SEN_Transport_agriculture-to-market_flood_2021-11-19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network analysis in Senegal\n",
    "\n",
    "### Objectives\n",
    "    1)\tUse measures of road-based accessibility to identify road segments that, if rehabilitated, would improve agricultural market activities in Senegal, including during flood conditions.\n",
    "    2)\tGain a better understanding of the accessibility, connectivity, and criticality of roads in Senegal in relationship to agricultural origins, processing & transfer sites, and markets.\n",
    "\n",
    "To this end, the team will develop an accessibility model which measures the travel time from sites of agricultural production to their nearest populated areas, processing centers, and markets. \n",
    "\n",
    "### Datasets for analysis\n",
    "#### ORIGIN\n",
    "    1) agriculture: MapSPAM 2017. Measuring value in international dollars.\n",
    "    2) agriculture: UMD Land Cover 2019 30m. Assign MapSPAM value onto land cover cropland class for more precise origin information.\n",
    "    3) population: WorldPop 2020, UN-adjusted.\n",
    "    4) settlement extent: GRID3 2020.\n",
    "#### DESTINATION\n",
    "    4) markets: derived from WorldPop 2020 and GRID3 2020 urban clusters.\n",
    "    5) agricultural processing hubs: to be acquired.\n",
    "#### TRAVEL ROUTE\n",
    "    6) roads: OpenStreetMap, July 2021.\n",
    "    7) elevation: \n",
    "#### OBSTACLE\n",
    "    8) flood: FATHOM. 1-in-10, 20, and 50 year flood return periods. \n",
    "#### INTERVENTION\n",
    "    9) upcoming road projects: AGEROUTE interventions separate from the World Bank-financed project\n",
    "    10) targeted road projects: critical road segments identified by this accessibility model's baseline outputs\n",
    "\n",
    "\n",
    "### Model design\n",
    "#### Basic formula: \n",
    "    (a) Off-road driving time from origin to closest road node\n",
    "    +\n",
    "    (b) Driving time from road node in (a) to a destination (closeness measured by road segments speeds)\n",
    "\n",
    "#### Model origin & destination (OD) sets:\n",
    "    A)\tTravel time from an area that has agricultural value/potential to the nearest processing hub (if provided).\n",
    "    B)\tTravel time from an area that has agricultural value/potential to the nearest larger settlement, (“larger” settlement identified using a case-appropriate population metric to be determined).\n",
    "    C)\tTravel time from an area that has agricultural value/potential to the nearest market.\n",
    "    D)\tTravel time from all settlements to the nearest market.\n",
    "    E)\tTravel time from larger settlements to the nearest market.\n",
    "\n",
    "#### Before/after scenarios for each OD set:\n",
    "    1)\tPre-project, baseline weather: No inclement weather. Road network status as of November 2021.\n",
    "    2)\tPre-project, flood: 1-in-10, 1-in-20 and 1-in-50 year flood return period. Road network status as of November 2021.\n",
    "    3)\tPost-project, baseline weather: No inclement weather. Road network status if X number of critical road segments to high-value areas are protected (i.e., their travel times reduced).\n",
    "    4)\tPost-project, flood: 1-in-10 year flood return period. Road network status if X number of critical road segments to high-value areas are protected (i.e., their travel times reduced).\n",
    "\n",
    "#### Notes:\n",
    "    --Destinations are expected to be proximal to the road network, so no measure is taken between road and destination.\n",
    "    --All travel times will be assigned to each model variation’s point of origin; the aggregation up to admin areas is possible if desired.\n",
    "    --Obstacles & interventions modify the road segment speeds. Basic formula is then applied to the modified road network.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prep workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\wb527163\\\\GEO-Cdrive-Grace'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os, sys\n",
    "GISFolder = os.getcwd()\n",
    "GISFolder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: needed to reinstall rtree due to geopandas import error. Did so in the console. \n",
    "# conda install -c conda-forge rtree=0.9.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and filter osm network (step 1)\n",
    "import geopandas as gpd\n",
    "from geopandas import GeoDataFrame\n",
    "import pandas as pd\n",
    "import time\n",
    "sys.path.append(r\"C:\\Users\\wb527163\\.conda\\envs\\geo\\GOSTnets-master\")\n",
    "import GOSTnets as gn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import osmnx as ox\n",
    "import numpy as np\n",
    "import rasterio as rt\n",
    "import shapely\n",
    "from shapely.geometry import Point, box\n",
    "from shapely.ops import unary_union\n",
    "from shapely.wkt import loads\n",
    "from shapely import wkt\n",
    "from shapely.geometry import LineString, MultiLineString, Point\n",
    "import peartree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Might not use these\n",
    "import fiona\n",
    "from osgeo import gdal\n",
    "import importlib\n",
    "import matplotlib.pyplot as plt\n",
    "import subprocess, glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\wb527163\\\\GEO-Cdrive-Grace\\\\SEN-Cdrive'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pth = os.path.join(GISFolder, \"SEN-Cdrive\") # Personal folder system for running model.\n",
    "pth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\wb527163\\\\GEO-Cdrive-Grace\\\\SEN-Cdrive\\\\outputs'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_pth = os.path.join(GISFolder, \"SEN-Cdrive\\outputs\") # For storing intermediate outputs from the model.\n",
    "out_pth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'R:\\\\SEN\\\\GEO'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_pth = 'R:\\\\SEN\\\\GEO' # This is where the unmodified input data is stored. Finalized outputs also housed here.\n",
    "team_pth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare and clean the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Return periods from FATHOM: 1-in-10, 20, and 50 year floods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import multiprocessing\n",
