mobility_landslide_2020-10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mobility in Puerto Rico following Hurricane Maria\n",
    "\n",
    "### Baseline model: Measuring travel time from all origins (~1 mil WorldPop cells) to key health facilities\n",
    "Baseline travel time is measured as the walking time (accounting for slope) from origin Point A to the closest node \n",
    "on the road network, plus driving time from there to the closest road node to a service.\n",
    "Facilities are expected to be proximal to the road network, so no measure is taken between \n",
    "road and service.\n",
    "\n",
    "Health facilities datasets:\n",
    "- dialysis facilities\n",
    "- hospitals\n",
    "- pharmacies\n",
    "\n",
    "### Storm disruption model: Accounting for storm-related disruptions to baseline travel time.\n",
    "The disruption model simulates travel slowdowns and alternate routing due to geohazards, infrastructural damage, and facility incapacity. Disrupted travel times are measured by i) full road obstruction requiring alternate routing and ii) road speed penalty affecting total travel time. \n",
    "\n",
    "Storm-related hazards indicating complete obstruction:\n",
    "- flooding intersection at 3 feet depth\n",
    "- landslide intersection\n",
    "\n",
    "Storm-related hazard indicating road speed penalty:\n",
    "- flooding intersection at 1 and 2 feet depth"
   ]
  },
  {
   "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": [
    "### 2. Get driving network for all islands in Puerto Rico. \n",
    "\n",
    "Reloading OSM data from file. Origins and health facilities are not yet incorporated. Travel measured in length (meters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime = nx.read_gpickle(\"gTime.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spatial join in Python on the gdf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grace\\Anaconda3\\envs\\access\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    }
   ],
   "source": [
    "gTime_node_gdf = gn.node_gdf_from_graph(gTime)\n",
    "gTime_edge_gdf = gn.edge_gdf_from_graph(gTime)\n",
    "# Takes just a few seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_edge_gdf = gTime_edge_gdf.drop(['oneway','lanes','ref','maxspeed','area', 'width','service','junction','access','tunnel','name', 'bridge'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 393582 entries, 0 to 393581\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count   Dtype   \n",
      "---  ------    --------------   -----   \n",
      " 0   stnode    393582 non-null  int64   \n",
      " 1   endnode   393582 non-null  int64   \n",
      " 2   highway   393582 non-null  object  \n",
      " 3   time      393582 non-null  float64 \n",
      " 4   length    393582 non-null  float64 \n",
      " 5   osmid     393582 non-null  object  \n",
      " 6   mode      393582 non-null  object  \n",
      " 7   geometry  393582 non-null  geometry\n",
      "dtypes: float64(2), geometry(1), int64(2), object(3)\n",
      "memory usage: 24.0+ MB\n"
     ]
    }
   ],
   "source": [
    "gTime_edge_gdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the flood insurance zones dataset. Using the meters depth column (0,1,2, or 3 meters deep).\n",
    "landslide = os.path.join(pth, \"2017landslide_1km.shp\")\n",
    "landslide = gpd.read_file(landslide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 71431 entries, 0 to 71430\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype   \n",
      "---  ------     --------------  -----   \n",
      " 0   Municipio  71431 non-null  object  \n",
      " 1   Barrio     71431 non-null  object  \n",
      " 2   geometry   71431 non-null  geometry\n",
      "dtypes: geometry(1), object(2)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "landslide.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "landslide = landslide[['Municipio', 'Barrio', 'geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: PROJCS[\"NAD83_2011_Puerto_Rico_and_Virgin_Is\",GEOG ...>\n",
       "Name: NAD83_2011_Puerto_Rico_and_Virgin_Is\n",
       "Axis Info [cartesian]:\n",
       "- E[east]: Easting (metre)\n",
       "- N[north]: Northing (metre)\n",
       "Area of Use:\n",
       "- undefined\n",
       "Coordinate Operation:\n",
       "- name: unnamed\n",
       "- method: Lambert Conic Conformal (2SP)\n",
       "Datum: NAD83 (National Spatial Reference System 2011)\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landslide.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: +init=epsg:4326 +type=crs>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- lon[east]: Longitude (degree)\n",
       "- lat[north]: Latitude (degree)\n",
       "Area of Use:\n",
       "- name: World\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gTime_edge_gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "landslide = landslide.to_crs(\"EPSG:4326\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_edge_gdf = gTime_edge_gdf.to_crs(\"EPSG:4326\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landslide.crs == gTime_edge_gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "join = gpd.sjoin(gTime_edge_gdf, landslide, how=\"left\", op=\"intersects\")\n",
    "# Doesn't take long with the landslide dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Int64Index: 404926 entries, 0 to 393581\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count   Dtype   \n",
      "---  ------       --------------   -----   \n",
      " 0   stnode       404926 non-null  int64   \n",
      " 1   endnode      404926 non-null  int64   \n",
      " 2   highway      404926 non-null  object  \n",
      " 3   time         404926 non-null  float64 \n",
      " 4   length       404926 non-null  float64 \n",
      " 5   osmid        404926 non-null  object  \n",
      " 6   mode         404926 non-null  object  \n",
      " 7   geometry     404926 non-null  geometry\n",
      " 8   index_right  20595 non-null   float64 \n",
      " 9   Municipio    20595 non-null   object  \n",
      " 10  Barrio       20595 non-null   object  \n",
      "dtypes: float64(3), geometry(1), int64(2), object(5)\n",
      "memory usage: 37.1+ MB\n"
     ]
    }
   ],
   "source": [
    "join.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nx.to_networkx_graph?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_landslide_df = pd.DataFrame(join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 404926 entries, 0 to 393581\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count   Dtype   \n",
      "---  ------       --------------   -----   \n",
      " 0   stnode       404926 non-null  int64   \n",
      " 1   endnode      404926 non-null  int64   \n",
      " 2   highway      404926 non-null  object  \n",
      " 3   time         404926 non-null  float64 \n",
      " 4   length       404926 non-null  float64 \n",
      " 5   osmid        404926 non-null  object  \n",
      " 6   mode         404926 non-null  object  \n",
      " 7   geometry     404926 non-null  geometry\n",
      " 8   index_right  20595 non-null   float64 \n",
      " 9   Municipio    20595 non-null   object  \n",
      " 10  Barrio       20595 non-null   object  \n",
      "dtypes: float64(3), geometry(1), int64(2), object(5)\n",
      "memory usage: 37.1+ MB\n"
     ]
    }
   ],
   "source": [
    "gTime_landslide_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_landslide_df.to_csv(os.path.join(pth, 'gTime_landslide_1km.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create speed penalties.\n",
    "This is easier in R, but I want to avoid saving and reloading the file due to the pickiness of converting back to a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_landslide_df[\"penalty_l\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
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       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.07267 18.21207, -66.07168 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.88704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404896</th>\n",
       "      <td>393552</td>\n",
       "      <td>238026666</td>\n",
       "      <td>238066349</td>\n",
       "      <td>residential</td>\n",
       "      <td>14.92920</td>\n",
       "      <td>82.940</td>\n",
       "      <td>22131483</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.07267 18.21207, -66.07261 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.92920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404897</th>\n",
       "      <td>393553</td>\n",
       "      <td>238026666</td>\n",
       "      <td>238006360</td>\n",
       "      <td>residential</td>\n",
       "      <td>15.06906</td>\n",
       "      <td>83.717</td>\n",
       "      <td>22131483</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.07267 18.21207, -66.07273 18.2...</td>\n",
       "      <td>493.0</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>Cagaboncito</td>\n",
       "      <td>15.06906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404898</th>\n",
       "      <td>393554</td>\n",
       "      <td>238026671</td>\n",
       "      <td>238026666</td>\n",
       "      <td>residential</td>\n",
       "      <td>18.88704</td>\n",
       "      <td>104.928</td>\n",
       "      <td>22128605</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.07168 18.21215, -66.07267 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.88704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404899</th>\n",
       "      <td>393555</td>\n",
       "      <td>238550963</td>\n",
       "      <td>238509482</td>\n",
       "      <td>residential</td>\n",
       "      <td>24.55074</td>\n",
       "      <td>136.393</td>\n",
       "      <td>22184945</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.85196 18.28374, -66.85146 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.55074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404900</th>\n",
       "      <td>393556</td>\n",
       "      <td>238550963</td>\n",
       "      <td>238472577</td>\n",
       "      <td>secondary</td>\n",
       "      <td>33.86388</td>\n",
       "      <td>282.199</td>\n",
       "      <td>22185844</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.85196 18.28374, -66.85183 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.86388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404901</th>\n",
       "      <td>393557</td>\n",
       "      <td>238550963</td>\n",
       "      <td>238528656</td>\n",
       "      <td>secondary</td>\n",
       "      <td>8.27388</td>\n",
       "      <td>68.949</td>\n",
       "      <td>22185844</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.85196 18.28374, -66.85207 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404902</th>\n",
       "      <td>393558</td>\n",
       "      <td>238026680</td>\n",
       "      <td>237993968</td>\n",
       "      <td>residential</td>\n",
       "      <td>94.70556</td>\n",
       "      <td>526.142</td>\n",
       "      <td>22128606</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.03143 18.26259, -66.03641 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.70556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404903</th>\n",
       "      <td>393559</td>\n",
       "      <td>238026687</td>\n",
       "      <td>700233767</td>\n",
       "      <td>motorway_link</td>\n",
       "      <td>14.76621</td>\n",
       "      <td>164.069</td>\n",
       "      <td>22128620</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.03843 18.26295, -66.03854 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.76621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404904</th>\n",
       "      <td>393560</td>\n",
       "      <td>238026687</td>\n",
       "      <td>1422368466</td>\n",
       "      <td>motorway_link</td>\n",
       "      <td>10.06605</td>\n",
       "      <td>111.845</td>\n",
       "      <td>57853943</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.03843 18.26295, -66.03835 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.06605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404905</th>\n",
       "      <td>393561</td>\n",
       "      <td>5248122836</td>\n",
       "      <td>237991427</td>\n",
       "      <td>residential</td>\n",
       "      <td>5.85450</td>\n",
       "      <td>32.525</td>\n",
       "      <td>22124214</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.84678 18.38056, -66.84696 18.3...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.85450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404906</th>\n",
       "      <td>393562</td>\n",
       "      <td>238026714</td>\n",
       "      <td>238026730</td>\n",
       "      <td>residential</td>\n",
       "      <td>46.93842</td>\n",
       "      <td>260.769</td>\n",
