mobility_baseline_2020-10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 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": [
    "### 3. Origins and destinations\n",
    "\n",
    "Measure distance from origin/destination to nearest node and save to file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime = nx.read_gpickle(\"gTime.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "### Create travel time values for the road nodes nearest to each service.\n",
    "\n",
    "Using calculate_OD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We only need to find the origin-destination pairs for nodes closest to the origins and services,\n",
    "# and some nodes will be the nearest for more than one service (and definitely for multiple origins).\n",
    "origins = list(inOsnap.NN.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "listD = list(inDsnap.NN.unique()) \n",
    "listH = list(inHsnap.NN.unique()) \n",
    "listP = list(inPsnap.NN.unique()) \n",
    "destslist = listD + listH + listP\n",
    "dests = list(set(destslist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142421"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(origins) # 142,421 unique nearest nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1011"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dests) # 1,011 unique nearest nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(listH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fail_value = 999999999 # If there is no shortest path, the OD pair will be assigned the fail value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(238026752, 238075156, {'osmid': 22137085, 'name': 'Calle F', 'highway': 'residential', 'oneway': False, 'length': 108.717, 'time': 19.569059999999997, 'mode': 'drive'})\n"
     ]
    }
   ],
   "source": [
    "gn.example_edge(gTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "OD = gn.calculate_OD(gTime, origins, dests, fail_value, weight = 'time')\n",
    "# Took maybe 10 min."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "OD_df = pd.DataFrame(OD, index = origins, columns = dests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   "source": [
    "OD_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "execution_count": 18,
     "metadata": {},
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   "source": [
    "OD_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to minutes and save to file.\n",
    "OD_min = OD_df[OD_df <fail_value] / 60\n",
    "OD_min.to_csv(os.path.join(pth, 'OD.csv'))\n",
    "# Takes a couple minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "<p>20 rows × 1011 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            233324546   4155721731  238944260   239097861   239310852   \\\n",
       "238061534   187.171666   63.549782  175.234161   28.914269  103.213949   \n",
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       "238261379    19.330812   44.116415   65.684366   24.054323   39.613442  \n",
       "238547482    54.708715   28.270925  126.332696   79.290446  100.261772  \n",
       "238537241    59.371255   32.933465  130.995236   83.952986  104.924312  \n",
       "5138198157   59.412088   32.974298  131.036069   83.993819  104.965145  \n",
       "238521463    59.688057   33.250267  131.312038   84.269788  105.241114  \n",
       "238217078    83.551775   57.113985  155.175756  108.133506  129.104832  \n",
       "238421388    87.874784   80.742644  151.216019   98.125120  122.596289  \n",
       "238402093    87.912787   80.780647  151.254022   98.163123  122.634292  \n",
       "238591938   101.965196   94.833056  165.306430  112.215532  136.686700  \n",
       "238923898   107.039621  128.237466  170.380856  117.289957  138.778429  \n",
       "238880909   108.966338  130.164183  172.307573  119.216674  140.705146  \n",
       "\n",
       "[20 rows x 1011 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OD_min.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create POI-specific OD and save to file.\n",
    "ODD = OD_df.loc[:, listD]\n",
    "ODD = ODD[ODD < fail_value] / 60 \n",
    "ODD.to_csv(os.path.join(pth, 'ODD.csv')) # Each takes a minute or so.\n",
    "\n",
    "ODH = OD_df.loc[:, listH]\n",
    "ODH = ODH[ODH < fail_value] / 60 \n",
    "ODH.to_csv(os.path.join(pth, 'ODH.csv'))\n",
    "\n",
    "ODP = OD_df.loc[:, listP]\n",
    "ODP = ODP[ODP < fail_value] / 60 \n",
    "ODP.to_csv(os.path.join(pth, 'ODP.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "[5 rows x 69 columns]"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ODH.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter 1st nearest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check each file to make sure nearest neighbor column is named correctly. If not, rename."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reload from file even if already loaded. Quickest way to ensure NN is a column rather than only the index.\n",