    "\n",
    "multiprocessing.cpu_count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_jobs=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flood = os.path.join(pth, \"FU_1in10.csv\")\n",
    "flood = pd.read_csv(flood)\n",
    "flood.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crs = \"EPSG:4326\"\n",
    "geometry = [Point(xy) for xy in zip(flood.x, flood.y)]\n",
    "flood = GeoDataFrame(flood, crs=crs, geometry=geometry)\n",
    "flood = gpd.read_file(flood)\n",
    "flood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flood = flood[['ID', 'FLD_ZONE', 'R10', 'R20', 'R50','geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flood.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_edge = os.path.join(out_pth, \"drive_time_edge.csv\")\n",
    "gTime_edge = pd.read_csv(gTime_edge)\n",
    "crs = 'epsg:4326'\n",
    "gTime_edge = GeoDataFrame(gTime_edge, crs=crs, geometry='geometry') \n",
    "gTime_edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_edge.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flood = flood.to_crs(\"EPSG:4326\")\n",
    "gTime_edge = gTime_edge.to_crs(\"EPSG:4326\")\n",
    "flood == gTime_edge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update driving times based on flood intersection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Join road network and flood raster into single table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "join = gpd.sjoin(gTime_edge, flood, how=\"left\", lsuffix='_edge', rsuffix='_fl') # This takes 30+ minutes.\n",
    "join.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fewer errors farther down when using dataframe instead of gdf\n",
    "gTime_flood = pd.DataFrame(join)\n",
    "gTime_flood.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_flood.to_csv(os.path.join(out_pth, 'gTime_flood.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create speed penalties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_flood.reset_index(inplace=True) # To avoid:  ValueError: cannot reindex from a duplicate axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_flood[\"time_R10\"] = 0 # This is the \"penalty\" column\n",
    "gTime_flood[\"time_R20\"] = 0\n",
    "gTime_flood[\"time_R50\"] = 0\n",
    "\n",
    "# 1-in-10 year flood\n",
    "gTime_flood.loc[(gTime_flood['R10'] == 1), # Select rows where depth is 1 foot\n",
    "       'time_R10'] = (gTime_flood['time'] * 1.25) # then slow down slightly on the penalty column\n",
    "gTime_flood.loc[(gTime_flood['R10'] == 2), \n",
    "       'time_R10'] = (gTime_flood['time'] * 2) # Double the travel time on that road edge.\n",
    "gTime_flood.loc[(gTime_flood['R10'] == 3), \n",
    "       'time_R10'] = (gTime_flood['time'] * 5) # Increase travel time five-fold.\n",
    "\n",
    "# 1-in-20 year flood\n",
    "gTime_flood.loc[(gTime_flood['R20'] == 1), # Select rows where depth is 1 foot\n",
    "       'time_R20'] = (gTime_flood['time'] * 1.25) # then slow down slightly on the penalty column\n",
    "gTime_flood.loc[(gTime_flood['R20'] == 2), \n",
    "       'time_R20'] = (gTime_flood['time'] * 2) # Double the travel time on that road edge.\n",
    "gTime_flood.loc[(gTime_flood['R20'] == 3), \n",
    "       'time_R20'] = (gTime_flood['time'] * 5) # Increase travel time five-fold.\n",
    "\n",
    "# 1-in-50 year flood\n",
    "gTime_flood.loc[(gTime_flood['R50'] == 1), # Select rows where depth is 1 foot\n",
    "       'time_R50'] = (gTime_flood['time'] * 1.25) # then slow down slightly on the penalty column\n",
    "gTime_flood.loc[(gTime_flood['R50'] == 2), \n",
    "       'time_R50'] = (gTime_flood['time'] * 2) # Double the travel time on that road edge.\n",
    "gTime_flood.loc[(gTime_flood['R50'] == 3), \n",
    "       'time_R50'] = (gTime_flood['time'] * 5) # Increase travel time five-fold.\n",
    "\n",
    "gTime_flood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_flood.to_csv(os.path.join(out_pth, 'gTime_flood.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert back to graph object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gTime_flood_G = nx.from_pandas_edgelist(\n",
    "    gTime_flood,\n",
    "    source=\"stnode\",\n",
    "    target=\"endnode\",\n",
    "    edge_attr=True,\n",
    ")\n",
    "# This should only take a few seconds.\n",
    "\n",
    "gn.example_edge(gTime_flood_G, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gn.save(gTime_flood_G, 'gTime_flood', pth, edges = False, nodes = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Origins and destinations\n",
    "\n",
    "Measure distance from origin/destination to nearest node and save to file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 319 entries, 0 to 318\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   Unnamed_ 0  319 non-null    int64   \n",
      " 1   mgrs_code   319 non-null    object  \n",
      " 2   type        319 non-null    object  \n",
      " 3   AREA_GEO    319 non-null    float64 \n",
      " 4   SUM         318 non-null    float64 \n",
      " 5   Total_Driv  312 non-null    float64 \n",
      " 6   WP_dens     319 non-null    float64 \n",
      " 7   urb         319 non-null    int64   \n",
      " 8   hd_urb      319 non-null    int64   \n",
      " 9   Urb_class   319 non-null    int64   \n",
      " 10  Shape_Leng  319 non-null    float64 \n",
      " 11  Shape_Area  319 non-null    float64 \n",
      " 12  geometry    319 non-null    geometry\n",
      "dtypes: float64(6), geometry(1), int64(4), object(2)\n",
      "memory usage: 32.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#%% If starting new session, reload graph from file\n",
    "gTime = nx.read_gpickle(\"SEN-Cdrive/outputs/gTime.pickle\")\n",
    "agriculture = gpd.read_file(\"SEN-Cdrive/outputs/agriculture.shp\")\n",
    "agriculture = agriculture.to_crs(\"EPSG:4326\")\n",
    "agriculture.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "Shape_Area     float64\n",
       "geometry      geometry\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agriculture.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "?gn.pandana_snap_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "      <td>28PHU0490_01</td>\n",
       "      <td>bua</td>\n",
       "      <td>12.646372</td>\n",