       "      <td>22128616</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86647 18.26430, -65.86662 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.93842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404907</th>\n",
       "      <td>393563</td>\n",
       "      <td>238026714</td>\n",
       "      <td>238024765</td>\n",
       "      <td>residential</td>\n",
       "      <td>34.63272</td>\n",
       "      <td>192.404</td>\n",
       "      <td>22130366</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86647 18.26430, -65.86635 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.63272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404908</th>\n",
       "      <td>393564</td>\n",
       "      <td>238026714</td>\n",
       "      <td>238038585</td>\n",
       "      <td>residential</td>\n",
       "      <td>20.79792</td>\n",
       "      <td>115.544</td>\n",
       "      <td>22130366</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86647 18.26430, -65.86647 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.79792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404909</th>\n",
       "      <td>393565</td>\n",
       "      <td>238026722</td>\n",
       "      <td>5167079179</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>33.30576</td>\n",
       "      <td>185.032</td>\n",
       "      <td>532490641</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98663 18.43177, -65.98753 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.30576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404910</th>\n",
       "      <td>393566</td>\n",
       "      <td>238026723</td>\n",
       "      <td>4133461016</td>\n",
       "      <td>primary</td>\n",
       "      <td>1.93527</td>\n",
       "      <td>21.503</td>\n",
       "      <td>202426342</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.03693 18.25840, -66.03689 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.93527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404911</th>\n",
       "      <td>393567</td>\n",
       "      <td>238026729</td>\n",
       "      <td>5167079184</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>16.31772</td>\n",
       "      <td>90.654</td>\n",
       "      <td>532491121</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98867 18.42985, -65.98938 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.31772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404912</th>\n",
       "      <td>393568</td>\n",
       "      <td>238026729</td>\n",
       "      <td>238026748</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>37.04274</td>\n",
       "      <td>205.793</td>\n",
       "      <td>532490641</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98867 18.42985, -65.98874 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.04274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404913</th>\n",
       "      <td>393569</td>\n",
       "      <td>238026729</td>\n",
       "      <td>5167079179</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>21.45060</td>\n",
       "      <td>119.170</td>\n",
       "      <td>532490641</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98867 18.42985, -65.98851 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.45060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404914</th>\n",
       "      <td>393570</td>\n",
       "      <td>238026730</td>\n",
       "      <td>238044145</td>\n",
       "      <td>residential</td>\n",
       "      <td>11.30382</td>\n",
       "      <td>62.799</td>\n",
       "      <td>22131258</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86817 18.26332, -65.86802 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.30382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404915</th>\n",
       "      <td>393571</td>\n",
       "      <td>238026730</td>\n",
       "      <td>238024741</td>\n",
       "      <td>residential</td>\n",
       "      <td>84.65670</td>\n",
       "      <td>470.315</td>\n",
       "      <td>22128616</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86817 18.26332, -65.86839 18.2...</td>\n",
       "      <td>45981.0</td>\n",
       "      <td>Can2vanas</td>\n",
       "      <td>Cubuy</td>\n",
       "      <td>84.65670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404916</th>\n",
       "      <td>393572</td>\n",
       "      <td>238026730</td>\n",
       "      <td>238026714</td>\n",
       "      <td>residential</td>\n",
       "      <td>46.93842</td>\n",
       "      <td>260.769</td>\n",
       "      <td>22128616</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86817 18.26332, -65.86804 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.93842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404917</th>\n",
       "      <td>393573</td>\n",
       "      <td>238551018</td>\n",
       "      <td>238542412</td>\n",
       "      <td>residential</td>\n",
       "      <td>28.61604</td>\n",
       "      <td>158.978</td>\n",
       "      <td>529224222</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.84656 18.28132, -66.84658 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.61604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404918</th>\n",
       "      <td>393574</td>\n",
       "      <td>238026733</td>\n",
       "      <td>238026752</td>\n",
       "      <td>residential</td>\n",
       "      <td>34.03782</td>\n",
       "      <td>189.099</td>\n",
       "      <td>22128613</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.05095 18.26338, -66.05086 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.03782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404919</th>\n",
       "      <td>393575</td>\n",
       "      <td>239075323</td>\n",
       "      <td>238000699</td>\n",
       "      <td>residential</td>\n",
       "      <td>42.51456</td>\n",
       "      <td>236.192</td>\n",
       "      <td>22239587</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.04037 18.31270, -66.04026 18.3...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.51456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404920</th>\n",
       "      <td>393576</td>\n",
       "      <td>239075323</td>\n",
       "      <td>238911905</td>\n",
       "      <td>residential</td>\n",
       "      <td>9.59148</td>\n",
       "      <td>53.286</td>\n",
       "      <td>22239587</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.04037 18.31270, -66.04045 18.3...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.59148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404921</th>\n",
       "      <td>393577</td>\n",
       "      <td>239075323</td>\n",
       "      <td>239075409</td>\n",
       "      <td>residential</td>\n",
       "      <td>31.01580</td>\n",
       "      <td>172.310</td>\n",
       "      <td>22239591</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.04037 18.31270, -66.04031 18.3...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.01580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404922</th>\n",
       "      <td>393578</td>\n",
       "      <td>239075323</td>\n",
       "      <td>238911886</td>\n",
       "      <td>residential</td>\n",
       "      <td>6.28632</td>\n",
       "      <td>34.924</td>\n",
       "      <td>22239591</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.04037 18.31270, -66.04058 18.3...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.28632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404923</th>\n",
       "      <td>393579</td>\n",
       "      <td>238026748</td>\n",
       "      <td>5167073092</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>39.56712</td>\n",
       "      <td>329.726</td>\n",
       "      <td>196950650</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98955 18.42824, -65.98967 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.56712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404924</th>\n",
       "      <td>393580</td>\n",
       "      <td>238026748</td>\n",
       "      <td>4220614629</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>5.76624</td>\n",
       "      <td>48.052</td>\n",
       "      <td>[22136906, 363774843, 422318206]</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98955 18.42824, -65.98948 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.76624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404925</th>\n",
       "      <td>393581</td>\n",
       "      <td>238026748</td>\n",
       "      <td>238026729</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>37.04274</td>\n",
       "      <td>205.793</td>\n",
       "      <td>532490641</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98955 18.42824, -65.98945 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.04274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         index      stnode     endnode        highway      time   length  \\\n",
       "404886  393542   238026643   238082805    residential   7.67628   42.646   \n",
       "404887  393543   238026643   238052057    residential  30.65508  170.306   \n",
       "404888  393544   238026646   238026643    residential  24.45822  135.879   \n",
       "404889  393545  5178916766  5178916767    residential  31.13190  172.955   \n",
       "404890  393546  5178916767  5178916768    residential  11.20104   62.228   \n",
       "404891  393547  5178916767  5178916766    residential  31.13190  172.955   \n",
       "404892  393548  5178916767  5178916749    residential  11.36016   63.112   \n",
       "404893  393549  5178916768  5178916767    residential  11.20104   62.228   \n",
       "404894  393550  5178916774  5178916749    residential  11.74752   65.264   \n",
       "404895  393551   238026666   238026671    residential  18.88704  104.928   \n",
       "404896  393552   238026666   238066349    residential  14.92920   82.940   \n",
       "404897  393553   238026666   238006360    residential  15.06906   83.717   \n",
       "404898  393554   238026671   238026666    residential  18.88704  104.928   \n",
       "404899  393555   238550963   238509482    residential  24.55074  136.393   \n",
       "404900  393556   238550963   238472577      secondary  33.86388  282.199   \n",
       "404901  393557   238550963   238528656      secondary   8.27388   68.949   \n",
       "404902  393558   238026680   237993968    residential  94.70556  526.142   \n",
       "404903  393559   238026687   700233767  motorway_link  14.76621  164.069   \n",
       "404904  393560   238026687  1422368466  motorway_link  10.06605  111.845   \n",
       "404905  393561  5248122836   237991427    residential   5.85450   32.525   \n",
       "404906  393562   238026714   238026730    residential  46.93842  260.769   \n",
       "404907  393563   238026714   238024765    residential  34.63272  192.404   \n",
       "404908  393564   238026714   238038585    residential  20.79792  115.544   \n",
       "404909  393565   238026722  5167079179   unclassified  33.30576  185.032   \n",
       "404910  393566   238026723  4133461016        primary   1.93527   21.503   \n",
       "404911  393567   238026729  5167079184   unclassified  16.31772   90.654   \n",
       "404912  393568   238026729   238026748   unclassified  37.04274  205.793   \n",
       "404913  393569   238026729  5167079179   unclassified  21.45060  119.170   \n",
       "404914  393570   238026730   238044145    residential  11.30382   62.799   \n",
       "404915  393571   238026730   238024741    residential  84.65670  470.315   \n",
       "404916  393572   238026730   238026714    residential  46.93842  260.769   \n",
       "404917  393573   238551018   238542412    residential  28.61604  158.978   \n",
       "404918  393574   238026733   238026752    residential  34.03782  189.099   \n",
       "404919  393575   239075323   238000699    residential  42.51456  236.192   \n",
       "404920  393576   239075323   238911905    residential   9.59148   53.286   \n",
       "404921  393577   239075323   239075409    residential  31.01580  172.310   \n",
       "404922  393578   239075323   238911886    residential   6.28632   34.924   \n",
       "404923  393579   238026748  5167073092       tertiary  39.56712  329.726   \n",
       "404924  393580   238026748  4220614629       tertiary   5.76624   48.052   \n",
       "404925  393581   238026748   238026729   unclassified  37.04274  205.793   \n",
       "\n",
       "                                   osmid   mode  \\\n",
       "404886                          22138283  drive   \n",
       "404887                          22138283  drive   \n",
       "404888                          22128603  drive   \n",
       "404889                          22193591  drive   \n",
       "404890                         533892307  drive   \n",
       "404891                          22193591  drive   \n",
       "404892                          22193591  drive   \n",
       "404893                         533892307  drive   \n",