    "ODD = os.path.join(pth, \"ODD.csv\")\n",
    "ODD = pd.read_csv(ODD)\n",
    "ODP = os.path.join(pth, \"ODP.csv\")\n",
    "ODP = pd.read_csv(ODP)\n",
    "ODH = os.path.join(pth, \"ODH.csv\")\n",
    "ODH = pd.read_csv(ODH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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       "      <th>142411</th>\n",
       "      <td>238547482</td>\n",
       "      <td>250.722303</td>\n",
       "      <td>234.125653</td>\n",
       "      <td>243.391662</td>\n",
       "      <td>149.668008</td>\n",
       "      <td>161.984366</td>\n",
       "      <td>162.750227</td>\n",
       "      <td>133.085593</td>\n",
       "      <td>71.998822</td>\n",
       "      <td>73.552705</td>\n",
       "      <td>...</td>\n",
       "      <td>58.624406</td>\n",
       "      <td>58.305095</td>\n",
       "      <td>57.173795</td>\n",
       "      <td>55.603295</td>\n",
       "      <td>57.676113</td>\n",
       "      <td>58.847477</td>\n",
       "      <td>199.470441</td>\n",
       "      <td>108.920592</td>\n",
       "      <td>57.615050</td>\n",
       "      <td>238.429547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142412</th>\n",
       "      <td>238537241</td>\n",
       "      <td>255.384843</td>\n",
       "      <td>238.788193</td>\n",
       "      <td>248.054202</td>\n",
       "      <td>154.330548</td>\n",
       "      <td>166.646906</td>\n",
       "      <td>167.412767</td>\n",
       "      <td>128.380875</td>\n",
       "      <td>76.661362</td>\n",
       "      <td>78.215245</td>\n",
       "      <td>...</td>\n",
       "      <td>63.286946</td>\n",
       "      <td>62.967634</td>\n",
       "      <td>61.836335</td>\n",
       "      <td>60.265835</td>\n",
       "      <td>62.338652</td>\n",
       "      <td>63.510018</td>\n",
       "      <td>204.132981</td>\n",
       "      <td>113.583132</td>\n",
       "      <td>52.910332</td>\n",
       "      <td>243.092087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142413</th>\n",
       "      <td>5138198157</td>\n",
       "      <td>255.425676</td>\n",
       "      <td>238.829026</td>\n",
       "      <td>248.095035</td>\n",
       "      <td>154.371381</td>\n",
       "      <td>166.687739</td>\n",
       "      <td>167.453600</td>\n",
       "      <td>128.062476</td>\n",
       "      <td>76.702195</td>\n",
       "      <td>78.256078</td>\n",
       "      <td>...</td>\n",
       "      <td>63.327779</td>\n",
       "      <td>63.008467</td>\n",
       "      <td>61.877168</td>\n",
       "      <td>60.306668</td>\n",
       "      <td>62.379485</td>\n",
       "      <td>63.550850</td>\n",
       "      <td>204.173814</td>\n",
       "      <td>113.623965</td>\n",
       "      <td>52.591933</td>\n",
       "      <td>243.132920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142414</th>\n",
       "      <td>238521463</td>\n",
       "      <td>255.701645</td>\n",
       "      <td>239.104995</td>\n",
       "      <td>248.371004</td>\n",
       "      <td>154.647350</td>\n",
       "      <td>166.963708</td>\n",
       "      <td>167.729569</td>\n",
       "      <td>127.119228</td>\n",
       "      <td>76.978164</td>\n",
       "      <td>78.532047</td>\n",
       "      <td>...</td>\n",
       "      <td>63.603748</td>\n",
       "      <td>63.284436</td>\n",
       "      <td>62.153137</td>\n",
       "      <td>60.582637</td>\n",
       "      <td>62.655454</td>\n",
       "      <td>63.826819</td>\n",
       "      <td>204.449784</td>\n",
       "      <td>113.899934</td>\n",
       "      <td>51.648685</td>\n",
       "      <td>243.408889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142415</th>\n",
       "      <td>238217078</td>\n",
       "      <td>279.565363</td>\n",
       "      <td>262.968713</td>\n",
       "      <td>272.234722</td>\n",
       "      <td>168.750242</td>\n",
       "      <td>190.827426</td>\n",
       "      <td>191.593287</td>\n",
       "      <td>127.443616</td>\n",
       "      <td>100.841882</td>\n",
       "      <td>102.395765</td>\n",
       "      <td>...</td>\n",
       "      <td>87.467466</td>\n",
       "      <td>87.148154</td>\n",
       "      <td>86.016855</td>\n",
       "      <td>84.446355</td>\n",
       "      <td>86.519172</td>\n",
       "      <td>87.690537</td>\n",
       "      <td>228.313502</td>\n",
       "      <td>137.763652</td>\n",
       "      <td>51.973073</td>\n",
       "      <td>257.511781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142416</th>\n",