       "      <td>36115.813159</td>\n",
       "      <td>158.705391</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.399690</td>\n",
       "      <td>0.001052</td>\n",
       "      <td>POINT (-12.19376 12.56342)</td>\n",
       "      <td>8597133978</td>\n",
       "      <td>13.243242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed_ 0     mgrs_code type   AREA_GEO            SUM  Total_Driv  \\\n",
       "0           0  28PCU1268_02  bua   6.825291   14992.022066   67.224775   \n",
       "1           1  28PCU6189_01  bua  27.151434  186046.609754    0.000000   \n",
       "2           2  28PCV6615_01  bua  12.946680   29013.022570   26.304661   \n",
       "3           3  28PDV3804_01  bua   7.396509   31580.806516   90.292621   \n",
       "4           4  28PHU0490_01  bua  12.646372   36115.813159  158.705391   \n",
       "\n",
       "       WP_dens  urb  hd_urb  Urb_class  Shape_Leng  Shape_Area  \\\n",
       "0  2196.539770    1       0          1    0.430449    0.000567   \n",
       "1  6852.183502    1       1          2    0.671746    0.002259   \n",
       "2  2240.962297    1       0          1    0.349253    0.001078   \n",
       "3  4269.691037    1       0          1    0.252963    0.000616   \n",
       "4  2855.824066    1       0          1    0.399690    0.001052   \n",
       "\n",
       "                     geometry          NN    NN_dist  \n",
       "0  POINT (-16.72670 12.37174)  2283044923  84.109763  \n",
       "1  POINT (-16.27319 12.56237)  2119246531  39.014876  \n",
       "2  POINT (-16.22947 12.79722)  1894405285  22.797121  \n",
       "3  POINT (-15.56341 12.70399)  3355695913  42.169725  \n",
       "4  POINT (-12.19376 12.56342)  8597133978  13.243242  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ag_snap = gn.pandana_snap_c(gTime, agriculture, source_crs = 'epsg:4326', target_crs = 'epsg:31028', add_dist_to_node_col = True)\n",
    "ag_snap.to_csv('SEN-Cdrive/outputs/ag_snap.csv', index=True)\n",
    "# Finished within a minute.\n",
    "ag_snap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create travel time values for the road nodes nearest to each service.\n",
    "\n",
    "Using calculate_OD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If starting a new session, load from file.\n",
    "HDurban_snap = os.path.join(out_pth, \"HDurban_snap.csv\")\n",
    "HDurban_snap = pd.read_csv(HDurban_snap)\n",
    "ag_snap = os.path.join(out_pth, \"ag_snap.csv\")\n",
    "ag_snap = pd.read_csv(ag_snap)\n",
    "gTime = nx.read_gpickle(\"SEN-Cdrive/outputs/gTime.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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(ag_snap.NN.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_HDurban = list(HDurban_snap.NN.unique()) \n",
    "dests = list_HDurban\n",
    "\n",
    "# If more than one set of destinations, you can combine the sets to go into a single OD matrix using this code:\n",
    "# list_ssa = list(ssa_snap.NN.unique()) \n",
    "# destslist = list_HDurban + list_ssa\n",
    "# dests = list(set(destslist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23108"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(origins) # 23108 unique nearest nodes (average of 5 origins per node)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "314"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dests) # 314 unique nearest nodes. This is only 5 less than the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(358284990, 5329792501, {'osmid': 59618174, 'ref': 'D 523', 'name': 'D 523', 'highway': 'unclassified', 'oneway': False, 'length': 73.22200000000001, 'geometry': <shapely.geometry.linestring.LineString object at 0x00000181229D7E20>, 'time': 5.271984000000001, 'mode': 'drive'})\n"
     ]
    }
   ],
   "source": [
    "gn.example_edge(gTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "OD = gn.calculate_OD(gTime, origins, dests, fail_value, weight = 'time')\n",
    "# This took several hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "OD_df = pd.DataFrame(OD, index = origins, columns = dests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 23108 entries, 7761872879 to 4072041221\n",
      "Columns: 314 entries, 2283044923 to 3306192793\n",
      "dtypes: float64(314)\n",
      "memory usage: 55.5 MB\n"
     ]
    }
   ],
   "source": [
    "OD_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "\n",
       "[5 rows x 314 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OD_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to minutes and save to file.\n",
    "OD_min = OD_df[OD_df <fail_value] / 60\n",
    "OD_min.to_csv(os.path.join(out_pth, 'fromagriculture/OD_flood.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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       "8452098512  483.775578  428.536108  403.341438  386.766278  586.361172   \n",
       "8449983186  488.847199  433.607729  408.413059  391.837899  591.432793   \n",
       "4072935008  494.307499  439.068029  413.873359  397.298199  596.893093   \n",
       "4072935012  494.874763  439.635293  414.440623  397.865463  597.460357   \n",
       "8558304561  511.931168  456.691698  431.497028  414.921868  614.516762   \n",
       "4072041221  495.490698  440.251228  415.056558  398.481398  598.076292   \n",
       "\n",
       "            3490825773  8636590476  3495281736  6029312286  6968809270  ...  \\\n",
       "8073203019  330.507954  225.126908  326.528981  287.453858  353.758264  ...   \n",
       "8071408643  326.560153  221.179107  322.581180  283.506057  349.810463  ...   \n",
       "3717192287  324.883012  219.501966  320.904039  281.828916  348.133322  ...   \n",
       "4186362309  542.424183  437.043136  330.256648  360.656559  302.678016  ...   \n",
       "7805596740  369.500026  264.118979  365.521052  326.445929  392.750335  ...   \n",
       "4071912759  382.855331  277.474285  378.876358  339.801235  406.105640  ...   \n",
       "6021242885  383.116269  277.735223  379.137296  340.062173  406.366579  ...   \n",
       "6026792324  388.278227  282.897181  384.299254  345.224131  411.528537  ...   \n",
       "2281084786  385.809719  280.428672  381.830746  342.755623  409.060028  ...   \n",
       "9206374054  414.721624  309.340578  410.742651  371.667528  407.857460  ...   \n",