       "404894                         533892308  drive   \n",
       "404895                          22128605  drive   \n",
       "404896                          22131483  drive   \n",
       "404897                          22131483  drive   \n",
       "404898                          22128605  drive   \n",
       "404899                          22184945  drive   \n",
       "404900                          22185844  drive   \n",
       "404901                          22185844  drive   \n",
       "404902                          22128606  drive   \n",
       "404903                          22128620  drive   \n",
       "404904                          57853943  drive   \n",
       "404905                          22124214  drive   \n",
       "404906                          22128616  drive   \n",
       "404907                          22130366  drive   \n",
       "404908                          22130366  drive   \n",
       "404909                         532490641  drive   \n",
       "404910                         202426342  drive   \n",
       "404911                         532491121  drive   \n",
       "404912                         532490641  drive   \n",
       "404913                         532490641  drive   \n",
       "404914                          22131258  drive   \n",
       "404915                          22128616  drive   \n",
       "404916                          22128616  drive   \n",
       "404917                         529224222  drive   \n",
       "404918                          22128613  drive   \n",
       "404919                          22239587  drive   \n",
       "404920                          22239587  drive   \n",
       "404921                          22239591  drive   \n",
       "404922                          22239591  drive   \n",
       "404923                         196950650  drive   \n",
       "404924  [22136906, 363774843, 422318206]  drive   \n",
       "404925                         532490641  drive   \n",
       "\n",
       "                                                 geometry  index_right  \\\n",
       "404886  LINESTRING (-65.95805 18.38797, -65.95802 18.3...         -1.0   \n",
       "404887  LINESTRING (-65.95805 18.38797, -65.95813 18.3...         -1.0   \n",
       "404888  LINESTRING (-65.95678 18.38780, -65.95805 18.3...         -1.0   \n",
       "404889  LINESTRING (-67.14905 18.22829, -67.14888 18.2...         -1.0   \n",
       "404890  LINESTRING (-67.14801 18.22883, -67.14859 18.2...         -1.0   \n",
       "404891  LINESTRING (-67.14801 18.22883, -67.14801 18.2...         -1.0   \n",
       "404892  LINESTRING (-67.14801 18.22883, -67.14800 18.2...         -1.0   \n",
       "404893  LINESTRING (-67.14859 18.22891, -67.14801 18.2...         -1.0   \n",
       "404894  LINESTRING (-67.14778 18.22983, -67.14783 18.2...         -1.0   \n",
       "404895  LINESTRING (-66.07267 18.21207, -66.07168 18.2...         -1.0   \n",
       "404896  LINESTRING (-66.07267 18.21207, -66.07261 18.2...         -1.0   \n",
       "404897  LINESTRING (-66.07267 18.21207, -66.07273 18.2...        493.0   \n",
       "404898  LINESTRING (-66.07168 18.21215, -66.07267 18.2...         -1.0   \n",
       "404899  LINESTRING (-66.85196 18.28374, -66.85146 18.2...         -1.0   \n",
       "404900  LINESTRING (-66.85196 18.28374, -66.85183 18.2...         -1.0   \n",
       "404901  LINESTRING (-66.85196 18.28374, -66.85207 18.2...         -1.0   \n",
       "404902  LINESTRING (-66.03143 18.26259, -66.03641 18.2...         -1.0   \n",
       "404903  LINESTRING (-66.03843 18.26295, -66.03854 18.2...         -1.0   \n",
       "404904  LINESTRING (-66.03843 18.26295, -66.03835 18.2...         -1.0   \n",
       "404905  LINESTRING (-66.84678 18.38056, -66.84696 18.3...         -1.0   \n",
       "404906  LINESTRING (-65.86647 18.26430, -65.86662 18.2...         -1.0   \n",
       "404907  LINESTRING (-65.86647 18.26430, -65.86635 18.2...         -1.0   \n",
       "404908  LINESTRING (-65.86647 18.26430, -65.86647 18.2...         -1.0   \n",
       "404909  LINESTRING (-65.98663 18.43177, -65.98753 18.4...         -1.0   \n",
       "404910  LINESTRING (-66.03693 18.25840, -66.03689 18.2...         -1.0   \n",
       "404911  LINESTRING (-65.98867 18.42985, -65.98938 18.4...         -1.0   \n",
       "404912  LINESTRING (-65.98867 18.42985, -65.98874 18.4...         -1.0   \n",
       "404913  LINESTRING (-65.98867 18.42985, -65.98851 18.4...         -1.0   \n",
       "404914  LINESTRING (-65.86817 18.26332, -65.86802 18.2...         -1.0   \n",
       "404915  LINESTRING (-65.86817 18.26332, -65.86839 18.2...      45981.0   \n",
       "404916  LINESTRING (-65.86817 18.26332, -65.86804 18.2...         -1.0   \n",
       "404917  LINESTRING (-66.84656 18.28132, -66.84658 18.2...         -1.0   \n",
       "404918  LINESTRING (-66.05095 18.26338, -66.05086 18.2...         -1.0   \n",
       "404919  LINESTRING (-66.04037 18.31270, -66.04026 18.3...         -1.0   \n",
       "404920  LINESTRING (-66.04037 18.31270, -66.04045 18.3...         -1.0   \n",
       "404921  LINESTRING (-66.04037 18.31270, -66.04031 18.3...         -1.0   \n",
       "404922  LINESTRING (-66.04037 18.31270, -66.04058 18.3...         -1.0   \n",
       "404923  LINESTRING (-65.98955 18.42824, -65.98967 18.4...         -1.0   \n",
       "404924  LINESTRING (-65.98955 18.42824, -65.98948 18.4...         -1.0   \n",
       "404925  LINESTRING (-65.98955 18.42824, -65.98945 18.4...         -1.0   \n",
       "\n",
       "        Municipio       Barrio  penalty_l  \n",
       "404886        NaN          NaN    7.67628  \n",
       "404887        NaN          NaN   30.65508  \n",
       "404888        NaN          NaN   24.45822  \n",
       "404889        NaN          NaN   31.13190  \n",
       "404890        NaN          NaN   11.20104  \n",
       "404891        NaN          NaN   31.13190  \n",
       "404892        NaN          NaN   11.36016  \n",
       "404893        NaN          NaN   11.20104  \n",
       "404894        NaN          NaN   11.74752  \n",
       "404895        NaN          NaN   18.88704  \n",
       "404896        NaN          NaN   14.92920  \n",
       "404897     Caguas  Cagaboncito   15.06906  \n",
       "404898        NaN          NaN   18.88704  \n",
       "404899        NaN          NaN   24.55074  \n",
       "404900        NaN          NaN   33.86388  \n",
       "404901        NaN          NaN    8.27388  \n",
       "404902        NaN          NaN   94.70556  \n",
       "404903        NaN          NaN   14.76621  \n",
       "404904        NaN          NaN   10.06605  \n",
       "404905        NaN          NaN    5.85450  \n",
       "404906        NaN          NaN   46.93842  \n",
       "404907        NaN          NaN   34.63272  \n",
       "404908        NaN          NaN   20.79792  \n",
       "404909        NaN          NaN   33.30576  \n",
       "404910        NaN          NaN    1.93527  \n",
       "404911        NaN          NaN   16.31772  \n",
       "404912        NaN          NaN   37.04274  \n",
       "404913        NaN          NaN   21.45060  \n",
       "404914        NaN          NaN   11.30382  \n",
       "404915  Can2vanas        Cubuy   84.65670  \n",
       "404916        NaN          NaN   46.93842  \n",
       "404917        NaN          NaN   28.61604  \n",
       "404918        NaN          NaN   34.03782  \n",
       "404919        NaN          NaN   42.51456  \n",
       "404920        NaN          NaN    9.59148  \n",
       "404921        NaN          NaN   31.01580  \n",
       "404922        NaN          NaN    6.28632  \n",
       "404923        NaN          NaN   39.56712  \n",
       "404924        NaN          NaN    5.76624  \n",
       "404925        NaN          NaN   37.04274  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gTime_landslide_df.tail(40) # Time is in seconds.\n",
    "# Not going to be nearly as many intersections as the flooding dataset. But when there is, the road is completely blocked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_landslide_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_landslide_df[\"penalty_l\"] = gTime_landslide_df['time'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_landslide_df['index_right'] = gTime_landslide_df['index_right'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_landslide_df[['index_right']] = gTime_landslide_df[['index_right']].fillna(value=-1) \n",
    "# Because it's complicated to use .loc (see below) on NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_landslide_df.loc[(gTime_landslide_df['index_right'] != -1), # Select rows where a landslide intersected.\n",
    "       'penalty_l'] = (gTime_landslide_df['time'] * 3000) # Then make drive time impossibly long (for a 30 second baseline time, it would take 24+ hours)\n",
    "# First time returned a ValueError: cannot reindex from a duplicate axis. Needed to do reset_index (see several lines up)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
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       "      <td>18.88704</td>\n",
       "      <td>104.928</td>\n",
       "      <td>22128605</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.07267 18.21207, -66.07168 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.88704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404896</th>\n",
       "      <td>393552</td>\n",
       "      <td>238026666</td>\n",
       "      <td>238066349</td>\n",
       "      <td>residential</td>\n",
       "      <td>14.92920</td>\n",
       "      <td>82.940</td>\n",
       "      <td>22131483</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.07267 18.21207, -66.07261 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.92920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404897</th>\n",
       "      <td>393553</td>\n",
       "      <td>238026666</td>\n",
       "      <td>238006360</td>\n",
       "      <td>residential</td>\n",
       "      <td>15.06906</td>\n",
       "      <td>83.717</td>\n",
       "      <td>22131483</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.07267 18.21207, -66.07273 18.2...</td>\n",
       "      <td>493.0</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>Cagaboncito</td>\n",
       "      <td>45207.18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404898</th>\n",
       "      <td>393554</td>\n",
       "      <td>238026671</td>\n",
       "      <td>238026666</td>\n",
       "      <td>residential</td>\n",
       "      <td>18.88704</td>\n",
       "      <td>104.928</td>\n",
       "      <td>22128605</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.07168 18.21215, -66.07267 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.88704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404899</th>\n",
       "      <td>393555</td>\n",
       "      <td>238550963</td>\n",
       "      <td>238509482</td>\n",
       "      <td>residential</td>\n",
       "      <td>24.55074</td>\n",
       "      <td>136.393</td>\n",
       "      <td>22184945</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.85196 18.28374, -66.85146 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.55074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404900</th>\n",
       "      <td>393556</td>\n",
       "      <td>238550963</td>\n",
       "      <td>238472577</td>\n",
       "      <td>secondary</td>\n",
       "      <td>33.86388</td>\n",
       "      <td>282.199</td>\n",
       "      <td>22185844</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.85196 18.28374, -66.85183 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.86388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404901</th>\n",
       "      <td>393557</td>\n",
       "      <td>238550963</td>\n",
       "      <td>238528656</td>\n",
       "      <td>secondary</td>\n",
       "      <td>8.27388</td>\n",
       "      <td>68.949</td>\n",
       "      <td>22185844</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.85196 18.28374, -66.85207 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404902</th>\n",
       "      <td>393558</td>\n",
       "      <td>238026680</td>\n",
       "      <td>237993968</td>\n",
       "      <td>residential</td>\n",
       "      <td>94.70556</td>\n",
       "      <td>526.142</td>\n",
       "      <td>22128606</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.03143 18.26259, -66.03641 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.70556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404903</th>\n",
       "      <td>393559</td>\n",
       "      <td>238026687</td>\n",
       "      <td>700233767</td>\n",
       "      <td>motorway_link</td>\n",
       "      <td>14.76621</td>\n",
       "      <td>164.069</td>\n",
       "      <td>22128620</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.03843 18.26295, -66.03854 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.76621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404904</th>\n",