       "      <td>238421388</td>\n",
       "      <td>241.972505</td>\n",
       "      <td>259.008976</td>\n",
       "      <td>268.274985</td>\n",
       "      <td>125.460415</td>\n",
       "      <td>186.867688</td>\n",
       "      <td>187.633549</td>\n",
       "      <td>84.589187</td>\n",
       "      <td>93.750471</td>\n",
       "      <td>94.873959</td>\n",
       "      <td>...</td>\n",
       "      <td>82.178917</td>\n",
       "      <td>81.328726</td>\n",
       "      <td>84.189454</td>\n",
       "      <td>85.974378</td>\n",
       "      <td>80.446971</td>\n",
       "      <td>81.618336</td>\n",
       "      <td>224.353764</td>\n",
       "      <td>133.803914</td>\n",
       "      <td>73.341172</td>\n",
       "      <td>214.221954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142417</th>\n",
       "      <td>238402093</td>\n",
       "      <td>242.010508</td>\n",
       "      <td>259.046979</td>\n",
       "      <td>268.312988</td>\n",
       "      <td>125.498418</td>\n",
       "      <td>186.905691</td>\n",
       "      <td>187.671552</td>\n",
       "      <td>84.627190</td>\n",
       "      <td>93.788474</td>\n",
       "      <td>94.911962</td>\n",
       "      <td>...</td>\n",
       "      <td>82.216920</td>\n",
       "      <td>81.366729</td>\n",
       "      <td>84.227457</td>\n",
       "      <td>86.012381</td>\n",
       "      <td>80.484974</td>\n",
       "      <td>81.656339</td>\n",
       "      <td>224.391767</td>\n",
       "      <td>133.841917</td>\n",
       "      <td>73.379175</td>\n",
       "      <td>214.259957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142418</th>\n",
       "      <td>238591938</td>\n",
       "      <td>196.677714</td>\n",
       "      <td>271.529266</td>\n",
       "      <td>269.485649</td>\n",
       "      <td>110.432711</td>\n",
       "      <td>200.958100</td>\n",
       "      <td>201.723961</td>\n",
       "      <td>36.901675</td>\n",
       "      <td>107.840882</td>\n",
       "      <td>108.964370</td>\n",
       "      <td>...</td>\n",
       "      <td>96.269329</td>\n",
       "      <td>95.419138</td>\n",
       "      <td>98.279866</td>\n",
       "      <td>100.064789</td>\n",
       "      <td>94.537383</td>\n",
       "      <td>95.708748</td>\n",
       "      <td>191.230468</td>\n",
       "      <td>147.894326</td>\n",
       "      <td>90.835023</td>\n",
       "      <td>168.927163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142419</th>\n",
       "      <td>238923898</td>\n",
       "      <td>143.263138</td>\n",
       "      <td>218.114691</td>\n",
       "      <td>216.071074</td>\n",
       "      <td>58.525861</td>\n",
       "      <td>175.604847</td>\n",
       "      <td>176.370708</td>\n",
       "      <td>40.303370</td>\n",
       "      <td>112.915308</td>\n",
       "      <td>114.038796</td>\n",
       "      <td>...</td>\n",
       "      <td>101.343755</td>\n",
       "      <td>100.493563</td>\n",
       "      <td>103.354292</td>\n",
       "      <td>105.139215</td>\n",
       "      <td>99.611809</td>\n",
       "      <td>100.783174</td>\n",
       "      <td>137.815892</td>\n",
       "      <td>152.968752</td>\n",
       "      <td>166.654393</td>\n",
       "      <td>115.512588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142420</th>\n",
       "      <td>238880909</td>\n",
       "      <td>144.318022</td>\n",
       "      <td>219.169575</td>\n",
       "      <td>217.125958</td>\n",
       "      <td>60.452578</td>\n",
       "      <td>176.659731</td>\n",
       "      <td>177.425592</td>\n",
       "      <td>42.230087</td>\n",
       "      <td>114.842025</td>\n",
       "      <td>115.965513</td>\n",
       "      <td>...</td>\n",
       "      <td>103.270472</td>\n",
       "      <td>102.420280</td>\n",
       "      <td>105.281009</td>\n",
       "      <td>107.065932</td>\n",
       "      <td>101.538526</td>\n",
       "      <td>102.709891</td>\n",
       "      <td>138.870776</td>\n",
       "      <td>154.895469</td>\n",
       "      <td>168.581110</td>\n",
       "      <td>116.567472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                NN   238527834   243546298   238597596   237778206  \\\n",
       "142411   238547482  250.722303  234.125653  243.391662  149.668008   \n",
       "142412   238537241  255.384843  238.788193  248.054202  154.330548   \n",
       "142413  5138198157  255.425676  238.829026  248.095035  154.371381   \n",
       "142414   238521463  255.701645  239.104995  248.371004  154.647350   \n",
       "142415   238217078  279.565363  262.968713  272.234722  168.750242   \n",
       "142416   238421388  241.972505  259.008976  268.274985  125.460415   \n",