       "4603303859  551.387919  446.006873  339.220385  369.620295  311.641752  ...   \n",
       "8382671047  524.261604  418.880558  345.032003  375.431914  317.453371  ...   \n",
       "8382671039  524.175151  418.794104  344.945550  375.345460  317.366917  ...   \n",
       "8463478851  438.630661  333.249615  434.651688  395.576565  461.880971  ...   \n",
       "8452098512  453.369230  347.988183  449.390256  410.315133  476.619539  ...   \n",
       "8449983186  458.440851  353.059804  454.461877  415.386754  481.691160  ...   \n",
       "4072935008  463.901150  358.520104  459.922177  420.847054  487.151460  ...   \n",
       "4072935012  464.468415  359.087368  460.489442  421.414319  487.718724  ...   \n",
       "8558304561  481.524819  376.143773  477.545846  438.470723  504.775129  ...   \n",
       "4072041221  465.084349  359.703303  461.105376  422.030253  488.334659  ...   \n",
       "\n",
       "            6045379010  6457408831  3783356554  7037053972  6045711032  \\\n",
       "8073203019  134.132904  361.294538  330.176853  339.439415  206.482904   \n",
       "8071408643  139.486675  366.648309  330.206267  339.468830  211.836675   \n",
       "3717192287  123.714349  324.195482  284.806059  294.068622  185.533737   \n",
       "4186362309  418.425640  177.003454  118.882474  128.145036  337.987056   \n",
       "7805596740  124.049748  352.990054  366.786843  376.049406  198.178420   \n",
       "4071912759   57.405258  284.566892  282.601937  291.864500  129.755258   \n",
       "6021242885   57.144320  284.305954  282.340999  291.603561  129.494320   \n",
       "6026792324   60.689634  283.910628  278.531514  287.794077  131.119460   \n",
       "2281084786   62.062304  285.283297  276.063006  285.325568  132.492130   \n",
       "9206374054  191.387696  220.472625  211.662694  220.925256  215.625927   \n",
       "4603303859  369.421250  127.999063   69.878083   79.140645  288.982666   \n",
       "8382671047  318.492579   77.509760   19.388780   28.651342  238.493363   \n",
       "8382671039  318.406126   77.423307   19.302327   28.564889  238.406909   \n",
       "8463478851   75.545068  232.789178  291.503846  289.075430   77.977544   \n",
       "8452098512   79.234504  223.885983  282.600651  280.172234   69.074348   \n",
       "8449983186   71.138156  212.273931  270.988599  268.560182   57.462297   \n",
       "4072935008  109.140970  197.683380  256.398048  253.969631   44.892213   \n",
       "4072935012  109.708234  197.669900  256.384568  253.956152   44.878733   \n",
       "8558304561  126.764639  204.611943  263.326611  260.898195   53.781979   \n",
       "4072041221   75.699289  214.660966  273.375635  270.947218   59.849332   \n",
       "\n",
       "            1863721184  3649063162  293471544   3916091448  3306192793  \n",
       "8073203019  308.871430  341.093917  325.231059   80.687359  345.071244  \n",
       "8071408643  314.225202  346.447689  321.283258   76.739558  345.100658  \n",
       "3717192287  271.772374  303.994861  319.606118   36.090202  299.700450  \n",
       "4186362309  229.426562  202.824827  475.507755  364.275842  106.254608  \n",
       "7805596740  300.566946  332.789433  364.223131  124.191002  381.681234  \n",
       "4071912759  232.143785  264.366272  377.578436  103.555840  297.496328  \n",
       "6021242885  231.882846  264.105333  377.839375  103.294901  297.235390  \n",
       "6026792324  231.487520  263.710007  383.001332   99.485417  293.425905  \n",
       "2281084786  232.860189  265.082676  380.532824   97.016908  290.957397  \n",
       "9206374054  168.049518  200.272005  409.444729  124.474588  226.557085  \n",
       "4603303859  180.422171  153.820436  484.471491  315.271451   48.206621  \n",
       "8382671047  129.932868  103.331133  490.283110  251.579471   34.283171  \n",
       "8382671039  129.846414  103.244679  490.196656  251.493018   34.196718  \n",
       "8463478851  180.366070  212.588557  433.353766  149.837851  330.726927  \n",
       "8452098512  171.462875  203.685362  448.092335  164.576419  321.823731  \n",
       "8449983186  159.850823  192.073310  453.163956  169.648040  310.211680  \n",
       "4072935008  145.260272  177.482759  458.624255  175.108340  295.621128  \n",
       "4072935012  145.246793  177.469280  459.191520  175.675604  295.607649  \n",
       "8558304561  152.188835  184.411322  476.247924  192.732009  302.549692  \n",
       "4072041221  162.237859  194.460346  459.807454  176.291539  312.598715  \n",
       "\n",
       "[20 rows x 314 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OD_min.tail(20)"
   ]
  },
  {
   "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": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reload from file even if already loaded. Quickest way to ensure NN is a column rather than only the index.\n",
    "OD = os.path.join(out_pth, \"fromagriculture/OD_flood.csv\")\n",
    "OD = pd.read_csv(OD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NN</th>\n",
       "      <th>2283044923</th>\n",
       "      <th>2119246531</th>\n",
       "      <th>1894405285</th>\n",
       "      <th>3355695913</th>\n",
       "      <th>8597133978</th>\n",
       "      <th>3490825773</th>\n",
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       "      <th>...</th>\n",
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       "      <th>1863721184</th>\n",
       "      <th>3649063162</th>\n",
       "      <th>293471544</th>\n",
       "      <th>3916091448</th>\n",
       "      <th>3306192793</th>\n",
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       "      <th>0</th>\n",
       "      <td>7761872879</td>\n",
       "      <td>3.601602</td>\n",
       "      <td>62.713982</td>\n",
       "      <td>84.233782</td>\n",
       "      <td>178.427721</td>\n",
       "      <td>505.950746</td>\n",
       "      <td>146.401548</td>\n",
       "      <td>139.744338</td>\n",
       "      <td>238.725532</td>\n",
       "      <td>199.650409</td>\n",
       "      <td>...</td>\n",
       "      <td>474.253205</td>\n",
       "      <td>634.036094</td>\n",
       "      <td>577.000206</td>\n",
       "      <td>586.262769</td>\n",
       "      <td>542.843904</td>\n",
       "      <td>581.612986</td>\n",
