  787-792-7708   \n",
       "3  22004  11652356  UNCLASSIFIED     ONE STOP PRESCRIPTION  787-977-2007   \n",
       "4  22005  11697024  UNCLASSIFIED          FARMACIA CARIDAD  787-253-7195   \n",
       "\n",
       "                          ADDRESS  \\\n",
       "0        AVENIDA EMILIANO POL 489   \n",
       "1           CALLE 31 904 SUROESTE   \n",
       "2  AVENIDA FRANKLIN D ROOSEVELT 1   \n",
       "3       AVENIDA JOSE DE DIEGO 114   \n",
       "4                 CALLE LUNA WD22   \n",
       "\n",
       "                                          ADDRESS2      CITY STATE  ZIP  ...  \\\n",
       "0                           URBANIZACION LA CUMBRE  SAN JUAN    PR  926  ...   \n",
       "1                                 BARRIO LAS LOMAS  SAN JUAN    PR  921  ...   \n",
       "2  URBANIZACION CAPARRA HEIGHTS SAN PATRICIO PLAZA  GUAYNABO    PR  968  ...   \n",
       "3                            SUPERMERCADO EL MONTE  SANTURCE    PR  907  ...   \n",
       "4                         URBANIZACION LOS ANGELES  CAROLINA    PR  979  ...   \n",
       "\n",
       "   HEALTH_95 PROVID_96  PROVID_97 HEALTH_99  PROVID_100  PROVID_101  \\\n",
       "0        NaN       NaN        NaN       NaN         NaN         NaN   \n",
       "1        NaN       NaN        NaN       NaN         NaN         NaN   \n",
       "2        NaN       NaN        NaN       NaN         NaN         NaN   \n",
       "3        NaN       NaN        NaN       NaN         NaN         NaN   \n",
       "4        NaN       NaN        NaN       NaN         NaN         NaN   \n",
       "\n",
       "   HEALTH_103 PROVID_104 PROVID_105                               geometry  \n",
       "0         NaN        NaN        NaN          POINT (-66.074557 18.3609946)  \n",
       "1         NaN        NaN        NaN         POINT (-66.0949918 18.3904596)  \n",
       "2         NaN        NaN        NaN          POINT (-66.1054762 18.409322)  \n",
       "3         NaN        NaN        NaN          POINT (-66.064924 18.4514286)  \n",
       "4         NaN        NaN        NaN  POINT (-66.00932229999999 18.4296024)  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfP = os.path.join(GISFolder, \"data\\working files\\gn\", \"pharmacies1.csv\")\n",
    "dfP = pd.read_csv(dfP)\n",
    "geometry = [Point(xy) for xy in zip(dfP.X, dfP.Y)]\n",
    "crs = {'init': 'epsg:4326'} \n",
    "inP = GeoDataFrame(dfP, crs=crs, geometry=geometry)\n",
    "inP.to_file(driver='ESRI Shapefile', filename='pharm3wgs84.shp') \n",
    "# 965 observations.\n",
    "inP.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean WorldPop data for network analysis.\n",
    "\n",
    "All cleaning for WorldPop detailed below performed in R and QGIS:\n",
    "\n",
    "1) Removed observations where population = 0, leaving 1,075,783 observations. (1,082,378 original observations.) \n",
    "\n",
    "R: tidyverse filter()\n",
    "\n",
    "2) Each WorldPop feature is assigned a unique ID (\"wid\") and its corresponding municipal ID (\"mid\") and municipio name.\n",
    "\n",
    "R: 1:nrow()\n",
    "    \n",
    "QGIS: Spatial join.\n",
    "Note: some coastal points fall outside of the municipio boundaries. To resolve, created a buffer of all municipal features, and merged those buffer zones onto the municipalities polygon shapefile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfO = os.path.join(GISFolder, \"data\\working files\\gn\", \"wpop1.csv\")\n",
    "dfO = pd.read_csv(dfO)\n",
    "geometry = [Point(xy) for xy in zip(dfO.X, dfO.Y)]\n",
    "crs = {'init': 'epsg:4326'} \n",
    "inO = GeoDataFrame(dfO, crs=crs, geometry=geometry)\n",
    "inO.to_file(driver='ESRI Shapefile', filename='wpop2wgs84.shp') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If starting new session, load the cleaned data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputOrigins = os.path.join(pth, \"wpop3wgs84.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputD = os.path.join(pth, \"dialysis2wgs84.shp\")\n",
    "inputH = os.path.join(pth, \"hospitals2.shp\")\n",
    "inputP = os.path.join(pth, \"pharm3wgs84.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "inO = gpd.read_file(inputOrigins) # Around 1 million rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "inD = gpd.read_file(inputD) # 45 rows\n",
    "inH = gpd.read_file(inputH) # 75 rows\n",
    "inP = gpd.read_file(inputP) # Around 1000 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>FID</th>\n",
       "      <th>ID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>ADDRESS2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>ZIPP4</th>\n",
       "      <th>COUNTY</th>\n",
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       "      <td>22001</td>\n",
       "      <td>11711758</td>\n",
       "      <td>FARMACIA VILLA ESPERANZA</td>\n",
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       "      <td>URBANIZACION LA CUMBRE</td>\n",
       "      <td>SAN JUAN</td>\n",
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       "      <td>11645399</td>\n",
       "      <td>FARMACIA YARIMAR</td>\n",
       "      <td>CALLE 31 904 SUROESTE</td>\n",
       "      <td>BARRIO LAS LOMAS</td>\n",
       "      <td>SAN JUAN</td>\n",
       "      <td>PR</td>\n",
       "      <td>921</td>\n",
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       "      <td>SAN JUAN</td>\n",
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       "      <th>2</th>\n",
       "      <td>22003</td>\n",
       "      <td>11656886</td>\n",
       "      <td>KMART - 4490</td>\n",