       "142417   238402093  242.010508  259.046979  268.312988  125.498418   \n",
       "142418   238591938  196.677714  271.529266  269.485649  110.432711   \n",
       "142419   238923898  143.263138  218.114691  216.071074   58.525861   \n",
       "142420   238880909  144.318022  219.169575  217.125958   60.452578   \n",
       "\n",
       "         237895435  2132540420   499948755   237982345  4156205993  ...  \\\n",
       "142411  161.984366  162.750227  133.085593   71.998822   73.552705  ...   \n",
       "142412  166.646906  167.412767  128.380875   76.661362   78.215245  ...   \n",
       "142413  166.687739  167.453600  128.062476   76.702195   78.256078  ...   \n",
       "142414  166.963708  167.729569  127.119228   76.978164   78.532047  ...   \n",
       "142415  190.827426  191.593287  127.443616  100.841882  102.395765  ...   \n",
       "142416  186.867688  187.633549   84.589187   93.750471   94.873959  ...   \n",
       "142417  186.905691  187.671552   84.627190   93.788474   94.911962  ...   \n",
       "142418  200.958100  201.723961   36.901675  107.840882  108.964370  ...   \n",
       "142419  175.604847  176.370708   40.303370  112.915308  114.038796  ...   \n",
       "142420  176.659731  177.425592   42.230087  114.842025  115.965513  ...   \n",
       "\n",
       "        2903017551  2705040978   238982313   238978858   238942305  \\\n",
       "142411   58.624406   58.305095   57.173795   55.603295   57.676113   \n",
       "142412   63.286946   62.967634   61.836335   60.265835   62.338652   \n",
       "142413   63.327779   63.008467   61.877168   60.306668   62.379485   \n",
       "142414   63.603748   63.284436   62.153137   60.582637   62.655454   \n",
       "142415   87.467466   87.148154   86.016855   84.446355   86.519172   \n",
       "142416   82.178917   81.328726   84.189454   85.974378   80.446971   \n",
       "142417   82.216920   81.366729   84.227457   86.012381   80.484974   \n",
       "142418   96.269329   95.419138   98.279866  100.064789   94.537383   \n",
       "142419  101.343755  100.493563  103.354292  105.139215   99.611809   \n",
       "142420  103.270472  102.420280  105.281009  107.065932  101.538526   \n",
       "\n",
       "        2902931361  5213391119  4155890330  5136150615   239272483  \n",
       "142411   58.847477  199.470441  108.920592   57.615050  238.429547  \n",
       "142412   63.510018  204.132981  113.583132   52.910332  243.092087  \n",
       "142413   63.550850  204.173814  113.623965   52.591933  243.132920  \n",
       "142414   63.826819  204.449784  113.899934   51.648685  243.408889  \n",
       "142415   87.690537  228.313502  137.763652   51.973073  257.511781  \n",
       "142416   81.618336  224.353764  133.803914   73.341172  214.221954  \n",
       "142417   81.656339  224.391767  133.841917   73.379175  214.259957  \n",
       "142418   95.708748  191.230468  147.894326   90.835023  168.927163  \n",
       "142419  100.783174  137.815892  152.968752  166.654393  115.512588  \n",
       "142420  102.709891  138.870776  154.895469  168.581110  116.567472  \n",
       "\n",
       "[10 rows x 70 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ODH.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ODH.rename(columns={'Unnamed: 0': 'NN'}, inplace=True) # Repeat for each OD set, if needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find first nearest POI for each origin node. Run this block for each variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ODD[\"1D\"] = 0\n",
    "sub = ODD.iloc[:,1:-1] # Filtering out the newly created field and the node ID column. (\"include everything between column 0 and the last column\")\n",
    "ODD[\"1D\"] = sub.min(axis=1) # Default is axis=0, meaning min value of each column selected. We want min of each row.\n",
    "D1 = ODD[['NN', '1D']] # Remove unnecessary OD values.\n",
    "D1.to_csv(os.path.join(pth, '1D_base.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NN</th>\n",
       "      <th>1D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>239148931</td>\n",
       "      <td>37.489956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>239077120</td>\n",
       "      <td>56.710245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>239127767</td>\n",
       "      <td>54.502221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>239136364</td>\n",
       "      <td>54.187776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>239077188</td>\n",
       "      <td>54.715068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>239176627</td>\n",
       "      <td>36.540948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>237676072</td>\n",