       "      <td>393560</td>\n",
       "      <td>238026687</td>\n",
       "      <td>1422368466</td>\n",
       "      <td>motorway_link</td>\n",
       "      <td>10.06605</td>\n",
       "      <td>111.845</td>\n",
       "      <td>57853943</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.03843 18.26295, -66.03835 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.06605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404905</th>\n",
       "      <td>393561</td>\n",
       "      <td>5248122836</td>\n",
       "      <td>237991427</td>\n",
       "      <td>residential</td>\n",
       "      <td>5.85450</td>\n",
       "      <td>32.525</td>\n",
       "      <td>22124214</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.84678 18.38056, -66.84696 18.3...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.85450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404906</th>\n",
       "      <td>393562</td>\n",
       "      <td>238026714</td>\n",
       "      <td>238026730</td>\n",
       "      <td>residential</td>\n",
       "      <td>46.93842</td>\n",
       "      <td>260.769</td>\n",
       "      <td>22128616</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86647 18.26430, -65.86662 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.93842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404907</th>\n",
       "      <td>393563</td>\n",
       "      <td>238026714</td>\n",
       "      <td>238024765</td>\n",
       "      <td>residential</td>\n",
       "      <td>34.63272</td>\n",
       "      <td>192.404</td>\n",
       "      <td>22130366</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86647 18.26430, -65.86635 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.63272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404908</th>\n",
       "      <td>393564</td>\n",
       "      <td>238026714</td>\n",
       "      <td>238038585</td>\n",
       "      <td>residential</td>\n",
       "      <td>20.79792</td>\n",
       "      <td>115.544</td>\n",
       "      <td>22130366</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86647 18.26430, -65.86647 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.79792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404909</th>\n",
       "      <td>393565</td>\n",
       "      <td>238026722</td>\n",
       "      <td>5167079179</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>33.30576</td>\n",
       "      <td>185.032</td>\n",
       "      <td>532490641</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98663 18.43177, -65.98753 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.30576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404910</th>\n",
       "      <td>393566</td>\n",
       "      <td>238026723</td>\n",
       "      <td>4133461016</td>\n",
       "      <td>primary</td>\n",
       "      <td>1.93527</td>\n",
       "      <td>21.503</td>\n",
       "      <td>202426342</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.03693 18.25840, -66.03689 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.93527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404911</th>\n",
       "      <td>393567</td>\n",
       "      <td>238026729</td>\n",
       "      <td>5167079184</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>16.31772</td>\n",
       "      <td>90.654</td>\n",
       "      <td>532491121</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98867 18.42985, -65.98938 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.31772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404912</th>\n",
       "      <td>393568</td>\n",
       "      <td>238026729</td>\n",
       "      <td>238026748</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>37.04274</td>\n",
       "      <td>205.793</td>\n",
       "      <td>532490641</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98867 18.42985, -65.98874 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.04274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404913</th>\n",
       "      <td>393569</td>\n",
       "      <td>238026729</td>\n",
       "      <td>5167079179</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>21.45060</td>\n",
       "      <td>119.170</td>\n",
       "      <td>532490641</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98867 18.42985, -65.98851 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.45060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404914</th>\n",
       "      <td>393570</td>\n",
       "      <td>238026730</td>\n",
       "      <td>238044145</td>\n",
       "      <td>residential</td>\n",
       "      <td>11.30382</td>\n",
       "      <td>62.799</td>\n",
       "      <td>22131258</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86817 18.26332, -65.86802 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.30382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404915</th>\n",
       "      <td>393571</td>\n",
       "      <td>238026730</td>\n",
       "      <td>238024741</td>\n",
       "      <td>residential</td>\n",
       "      <td>84.65670</td>\n",
       "      <td>470.315</td>\n",
       "      <td>22128616</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86817 18.26332, -65.86839 18.2...</td>\n",
       "      <td>45981.0</td>\n",
       "      <td>Can2vanas</td>\n",
       "      <td>Cubuy</td>\n",
       "      <td>253970.10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404916</th>\n",
       "      <td>393572</td>\n",
       "      <td>238026730</td>\n",
       "      <td>238026714</td>\n",
       "      <td>residential</td>\n",
       "      <td>46.93842</td>\n",
       "      <td>260.769</td>\n",
       "      <td>22128616</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.86817 18.26332, -65.86804 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.93842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404917</th>\n",
       "      <td>393573</td>\n",
       "      <td>238551018</td>\n",
       "      <td>238542412</td>\n",
       "      <td>residential</td>\n",
       "      <td>28.61604</td>\n",
       "      <td>158.978</td>\n",
       "      <td>529224222</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.84656 18.28132, -66.84658 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.61604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404918</th>\n",
       "      <td>393574</td>\n",
       "      <td>238026733</td>\n",
       "      <td>238026752</td>\n",
       "      <td>residential</td>\n",
       "      <td>34.03782</td>\n",
       "      <td>189.099</td>\n",
       "      <td>22128613</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.05095 18.26338, -66.05086 18.2...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.03782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404919</th>\n",
       "      <td>393575</td>\n",
       "      <td>239075323</td>\n",
       "      <td>238000699</td>\n",
       "      <td>residential</td>\n",
       "      <td>42.51456</td>\n",
       "      <td>236.192</td>\n",
       "      <td>22239587</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.04037 18.31270, -66.04026 18.3...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.51456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404920</th>\n",
       "      <td>393576</td>\n",
       "      <td>239075323</td>\n",
       "      <td>238911905</td>\n",
       "      <td>residential</td>\n",
       "      <td>9.59148</td>\n",
       "      <td>53.286</td>\n",
       "      <td>22239587</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.04037 18.31270, -66.04045 18.3...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.59148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404921</th>\n",
       "      <td>393577</td>\n",
       "      <td>239075323</td>\n",
       "      <td>239075409</td>\n",
       "      <td>residential</td>\n",
       "      <td>31.01580</td>\n",
       "      <td>172.310</td>\n",
       "      <td>22239591</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.04037 18.31270, -66.04031 18.3...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.01580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404922</th>\n",
       "      <td>393578</td>\n",
       "      <td>239075323</td>\n",
       "      <td>238911886</td>\n",
       "      <td>residential</td>\n",
       "      <td>6.28632</td>\n",
       "      <td>34.924</td>\n",
       "      <td>22239591</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-66.04037 18.31270, -66.04058 18.3...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.28632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404923</th>\n",
       "      <td>393579</td>\n",
       "      <td>238026748</td>\n",
       "      <td>5167073092</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>39.56712</td>\n",
       "      <td>329.726</td>\n",
       "      <td>196950650</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98955 18.42824, -65.98967 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.56712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404924</th>\n",
       "      <td>393580</td>\n",
       "      <td>238026748</td>\n",
       "      <td>4220614629</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>5.76624</td>\n",
       "      <td>48.052</td>\n",
       "      <td>[22136906, 363774843, 422318206]</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98955 18.42824, -65.98948 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.76624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404925</th>\n",
       "      <td>393581</td>\n",
       "      <td>238026748</td>\n",
       "      <td>238026729</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>37.04274</td>\n",
       "      <td>205.793</td>\n",
       "      <td>532490641</td>\n",
       "      <td>drive</td>\n",
       "      <td>LINESTRING (-65.98955 18.42824, -65.98945 18.4...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.04274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         index      stnode     endnode        highway      time   length  \\\n",
       "404886  393542   238026643   238082805    residential   7.67628   42.646   \n",
       "404887  393543   238026643   238052057    residential  30.65508  170.306   \n",
       "404888  393544   238026646   238026643    residential  24.45822  135.879   \n",
       "404889  393545  5178916766  5178916767    residential  31.13190  172.955   \n",
       "404890  393546  5178916767  5178916768    residential  11.20104   62.228   \n",
       "404891  393547  5178916767  5178916766    residential  31.13190  172.955   \n",
       "404892  393548  5178916767  5178916749    residential  11.36016   63.112   \n",
       "404893  393549  5178916768  5178916767    residential  11.20104   62.228   \n",
       "404894  393550  5178916774  5178916749    residential  11.74752   65.264   \n",
       "404895  393551   238026666   238026671    residential  18.88704  104.928   \n",
       "404896  393552   238026666   238066349    residential  14.92920   82.940   \n",
       "404897  393553   238026666   238006360    residential  15.06906   83.717   \n",
       "404898  393554   238026671   238026666    residential  18.88704  104.928   \n",
       "404899  393555   238550963   238509482    residential  24.55074  136.393   \n",
       "404900  393556   238550963   238472577      secondary  33.86388  282.199   \n",
       "404901  393557   238550963   238528656      secondary   8.27388   68.949   \n",
       "404902  393558   238026680   237993968    residential  94.70556  526.142   \n",
       "404903  393559   238026687   700233767  motorway_link  14.76621  164.069   \n",
       "404904  393560   238026687  1422368466  motorway_link  10.06605  111.845   \n",
       "404905  393561  5248122836   237991427    residential   5.85450   32.525   \n",
       "404906  393562   238026714   238026730    residential  46.93842  260.769   \n",
       "404907  393563   238026714   238024765    residential  34.63272  192.404   \n",
       "404908  393564   238026714   238038585    residential  20.79792  115.544   \n",
       "404909  393565   238026722  5167079179   unclassified  33.30576  185.032   \n",
       "404910  393566   238026723  4133461016        primary   1.93527   21.503   \n",
       "404911  393567   238026729  5167079184   unclassified  16.31772   90.654   \n",
       "404912  393568   238026729   238026748   unclassified  37.04274  205.793   \n",
       "404913  393569   238026729  5167079179   unclassified  21.45060  119.170   \n",
       "404914  393570   238026730   238044145    residential  11.30382   62.799   \n",
       "404915  393571   238026730   238024741    residential  84.65670  470.315   \n",
       "404916  393572   238026730   238026714    residential  46.93842  260.769   \n",
       "404917  393573   238551018   238542412    residential  28.61604  158.978   \n",