       "      <td>613.835473</td>\n",
       "      <td>84.746116</td>\n",
       "      <td>324.588514</td>\n",
       "      <td>591.894597</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7761872860</td>\n",
       "      <td>2.879157</td>\n",
       "      <td>61.991537</td>\n",
       "      <td>83.511337</td>\n",
       "      <td>177.705276</td>\n",
       "      <td>505.228301</td>\n",
       "      <td>145.679103</td>\n",
       "      <td>139.021893</td>\n",
       "      <td>238.003087</td>\n",
       "      <td>198.927964</td>\n",
       "      <td>...</td>\n",
       "      <td>473.530760</td>\n",
       "      <td>633.313649</td>\n",
       "      <td>576.277761</td>\n",
       "      <td>585.540324</td>\n",
       "      <td>542.121459</td>\n",
       "      <td>580.890541</td>\n",
       "      <td>613.113028</td>\n",
       "      <td>84.023671</td>\n",
       "      <td>323.866069</td>\n",
       "      <td>591.172152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7761872847</td>\n",
       "      <td>1.830102</td>\n",
       "      <td>60.942482</td>\n",
       "      <td>82.462282</td>\n",
       "      <td>176.656221</td>\n",
       "      <td>504.179246</td>\n",
       "      <td>144.630048</td>\n",
       "      <td>137.972838</td>\n",
       "      <td>236.954032</td>\n",
       "      <td>197.878909</td>\n",
       "      <td>...</td>\n",
       "      <td>472.481705</td>\n",
       "      <td>632.264594</td>\n",
       "      <td>575.228706</td>\n",
       "      <td>584.491269</td>\n",
       "      <td>541.072404</td>\n",
       "      <td>579.841486</td>\n",
       "      <td>612.063973</td>\n",
       "      <td>82.974616</td>\n",
       "      <td>322.817014</td>\n",
       "      <td>590.123097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8604836683</td>\n",
       "      <td>127.855787</td>\n",
       "      <td>69.841801</td>\n",
       "      <td>92.811330</td>\n",
       "      <td>187.005269</td>\n",
       "      <td>501.750641</td>\n",
       "      <td>154.979097</td>\n",
       "      <td>148.321887</td>\n",
       "      <td>234.525427</td>\n",
       "      <td>195.450304</td>\n",
       "      <td>...</td>\n",
       "      <td>482.830754</td>\n",
       "      <td>634.418102</td>\n",
       "      <td>576.297122</td>\n",
       "      <td>585.559684</td>\n",
       "      <td>551.421452</td>\n",
       "      <td>590.190535</td>\n",
       "      <td>622.413022</td>\n",
       "      <td>92.151918</td>\n",
       "      <td>333.166063</td>\n",
       "      <td>590.886269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8604829828</td>\n",
       "      <td>133.448610</td>\n",
       "      <td>75.434624</td>\n",
       "      <td>98.404153</td>\n",
       "      <td>192.598092</td>\n",
       "      <td>507.343464</td>\n",
       "      <td>160.571920</td>\n",
       "      <td>153.914710</td>\n",
       "      <td>240.118250</td>\n",
       "      <td>201.043127</td>\n",
       "      <td>...</td>\n",
       "      <td>488.423577</td>\n",
       "      <td>640.010925</td>\n",
       "      <td>581.889945</td>\n",
       "      <td>591.152507</td>\n",
       "      <td>557.014275</td>\n",
       "      <td>595.783358</td>\n",
       "      <td>628.005845</td>\n",
       "      <td>97.744741</td>\n",
       "      <td>338.758886</td>\n",
       "      <td>596.479092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23103</th>\n",
       "      <td>8449983186</td>\n",
       "      <td>488.847199</td>\n",
       "      <td>433.607729</td>\n",
       "      <td>408.413059</td>\n",
       "      <td>391.837899</td>\n",
       "      <td>591.432793</td>\n",
       "      <td>458.440851</td>\n",
       "      <td>353.059804</td>\n",
       "      <td>454.461877</td>\n",
       "      <td>415.386754</td>\n",
       "      <td>...</td>\n",
       "      <td>71.138156</td>\n",
       "      <td>212.273931</td>\n",
       "      <td>270.988599</td>\n",
       "      <td>268.560182</td>\n",
       "      <td>57.462297</td>\n",
       "      <td>159.850823</td>\n",
       "      <td>192.073310</td>\n",
       "      <td>453.163956</td>\n",
       "      <td>169.648040</td>\n",
       "      <td>310.211680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23104</th>\n",
       "      <td>4072935008</td>\n",
       "      <td>494.307499</td>\n",
       "      <td>439.068029</td>\n",
       "      <td>413.873359</td>\n",
       "      <td>397.298199</td>\n",
       "      <td>596.893093</td>\n",
       "      <td>463.901150</td>\n",
       "      <td>358.520104</td>\n",
       "      <td>459.922177</td>\n",
       "      <td>420.847054</td>\n",
       "      <td>...</td>\n",
       "      <td>109.140970</td>\n",
       "      <td>197.683380</td>\n",
       "      <td>256.398048</td>\n",
       "      <td>253.969631</td>\n",
       "      <td>44.892213</td>\n",
       "      <td>145.260272</td>\n",
       "      <td>177.482759</td>\n",
       "      <td>458.624255</td>\n",
       "      <td>175.108340</td>\n",
       "      <td>295.621128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23105</th>\n",
       "      <td>4072935012</td>\n",
       "      <td>494.874763</td>\n",
       "      <td>439.635293</td>\n",
       "      <td>414.440623</td>\n",
       "      <td>397.865463</td>\n",
       "      <td>597.460357</td>\n",
       "      <td>464.468415</td>\n",
       "      <td>359.087368</td>\n",
       "      <td>460.489442</td>\n",
       "      <td>421.414319</td>\n",
       "      <td>...</td>\n",
       "      <td>109.708234</td>\n",
       "      <td>197.669900</td>\n",
       "      <td>256.384568</td>\n",
       "      <td>253.956152</td>\n",
       "      <td>44.878733</td>\n",
       "      <td>145.246793</td>\n",
       "      <td>177.469280</td>\n",
       "      <td>459.191520</td>\n",
       "      <td>175.675604</td>\n",
       "      <td>295.607649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23106</th>\n",
       "      <td>8558304561</td>\n",
       "      <td>511.931168</td>\n",
       "      <td>456.691698</td>\n",
       "      <td>431.497028</td>\n",
       "      <td>414.921868</td>\n",
       "      <td>614.516762</td>\n",
       "      <td>481.524819</td>\n",
       "      <td>376.143773</td>\n",