       "      <td>AVENIDA FRANKLIN D ROOSEVELT 1</td>\n",
       "      <td>URBANIZACION CAPARRA HEIGHTS SAN PATRICIO PLAZA</td>\n",
       "      <td>GUAYNABO</td>\n",
       "      <td>PR</td>\n",
       "      <td>968</td>\n",
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       "      <td>GUAYNABO</td>\n",
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       "      <td>22004</td>\n",
       "      <td>11652356</td>\n",
       "      <td>ONE STOP PRESCRIPTION</td>\n",
       "      <td>AVENIDA JOSE DE DIEGO 114</td>\n",
       "      <td>SUPERMERCADO EL MONTE</td>\n",
       "      <td>SANTURCE</td>\n",
       "      <td>PR</td>\n",
       "      <td>907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAN JUAN</td>\n",
       "      <td>72127</td>\n",
       "      <td>-66.064924</td>\n",
       "      <td>18.451429</td>\n",
       "      <td>POINT (-66.06492 18.45143)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22005</td>\n",
       "      <td>11697024</td>\n",
       "      <td>FARMACIA CARIDAD</td>\n",
       "      <td>CALLE LUNA WD22</td>\n",
       "      <td>URBANIZACION LOS ANGELES</td>\n",
       "      <td>CAROLINA</td>\n",
       "      <td>PR</td>\n",
       "      <td>979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CAROLINA</td>\n",
       "      <td>72031</td>\n",
       "      <td>-66.009322</td>\n",
       "      <td>18.429602</td>\n",
       "      <td>POINT (-66.00932 18.42960)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FID        ID                      NAME                         ADDRESS  \\\n",
       "0  22001  11711758  FARMACIA VILLA ESPERANZA        AVENIDA EMILIANO POL 489   \n",
       "1  22002  11645399          FARMACIA YARIMAR           CALLE 31 904 SUROESTE   \n",
       "2  22003  11656886              KMART - 4490  AVENIDA FRANKLIN D ROOSEVELT 1   \n",
       "3  22004  11652356     ONE STOP PRESCRIPTION       AVENIDA JOSE DE DIEGO 114   \n",
       "4  22005  11697024          FARMACIA CARIDAD                 CALLE LUNA WD22   \n",
       "\n",
       "                                          ADDRESS2      CITY STATE  ZIP  \\\n",
       "0                           URBANIZACION LA CUMBRE  SAN JUAN    PR  926   \n",
       "1                                 BARRIO LAS LOMAS  SAN JUAN    PR  921   \n",
       "2  URBANIZACION CAPARRA HEIGHTS SAN PATRICIO PLAZA  GUAYNABO    PR  968   \n",
       "3                            SUPERMERCADO EL MONTE  SANTURCE    PR  907   \n",
       "4                         URBANIZACION LOS ANGELES  CAROLINA    PR  979   \n",
       "\n",
       "   ZIPP4    COUNTY   FIPS          X          Y                    geometry  \n",
       "0    NaN  SAN JUAN  72127 -66.074557  18.360995  POINT (-66.07456 18.36099)  \n",
       "1    NaN  SAN JUAN  72127 -66.094992  18.390460  POINT (-66.09499 18.39046)  \n",
       "2    NaN  GUAYNABO  72061 -66.105476  18.409322  POINT (-66.10548 18.40932)  \n",
       "3    NaN  SAN JUAN  72127 -66.064924  18.451429  POINT (-66.06492 18.45143)  \n",
       "4    NaN  CAROLINA  72031 -66.009322  18.429602  POINT (-66.00932 18.42960)  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inP.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>Adjuntas</td>\n",
       "      <td>5</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POINT (-66.79017 18.24981)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wpop  xmid xxmunicipi  wid municipio  mid                    geometry\n",
       "0  0.818646   0.0   Adjuntas    1  Adjuntas  0.0  POINT (-66.79433 18.24981)\n",
       "1  0.731308   0.0   Adjuntas    2  Adjuntas  0.0  POINT (-66.79350 18.24981)\n",
       "2  0.642141   0.0   Adjuntas    3  Adjuntas  0.0  POINT (-66.79267 18.24981)\n",
       "3  0.612746   0.0   Adjuntas    4  Adjuntas  0.0  POINT (-66.79183 18.24981)\n",
       "4  0.699177   0.0   Adjuntas    5  Adjuntas  0.0  POINT (-66.79017 18.24981)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inO.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate shape from shapefile for the bounding box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-68.10134661192134 17.72091685844226 3.033351892037942 0.9790309669160919\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,36.42086468380061)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.06066703784075884\" opacity=\"0.6\" d=\"M -67.98900024554956,18.587601458986576 L -65.18034108625518,18.573368565151693 L -65.18317550453337,17.833263224814033 L -67.98243490319383,17.84752250960653 L -67.98900024554956,18.587601458986576 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x1a53f5d7eb8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aoi = r'prboundingwgs84.shp' \n",
    "# Graph didn't populate on the smaller islands when using the Puerto Rico admin boundary.\n",
    "# Created a rectangular bounding box (in QGIS) to use as the AOI instead.\n",
    "shp = gpd.read_file(os.path.join(pth, aoi))\n",
    "bound = shp.geometry.iloc[0]\n",
    "bound # Check that it's a rectangle (short and wide)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Get driving network for all islands in Puerto Rico. \n",
    "\n",
    "This step pulls live OpenStreetMap road data. Origins and health facilities are not yet incorporated. Travel measured in length (meters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "gDrive = ox.graph_from_polygon(bound, network_type= 'drive')\n",
    "# This took about 8 minutes on a fast machine.\n",
    "# Note: length is measured in meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save all road nodes (points on the road) to file.\n",