       "404918  393574   238026733   238026752    residential  34.03782  189.099   \n",
       "404919  393575   239075323   238000699    residential  42.51456  236.192   \n",
       "404920  393576   239075323   238911905    residential   9.59148   53.286   \n",
       "404921  393577   239075323   239075409    residential  31.01580  172.310   \n",
       "404922  393578   239075323   238911886    residential   6.28632   34.924   \n",
       "404923  393579   238026748  5167073092       tertiary  39.56712  329.726   \n",
       "404924  393580   238026748  4220614629       tertiary   5.76624   48.052   \n",
       "404925  393581   238026748   238026729   unclassified  37.04274  205.793   \n",
       "\n",
       "                                   osmid   mode  \\\n",
       "404886                          22138283  drive   \n",
       "404887                          22138283  drive   \n",
       "404888                          22128603  drive   \n",
       "404889                          22193591  drive   \n",
       "404890                         533892307  drive   \n",
       "404891                          22193591  drive   \n",
       "404892                          22193591  drive   \n",
       "404893                         533892307  drive   \n",
       "404894                         533892308  drive   \n",
       "404895                          22128605  drive   \n",
       "404896                          22131483  drive   \n",
       "404897                          22131483  drive   \n",
       "404898                          22128605  drive   \n",
       "404899                          22184945  drive   \n",
       "404900                          22185844  drive   \n",
       "404901                          22185844  drive   \n",
       "404902                          22128606  drive   \n",
       "404903                          22128620  drive   \n",
       "404904                          57853943  drive   \n",
       "404905                          22124214  drive   \n",
       "404906                          22128616  drive   \n",
       "404907                          22130366  drive   \n",
       "404908                          22130366  drive   \n",
       "404909                         532490641  drive   \n",
       "404910                         202426342  drive   \n",
       "404911                         532491121  drive   \n",
       "404912                         532490641  drive   \n",
       "404913                         532490641  drive   \n",
       "404914                          22131258  drive   \n",
       "404915                          22128616  drive   \n",
       "404916                          22128616  drive   \n",
       "404917                         529224222  drive   \n",
       "404918                          22128613  drive   \n",
       "404919                          22239587  drive   \n",
       "404920                          22239587  drive   \n",
       "404921                          22239591  drive   \n",
       "404922                          22239591  drive   \n",
       "404923                         196950650  drive   \n",
       "404924  [22136906, 363774843, 422318206]  drive   \n",
       "404925                         532490641  drive   \n",
       "\n",
       "                                                 geometry  index_right  \\\n",
       "404886  LINESTRING (-65.95805 18.38797, -65.95802 18.3...         -1.0   \n",
       "404887  LINESTRING (-65.95805 18.38797, -65.95813 18.3...         -1.0   \n",
       "404888  LINESTRING (-65.95678 18.38780, -65.95805 18.3...         -1.0   \n",
       "404889  LINESTRING (-67.14905 18.22829, -67.14888 18.2...         -1.0   \n",
       "404890  LINESTRING (-67.14801 18.22883, -67.14859 18.2...         -1.0   \n",
       "404891  LINESTRING (-67.14801 18.22883, -67.14801 18.2...         -1.0   \n",
       "404892  LINESTRING (-67.14801 18.22883, -67.14800 18.2...         -1.0   \n",
       "404893  LINESTRING (-67.14859 18.22891, -67.14801 18.2...         -1.0   \n",
       "404894  LINESTRING (-67.14778 18.22983, -67.14783 18.2...         -1.0   \n",
       "404895  LINESTRING (-66.07267 18.21207, -66.07168 18.2...         -1.0   \n",
       "404896  LINESTRING (-66.07267 18.21207, -66.07261 18.2...         -1.0   \n",
       "404897  LINESTRING (-66.07267 18.21207, -66.07273 18.2...        493.0   \n",
       "404898  LINESTRING (-66.07168 18.21215, -66.07267 18.2...         -1.0   \n",
       "404899  LINESTRING (-66.85196 18.28374, -66.85146 18.2...         -1.0   \n",
       "404900  LINESTRING (-66.85196 18.28374, -66.85183 18.2...         -1.0   \n",
       "404901  LINESTRING (-66.85196 18.28374, -66.85207 18.2...         -1.0   \n",
       "404902  LINESTRING (-66.03143 18.26259, -66.03641 18.2...         -1.0   \n",
       "404903  LINESTRING (-66.03843 18.26295, -66.03854 18.2...         -1.0   \n",
       "404904  LINESTRING (-66.03843 18.26295, -66.03835 18.2...         -1.0   \n",
       "404905  LINESTRING (-66.84678 18.38056, -66.84696 18.3...         -1.0   \n",
       "404906  LINESTRING (-65.86647 18.26430, -65.86662 18.2...         -1.0   \n",
       "404907  LINESTRING (-65.86647 18.26430, -65.86635 18.2...         -1.0   \n",
       "404908  LINESTRING (-65.86647 18.26430, -65.86647 18.2...         -1.0   \n",
       "404909  LINESTRING (-65.98663 18.43177, -65.98753 18.4...         -1.0   \n",
       "404910  LINESTRING (-66.03693 18.25840, -66.03689 18.2...         -1.0   \n",
       "404911  LINESTRING (-65.98867 18.42985, -65.98938 18.4...         -1.0   \n",
       "404912  LINESTRING (-65.98867 18.42985, -65.98874 18.4...         -1.0   \n",
       "404913  LINESTRING (-65.98867 18.42985, -65.98851 18.4...         -1.0   \n",
       "404914  LINESTRING (-65.86817 18.26332, -65.86802 18.2...         -1.0   \n",
       "404915  LINESTRING (-65.86817 18.26332, -65.86839 18.2...      45981.0   \n",
       "404916  LINESTRING (-65.86817 18.26332, -65.86804 18.2...         -1.0   \n",
       "404917  LINESTRING (-66.84656 18.28132, -66.84658 18.2...         -1.0   \n",
       "404918  LINESTRING (-66.05095 18.26338, -66.05086 18.2...         -1.0   \n",
       "404919  LINESTRING (-66.04037 18.31270, -66.04026 18.3...         -1.0   \n",
       "404920  LINESTRING (-66.04037 18.31270, -66.04045 18.3...         -1.0   \n",
       "404921  LINESTRING (-66.04037 18.31270, -66.04031 18.3...         -1.0   \n",
       "404922  LINESTRING (-66.04037 18.31270, -66.04058 18.3...         -1.0   \n",
       "404923  LINESTRING (-65.98955 18.42824, -65.98967 18.4...         -1.0   \n",
       "404924  LINESTRING (-65.98955 18.42824, -65.98948 18.4...         -1.0   \n",
       "404925  LINESTRING (-65.98955 18.42824, -65.98945 18.4...         -1.0   \n",
       "\n",
       "        Municipio       Barrio     penalty_l  \n",
       "404886        NaN          NaN       7.67628  \n",
       "404887        NaN          NaN      30.65508  \n",
       "404888        NaN          NaN      24.45822  \n",
       "404889        NaN          NaN      31.13190  \n",
       "404890        NaN          NaN      11.20104  \n",
       "404891        NaN          NaN      31.13190  \n",
       "404892        NaN          NaN      11.36016  \n",
       "404893        NaN          NaN      11.20104  \n",
       "404894        NaN          NaN      11.74752  \n",
       "404895        NaN          NaN      18.88704  \n",
       "404896        NaN          NaN      14.92920  \n",
       "404897     Caguas  Cagaboncito   45207.18000  \n",
       "404898        NaN          NaN      18.88704  \n",
       "404899        NaN          NaN      24.55074  \n",
       "404900        NaN          NaN      33.86388  \n",
       "404901        NaN          NaN       8.27388  \n",
       "404902        NaN          NaN      94.70556  \n",
       "404903        NaN          NaN      14.76621  \n",
       "404904        NaN          NaN      10.06605  \n",
       "404905        NaN          NaN       5.85450  \n",
       "404906        NaN          NaN      46.93842  \n",
       "404907        NaN          NaN      34.63272  \n",
       "404908        NaN          NaN      20.79792  \n",
       "404909        NaN          NaN      33.30576  \n",
       "404910        NaN          NaN       1.93527  \n",
       "404911        NaN          NaN      16.31772  \n",
       "404912        NaN          NaN      37.04274  \n",
       "404913        NaN          NaN      21.45060  \n",
       "404914        NaN          NaN      11.30382  \n",
       "404915  Can2vanas        Cubuy  253970.10000  \n",
       "404916        NaN          NaN      46.93842  \n",
       "404917        NaN          NaN      28.61604  \n",
       "404918        NaN          NaN      34.03782  \n",
       "404919        NaN          NaN      42.51456  \n",
       "404920        NaN          NaN       9.59148  \n",
       "404921        NaN          NaN      31.01580  \n",
       "404922        NaN          NaN       6.28632  \n",
       "404923        NaN          NaN      39.56712  \n",
       "404924        NaN          NaN       5.76624  \n",
       "404925        NaN          NaN      37.04274  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gTime_landslide_df.tail(40)\n",
    "# Check that it changed row 404897 and 404915, the two rows of the last 40 that intersected a landslide."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert back to graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime2 = nx.from_pandas_edgelist(\n",
    "    gTime_landslide_df,\n",
    "    source=\"stnode\",\n",
    "    target=\"endnode\",\n",
    "    edge_attr=True,\n",
    ")\n",
    "# It ran. Only took a few seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(238026752, 238075156, {'index': 39981, 'highway': 'residential', 'time': 19.569059999999997, 'length': 108.717, 'osmid': 22137085, 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E830477CC0>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 19.569059999999997})\n",
      "(238026752, 238026733, {'index': 393574, 'highway': 'residential', 'time': 34.03782, 'length': 189.099, 'osmid': 22128613, 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E6BE47FAC8>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 34.03782})\n",
      "(238026752, 238072036, {'index': 37001, 'highway': 'residential', 'time': 30.338100000000004, 'length': 168.54500000000002, 'osmid': [22136924, 22137599], 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E825328CF8>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 30.338100000000004})\n",
      "(238026752, 237995790, {'index': 365726, 'highway': 'residential', 'time': 12.39138, 'length': 68.841, 'osmid': 22137599, 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E6DF349E48>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 12.39138})\n",
      "(238072036, 238053029, {'index': 37000, 'highway': 'residential', 'time': 10.603439999999999, 'length': 58.908, 'osmid': 22136654, 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E825783F28>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 10.603439999999999})\n",
      "(238072036, 237991523, {'index': 361248, 'highway': 'residential', 'time': 24.789239999999996, 'length': 137.718, 'osmid': 22136654, 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E6B981AB38>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 24.789239999999996})\n",
      "(237995790, 238075861, {'index': 365723, 'highway': 'residential', 'time': 12.166919999999998, 'length': 67.594, 'osmid': 22137599, 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E6DF349DA0>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 12.166919999999998})\n",
      "(237995790, 237988204, {'index': 365725, 'highway': 'residential', 'time': 53.44307999999999, 'length': 296.90599999999995, 'osmid': [22137238, 22123678], 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E6DF349E10>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 53.44307999999999})\n",
      "(237995790, 237995798, {'index': 365737, 'highway': 'residential', 'time': 31.1913, 'length': 173.285, 'osmid': 22123678, 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E6DF3480F0>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 31.1913})\n",
      "(238026754, 4220614634, {'index': 4, 'highway': 'tertiary', 'time': 2.56236, 'length': 21.353, 'osmid': 422318208, 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E83BDCC5C0>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 2.56236})\n"
     ]
    }
   ],
   "source": [
    "gn.example_edge(gTime2, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "gn.save(gTime2, 'gTime_landslide', pth, edges = False, nodes = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "gn.save? # Tempting to make edges and nodes = True, but this returns an error. Better to save just the pickle file."