       "      <td>39.801646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>239149053</td>\n",
       "      <td>52.248120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>237686017</td>\n",
       "      <td>58.995387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>239158919</td>\n",
       "      <td>33.653093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          NN         1D\n",
       "0  239148931  37.489956\n",
       "1  239077120  56.710245\n",
       "2  239127767  54.502221\n",
       "3  239136364  54.187776\n",
       "4  239077188  54.715068\n",
       "5  239176627  36.540948\n",
       "6  237676072  39.801646\n",
       "7  239149053  52.248120\n",
       "8  237686017  58.995387\n",
       "9  239158919  33.653093"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D1.head(10) # Validation: The nodes that had NaN values in the OD matrix show a higher travel time in the landslide set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ODH[\"1H\"] = 0\n",
    "sub = ODH.iloc[:,1:-1] # Filtering out the newly created field and the node ID column.\n",
    "ODH[\"1H\"] = sub.min(axis=1) # Default is axis=0, meaning min value of each column selected. We want min of each row.\n",
    "H1 = ODH[['NN', '1H']] # Remove unnecessary OD values.\n",
    "H1.to_csv(os.path.join(pth, '1H_base.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1H</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>239148931</td>\n",
       "      <td>39.231936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>239077120</td>\n",
       "      <td>34.486956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>239127767</td>\n",
       "      <td>32.278933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>239136364</td>\n",
       "      <td>31.964488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>239077188</td>\n",
       "      <td>32.491779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>239176627</td>\n",
       "      <td>38.282928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>237676072</td>\n",
       "      <td>28.394310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>239149053</td>\n",
       "      <td>30.024832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>237686017</td>\n",
       "      <td>36.772098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>239158919</td>\n",
       "      <td>28.402349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NN         1H\n",
       "0  239148931  39.231936\n",
       "1  239077120  34.486956\n",
       "2  239127767  32.278933\n",
       "3  239136364  31.964488\n",
       "4  239077188  32.491779\n",
       "5  239176627  38.282928\n",
       "6  237676072  28.394310\n",
       "7  239149053  30.024832\n",
       "8  237686017  36.772098\n",
       "9  239158919  28.402349"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ODP[\"1P\"] = 0\n",
    "sub = ODP.iloc[:,1:-1] # Filtering out the newly created field and the node ID column.\n",
    "ODP[\"1P\"] = sub.min(axis=1) # Default is axis=0, meaning min value of each column selected. We want min of each row.\n",
    "P1 = ODP[['NN', '1P']] # Remove unnecessary OD values.\n",
    "P1.to_csv(os.path.join(pth, '1P_base.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>19.665247</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>239127767</td>\n",
       "      <td>17.457223</td>\n",
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       "      <th>3</th>\n",
       "      <td>239136364</td>\n",
       "      <td>17.142778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>239077188</td>\n",
       "      <td>17.670070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>239176627</td>\n",
       "      <td>13.313688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>237676072</td>\n",
       "      <td>28.394310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>239149053</td>\n",
       "      <td>15.203122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>237686017</td>\n",
       "      <td>21.950389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>239158919</td>\n",
       "      <td>24.843397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          NN         1P\n",
       "0  239148931  14.262696\n",
       "1  239077120  19.665247\n",
       "2  239127767  17.457223\n",
       "3  239136364  17.142778\n",
       "4  239077188  17.670070\n",
       "5  239176627  13.313688\n",
       "6  237676072  28.394310\n",
       "7  239149053  15.203122\n",
       "8  237686017  21.950389\n",
       "9  239158919  24.843397"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P1.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare to baseline values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create multi-modal travel times by combining walk time to road with drive time to nth nearest service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If starting new session, re-load from disk.\n",