    "gDrive_node_gdf = gn.node_gdf_from_graph(gDrive)\n",
    "gDrive_node_gdf.to_csv(os.path.join(pth, 'drive_dist_node.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add a baseline time measure using a speed dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "speed_dict = {\n",
    "                'residential': 20,  # kmph\n",
    "                'primary': 40, # kmph\n",
    "                'primary_link':35,\n",
    "                'motorway':45,\n",
    "                'motorway_link': 40,\n",
    "                'trunk': 40,\n",
    "                'trunk_link':35,\n",
    "                'secondary': 30, # kmph\n",
    "                'secondary_link':25,\n",
    "                'tertiary':30,\n",
    "                'tertiary_link': 25,\n",
    "                'unclassified':20, \n",
    "                'road':20,\n",
    "                'crossing':20,\n",
    "                'living_street':20\n",
    "                }\n",
    "\n",
    "networkType = 'drive'\n",
    "gTime = gn.convert_network_to_time(gDrive, distance_tag = 'length', graph_type = networkType, speed_dict = speed_dict)\n",
    "# Note: time is in seconds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save road nodes and road edges to file. Edges contains time measure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = gn.edge_gdf_from_graph(gTime)\n",
    "nodes = gn.node_gdf_from_graph(gTime)\n",
    "edges.to_csv(os.path.join(pth, 'drive_time_edge.csv'))\n",
    "nodes.to_csv(os.path.join(pth, 'drive_time_node.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Int64Index: 171850 entries, 0 to 171849\n",
      "Data columns (total 7 columns):\n",
      "node_ID     171850 non-null int64\n",
      "ref         407 non-null object\n",
      "osmid       171850 non-null int64\n",
      "y           171850 non-null float64\n",
      "x           171850 non-null float64\n",
      "highway     3358 non-null object\n",
      "geometry    171850 non-null geometry\n",
      "dtypes: float64(2), geometry(1), int64(2), object(2)\n",
      "memory usage: 10.5+ MB\n"
     ]
    }
   ],
   "source": [
    "nodes.info()\n",
    "# There are 171,850 road nodes in Puerto Rico that an origin cell can snap to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "?gn.save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save a pickle of the graph with the time measure for easy recall.\n",
    "gn.save(gTime, 'gTime', edges = False, nodes = False) # Switched this to working directory (removed  pth, from before edges=) but haven't tried it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% If starting new session, reload graph from file\n",
    "gTime = nx.read_gpickle(\"gTime.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Origins and destinations\n",
    "\n",
    "Measure distance from origin/destination to nearest node and save to file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pandana_snap calculates the great circle distance between the origin and nearest road node.\n",
    "# Took maybe 1 min for Origins. Much shorter for the rest.\n",
    "# Adding x,y fields on origins file for later use with add_elevation function.\n",
    "inO['x'] = inO['geometry'].x\n",
    "inO['y'] = inO['geometry'].y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "inD['x'] = inD['geometry'].x\n",
    "inD['y'] = inD['geometry'].y\n",
    "\n",
    "inP['x'] = inP['geometry'].x\n",
    "inP['y'] = inP['geometry'].y\n",
    "\n",
    "inH['x'] = inH['geometry'].x\n",
    "inH['y'] = inH['geometry'].y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>OBJECTID</th>\n",
       "      <th>ID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>ZIP4</th>\n",
       "      <th>TELEPHONE</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>...</th>\n",
       "      <th>ALT_NAME</th>\n",
       "      <th>ST_FIPS</th>\n",
       "      <th>OWNER</th>\n",
       "      <th>TTL_STAFF</th>\n",
       "      <th>BEDS</th>\n",
       "      <th>TRAUMA</th>\n",
       "      <th>HELIPAD</th>\n",
       "      <th>geometry</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7990</td>\n",
       "      <td>400631</td>\n",
       "      <td>HOSP GENERAL CASTANER INC</td>\n",
       "      <td>KM 64-2 ROUTE 135</td>\n",
       "      <td>CASTAÑER</td>\n",
       "      <td>PR</td>\n",
       "      <td>631</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>(787) 829-5010</td>\n",
       "      <td>GENERAL ACUTE CARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>72</td>\n",
       "      <td>NON-PROFIT</td>\n",
       "      <td>-999</td>\n",
       "      <td>24</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>POINT (-66.83318 18.18101)</td>\n",
       "      <td>-66.833182</td>\n",
       "      <td>18.181013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7995</td>\n",
       "      <td>900605</td>\n",
       "      <td>HOSPITAL BUEN SAMARITANO</td>\n",
       "      <td>CARR #2 KM 141-1 AVE SEVERIANO CUEVAS #18 BO. ...</td>\n",
       "      <td>AGUADILLA</td>\n",
       "      <td>PR</td>\n",
       "      <td>605</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>(787) 658-0000</td>\n",
       "      <td>GENERAL ACUTE CARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>72</td>\n",
       "      <td>NON-PROFIT</td>\n",
       "      <td>-999</td>\n",
       "      <td>154</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>POINT (-67.15089 18.44281)</td>\n",
       "      <td>-67.150885</td>\n",
       "      <td>18.442809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7996</td>\n",