   ]
  },
  {
   "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": 80,
   "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 flood-affected travel time values for the road nodes nearest to each service.\n",
    "\n",
    "Using calculate_OD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "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": 183,
   "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": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142421"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(origins) # 142,421 unique nearest nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1011"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dests) # 1,011 unique nearest nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(listH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(238026752, 238075156, {'index': 39981, 'highway': 'residential', 'time': 19.569059999999997, 'length': 108.717, 'osmid': 22137085, 'mode': 'drive', 'geometry': <shapely.geometry.linestring.LineString object at 0x000001E830477CC0>, 'index_right': -1.0, 'Municipio': nan, 'Barrio': nan, 'penalty_l': 19.569059999999997})\n"
     ]
    }
   ],
   "source": [
    "gn.example_edge(gTime2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'GOSTnet' from 'C:\\\\Users\\\\grace\\\\Anaconda3\\\\envs\\\\access\\\\lib\\\\site-packages\\\\GOSTnet.py'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(gn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gn.calculate_OD?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "OD_landslide = gn.calculate_OD(gTime2, origins, dests, fail_value, weight = 'penalty_l')\n",
    "# Took maybe 10 min."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "OD_landslide_df = pd.DataFrame(OD_landslide, index = origins, columns = dests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 142421 entries, 239148931 to 238880909\n",
      "Columns: 1011 entries, 233324546 to 238196735\n",
      "dtypes: float64(1011)\n",
      "memory usage: 1.1 GB\n"
     ]
    }
   ],
   "source": [
    "OD_landslide_df.info()\n",
    "# Created a 142421 x 1011 matrix. Same dimensions and size as the non-flood (1.1 GB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "\n",
       "[5 rows x 1011 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OD_landslide_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to minutes and save to file.\n",
    "OD_landslide_min = OD_landslide_df[OD_landslide_df < fail_value] / 60\n",
    "OD_landslide_min.to_csv(os.path.join(pth, 'OD_landslide.csv'))\n",
    "# Takes a couple minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
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       "<p>20 rows × 1011 columns</p>\n",
       "</div>"
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      "text/plain": [
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       "\n",
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       "\n",
       "[20 rows x 1011 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OD_landslide_min.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create POI-specific OD and save to file.\n",
    "ODD_landslide = OD_landslide_df.loc[:, listD]\n",
    "ODD_landslide = ODD_landslide[ODD_landslide < fail_value] / 60 \n",
    "ODD_landslide.to_csv(os.path.join(pth, 'ODD_landslide.csv')) # Each takes a minute or so.\n",
    "\n",
    "ODH_landslide = OD_landslide_df.loc[:, listH]\n",
    "ODH_landslide = ODH_landslide[ODH_landslide < fail_value] / 60 \n",
    "ODH_landslide.to_csv(os.path.join(pth, 'ODH_landslide.csv'))\n",
    "\n",
    "ODP_landslide = OD_landslide_df.loc[:, listP]\n",
    "ODP_landslide = ODP_landslide[ODP_landslide < fail_value] / 60 \n",
    "ODP_landslide.to_csv(os.path.join(pth, 'ODP_landslide.csv'))"
   ]
  },
  {
   "cell_type": "code",
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     "execution_count": 197,
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   "source": [
    "ODP_landslide.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare flooding and baseline results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this even if already loaded. Name change for hazard file.\n",
    "OD_base = os.path.join(pth, \"OD.csv\")\n",
    "OD_base = pd.read_csv(OD_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "OD_landslide = os.path.join(pth, \"OD_landslide.csv\")\n",
    "OD_landslide = pd.read_csv(OD_landslide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
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       "      <td>223.402658</td>\n",
       "      <td>106.393048</td>\n",
       "      <td>172.172835</td>\n",
       "      <td>165.803737</td>\n",
       "      <td>91.317608</td>\n",
       "      <td>...</td>\n",
       "      <td>203.567827</td>\n",
       "      <td>83.680500</td>\n",
       "      <td>169.662747</td>\n",
       "      <td>194.153779</td>\n",
       "      <td>173.156158</td>\n",
       "      <td>171.650362</td>\n",
       "      <td>196.435965</td>\n",
       "      <td>104.838151</td>\n",
       "      <td>165.185286</td>\n",
       "      <td>141.942063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>239136364</td>\n",
       "      <td>131.674608</td>\n",
       "      <td>105.023504</td>\n",
       "      <td>82.471018</td>\n",
       "      <td>178.580743</td>\n",
       "      <td>223.088213</td>\n",
       "      <td>106.078603</td>\n",
       "      <td>171.858390</td>\n",
       "      <td>165.489292</td>\n",
       "      <td>91.003163</td>\n",
       "      <td>...</td>\n",
       "      <td>203.253382</td>\n",
       "      <td>83.366055</td>\n",
       "      <td>169.348302</td>\n",
       "      <td>193.839334</td>\n",
       "      <td>172.841713</td>\n",
       "      <td>171.335917</td>\n",
       "      <td>196.121520</td>\n",
       "      <td>104.523706</td>\n",
       "      <td>164.870841</td>\n",
       "      <td>141.627618</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>239077188</td>\n",
       "      <td>132.201900</td>\n",
       "      <td>105.550796</td>\n",
       "      <td>82.998310</td>\n",
       "      <td>179.108035</td>\n",
       "      <td>223.615505</td>\n",
       "      <td>106.605895</td>\n",
       "      <td>172.385682</td>\n",
       "      <td>166.016584</td>\n",
       "      <td>91.530455</td>\n",
       "      <td>...</td>\n",
       "      <td>203.780674</td>\n",
       "      <td>83.893347</td>\n",
       "      <td>169.875594</td>\n",
       "      <td>194.366626</td>\n",
       "      <td>173.369005</td>\n",
       "      <td>171.863209</td>\n",
       "      <td>196.648812</td>\n",
       "      <td>105.050998</td>\n",
       "      <td>165.398133</td>\n",
       "      <td>142.154910</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>142416</th>\n",
       "      <td>238421388</td>\n",
       "      <td>274.348489</td>\n",
       "      <td>150.726605</td>\n",
       "      <td>262.410984</td>\n",
       "      <td>86.810998</td>\n",
       "      <td>39.038662</td>\n",
       "      <td>150.839943</td>\n",
       "      <td>80.654407</td>\n",
       "      <td>84.603840</td>\n",
       "      <td>172.531964</td>\n",
       "      <td>...</td>\n",
       "      <td>48.695262</td>\n",
       "      <td>261.493238</td>\n",
       "      <td>83.006299</td>\n",
       "      <td>57.307928</td>\n",
       "      <td>96.245695</td>\n",
       "      <td>87.874784</td>\n",
       "      <td>80.742644</td>\n",
       "      <td>151.216019</td>\n",
       "      <td>98.125120</td>\n",
       "      <td>122.596289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142417</th>\n",
       "      <td>238402093</td>\n",
       "      <td>274.386492</td>\n",
       "      <td>150.764608</td>\n",
       "      <td>262.448987</td>\n",
       "      <td>86.849001</td>\n",
       "      <td>39.076665</td>\n",
       "      <td>150.877946</td>\n",
       "      <td>80.692410</td>\n",
       "      <td>84.641843</td>\n",
       "      <td>172.569967</td>\n",
       "      <td>...</td>\n",
       "      <td>48.733265</td>\n",
       "      <td>261.531241</td>\n",
       "      <td>83.044302</td>\n",
       "      <td>57.345931</td>\n",
       "      <td>96.283698</td>\n",
       "      <td>87.912787</td>\n",
       "      <td>80.780647</td>\n",
       "      <td>151.254022</td>\n",
       "      <td>98.163123</td>\n",
       "      <td>122.634292</td>\n",
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       "    <tr>\n",
       "      <th>142418</th>\n",
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       "      <td>100.901409</td>\n",
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       "      <td>94.744819</td>\n",
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       "      <td>127.237173</td>\n",
       "      <td>...</td>\n",
       "      <td>62.785673</td>\n",
       "      <td>248.332302</td>\n",
       "      <td>97.096710</td>\n",
       "      <td>71.398340</td>\n",
       "      <td>110.336106</td>\n",
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       "      <td>94.833056</td>\n",
       "      <td>165.306430</td>\n",
       "      <td>112.215532</td>\n",
       "      <td>136.686700</td>\n",
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       "      <th>142419</th>\n",
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       "      <td>91.021763</td>\n",
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       "      <td>99.819245</td>\n",
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       "      <td>73.822598</td>\n",
       "      <td>...</td>\n",
       "      <td>85.535147</td>\n",
       "      <td>194.917727</td>\n",
       "      <td>102.171136</td>\n",
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       "      <td>128.237466</td>\n",
       "      <td>170.380856</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>87.461864</td>\n",
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       "<p>142421 rows × 1012 columns</p>\n",
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       "...            ...         ...         ...         ...         ...   \n",
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       "142420   92.948480  171.931497  101.745962  105.695394   74.877482  ...   \n",
       "\n",
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       "0       204.024088   66.515588  175.284588  195.431551  178.777999   \n",
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       "...            ...         ...         ...         ...         ...   \n",
       "142416   48.695262  261.493238   83.006299   57.307928   96.245695   \n",
       "142417   48.733265  261.531241   83.044302   57.345931   96.283698   \n",
       "142418   62.785673  248.332302   97.096710   71.398340  110.336106   \n",
       "142419   85.535147  194.917727  102.171136   76.942609  115.410532   \n",
       "142420   87.461864  195.972611  104.097853   78.869326  117.337249   \n",
       "\n",
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       "0       177.272204  202.057806  110.459993  170.807127  147.563904  \n",
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       "3       171.335917  196.121520  104.523706  164.870841  141.627618  \n",
       "4       171.863209  196.648812  105.050998  165.398133  142.154910  \n",
       "...            ...         ...         ...         ...         ...  \n",
       "142416   87.874784   80.742644  151.216019   98.125120  122.596289  \n",
       "142417   87.912787   80.780647  151.254022   98.163123  122.634292  \n",
       "142418  101.965196   94.833056  165.306430  112.215532  136.686700  \n",
       "142419  107.039621  128.237466  170.380856  117.289957  138.778429  \n",
       "142420  108.966338  130.164183  172.307573  119.216674  140.705146  \n",
       "\n",
       "[142421 rows x 1012 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OD_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
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       "[142421 rows x 1012 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OD_landslide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>142420</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>142421 rows × 1012 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  233324546  4155721731  238944260  239097861  239310852  \\\n",
       "0             True      False       False      False      False      False   \n",
       "1             True      False       False      False      False      False   \n",
       "2             True      False       False      False      False      False   \n",
       "3             True      False       False      False      False      False   \n",
       "4             True      False       False      False      False      False   \n",
       "...            ...        ...         ...        ...        ...        ...   \n",
       "142416        True      False       False      False      False      False   \n",
       "142417        True      False       False      False      False      False   \n",
       "142418        True      False       False      False      False       True   \n",
       "142419        True      False       False      False      False      False   \n",
       "142420        True      False       False      False      False      False   \n",
       "\n",
       "        4155721735  238962698  238981133  238845965  ...  238372836  \\\n",
       "0            False      False      False      False  ...      False   \n",
       "1            False      False      False      False  ...      False   \n",
       "2            False      False      False      False  ...      False   \n",
       "3            False      False      False      False  ...      False   \n",
       "4            False      False      False      False  ...      False   \n",
       "...            ...        ...        ...        ...  ...        ...   \n",
       "142416       False      False      False      False  ...      False   \n",
       "142417       False      False      False      False  ...      False   \n",
       "142418       False      False      False      False  ...      False   \n",
       "142419       False      False       True      False  ...      False   \n",
       "142420       False      False      False      False  ...      False   \n",
       "\n",
       "        238995433  763959274  238055405  238071790  2149629933  238018545  \\\n",
       "0           False      False      False      False       False      False   \n",
       "1           False      False      False      False       False      False   \n",
       "2           False      False      False      False       False      False   \n",
       "3           False      False      False      False       False      False   \n",
       "4           False      False      False      False       False      False   \n",
       "...           ...        ...        ...        ...         ...        ...   \n",
       "142416      False      False      False      False       False      False   \n",
       "142417      False      False      False      False       False      False   \n",
       "142418      False      False      False      False       False      False   \n",
       "142419      False       True      False      False       False      False   \n",
       "142420      False      False      False      False       False      False   \n",
       "\n",
       "        4155721720  237981689  238196735  \n",
       "0            False      False      False  \n",
       "1            False      False      False  \n",
       "2            False      False      False  \n",
       "3            False      False      False  \n",
       "4            False      False      False  \n",
       "...            ...        ...        ...  \n",
       "142416       False      False      False  \n",
       "142417       False      False      False  \n",
       "142418       False      False      False  \n",
       "142419       False       True      False  \n",
       "142420       False      False      False  \n",
       "\n",
       "[142421 rows x 1012 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OD_base == OD_landslide\n",
    "# Returns True/False matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3434272"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((OD_base == OD_landslide).sum()).sum()\n",
    "# Number of matching values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.023865219139646243"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((OD_base == OD_landslide).sum()).sum() / ((OD_base == OD_base).sum()).sum()\n",
    "# Percent of routes remaining the same after the hazard simulation."