       "      <td>477.545846</td>\n",
       "      <td>438.470723</td>\n",
       "      <td>...</td>\n",
       "      <td>126.764639</td>\n",
       "      <td>204.611943</td>\n",
       "      <td>263.326611</td>\n",
       "      <td>260.898195</td>\n",
       "      <td>53.781979</td>\n",
       "      <td>152.188835</td>\n",
       "      <td>184.411322</td>\n",
       "      <td>476.247924</td>\n",
       "      <td>192.732009</td>\n",
       "      <td>302.549692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23107</th>\n",
       "      <td>4072041221</td>\n",
       "      <td>495.490698</td>\n",
       "      <td>440.251228</td>\n",
       "      <td>415.056558</td>\n",
       "      <td>398.481398</td>\n",
       "      <td>598.076292</td>\n",
       "      <td>465.084349</td>\n",
       "      <td>359.703303</td>\n",
       "      <td>461.105376</td>\n",
       "      <td>422.030253</td>\n",
       "      <td>...</td>\n",
       "      <td>75.699289</td>\n",
       "      <td>214.660966</td>\n",
       "      <td>273.375635</td>\n",
       "      <td>270.947218</td>\n",
       "      <td>59.849332</td>\n",
       "      <td>162.237859</td>\n",
       "      <td>194.460346</td>\n",
       "      <td>459.807454</td>\n",
       "      <td>176.291539</td>\n",
       "      <td>312.598715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23108 rows × 315 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               NN  2283044923  2119246531  1894405285  3355695913  8597133978  \\\n",
       "0      7761872879    3.601602   62.713982   84.233782  178.427721  505.950746   \n",
       "1      7761872860    2.879157   61.991537   83.511337  177.705276  505.228301   \n",
       "2      7761872847    1.830102   60.942482   82.462282  176.656221  504.179246   \n",
       "3      8604836683  127.855787   69.841801   92.811330  187.005269  501.750641   \n",
       "4      8604829828  133.448610   75.434624   98.404153  192.598092  507.343464   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "23103  8449983186  488.847199  433.607729  408.413059  391.837899  591.432793   \n",
       "23104  4072935008  494.307499  439.068029  413.873359  397.298199  596.893093   \n",
       "23105  4072935012  494.874763  439.635293  414.440623  397.865463  597.460357   \n",
       "23106  8558304561  511.931168  456.691698  431.497028  414.921868  614.516762   \n",
       "23107  4072041221  495.490698  440.251228  415.056558  398.481398  598.076292   \n",
       "\n",
       "       3490825773  8636590476  3495281736  6029312286  ...  6045379010  \\\n",
       "0      146.401548  139.744338  238.725532  199.650409  ...  474.253205   \n",
       "1      145.679103  139.021893  238.003087  198.927964  ...  473.530760   \n",
       "2      144.630048  137.972838  236.954032  197.878909  ...  472.481705   \n",
       "3      154.979097  148.321887  234.525427  195.450304  ...  482.830754   \n",
       "4      160.571920  153.914710  240.118250  201.043127  ...  488.423577   \n",
       "...           ...         ...         ...         ...  ...         ...   \n",
       "23103  458.440851  353.059804  454.461877  415.386754  ...   71.138156   \n",
       "23104  463.901150  358.520104  459.922177  420.847054  ...  109.140970   \n",
       "23105  464.468415  359.087368  460.489442  421.414319  ...  109.708234   \n",
       "23106  481.524819  376.143773  477.545846  438.470723  ...  126.764639   \n",
       "23107  465.084349  359.703303  461.105376  422.030253  ...   75.699289   \n",
       "\n",
       "       6457408831  3783356554  7037053972  6045711032  1863721184  3649063162  \\\n",
       "0      634.036094  577.000206  586.262769  542.843904  581.612986  613.835473   \n",
       "1      633.313649  576.277761  585.540324  542.121459  580.890541  613.113028   \n",
       "2      632.264594  575.228706  584.491269  541.072404  579.841486  612.063973   \n",
       "3      634.418102  576.297122  585.559684  551.421452  590.190535  622.413022   \n",
       "4      640.010925  581.889945  591.152507  557.014275  595.783358  628.005845   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "23103  212.273931  270.988599  268.560182   57.462297  159.850823  192.073310   \n",
       "23104  197.683380  256.398048  253.969631   44.892213  145.260272  177.482759   \n",
       "23105  197.669900  256.384568  253.956152   44.878733  145.246793  177.469280   \n",
       "23106  204.611943  263.326611  260.898195   53.781979  152.188835  184.411322   \n",
       "23107  214.660966  273.375635  270.947218   59.849332  162.237859  194.460346   \n",
       "\n",
       "        293471544  3916091448  3306192793  \n",
       "0       84.746116  324.588514  591.894597  \n",
       "1       84.023671  323.866069  591.172152  \n",
       "2       82.974616  322.817014  590.123097  \n",
       "3       92.151918  333.166063  590.886269  \n",
       "4       97.744741  338.758886  596.479092  \n",
       "...           ...         ...         ...  \n",
       "23103  453.163956  169.648040  310.211680  \n",
       "23104  458.624255  175.108340  295.621128  \n",
       "23105  459.191520  175.675604  295.607649  \n",
       "23106  476.247924  192.732009  302.549692  \n",
       "23107  459.807454  176.291539  312.598715  \n",
       "\n",
       "[23108 rows x 315 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "OD.rename(columns={'Unnamed: 0': 'NN'}, inplace=True) # Repeat for each OD set, if needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find first, second, and third nearest destination for each origin node. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "fail_value = 999999999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>NN</th>\n",
       "      <th>HDurban1</th>\n",
       "      <th>HDurban2</th>\n",
       "      <th>HDurban3</th>\n",
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       "      <td>4.610503</td>\n",
       "      <td>6.780075</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7761872860</td>\n",
       "      <td>2.879157</td>\n",
       "      <td>3.888058</td>\n",
       "      <td>6.057630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7761872847</td>\n",
       "      <td>1.830102</td>\n",
       "      <td>2.839003</td>\n",