    "zwalk = os.path.join(pth, \"zwalk.csv\") \n",
    "zwalk = pd.read_csv(zwalk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "D1 = os.path.join(pth, \"1D.csv\")\n",
    "D1 = pd.read_csv(D1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <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>4</td>\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",
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      ],
      "text/plain": [
       "   Unnamed: 0      wpop  xmid  wid municipio         NN      osmid  walkspeed  \\\n",
       "0           0  0.818646   0.0    1  Adjuntas  239148931  239148931   2.284031   \n",
       "1           1  0.731308   0.0    2  Adjuntas  239148931  239148931   3.250490   \n",
       "2           2  0.642141   0.0    3  Adjuntas  239148931  239148931   4.286563   \n",
       "3           3  0.612746   0.0    4  Adjuntas  239148931  239148931   4.613557   \n",
       "4           4  0.699177   0.0    5  Adjuntas  239077120  239077120   3.360424   \n",
       "\n",
       "   walk_time  \n",
       "0   9.998605  \n",
       "1   8.260954  \n",
       "2   7.290488  \n",
       "3   7.779941  \n",
       "4  12.971183  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zwalk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "      <td>54.502221</td>\n",
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       "      <th>3</th>\n",
       "      <td>239136364</td>\n",
       "      <td>54.187776</td>\n",
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       "      <th>4</th>\n",
       "      <td>239077188</td>\n",
       "      <td>54.715068</td>\n",
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      "text/plain": [
       "          NN         1D\n",
       "0  239148931  37.489956\n",
       "1  239077120  56.710245\n",
       "2  239127767  54.502221\n",
       "3  239136364  54.187776\n",
       "4  239077188  54.715068"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>0.612746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
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       "      <td>239148931</td>\n",
       "      <td>4.613557</td>\n",
       "      <td>7.779941</td>\n",
       "      <td>37.489956</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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",
       "      <td>56.710245</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0      wpop  xmid  wid municipio         NN      osmid  walkspeed  \\\n",
       "0           0  0.818646   0.0    1  Adjuntas  239148931  239148931   2.284031   \n",
       "1           1  0.731308   0.0    2  Adjuntas  239148931  239148931   3.250490   \n",
       "2           2  0.642141   0.0    3  Adjuntas  239148931  239148931   4.286563   \n",
       "3           3  0.612746   0.0    4  Adjuntas  239148931  239148931   4.613557   \n",
       "4           4  0.699177   0.0    5  Adjuntas  239077120  239077120   3.360424   \n",
       "\n",
       "   walk_time         1D  \n",
       "0   9.998605  37.489956  \n",
       "1   8.260954  37.489956  \n",
       "2   7.290488  37.489956  \n",
       "3   7.779941  37.489956  \n",
       "4  12.971183  56.710245  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge nearest POIs and walktimes\n",
    "zwalkD = zwalk.merge(D1, how='left', left_on='NN', right_on='NN', sort=False)\n",
    "zwalkD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.731308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>239148931</td>\n",
       "      <td>239148931</td>\n",
       "      <td>3.250490</td>\n",
       "      <td>8.260954</td>\n",
       "      <td>14.262696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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",
       "      <td>14.262696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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",
       "      <td>14.262696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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",
       "      <td>19.665247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      wpop  xmid  wid municipio         NN      osmid  walkspeed  \\\n",
       "0           0  0.818646   0.0    1  Adjuntas  239148931  239148931   2.284031   \n",
       "1           1  0.731308   0.0    2  Adjuntas  239148931  239148931   3.250490   \n",
       "2           2  0.642141   0.0    3  Adjuntas  239148931  239148931   4.286563   \n",
       "3           3  0.612746   0.0    4  Adjuntas  239148931  239148931   4.613557   \n",