       "      <td>1000676</td>\n",
       "      <td>HOSP SAN CARLOS BORROMEO</td>\n",
       "      <td>550 CONCEPCION VERA AYALA</td>\n",
       "      <td>MOCA</td>\n",
       "      <td>PR</td>\n",
       "      <td>676</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>(787) 877-8000</td>\n",
       "      <td>GENERAL ACUTE CARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>72</td>\n",
       "      <td>NON-PROFIT</td>\n",
       "      <td>-999</td>\n",
       "      <td>106</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>POINT (-67.10997 18.39019)</td>\n",
       "      <td>-67.109969</td>\n",
       "      <td>18.390194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8007</td>\n",
       "      <td>3400705</td>\n",
       "      <td>HOSP GENERAL MENONITA - AIBONITO</td>\n",
       "      <td>C/JOSE C VAZQUEZ - SALIDA BO CAONILLA</td>\n",
       "      <td>AIBONITO</td>\n",
       "      <td>PR</td>\n",
       "      <td>705</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>(787) 735-8001</td>\n",
       "      <td>GENERAL ACUTE CARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>72</td>\n",
       "      <td>PROPRIETARY</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>POINT (-66.26171 18.14379)</td>\n",
       "      <td>-66.261707</td>\n",
       "      <td>18.143794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7988</td>\n",
       "      <td>100614</td>\n",
       "      <td>HOSP METROPOLITANO DR SUSONI</td>\n",
       "      <td>55 NICOMEDES RIVERA STREET</td>\n",
       "      <td>ARECIBO PUEBLO</td>\n",
       "      <td>PR</td>\n",
       "      <td>614</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>(787) 650-1030</td>\n",
       "      <td>GENERAL ACUTE CARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>72</td>\n",
       "      <td>PROPRIETARY</td>\n",
       "      <td>-999</td>\n",
       "      <td>138</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>NOT AVAILABLE</td>\n",
       "      <td>POINT (-66.71691 18.47329)</td>\n",
       "      <td>-66.716915</td>\n",
       "      <td>18.473288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID       ID                              NAME  \\\n",
       "0      7990   400631         HOSP GENERAL CASTANER INC   \n",
       "1      7995   900605          HOSPITAL BUEN SAMARITANO   \n",
       "2      7996  1000676          HOSP SAN CARLOS BORROMEO   \n",
       "3      8007  3400705  HOSP GENERAL MENONITA - AIBONITO   \n",
       "4      7988   100614      HOSP METROPOLITANO DR SUSONI   \n",
       "\n",
       "                                             ADDRESS            CITY STATE  \\\n",
       "0                                  KM 64-2 ROUTE 135        CASTAÑER    PR   \n",
       "1  CARR #2 KM 141-1 AVE SEVERIANO CUEVAS #18 BO. ...       AGUADILLA    PR   \n",
       "2                          550 CONCEPCION VERA AYALA            MOCA    PR   \n",
       "3              C/JOSE C VAZQUEZ - SALIDA BO CAONILLA        AIBONITO    PR   \n",
       "4                         55 NICOMEDES RIVERA STREET  ARECIBO PUEBLO    PR   \n",
       "\n",
       "   ZIP           ZIP4       TELEPHONE                TYPE  ...       ALT_NAME  \\\n",
       "0  631  NOT AVAILABLE  (787) 829-5010  GENERAL ACUTE CARE  ...  NOT AVAILABLE   \n",
       "1  605  NOT AVAILABLE  (787) 658-0000  GENERAL ACUTE CARE  ...  NOT AVAILABLE   \n",
       "2  676  NOT AVAILABLE  (787) 877-8000  GENERAL ACUTE CARE  ...  NOT AVAILABLE   \n",
       "3  705  NOT AVAILABLE  (787) 735-8001  GENERAL ACUTE CARE  ...  NOT AVAILABLE   \n",
       "4  614  NOT AVAILABLE  (787) 650-1030  GENERAL ACUTE CARE  ...  NOT AVAILABLE   \n",
       "\n",
       "   ST_FIPS        OWNER  TTL_STAFF BEDS         TRAUMA        HELIPAD  \\\n",
       "0       72   NON-PROFIT       -999   24  NOT AVAILABLE  NOT AVAILABLE   \n",
       "1       72   NON-PROFIT       -999  154  NOT AVAILABLE  NOT AVAILABLE   \n",
       "2       72   NON-PROFIT       -999  106  NOT AVAILABLE  NOT AVAILABLE   \n",
       "3       72  PROPRIETARY       -999 -999  NOT AVAILABLE  NOT AVAILABLE   \n",
       "4       72  PROPRIETARY       -999  138  NOT AVAILABLE  NOT AVAILABLE   \n",
       "\n",
       "                     geometry          x          y  \n",
       "0  POINT (-66.83318 18.18101) -66.833182  18.181013  \n",
       "1  POINT (-67.15089 18.44281) -67.150885  18.442809  \n",
       "2  POINT (-67.10997 18.39019) -67.109969  18.390194  \n",
       "3  POINT (-66.26171 18.14379) -66.261707  18.143794  \n",
       "4  POINT (-66.71691 18.47329) -66.716915  18.473288  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inH.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "inOsnap = gn.pandana_snap(gTime, inO, source_crs = 'epsg:4326', target_crs = 'epsg:3920', add_dist_to_node_col = True)\n",
    "inOsnap.to_csv('inOsnap.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "inDsnap = gn.pandana_snap(gTime, inD, source_crs = 'epsg:4326', target_crs = 'epsg:3920', add_dist_to_node_col = True)\n",
    "inDsnap.to_csv('inDsnap.csv', index=True)\n",
    "inHsnap = gn.pandana_snap(gTime, inH, source_crs = 'epsg:4326', target_crs = 'epsg:3920', add_dist_to_node_col = True)\n",
    "inHsnap.to_csv('inHsnap.csv', index=True)\n",
    "inPsnap = gn.pandana_snap(gTime, inP, source_crs = 'epsg:4326', target_crs = 'epsg:3920', add_dist_to_node_col = True)\n",
    "inPsnap.to_csv('inPsnap.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to file\n",