   ]
  },
  {
   "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": 205,
   "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_landslide = os.path.join(pth, \"ODD_landslide.csv\")\n",
    "ODD_landslide = pd.read_csv(ODD_landslide)\n",
    "ODP_landslide = os.path.join(pth, \"ODP_landslide.csv\")\n",
    "ODP_landslide = pd.read_csv(ODP_landslide)\n",
    "ODH_landslide = os.path.join(pth, \"ODH_landslide.csv\")\n",
    "ODH_landslide = pd.read_csv(ODH_landslide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>86.932902</td>\n",
       "      <td>87.694849</td>\n",
       "      <td>27.810718</td>\n",
       "      <td>55.368938</td>\n",
       "      <td>...</td>\n",
       "      <td>62.321859</td>\n",
       "      <td>62.784538</td>\n",
       "      <td>63.713292</td>\n",
       "      <td>60.084013</td>\n",
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       "      <td>52.686525</td>\n",
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       "      <th>142414</th>\n",
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       "      <th>142415</th>\n",
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       "      <td>58.755145</td>\n",
       "      <td>82.897092</td>\n",
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       "      <td>138.472458</td>\n",
       "      <td>71.669812</td>\n",
       "      <td>213.251541</td>\n",
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       "      <th>142418</th>\n",
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       "      <td>152.801396</td>\n",
       "      <td>90.647509</td>\n",
       "      <td>168.204499</td>\n",
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       "    <tr>\n",
       "      <th>142419</th>\n",
       "      <td>238923898</td>\n",
       "      <td>226.392459</td>\n",
       "      <td>219.764318</td>\n",
       "      <td>215.242171</td>\n",
       "      <td>221.316634</td>\n",
       "      <td>114.117552</td>\n",
       "      <td>73.636285</td>\n",
       "      <td>72.182481</td>\n",
       "      <td>131.236963</td>\n",
       "      <td>105.948347</td>\n",
       "      <td>...</td>\n",
       "      <td>99.198169</td>\n",
       "      <td>101.343755</td>\n",
       "      <td>102.367402</td>\n",
       "      <td>108.002496</td>\n",
       "      <td>243.547924</td>\n",
       "      <td>121.738308</td>\n",
       "      <td>145.507543</td>\n",
       "      <td>158.157510</td>\n",
       "      <td>165.934425</td>\n",
       "      <td>115.357415</td>\n",
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       "    <tr>\n",
       "      <th>142420</th>\n",
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       "      <td>227.527924</td>\n",
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       "      <td>75.186060</td>\n",
       "      <td>134.240542</td>\n",
       "      <td>108.951926</td>\n",
       "      <td>...</td>\n",
       "      <td>102.201748</td>\n",
       "      <td>104.347334</td>\n",
       "      <td>105.370981</td>\n",
       "      <td>111.006075</td>\n",
       "      <td>244.683389</td>\n",
       "      <td>124.741887</td>\n",
       "      <td>148.511122</td>\n",
       "      <td>161.161089</td>\n",
       "      <td>168.938004</td>\n",
       "      <td>116.492880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                NN   243545119   238600357   237883590   237875741  \\\n",
       "142411   238547482  235.351098  248.165838  171.022448  177.096912   \n",
       "142412   238537241  240.023305  252.838044  175.694655  181.769118   \n",
       "142413  5138198157  240.064138  252.878877  175.735488  181.809951   \n",
       "142414   238521463  240.340107  253.154846  176.011457  182.085920   \n",
       "142415   238217078  264.181473  276.996213  199.852823  205.927287   \n",
       "142416   238421388  259.988192  272.802931  195.519116  201.593579   \n",
       "142417   238402093  260.026195  272.840934  195.557119  201.631582   \n",
       "142418   238591938  274.355133  272.611402  209.886057  215.960520   \n",
       "142419   238923898  226.392459  219.764318  215.242171  221.316634   \n",
       "142420   238880909  227.527924  220.899783  218.245750  224.320213   \n",
       "\n",
       "         237992953  4377866427   238060667   553993016   239097861  ...  \\\n",
       "142411   75.453439   82.220353   82.981809   23.097679   50.656389  ...   \n",
       "142412   80.125646   86.892069   87.654016   27.769885   55.328105  ...   \n",
       "142413   80.166479   86.932902   87.694849   27.810718   55.368938  ...   \n",
       "142414   80.442448   87.208871   87.970818   28.086687   55.644907  ...   \n",
       "142415  104.283814   99.687925  101.930786   51.928054   79.486273  ...   \n",
       "142416   94.394497   56.474280   58.717142   82.859089   85.827683  ...   \n",
       "142417   94.432500   56.512283   58.755145   82.897092   85.865686  ...   \n",
       "142418  108.761438   70.605538   72.848399   97.297414  100.269317  ...   \n",
       "142419  114.117552   73.636285   72.182481  131.236963  105.948347  ...   \n",
       "142420  117.121131   76.639864   75.186060  134.240542  108.951926  ...   \n",
       "\n",
       "         238957886  2903017551   238975872   238997632   238965771  \\\n",
       "142411   57.609310   58.071499   59.000253   55.370973  238.599409   \n",
       "142412   62.281026   62.743705   63.672459   60.043180  243.271616   \n",
       "142413   62.321859   62.784538   63.713292   60.084013  243.312449   \n",
       "142414   62.597828   63.060507   63.989261   60.359982  243.588418   \n",
       "142415   86.439194   86.901874   87.830628   84.201348  267.429784   \n",
       "142416   79.077504   81.593099   82.521853   87.890640  263.096077   \n",
       "142417   79.115507   81.631102   82.559856   87.928643  263.134080   \n",
       "142418   93.519138   95.987641   96.963487  102.332274  277.463018   \n",
       "142419   99.198169  101.343755  102.367402  108.002496  243.547924   \n",
       "142420  102.201748  104.347334  105.370981  111.006075  244.683389   \n",
       "\n",
       "         238065313  4155927397   239209720  5136150615   239344045  \n",
       "142411   77.168707  101.287820  113.507419   57.709642  237.512236  \n",
       "142412   81.840913  105.960026  118.179626   53.004924  242.184443  \n",
       "142413   81.881746  106.000859  118.220459   52.686525  242.225276  \n",
       "142414   82.157715  106.276828  118.496428   51.743277  242.501245  \n",
       "142415  105.999082  130.118195  142.337794   52.010507  256.427183  \n",
       "142416  102.015253  125.784487  138.434455   71.631809  213.213538  \n",
       "142417  102.053256  125.822490  138.472458   71.669812  213.251541  \n",
       "142418  116.382194  140.151428  152.801396   90.647509  168.204499  \n",
       "142419  121.738308  145.507543  158.157510  165.934425  115.357415  \n",
       "142420  124.741887  148.511122  161.161089  168.938004  116.492880  \n",
       "\n",
       "[10 rows x 44 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ODD_landslide.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "ODD_landslide.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": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "ODD_landslide[\"1D\"] = 0\n",
    "sub = ODD_landslide.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_landslide[\"1D\"] = sub.min(axis=1) # Default is axis=0, meaning min value of each column selected. We want min of each row.\n",
    "D1_landslide = ODD_landslide[['NN', '1D']] # Remove unnecessary OD values.\n",
    "D1_landslide.to_csv(os.path.join(pth, '1D_landslide.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
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       "      <td>15.684400</td>\n",
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       "      <td>238217078</td>\n",
       "      <td>13.791444</td>\n",
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       "      <td>18.481299</td>\n",
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      "text/plain": [
       "                NN         1D\n",
       "142381  3422672432  18.638312\n",
       "142382   233331747  26.318761\n",
       "142383   233370841  23.569514\n",
       "142384   233363808  26.201311\n",
       "142385   233324358  30.779051\n",
       "142386  5125256148  43.283626\n",
       "142387   238797934  42.141003\n",
       "142388   238672387   4.420182\n",
       "142389  5186570054  30.226955\n",
       "142390   238256805  16.589999\n",
       "142391   237951964  57.995002\n",
       "142392  5188056947  33.144038\n",
       "142393   238244013  31.955887\n",
       "142394   239023079  41.127515\n",
       "142395   238936421  18.041609\n",
       "142396   238910207  12.954386\n",
       "142397   238931880  12.075073\n",
       "142398   238507813  13.820209\n",
       "142399   239010465  11.490877\n",
       "142400   753289645   4.022597\n",
       "142401   238061534  11.544436\n",
       "142402  6242335058   9.483847\n",
       "142403   238061543  11.786382\n",
       "142404  5148259911  15.033005\n",
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       "142412   238537241  16.946047\n",
       "142413  5138198157  16.627648\n",
       "142414   238521463  15.684400\n",
       "142415   238217078  13.791444\n",
       "142416   238421388  18.481299\n",
       "142417   238402093  18.519302\n",
       "142418   238591938  36.268174\n",
       "142419   238923898  28.688046\n",
       "142420   238880909  31.691625"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D1_landslide.tail(40) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "ODH_landslide[\"1H\"] = 0\n",
    "sub = ODH_landslide.iloc[:,1:-1] # Filtering out the newly created field and the node ID column.\n",
    "ODH_landslide[\"1H\"] = sub.min(axis=1) # Default is axis=0, meaning min value of each column selected. We want min of each row.\n",
    "H1_landslide = ODH_landslide[['NN', '1H']] # Remove unnecessary OD values.\n",
    "H1_landslide.to_csv(os.path.join(pth, '1H_landslide.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
       "      <td>239077188</td>\n",
       "      <td>11448.167825</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>239176627</td>\n",
       "      <td>22528.752462</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>237676072</td>\n",
       "      <td>10702.819166</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>239149053</td>\n",
       "      <td>11445.700877</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>24289.124825</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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      "text/plain": [
       "          NN            1H\n",
       "0  239148931  26941.704312\n",
       "1  239077120  11450.163002\n",
       "2  239127767  12390.975533\n",
       "3  239136364  11447.640533\n",
       "4  239077188  11448.167825\n",
       "5  239176627  22528.752462\n",
       "6  237676072  10702.819166\n",
       "7  239149053  11445.700877\n",
       "8  237686017  24289.124825\n",
       "9  239158919  10702.827205"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H1_landslide.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "ODP_landslide[\"1P\"] = 0\n",
    "sub = ODP_landslide.iloc[:,1:-1] # Filtering out the newly created field and the node ID column.\n",
    "ODP_landslide[\"1P\"] = sub.min(axis=1) # Default is axis=0, meaning min value of each column selected. We want min of each row.\n",
    "P1_landslide = ODP_landslide[['NN', '1P']] # Remove unnecessary OD values.\n",
    "P1_landslide.to_csv(os.path.join(pth, '1P_landslide.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
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       "      <td>18069.731552</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NN            1P\n",
       "0  239148931  22482.683402\n",
       "1  239077120   4634.970348\n",
       "2  239127767   5575.782879\n",
       "3  239136364   4632.447879\n",
       "4  239077188   4632.975171\n",
       "5  239176627  18069.731552\n",
       "6  237676072  10665.960198\n",
       "7  239149053   4630.508223\n",
       "8  237686017  17473.932171\n",
       "9  239158919  10665.968236"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P1_landslide.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare to baseline values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "D1_base = os.path.join(pth, \"1D.csv\")\n",
    "D1_base = pd.read_csv(D1_base)\n",
    "P1_base = os.path.join(pth, \"1P.csv\")\n",
    "P1_base = pd.read_csv(P1_base)\n",
    "H1_base = os.path.join(pth, \"1H.csv\")\n",
    "H1_base = pd.read_csv(H1_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NN</th>\n",
       "      <th>1P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>239148931</td>\n",
       "      <td>14.262696</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>239077120</td>\n",
       "      <td>19.665247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>239127767</td>\n",
       "      <td>17.457223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P1_base.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "P1_base = P1_base.loc[:,['NN', '1P']] # Remove unnecessary Unnamed: 0 column.\n",
    "D1_base = D1_base.loc[:,['NN', '1D']]\n",
    "H1_base = H1_base.loc[:,['NN', '1H']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NN</th>\n",
       "      <th>1P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>239148931</td>\n",
       "      <td>22482.683402</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>239077120</td>\n",
       "      <td>4634.970348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>239127767</td>\n",
       "      <td>5575.782879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>239136364</td>\n",
       "      <td>4632.447879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>239077188</td>\n",
       "      <td>4632.975171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NN            1P\n",
       "0  239148931  22482.683402\n",
       "1  239077120   4634.970348\n",
       "2  239127767   5575.782879\n",
       "3  239136364   4632.447879\n",
       "4  239077188   4632.975171"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P1_landslide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NN</th>\n",
       "      <th>1P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142416</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142417</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142418</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142419</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142420</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142421 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          NN     1P\n",
       "0       True  False\n",
       "1       True  False\n",
       "2       True  False\n",
       "3       True  False\n",
       "4       True  False\n",
       "...      ...    ...\n",
       "142416  True  False\n",
       "142417  True  False\n",
       "142418  True   True\n",
       "142419  True  False\n",
       "142420  True  False\n",
       "\n",
       "[142421 rows x 2 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P1_base == P1_landslide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67403"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((P1_base['1P'] == P1_landslide['1P']).sum()).sum()\n",
    "# Number of routes that remained the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4735052582034296"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((P1_base['1P'] == P1_landslide['1P']).sum()).sum() / ((P1_base['1P'] == P1_base['1P']).sum()).sum()\n",
    "# 47.4 percent of routes remained the same after the hazard simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30787"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((D1_base['1D'] == D1_landslide['1D']).sum()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2162950160884655"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((D1_base['1D'] == D1_landslide['1D']).sum()).sum() / ((D1_base['1D'] == D1_base['1D']).sum()).sum()\n",
    "# 21.6 percent of routes remained the same after the hazard simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26694"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((H1_base['1H'] == H1_landslide['1H']).sum()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18753951861063103"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((H1_base['1H'] == H1_landslide['1H']).sum()).sum() / ((H1_base['1H'] == H1_base['1H']).sum()).sum()\n",
    "# 18.8 percent of routes remained the same after the hazard simulation."