       "      <td>5.008575</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8604836683</td>\n",
       "      <td>68.078912</td>\n",
       "      <td>69.841801</td>\n",
       "      <td>72.851560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8604829828</td>\n",
       "      <td>73.671735</td>\n",
       "      <td>75.434624</td>\n",
       "      <td>78.444383</td>\n",
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      "text/plain": [
       "           NN   HDurban1   HDurban2   HDurban3\n",
       "0  7761872879   3.601602   4.610503   6.780075\n",
       "1  7761872860   2.879157   3.888058   6.057630\n",
       "2  7761872847   1.830102   2.839003   5.008575\n",
       "3  8604836683  68.078912  69.841801  72.851560\n",
       "4  8604829828  73.671735  75.434624  78.444383"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since we only have one destination set, renaming the OD object for quick find/replace on this section of code.\n",
    "OD_HDurban = OD\n",
    "\n",
    "# Nearest\n",
    "OD_HDurban[\"HDurban1\"] = 0\n",
    "sub = OD_HDurban.iloc[:,1:-1] # Filtering out the newly created field and the node ID column. (\"include everything between column 0 and the last column\")\n",
    "OD_HDurban[\"HDurban1\"] = sub.min(axis=1) # Default is axis=0, meaning min value of each column selected. We want min of each row.\n",
    "HDurban1 = OD_HDurban[['NN', 'HDurban1']] # Remove unnecessary OD values.\n",
    "HDurban1.to_csv(os.path.join(out_pth, 'fromagriculture/HDurban1_flood.csv'))\n",
    "\n",
    "\n",
    "# Second nearest\n",
    "dupes = OD_HDurban.apply(pd.Series.duplicated, axis = 1, keep=False) # If a number is repeated within a row, value is True. If not, False.\n",
    "# The first time this is done, there should be two True values per row, unless any POIs are equidistant.\n",
    "dupes = OD_HDurban.where(~dupes, fail_value) # For any value that appears more than once in its row, it is replaced with the fail_value.\n",
    "\n",
    "OD_HDurban[\"HDurban2\"] = 0\n",
    "Dsub = dupes.iloc[:,1:] # Filtering out the node ID column. No need to filter 1st nearest as its new \"dupes\" value is too high to be caught.\n",
    "OD_HDurban[\"HDurban2\"] = Dsub.min(axis=1) \n",
    "HDurban2 = OD_HDurban.loc[:,['NN', 'HDurban2']] \n",
    "HDurban2.to_csv(os.path.join(out_pth, 'fromagriculture/HDurban2_flood.csv'))\n",
    "\n",
    "\n",
    "# Third nearest\n",
    "dupes = OD_HDurban.apply(pd.Series.duplicated, axis = 1, keep=False)\n",
    "# Since this includes both first and second nearest columns, there should be four True values per row, unless POIs are equidistant.\n",
    "dupes = OD_HDurban.where(~dupes, fail_value)\n",
    " \n",
    "OD_HDurban[\"HDurban3\"] = 0\n",
    "Dsub = dupes.iloc[:,1:] # Filtering out the node ID column.\n",
    "OD_HDurban[\"HDurban3\"] = Dsub.min(axis=1)\n",
    "HDurban3 = OD_HDurban.loc[:,['NN', 'HDurban3']]\n",
    "HDurban3.to_csv(os.path.join(out_pth, 'fromagriculture/HDurban3_flood.csv'))\n",
    "\n",
    "\n",
    "# Combine and write to file\n",
    "HDurban_all = OD_HDurban.loc[:,['NN', 'HDurban1', 'HDurban2', 'HDurban3']]\n",
    "HDurban_all.to_csv(os.path.join(out_pth, 'fromagriculture/HDurban_all_flood.csv'))\n",
    "HDurban_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join back to georeferenced _snap file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>125881</td>\n",
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       "      <td>125882</td>\n",
       "      <td>125882</td>\n",
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       "      <td>hamlet</td>\n",
       "      <td>{20205A44-8B9D-4FCE-B14C-53826594DB5A}</td>\n",
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       "      <th>125883</th>\n",
       "      <td>125883</td>\n",
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       "<p>125886 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "        Unnamed: 0  Unnamed_ 0     mgrs_code    type  \\\n",
       "0                0           0  28PCU1265_01  hamlet   \n",
       "1                1           1  28PCU1365_01  hamlet   \n",
       "2                2           2  28PCU1365_02  hamlet   \n",
       "3                3           3  28PCU1566_01  hamlet   \n",
       "4                4           4  28PCU1566_02  hamlet   \n",
       "...            ...         ...           ...     ...   \n",
       "125881      125881      125881  28QED6412_03  hamlet   \n",
       "125882      125882      125882  28QED6413_03  hamlet   \n",
       "125883      125883      125883  28QED6413_04  hamlet   \n",
       "125884      125884      125884  28QED6424_03  hamlet   \n",
       "125885      125885      125885  28QED6438_03  hamlet   \n",
       "\n",
       "                                      GlobalID  Shape_Leng  Shape_Area  \\\n",
       "0       {ED2CCDD5-C78F-40B6-A18A-3A01B61A4998}    0.004314    0.000001   \n",
       "1       {372B104B-B208-4D14-84E2-8ABFD4D8C37A}    0.009910    0.000006   \n",
       "2       {D03C2B85-5F35-4EE8-8346-B83494628F26}    0.003754    0.000001   \n",
       "3       {5EAFF1C3-6EE5-4F96-99FC-78F924454480}    0.004401    0.000002   \n",
       "4       {1D6A9E17-0D49-446D-A23B-7A47B155DC64}    0.005357    0.000002   \n",
       "...                                        ...         ...         ...   \n",
       "125881  {5555A010-36B2-47D2-96C4-BDD1E59111ED}    0.005397    0.000002   \n",
       "125882  {20205A44-8B9D-4FCE-B14C-53826594DB5A}    0.003610    0.000001   \n",
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       "125884  {51593C65-B268-4BA1-8212-E43232C021FF}    0.003883    0.000001   \n",
       "125885  {05114E26-63E9-4384-9F9E-8533D8AE2F1F}    0.007869    0.000004   \n",
       "\n",
       "                                              geometry          NN  \\\n",
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       "1        POINT (-16.716456507935607 12.34788723564788)  7761872860   \n",
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       "...                                                ...         ...   \n",
       "125881  POINT (-14.397827933065358 16.394142941310925)  8592243241   \n",
       "125882  POINT (-14.397473236932905 16.405676938062538)  8592243457   \n",
       "125883  POINT (-14.400364192239715 16.404303134272737)  8592243457   \n",