    "inOsnap.to_csv(os.path.join(pth, 'inOsnap.csv'), index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "inDsnap.to_csv(os.path.join(pth, 'inDsnap.csv'), index=True)\n",
    "inHsnap.to_csv(os.path.join(pth, 'inHsnap.csv'), index=True)\n",
    "inPsnap.to_csv(os.path.join(pth, 'inPsnap.csv'), index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% If already created, load from file.\n",
    "inOsnap = os.path.join(GISFolder, \"data\\working files\\gn\", \"inOsnap.csv\")\n",
    "inOsnap = pd.read_csv(inOsnap)\n",
    "inDsnap = os.path.join(GISFolder, \"data\\working files\\gn\", \"inDsnap.csv\")\n",
    "inDsnap = pd.read_csv(inDsnap)\n",
    "inHsnap = os.path.join(GISFolder, \"data\\working files\\gn\", \"inHsnap.csv\")\n",
    "inHsnap = pd.read_csv(inHsnap)\n",
    "inPsnap = os.path.join(GISFolder, \"data\\working files\\gn\", \"inPsnap.csv\")\n",
    "inPsnap = pd.read_csv(inPsnap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map elevation onto road nodes and points of origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If starting new session, load road nodes from disk.\n",
    "nodes = os.path.join(GISFolder, \"data\\working files\\gn\", \"drive_time_node.csv\")\n",
    "nodes = gpd.read_file(nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>x</th>\n",
       "      <th>osmid</th>\n",
       "      <th>highway</th>\n",
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      "text/plain": [
       "     node_ID  ref          y          x      osmid          highway  \\\n",
       "0  238026752  NaN  18.261897 -66.050248  238026752              NaN   \n",
       "1  238026754  NaN  18.427812 -65.990280  238026754  traffic_signals   \n",
       "2  238026757  NaN  18.427999 -65.990606  238026757              NaN   \n",
       "3  238026760  NaN  18.236478 -66.025554  238026760              NaN   \n",
       "4  238026764  NaN  18.236759 -66.025735  238026764  traffic_signals   \n",
       "\n",
       "                                geometry  \n",
       "0           POINT (-66.050248 18.261897)  \n",
       "1         POINT (-65.9902804 18.4278116)  \n",
       "2  POINT (-65.99060559999999 18.4279988)  \n",
       "3           POINT (-66.025554 18.236478)  \n",
       "4           POINT (-66.025735 18.236759)  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure nodes coordinates and unique ID field are in the right data type.\n",
    "nodes['x'] = nodes['x'].astype('float')\n",
    "nodes['y'] = nodes['y'].astype('float')\n",
    "nodes['osmid'] = nodes['osmid'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shout out to Charles Fox for add_elevation() and generate_walktimes() functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_elevation(df, x, y, srtm_pth):\n",
    "    # walk all tiles, find path\n",
    "\n",
    "    tiles = []\n",
    "    for root, folder, files in os.walk(os.path.join(srtm_pth,'high_res')):\n",
    "        for f in files:\n",
    "            if f[-3:] == 'hgt':\n",
    "                tiles.append(f[:-4])\n",
    "\n",
    "    # load dictionary of tiles\n",
    "    arrs = {}\n",
    "    for t in tiles:\n",
    "         arrs[t] = rt.open(srtm_pth+r'\\high_res\\{}.hgt\\{}.hgt'.format(t, t), 'r')\n",
    "    # assign a code\n",
    "    uniques = []\n",
    "    df['code'] = 'placeholder'\n",
    "    def tile_code(z):\n",
    "        E = str(abs(z[x])+1)[:2]\n",
    "        N = str(abs(z[y]))[:2]\n",
    "        return 'N{}W0{}'.format(N, E)\n",
    "    df['code'] = df.apply(lambda z: tile_code(z), axis = 1)\n",
    "    unique_codes = list(set(df['code'].unique()))\n",
    "\n",
    "    z = {}\n",
    "    # Match on High Precision Elevation\n",
    "    property_name = 'elevation'\n",
    "    for code in unique_codes:\n",
    "\n",
    "        df2 = df.copy()\n",
    "        df2 = df2.loc[df2['code'] == code]\n",
    "        dataset = arrs[code]\n",
    "        b = dataset.bounds\n",
    "        datasetBoundary = box(b[0], b[1], b[2], b[3])\n",
    "        selKeys = []\n",
    "        selPts = []\n",
    "        for index, row in df2.iterrows():\n",
    "            selPts.append((row[x],row[y]))\n",
    "            selKeys.append(index)\n",
    "        raster_values = list(dataset.sample(selPts))\n",
    "        raster_values = [x[0] for x in raster_values]\n",
    "        # generate new dictionary of {node ID: raster values}\n",
    "        z.update(zip(selKeys, raster_values))\n",
    "    \n",
    "    elev_df = pd.DataFrame.from_dict(z, orient='index')\n",
    "    elev_df.columns = ['elevation']\n",
    "    \n",
    "    missing = elev_df.copy()\n",
    "    missing = missing.loc[missing.elevation < 0]\n",
    "    \n",
    "    print('missing: %s' % len(missing))\n",
    "    \n",
    "    if len(missing) > 0:\n",
    "        missing_df = df.copy()\n",
    "        missing_df = missing_df.loc[missing.index]\n",
    "        low_res_tifpath = os.path.join(srtm_pth, 'clipped', 'W100N40.GIF')\n",
    "        dataset = rt.open(low_res_tifpath, 'r')\n",
    "        b = dataset.bounds\n",
    "        datasetBoundary = box(b[0], b[1], b[2], b[3])\n",
    "        selKeys = []\n",
    "        selPts = []\n",
    "        for index, row in missing_df.iterrows():\n",
    "            if Point(row[x], row[y]).intersects(datasetBoundary):\n",
    "                selPts.append((row[x],row[y]))\n",
    "                selKeys.append(index)\n",
    "        raster_values = list(dataset.sample(selPts))\n",
    "        raster_values = [x[0] for x in raster_values]\n",
    "        z.update(zip(selKeys, raster_values))\n",
    "\n",
    "        elev_df = pd.DataFrame.from_dict(z, orient='index')\n",