       "4           4  0.699177   0.0    5  Adjuntas  239077120  239077120   3.360424   \n",
       "\n",
       "   walk_time         1P  \n",
       "0   9.998605  14.262696  \n",
       "1   8.260954  14.262696  \n",
       "2   7.290488  14.262696  \n",
       "3   7.779941  14.262696  \n",
       "4  12.971183  19.665247  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge nearest POIs and walktimes\n",
    "zwalkP = zwalk.merge(P1, how='left', left_on='NN', right_on='NN', sort=False)\n",
    "zwalkP.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>wpop</th>\n",
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       "      <td>Adjuntas</td>\n",
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       "      <td>2.284031</td>\n",
       "      <td>9.998605</td>\n",
       "      <td>39.231936</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>239148931</td>\n",
       "      <td>3.250490</td>\n",
       "      <td>8.260954</td>\n",
       "      <td>39.231936</td>\n",
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       "      <th>2</th>\n",
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       "      <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",
       "      <td>39.231936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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",
       "      <td>39.231936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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",
       "      <td>34.486956</td>\n",
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      "text/plain": [
       "   Unnamed: 0      wpop  xmid  wid municipio         NN      osmid  walkspeed  \\\n",
       "0           0  0.818646   0.0    1  Adjuntas  239148931  239148931   2.284031   \n",
       "1           1  0.731308   0.0    2  Adjuntas  239148931  239148931   3.250490   \n",
       "2           2  0.642141   0.0    3  Adjuntas  239148931  239148931   4.286563   \n",
       "3           3  0.612746   0.0    4  Adjuntas  239148931  239148931   4.613557   \n",
       "4           4  0.699177   0.0    5  Adjuntas  239077120  239077120   3.360424   \n",
       "\n",
       "   walk_time         1H  \n",
       "0   9.998605  39.231936  \n",
       "1   8.260954  39.231936  \n",
       "2   7.290488  39.231936  \n",
       "3   7.779941  39.231936  \n",
       "4  12.971183  34.486956  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge nearest POIs and walktimes\n",
    "zwalkH = zwalk.merge(H1, how='left', left_on='NN', right_on='NN', sort=False)\n",
    "zwalkH.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine walk time from WorldPop point to nearest road node, and from road node to facility.\n",
    "zwalkH[\"mm1H\"] = 0\n",
    "zwalkH[\"mm1H\"] = zwalkH[\"walk_time\"] + zwalkH[\"1H\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7.290488</td>\n",
       "      <td>39.231936</td>\n",
       "      <td>46.522424</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.612746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Adjuntas</td>\n",
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       "      <td>239148931</td>\n",
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       "      <td>7.779941</td>\n",
       "      <td>39.231936</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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",
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      ],
      "text/plain": [
       "   Unnamed: 0      wpop  xmid  wid municipio         NN      osmid  walkspeed  \\\n",
       "0           0  0.818646   0.0    1  Adjuntas  239148931  239148931   2.284031   \n",
       "1           1  0.731308   0.0    2  Adjuntas  239148931  239148931   3.250490   \n",
       "2           2  0.642141   0.0    3  Adjuntas  239148931  239148931   4.286563   \n",
       "3           3  0.612746   0.0    4  Adjuntas  239148931  239148931   4.613557   \n",
       "4           4  0.699177   0.0    5  Adjuntas  239077120  239077120   3.360424   \n",
       "\n",
       "   walk_time         1H       mm1H  \n",
       "0   9.998605  39.231936  49.230541  \n",
       "1   8.260954  39.231936  47.492890  \n",
       "2   7.290488  39.231936  46.522424  \n",
       "3   7.779941  39.231936  47.011877  \n",
       "4  12.971183  34.486956  47.458140  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zwalkH.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine walk time from WorldPop point to nearest road node, and from road node to facility.\n",
    "zwalkP[\"mm1P\"] = 0\n",
    "zwalkP[\"mm1P\"] = zwalkP[\"walk_time\"] + zwalkP[\"1P\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.612746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Adjuntas</td>\n",