   ]
  },
  {
   "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": 234,
   "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": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "D1_landslide = os.path.join(pth, \"1D_landslide.csv\")\n",
    "D1_landslide = pd.read_csv(D1_landslide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</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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       "      <td>Adjuntas</td>\n",
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       "      <td>2.284031</td>\n",
       "      <td>9.998605</td>\n",
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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",
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       "      <td>239148931</td>\n",
       "      <td>3.250490</td>\n",
       "      <td>8.260954</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",
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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",
       "      <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",
       "  </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  \n",
       "0   9.998605  \n",
       "1   8.260954  \n",
       "2   7.290488  \n",
       "3   7.779941  \n",
       "4  12.971183  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zwalk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1D</th>\n",
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       "      <td>26922.071692</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>239077120</td>\n",
       "      <td>11430.530382</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>239127767</td>\n",
       "      <td>12371.342913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>239136364</td>\n",
       "      <td>11428.007913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>239077188</td>\n",
       "      <td>11428.535205</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0         NN            1D\n",
       "0           0  239148931  26922.071692\n",
       "1           1  239077120  11430.530382\n",
       "2           2  239127767  12371.342913\n",
       "3           3  239136364  11428.007913\n",
       "4           4  239077188  11428.535205"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D1_landslide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0_x</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",
       "      <th>walkspeed</th>\n",
       "      <th>walk_time</th>\n",
       "      <th>Unnamed: 0_y</th>\n",
       "      <th>1D</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>0.0</td>\n",
       "      <td>26922.071692</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>0.0</td>\n",
       "      <td>26922.071692</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>0.0</td>\n",
       "      <td>26922.071692</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>0.0</td>\n",
       "      <td>26922.071692</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>1.0</td>\n",
       "      <td>11430.530382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0_x      wpop  xmid  wid municipio         NN      osmid  \\\n",
       "0             0  0.818646   0.0    1  Adjuntas  239148931  239148931   \n",
       "1             1  0.731308   0.0    2  Adjuntas  239148931  239148931   \n",
       "2             2  0.642141   0.0    3  Adjuntas  239148931  239148931   \n",
       "3             3  0.612746   0.0    4  Adjuntas  239148931  239148931   \n",
       "4             4  0.699177   0.0    5  Adjuntas  239077120  239077120   \n",
       "\n",
       "   walkspeed  walk_time  Unnamed: 0_y            1D  \n",
       "0   2.284031   9.998605           0.0  26922.071692  \n",
       "1   3.250490   8.260954           0.0  26922.071692  \n",
       "2   4.286563   7.290488           0.0  26922.071692  \n",
       "3   4.613557   7.779941           0.0  26922.071692  \n",
       "4   3.360424  12.971183           1.0  11430.530382  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge nearest POIs and walktimes\n",
    "zwalkD_landslide = zwalk.merge(D1_landslide, how='left', left_on='NN', right_on='NN', sort=False)\n",
    "zwalkD_landslide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
       "      <td>0.642141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Adjuntas</td>\n",
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       "      <td>239148931</td>\n",
       "      <td>4.286563</td>\n",
       "      <td>7.290488</td>\n",
       "      <td>22482.683402</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",
       "      <td>239148931</td>\n",
       "      <td>239148931</td>\n",
       "      <td>4.613557</td>\n",
       "      <td>7.779941</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>4634.970348</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            1P  \n",
       "0   9.998605  22482.683402  \n",
       "1   8.260954  22482.683402  \n",
       "2   7.290488  22482.683402  \n",
       "3   7.779941  22482.683402  \n",
       "4  12.971183   4634.970348  "
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge nearest POIs and walktimes\n",
    "zwalkP_landslide = zwalk.merge(P1_landslide, how='left', left_on='NN', right_on='NN', sort=False)\n",
    "zwalkP_landslide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>Adjuntas</td>\n",
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       "      <th>3</th>\n",
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       "      <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>26941.704312</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>11450.163002</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  26941.704312  \n",
       "1   8.260954  26941.704312  \n",
       "2   7.290488  26941.704312  \n",
       "3   7.779941  26941.704312  \n",
       "4  12.971183  11450.163002  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge nearest POIs and walktimes\n",
    "zwalkH_landslide = zwalk.merge(H1_landslide, how='left', left_on='NN', right_on='NN', sort=False)\n",
    "zwalkH_landslide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine walk time from WorldPop point to nearest road node, and from road node to facility.\n",
    "zwalkH_landslide[\"mm1H\"] = 0\n",
    "zwalkH_landslide[\"mm1H\"] = zwalkH_landslide[\"walk_time\"] + zwalkH_landslide[\"1H\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>26941.704312</td>\n",
       "      <td>26948.994800</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",
       "      <td>239148931</td>\n",
       "      <td>239148931</td>\n",
       "      <td>4.613557</td>\n",
       "      <td>7.779941</td>\n",
       "      <td>26941.704312</td>\n",
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       "      <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>11450.163002</td>\n",
       "      <td>11463.134185</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            1H          mm1H  \n",
       "0   9.998605  26941.704312  26951.702917  \n",
       "1   8.260954  26941.704312  26949.965266  \n",
       "2   7.290488  26941.704312  26948.994800  \n",
       "3   7.779941  26941.704312  26949.484253  \n",
       "4  12.971183  11450.163002  11463.134185  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zwalkH_landslide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine walk time from WorldPop point to nearest road node, and from road node to facility.\n",
    "zwalkP_landslide[\"mm1P\"] = 0\n",
    "zwalkP_landslide[\"mm1P\"] = zwalkP_landslide[\"walk_time\"] + zwalkP_landslide[\"1P\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>238881983</td>\n",
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       "      <td>1075781</td>\n",
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       "      <td>1075783</td>\n",
       "      <td>Salinas</td>\n",
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       "      <td>25.605756</td>\n",
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      ],
      "text/plain": [
       "         Unnamed: 0      wpop  xmid      wid municipio         NN      osmid  \\\n",
       "1076135     1076135  0.888915   NaN  1075779   Salinas  238881983  238881983   \n",
       "1076136     1076136  0.889920   NaN  1075780   Salinas  238881983  238881983   \n",
       "1076137     1076137  0.900318   NaN  1075781   Salinas  238859820  238859820   \n",
       "1076138     1076138  0.894649   NaN  1075782   Salinas  238859820  238859820   \n",
       "1076139     1076139  0.858682   NaN  1075783   Salinas  238289242  238289242   \n",
       "\n",
       "         walkspeed  walk_time         1P       mm1P  \n",
       "1076135   5.041526  43.878211  17.351342  61.229553  \n",
       "1076136   5.032043  44.713233  17.351342  62.064575  \n",
       "1076137   5.027474  45.359462   8.687345  54.046807  \n",
       "1076138   5.032116  45.417854   8.687345  54.105199  \n",
       "1076139   5.022637  44.728867  25.605756  70.334623  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zwalkP_landslide.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine walk time from WorldPop point to nearest road node, and from road node to facility.\n",
    "zwalkD_landslide[\"mm1D\"] = 0\n",
    "zwalkD_landslide[\"mm1D\"] = zwalkD_landslide[\"walk_time\"] + zwalkD_landslide[\"1D\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>municipio</th>\n",
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       "      <th>1076139</th>\n",
       "      <td>0.858682</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1075783</td>\n",
       "      <td>Salinas</td>\n",
       "      <td>238289242</td>\n",
       "      <td>44.728867</td>\n",
       "      <td>24.840352</td>\n",
       "      <td>69.569219</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             wpop  xmid      wid municipio         NN  walk_time         1D  \\\n",
       "1076135  0.888915   NaN  1075779   Salinas  238881983  43.878211  41.235351   \n",
       "1076136  0.889920   NaN  1075780   Salinas  238881983  44.713233  41.235351   \n",
       "1076137  0.900318   NaN  1075781   Salinas  238859820  45.359462  31.589310   \n",
       "1076138  0.894649   NaN  1075782   Salinas  238859820  45.417854  31.589310   \n",
       "1076139  0.858682   NaN  1075783   Salinas  238289242  44.728867  24.840352   \n",
       "\n",
       "              mm1D  \n",
       "1076135  85.113561  \n",
       "1076136  85.948584  \n",
       "1076137  76.948771  \n",
       "1076138  77.007163  \n",
       "1076139  69.569219  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zwalkD_landslide.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "zwalkD_landslide = zwalkD_landslide[['wpop', 'xmid', 'wid', 'municipio', 'NN', 'walk_time', '1D', 'mm1D']]\n",
    "zwalkH_landslide = zwalkH_landslide[['wpop', 'xmid', 'wid', 'municipio', 'NN', 'walk_time', '1H', 'mm1H']]\n",
    "zwalkP_landslide = zwalkP_landslide[['wpop', 'xmid', 'wid', 'municipio', 'NN', 'walk_time', '1P', 'mm1P']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "zwalkH_landslide.to_csv(os.path.join(pth, 'H_mm_landslide.csv'))\n",
    "zwalkP_landslide.to_csv(os.path.join(pth, 'P_mm_landslide.csv'))\n",
    "zwalkD_landslide.to_csv(os.path.join(pth, 'D_mm_landslide.csv'))"
   ]
  }
 ],
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