    "        elev_df.columns = ['elevation']\n",
    "    df['point_elev'] = elev_df['elevation']\n",
    "    df = df.drop('code', axis = 1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing: 71\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grace\\Anaconda3\\envs\\access\\lib\\site-packages\\rasterio\\__init__.py:193: UserWarning: Dataset has no geotransform set.  Default transform will be applied (Affine.identity())\n",
      "  s.start()\n"
     ]
    }
   ],
   "source": [
    "nodes_elev = add_elevation(nodes, \"x\", \"y\", pth) # Takes a few minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Origin points are different from road nodes. Need elevation for both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing: 1966\n"
     ]
    }
   ],
   "source": [
    "# Using inOsnap was giving the error: 'Series' object has no attribute 'x'. \n",
    "    # To work around this, merged the new NN fields from snap onto the original inO.\n",
    "inO.dtypes\n",
    "inOsnap2 = pd.merge(inO[['wpop', 'xmid', 'wid', 'municipio', 'geometry', 'x', 'y']], \n",
    "                    inOsnap[['wid', 'NN', 'NN_dist']], on='wid', how='left')\n",
    "inOsnap2.dtypes\n",
    "inOsnap2.isna().sum()\n",
    "O_elev = add_elevation(inOsnap2, \"x\", \"y\", pth) # Takes maybe 10 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to file.\n",
    "nodes_elev.to_csv(os.path.join(pth, 'nodes_elev.csv'))\n",
    "O_elev.to_csv(os.path.join(pth, 'O_elev.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reload from disk.\n",
    "nodes_elev = os.path.join(pth, \"nodes_elev.csv\")\n",
    "nodes_elev = gpd.read_file(nodes_elev)\n",
    "O_elev = os.path.join(pth, \"O_elev.csv\")\n",
    "O_elev = gpd.read_file(O_elev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elevation-adjusted walk time to road."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     node_ID  ref          y          x      osmid          highway  \\\n",
       "0  238026752  NaN  18.261897 -66.050248  238026752              NaN   \n",
       "1  238026754  NaN  18.427812 -65.990280  238026754  traffic_signals   \n",
       "2  238026757  NaN  18.427999 -65.990606  238026757              NaN   \n",
       "3  238026760  NaN  18.236478 -66.025554  238026760              NaN   \n",
       "4  238026764  NaN  18.236759 -66.025735  238026764  traffic_signals   \n",
       "\n",
       "                                geometry  point_elev  \n",
       "0           POINT (-66.050248 18.261897)          93  \n",
       "1         POINT (-65.9902804 18.4278116)           6  \n",
       "2  POINT (-65.99060559999999 18.4279988)           5  \n",
       "3           POINT (-66.025554 18.236478)          65  \n",
       "4           POINT (-66.025735 18.236759)          65  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes_elev.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate_walktimes function takes a single dataframe, and all numbers must be float.\n",
    "# Merging the two datasets and cleaning up any naming issues.\n",
    "nodes_elev.rename(columns={'node_ID':'NN'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "       wpop  xmid  wid municipio  \\\n",
       "0  0.818646   0.0    1  Adjuntas   \n",
       "1  0.731308   0.0    2  Adjuntas   \n",
       "2  0.642141   0.0    3  Adjuntas   \n",
       "3  0.612746   0.0    4  Adjuntas   \n",
       "4  0.699177   0.0    5  Adjuntas   \n",
       "\n",
       "                                     geometry_x        x_x        y_x  \\\n",
       "0  POINT (-66.79433386730727 18.24980920687696) -66.794334  18.249809   \n",
       "1  POINT (-66.79350056730728 18.24980920687693) -66.793501  18.249809   \n",
       "2  POINT (-66.79266726730728 18.24980920687693) -66.792667  18.249809   \n",
       "3  POINT (-66.79183396730728 18.24980920687692) -66.791834  18.249809   \n",
       "4  POINT (-66.79016736730729 18.24980920687696) -66.790167  18.249809   \n",
       "\n",
       "          NN     NN_dist  point_elev_x  ref        y_y        x_y      osmid  \\\n",
       "0  239148931  380.618736           486  NaN  18.252231 -66.796881  239148931   \n",
       "1  239148931  447.535794           516  NaN  18.252231 -66.796881  239148931   \n",
       "2  239148931  520.852340           548  NaN  18.252231 -66.796881  239148931   \n",
       "3  239148931  598.220028           557  NaN  18.252231 -66.796881  239148931   \n",
       "4  239077120  726.478000           555  NaN  18.249867 -66.783309  239077120   \n",
       "\n",
       "  highway                    geometry_y  point_elev_y  \n",
       "0     NaN  POINT (-66.796881 18.252231)           572  \n",
       "1     NaN  POINT (-66.796881 18.252231)           572  \n",
       "2     NaN  POINT (-66.796881 18.252231)           572  \n",
       "3     NaN  POINT (-66.796881 18.252231)           572  \n",
       "4     NaN  POINT (-66.783309 18.249867)           639  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zvalues = pd.merge(O_elev, nodes_elev, on='NN', how='left')\n",
    "zvalues.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wpop          float64\n",
       "xmid          float64\n",
       "wid             int64\n",
       "municipio      object\n",
       "geometry_x     object\n",
       "x_x           float64\n",
       "y_x           float64\n",
       "NN              int64\n",
       "NN_dist       float64\n",
       "point_elev      int64\n",
       "ref            object\n",
       "y_y           float64\n",
       "x_y           float64\n",
       "osmid           int32\n",