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       "      <td>239148931</td>\n",
       "      <td>4.613557</td>\n",
       "      <td>7.779941</td>\n",
       "      <td>14.262696</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.699177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Adjuntas</td>\n",
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       "      <td>239077120</td>\n",
       "      <td>3.360424</td>\n",
       "      <td>12.971183</td>\n",
       "      <td>19.665247</td>\n",
       "      <td>32.636430</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      wpop  xmid  wid municipio         NN      osmid  walkspeed  \\\n",
       "0           0  0.818646   0.0    1  Adjuntas  239148931  239148931   2.284031   \n",
       "1           1  0.731308   0.0    2  Adjuntas  239148931  239148931   3.250490   \n",
       "2           2  0.642141   0.0    3  Adjuntas  239148931  239148931   4.286563   \n",
       "3           3  0.612746   0.0    4  Adjuntas  239148931  239148931   4.613557   \n",
       "4           4  0.699177   0.0    5  Adjuntas  239077120  239077120   3.360424   \n",
       "\n",
       "   walk_time         1P       mm1P  \n",
       "0   9.998605  14.262696  24.261301  \n",
       "1   8.260954  14.262696  22.523650  \n",
       "2   7.290488  14.262696  21.553184  \n",
       "3   7.779941  14.262696  22.042637  \n",
       "4  12.971183  19.665247  32.636430  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zwalkP.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine walk time from WorldPop point to nearest road node, and from road node to facility.\n",
    "zwalkD[\"mm1D\"] = 0\n",
    "zwalkD[\"mm1D\"] = zwalkD[\"walk_time\"] + zwalkD[\"1D\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>wid</th>\n",
       "      <th>municipio</th>\n",
       "      <th>NN</th>\n",
       "      <th>osmid</th>\n",
       "      <th>walkspeed</th>\n",
       "      <th>walk_time</th>\n",
       "      <th>1D</th>\n",
       "      <th>mm1D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.818646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>239148931</td>\n",
       "      <td>239148931</td>\n",
       "      <td>2.284031</td>\n",
       "      <td>9.998605</td>\n",
       "      <td>37.489956</td>\n",
       "      <td>47.488561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.731308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Adjuntas</td>\n",
       "      <td>239148931</td>\n",
       "      <td>239148931</td>\n",
       "      <td>3.250490</td>\n",
       "      <td>8.260954</td>\n",
       "      <td>37.489956</td>\n",
       "      <td>45.750910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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",
       "      <td>37.489956</td>\n",
       "      <td>44.780444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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",
       "      <td>37.489956</td>\n",
       "      <td>45.269897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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",
       "      <td>56.710245</td>\n",
       "      <td>69.681428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      wpop  xmid  wid municipio         NN      osmid  walkspeed  \\\n",
       "0           0  0.818646   0.0    1  Adjuntas  239148931  239148931   2.284031   \n",
       "1           1  0.731308   0.0    2  Adjuntas  239148931  239148931   3.250490   \n",
       "2           2  0.642141   0.0    3  Adjuntas  239148931  239148931   4.286563   \n",
       "3           3  0.612746   0.0    4  Adjuntas  239148931  239148931   4.613557   \n",
       "4           4  0.699177   0.0    5  Adjuntas  239077120  239077120   3.360424   \n",
       "\n",
       "   walk_time         1D       mm1D  \n",
       "0   9.998605  37.489956  47.488561  \n",
       "1   8.260954  37.489956  45.750910  \n",
       "2   7.290488  37.489956  44.780444  \n",
       "3   7.779941  37.489956  45.269897  \n",
       "4  12.971183  56.710245  69.681428  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zwalkD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "zwalkD = zwalkD[['wpop', 'xmid', 'wid', 'municipio', 'NN', 'walk_time', '1D', 'mm1D']]\n",
    "zwalkH = zwalkH[['wpop', 'xmid', 'wid', 'municipio', 'NN', 'walk_time', '1H', 'mm1H']]\n",
    "zwalkP = zwalkP[['wpop', 'xmid', 'wid', 'municipio', 'NN', 'walk_time', '1P', 'mm1P']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "zwalkH.to_csv(os.path.join(pth, 'H_mm.csv'))\n",
    "zwalkP.to_csv(os.path.join(pth, 'P_mm.csv'))\n",
    "zwalkD.to_csv(os.path.join(pth, 'D_mm.csv'))"
   ]
  }
 ],
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