       "125884   POINT (-14.396243884215123 16.49715066208162)  3646207611   \n",
       "125885  POINT (-14.395109427617555 16.631548057253372)  8538238600   \n",
       "\n",
       "            NN_dist  \n",
       "0        396.520984  \n",
       "1        918.665696  \n",
       "2        952.759215  \n",
       "3       1923.779554  \n",
       "4       2181.254121  \n",
       "...             ...  \n",
       "125881  5089.086102  \n",
       "125882  4062.119008  \n",
       "125883  4008.851333  \n",
       "125884  1826.174300  \n",
       "125885   840.588582  \n",
       "\n",
       "[125886 rows x 10 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ag_snap = os.path.join(out_pth, \"ag_snap.csv\")\n",
    "ag_snap = pd.read_csv(ag_snap)\n",
    "ag_snap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 125886 entries, 0 to 125885\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   Unnamed: 0  125886 non-null  int64  \n",
      " 1   Unnamed_ 0  125886 non-null  int64  \n",
      " 2   mgrs_code   125886 non-null  object \n",
      " 3   type        125886 non-null  object \n",
      " 4   GlobalID    125886 non-null  object \n",
      " 5   Shape_Leng  125886 non-null  float64\n",
      " 6   Shape_Area  125886 non-null  float64\n",
      " 7   geometry    125886 non-null  object \n",
      " 8   NN          125886 non-null  int64  \n",
      " 9   NN_dist     125886 non-null  float64\n",
      "dtypes: float64(3), int64(3), object(4)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "ag_snap.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>125886 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "        Unnamed: 0  Unnamed_ 0     mgrs_code    type  \\\n",
       "0                0           0  28PCU1265_01  hamlet   \n",
       "1                1           1  28PCU1365_01  hamlet   \n",
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       "...            ...         ...           ...     ...   \n",
       "125881      125881      125881  28QED6412_03  hamlet   \n",
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       "125884      125884      125884  28QED6424_03  hamlet   \n",
       "125885      125885      125885  28QED6438_03  hamlet   \n",
       "\n",
       "                                      GlobalID  Shape_Leng  Shape_Area  \\\n",
       "0       {ED2CCDD5-C78F-40B6-A18A-3A01B61A4998}    0.004314    0.000001   \n",
       "1       {372B104B-B208-4D14-84E2-8ABFD4D8C37A}    0.009910    0.000006   \n",
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       "4       {1D6A9E17-0D49-446D-A23B-7A47B155DC64}    0.005357    0.000002   \n",
       "...                                        ...         ...         ...   \n",
       "125881  {5555A010-36B2-47D2-96C4-BDD1E59111ED}    0.005397    0.000002   \n",
       "125882  {20205A44-8B9D-4FCE-B14C-53826594DB5A}    0.003610    0.000001   \n",
       "125883  {AC6A169C-FD0E-4DF6-BDAB-B69FBD04BFAF}    0.015471    0.000008   \n",
       "125884  {51593C65-B268-4BA1-8212-E43232C021FF}    0.003883    0.000001   \n",
       "125885  {05114E26-63E9-4384-9F9E-8533D8AE2F1F}    0.007869    0.000004   \n",
       "\n",
       "                                              geometry          NN  \\\n",
       "0       POINT (-16.721473282454415 12.348636090165247)  7761872879   \n",
       "1        POINT (-16.716456507935607 12.34788723564788)  7761872860   \n",
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       "4       POINT (-16.698773736706396 12.356804484409668)  7761872847   \n",
       "...                                                ...         ...   \n",
       "125881  POINT (-14.397827933065358 16.394142941310925)  8592243241   \n",
       "125882  POINT (-14.397473236932905 16.405676938062538)  8592243457   \n",
       "125883  POINT (-14.400364192239715 16.404303134272737)  8592243457   \n",
       "125884   POINT (-14.396243884215123 16.49715066208162)  3646207611   \n",
       "125885  POINT (-14.395109427617555 16.631548057253372)  8538238600   \n",
       "\n",
       "            NN_dist   HDurban1   HDurban2   HDurban3  \n",
       "0        396.520984   3.601602   4.610503   6.780075  \n",
       "1        918.665696   2.879157   3.888058   6.057630  \n",
       "2        952.759215   1.830102   2.839003   5.008575  \n",
       "3       1923.779554   1.830102   2.839003   5.008575  \n",
       "4       2181.254121   1.830102   2.839003   5.008575  \n",
       "...             ...        ...        ...        ...  \n",
       "125881  5089.086102   6.893470  13.243532  14.366722  \n",
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       "125885   840.588582  35.864992  35.905421  52.126480  \n",
       "\n",
       "[125886 rows x 13 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ag_to_HDurban = pd.merge(ag_snap, HDurban_all, on='NN',how='left')\n",
    "ag_to_HDurban"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 125886 entries, 0 to 125885\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   Unnamed: 0  125886 non-null  int64  \n",
      " 1   Unnamed_ 0  125886 non-null  int64  \n",
      " 2   mgrs_code   125886 non-null  object \n",
      " 3   type        125886 non-null  object \n",
      " 4   GlobalID    125886 non-null  object \n",
      " 5   Shape_Leng  125886 non-null  float64\n",
      " 6   Shape_Area  125886 non-null  float64\n",
      " 7   geometry    125886 non-null  object \n",
      " 8   NN          125886 non-null  int64  \n",
      " 9   NN_dist     125886 non-null  float64\n",
      " 10  HDurban1    125883 non-null  float64\n",
      " 11  HDurban2    125886 non-null  float64\n",
      " 12  HDurban3    125886 non-null  float64\n",
      "dtypes: float64(6), int64(3), object(4)\n",
      "memory usage: 13.4+ MB\n"
     ]
    }
   ],
   "source": [
    "ag_to_HDurban.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ag_to_HDurban.to_csv(os.path.join(out_pth, 'fromagriculture/ag_to_HDurban_flood.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End of script. Load into QGIS or Arc and visualize at 10 min intervals. \n",
    "QML file for symbology in QGIS:\n",
    "R:\\GEOGlobal\\Design\\symb_traveltimes_10min.qml"
   ]
  }
 ],
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