       "highway        object\n",
       "geometry_y     object\n",
       "node_elev       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zvalues.rename(columns={'point_elev_y':'node_elev'}, inplace=True)\n",
    "zvalues.rename(columns={'point_elev_x':'point_elev'}, inplace=True)\n",
    "zvalues.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wpop          float64\n",
       "xmid          float64\n",
       "wid             int64\n",
       "municipio      object\n",
       "geometry_x     object\n",
       "x_x           float64\n",
       "y_x           float64\n",
       "NN              int64\n",
       "NN_dist       float64\n",
       "point_elev    float64\n",
       "ref            object\n",
       "y_y           float64\n",
       "x_y           float64\n",
       "osmid           int32\n",
       "highway        object\n",
       "geometry_y     object\n",
       "node_elev     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zvalues['point_elev'] = zvalues['point_elev'].astype(float)\n",
    "zvalues['node_elev'] = zvalues['node_elev'].astype(float)\n",
    "zvalues.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time is in seconds.\n",
    "def generate_walktimes(df, start = 'point_elev', end = 'node_elev', dist = 'NN_dist', max_walkspeed = 6, min_speed = 0.1):\n",
    "    # Tobler's hiking function: https://en.wikipedia.org/wiki/Tobler%27s_hiking_function\n",
    "    def speed(incline_ratio, max_speed):\n",
    "        walkspeed = max_speed * np.exp(-3.5 * abs(incline_ratio + 0.05)) \n",
    "        return walkspeed\n",
    "\n",
    "    speeds = {}\n",
    "    times = {}\n",
    "\n",
    "    for index, data in df.iterrows():\n",
    "        if data[dist] > 0:\n",
    "            delta_elevation = data[end] - data[start]\n",
    "            incline_ratio = delta_elevation / data[dist]\n",
    "            speed_kmph = speed(incline_ratio = incline_ratio, max_speed = max_walkspeed)\n",
    "            speed_kmph = max(speed_kmph, min_speed)\n",
    "            speeds[index] = (speed_kmph)\n",
    "            times[index] = (data[dist] / 1000 * 3600 / speed_kmph)\n",
    "\n",
    "    speed_df = pd.DataFrame.from_dict(speeds, orient = 'index')\n",
    "    time_df = pd.DataFrame.from_dict(times, orient = 'index')\n",
    "\n",
    "    df['walkspeed'] = speed_df[0]\n",
    "    df['walk_time'] = time_df[0]\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wpop</th>\n",
       "      <th>xmid</th>\n",
       "      <th>wid</th>\n",
       "      <th>municipio</th>\n",
       "      <th>geometry_x</th>\n",
       "      <th>x_x</th>\n",
       "      <th>y_x</th>\n",
       "      <th>NN</th>\n",
       "      <th>NN_dist</th>\n",
       "      <th>point_elev</th>\n",
       "      <th>ref</th>\n",
       "      <th>y_y</th>\n",
       "      <th>x_y</th>\n",
       "      <th>osmid</th>\n",
       "      <th>highway</th>\n",
       "      <th>geometry_y</th>\n",
       "      <th>node_elev</th>\n",
       "      <th>walkspeed</th>\n",
       "      <th>walk_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.818646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>POINT (-66.79433386730727 18.24980920687696)</td>\n",
       "      <td>-66.794334</td>\n",
       "      <td>18.249809</td>\n",
       "      <td>239148931</td>\n",
       "      <td>380.618736</td>\n",
       "      <td>486.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.252231</td>\n",
       "      <td>-66.796881</td>\n",
       "      <td>239148931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-66.796881 18.252231)</td>\n",
       "      <td>572.0</td>\n",
       "      <td>2.284031</td>\n",
       "      <td>599.916314</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.731308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>POINT (-66.79350056730728 18.24980920687693)</td>\n",
       "      <td>-66.793501</td>\n",
       "      <td>18.249809</td>\n",
       "      <td>239148931</td>\n",
       "      <td>447.535794</td>\n",
       "      <td>516.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.252231</td>\n",
       "      <td>-66.796881</td>\n",
       "      <td>239148931</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>572.0</td>\n",
       "      <td>3.250490</td>\n",
       "      <td>495.657267</td>\n",
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       "      <td>0.642141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Adjuntas</td>\n",
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       "      <td>-66.792667</td>\n",
       "      <td>18.249809</td>\n",
       "      <td>239148931</td>\n",
       "      <td>520.852340</td>\n",
       "      <td>548.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.252231</td>\n",
       "      <td>-66.796881</td>\n",
       "      <td>239148931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-66.796881 18.252231)</td>\n",
       "      <td>572.0</td>\n",
       "      <td>4.286563</td>\n",
       "      <td>437.429293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.612746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>POINT (-66.79183396730728 18.24980920687692)</td>\n",
       "      <td>-66.791834</td>\n",
       "      <td>18.249809</td>\n",
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       "      <td>598.220028</td>\n",
       "      <td>557.0</td>\n",
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       "      <td>572.0</td>\n",
       "      <td>4.613557</td>\n",
       "      <td>466.796478</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.699177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>POINT (-66.79016736730729 18.24980920687696)</td>\n",
       "      <td>-66.790167</td>\n",
       "      <td>18.249809</td>\n",
       "      <td>239077120</td>\n",
       "      <td>726.478000</td>\n",
       "      <td>555.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.249867</td>\n",
       "      <td>-66.783309</td>\n",
       "      <td>239077120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-66.783309 18.249867)</td>\n",
       "      <td>639.0</td>\n",
       "      <td>3.360424</td>\n",
       "      <td>778.271009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wpop  xmid  wid municipio  \\\n",
       "0  0.818646   0.0    1  Adjuntas   \n",
       "1  0.731308   0.0    2  Adjuntas   \n",
       "2  0.642141   0.0    3  Adjuntas   \n",
       "3  0.612746   0.0    4  Adjuntas   \n",
       "4  0.699177   0.0    5  Adjuntas   \n",
       "\n",
       "                                     geometry_x        x_x        y_x  \\\n",
       "0  POINT (-66.79433386730727 18.24980920687696) -66.794334  18.249809   \n",
       "1  POINT (-66.79350056730728 18.24980920687693) -66.793501  18.249809   \n",
       "2  POINT (-66.79266726730728 18.24980920687693) -66.792667  18.249809   \n",
       "3  POINT (-66.79183396730728 18.24980920687692) -66.791834  18.249809   \n",
       "4  POINT (-66.79016736730729 18.24980920687696) -66.790167  18.249809   \n",
       "\n",
       "          NN     NN_dist  point_elev  ref        y_y        x_y      osmid  \\\n",
       "0  239148931  380.618736       486.0  NaN  18.252231 -66.796881  239148931   \n",
       "1  239148931  447.535794       516.0  NaN  18.252231 -66.796881  239148931   \n",
       "2  239148931  520.852340       548.0  NaN  18.252231 -66.796881  239148931   \n",
       "3  239148931  598.220028       557.0  NaN  18.252231 -66.796881  239148931   \n",
       "4  239077120  726.478000       555.0  NaN  18.249867 -66.783309  239077120   \n",
       "\n",
       "  highway                    geometry_y  node_elev  walkspeed   walk_time  \n",
       "0     NaN  POINT (-66.796881 18.252231)      572.0   2.284031  599.916314  \n",
       "1     NaN  POINT (-66.796881 18.252231)      572.0   3.250490  495.657267  \n",
       "2     NaN  POINT (-66.796881 18.252231)      572.0   4.286563  437.429293  \n",
       "3     NaN  POINT (-66.796881 18.252231)      572.0   4.613557  466.796478  \n",
       "4     NaN  POINT (-66.783309 18.249867)      639.0   3.360424  778.271009  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zwalk = generate_walktimes(zvalues) # Takes about 10 minutes.\n",
    "zwalk.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "zwalk.to_csv('zwalk_full.csv', index=True) # Takes a few minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wpop         float64\n",
       "xmid         float64\n",
       "wid            int64\n",
       "municipio     object\n",
       "NN             int64\n",
       "osmid          int32\n",
       "walkspeed    float64\n",
       "walk_time    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean up the file for only the necessary columns.\n",
    "zwalk2 = zwalk.drop(columns=['geometry_x', 'x_x', 'y_x', 'NN_dist', 'point_elev', \n",
    "                                   'x_y', 'y_y', 'ref', 'highway', 'geometry_y', 'node_elev'])\n",
    "zwalk2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>wpop</th>\n",
       "      <th>xmid</th>\n",
       "      <th>wid</th>\n",
       "      <th>municipio</th>\n",
       "      <th>NN</th>\n",
       "      <th>osmid</th>\n",
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       "      <th>walk_time</th>\n",
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       "  </thead>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.731308</td>\n",
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       "      <td>2</td>\n",
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       "      <td>3.250490</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.642141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>239148931</td>\n",
       "      <td>239148931</td>\n",
       "      <td>4.286563</td>\n",
       "      <td>7.290488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.612746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>239148931</td>\n",
       "      <td>239148931</td>\n",
       "      <td>4.613557</td>\n",
       "      <td>7.779941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.699177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>239077120</td>\n",
       "      <td>239077120</td>\n",
       "      <td>3.360424</td>\n",
       "      <td>12.971183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wpop  xmid  wid municipio         NN      osmid  walkspeed  walk_time\n",
       "0  0.818646   0.0    1  Adjuntas  239148931  239148931   2.284031   9.998605\n",
       "1  0.731308   0.0    2  Adjuntas  239148931  239148931   3.250490   8.260954\n",
       "2  0.642141   0.0    3  Adjuntas  239148931  239148931   4.286563   7.290488\n",
       "3  0.612746   0.0    4  Adjuntas  239148931  239148931   4.613557   7.779941\n",
       "4  0.699177   0.0    5  Adjuntas  239077120  239077120   3.360424  12.971183"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert from seconds to minutes\n",
    "zwalk2['walk_time'] = zwalk2['walk_time'] / 60 \n",
    "zwalk2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to disk.\n",
    "zwalk2.to_csv('zwalk.csv', index=True)"
   ]
  }
 ],
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