FLP/code/distanceMatrix.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c505e198",
   "metadata": {},
   "source": [
    "### Calculate the road distances between each PAL field - candidate location "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5168903e-cf9d-4863-9d28-840deea7cc95",
   "metadata": {},
   "outputs": [],
   "source": [
    "import osmnx as ox\n",
    "import geopandas as gpd\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import csv\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a49cc868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\Users\\\\isaldiviagonzatti\\\\Downloads\\\\gitFiles\\\\MScThesisCode\\\\FLP'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define paths\n",
    "data_path = 'data'\n",
    "output_path = 'output'\n",
    "\n",
    "# set parent folder as working directory\n",
    "os.chdir('..')\n",
    "os.getcwd()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e61035fa",
   "metadata": {},
   "source": [
    "### Example with Pital (CR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "840f59ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "xlon = -84.278967\n",
    "ylat = 10.476319\n",
    "point = ylat, xlon # center of PA field near Pital\n",
    "\n",
    "examG = ox.graph_from_point(point, network_type='all_private', dist=1000)\n",
    "\n",
    "# impute missing edge speeds and add travel times\n",
    "#G = ox.add_edge_speeds(G)\n",
    "#G = ox.add_edge_travel_times(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eca5f719",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate route minimizing some weight (distance in this case)\n",
    "orig, dest = list(examG)[4], list(examG)[-1]\n",
    "route = nx.shortest_path_length(examG, orig, dest, weight='length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2f9f8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lenght of the route between the two points in meters is 893\n"
     ]
    }
   ],
   "source": [
    "print('Lenght of the route between the two points in meters is ' + str(round(route)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eae11c3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot between the two points\n",
    "unique_edges = list(examG)[4], list(examG)[-1]\n",
    "ec = ['r' if e in unique_edges else '#666666' for e in examG.nodes]\n",
    "#fig, ax = ox.plot_graph(G, edge_color=ec, edge_linewidth=1, node_size=0, show=False)\n",
    "fig, ax = ox.plot_graph(examG, node_color=ec, edge_linewidth=1, node_size=20, show=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f17d80d1-a65b-4329-bd72-edc71a4970e9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# load PAL field nodes\n",
    "with open(os.path.join(output_path, \"nearestNodePAL.csv\"), newline='') as f:\n",
    "    reader = csv.reader(f)\n",
    "    palFields = list(reader)\n",
    "\n",
    "origs = list(set(palFields[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cbe5b4fe-3c71-41b1-bde4-456c62c5675a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0, len(origs)):\n",
    "    origs[i] = int(origs[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7c9baa4e-5130-49a7-8aa1-8fce97855c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load facility candidates nodes\n",
    "with open(os.path.join(output_path, \"canNetwork.csv\"), newline='') as f:\n",
    "    reader = csv.reader(f)\n",
    "    candidates = list(reader)\n",
    "\n",
    "dests = list(set(candidates[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9ec4deba-b6a6-4091-94a9-f2132a3f06cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0, len(dests)):\n",
    "    dests[i] = int(dests[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "edd0c1f0-5d9b-435f-94be-f827991392a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframes for origs and dests\n",
    "origs = pd.DataFrame(origs, columns=['origs'])\n",
    "dests = pd.DataFrame(dests, columns=['dests'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d247e231-e429-40ce-b09f-cda05691885d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 570174 paths\n"
     ]
    }
   ],
   "source": [
    "# Create a dataframe with all combinations of origin-candidates\n",
    "matrix = origs.merge(dests, how='cross')\n",
    "print('We have ' + str(len(matrix)) + ' paths')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a7f83b08-d187-46e7-bef3-67d37ba0e562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "origs    int64\n",
       "dests    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e8676577-90e3-447d-b470-4b20743f9d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origs</th>\n",
       "      <th>dests</th>\n",
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       "      <th>0</th>\n",
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       "      <td>9321067326</td>\n",
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       "      <th>3</th>\n",
       "      <td>9321067326</td>\n",
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       "      <td>9321067326</td>\n",
       "      <td>5326294638</td>\n",
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       "      <td>3940030173</td>\n",
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       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>3940030173</td>\n",
       "      <td>332722599</td>\n",
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       "      <th>498</th>\n",
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       "      <td>3940030173</td>\n",
       "      <td>1951202736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          origs       dests\n",
       "0    9321067326  3934113197\n",
       "1    9321067326   990276754\n",
       "2    9321067326  2390780609\n",
       "3    9321067326  1049251210\n",
       "4    9321067326  5326294638\n",
       "..          ...         ...\n",
       "495  3940030173   312587257\n",
       "496  3940030173  5605082613\n",
       "497  3940030173   332722599\n",
       "498  3940030173  3874140238\n",
       "499  3940030173  1951202736\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampl_df = matrix.head(500).copy()\n",
    "sampl_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ded82374-2c46-442d-91ec-9fa9b319e0ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 7s\n",
      "Wall time: 1min 10s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# create network from OSMnx (loading locally saved network produces errors)\n",
    "G = ox.graph.graph_from_bbox(11.2, 10.00, -83, -85.2, network_type = 'drive_service')\n",
    "Gs = ox.utils_graph.get_largest_component(G, strongly=True) #for strongly connected network"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0843cfb9",
   "metadata": {},
   "source": [
    "To get shortest path, we can use networkx or OSMnx. For the entire matrix, this took more than 24 hours (not shown here). Perhaps there is a faster way, and perhaps using euclidean distances is sufficiently good (and it is fast). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b86f8ce2-fce0-4ea7-9e92-892d8d6958a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 16s\n",
      "Wall time: 1min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# try to get shortest path with networkx\n",
    "def short_path_length(row):\n",
    "    return nx.shortest_path_length(Gs, row['origs'], row['dests'], weight='length')\n",
    "sampl_df['path_length'] = sampl_df.apply(short_path_length, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5dc35f07-b5f8-4233-a172-b6a8eb3bfc6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 56.7 s\n",
      "Wall time: 57.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# try with OSMnx, it returns all the edges connecting the orig-dest\n",
    "sampl_df['path_OX'] = ox.shortest_path(Gs, sampl_df['origs'], sampl_df['dests'], weight='length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a86d1bb4-42c1-4387-babe-211ff7a281b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origs</th>\n",
       "      <th>dests</th>\n",
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       "      <th>path_lengthOX</th>\n",
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       "      <td>[9321067326, 9854494186, 9321065684, 932106567...</td>\n",
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       "      <td>9321067326</td>\n",
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       "      <td>[9321067326, 9854494186, 9321065684, 932106567...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>52229.198</td>\n",
       "      <td>[9321067326, 9854494186, 9321065684, 932106567...</td>\n",
       "      <td>[9321067326, 9854494186, 9321065684, 932106567...</td>\n",
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       "      <th>3</th>\n",
       "      <td>9321067326</td>\n",
       "      <td>1049251210</td>\n",
       "      <td>155783.352</td>\n",
       "      <td>[9321067326, 9854494186, 9321065684, 932106567...</td>\n",
       "      <td>[9321067326, 9854494186, 9321065684, 932106567...</td>\n",
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       "      <td>9321067326</td>\n",
       "      <td>5326294638</td>\n",
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       "      <td>[9321067326, 9853696637, 9853696643, 985369664...</td>\n",
       "      <td>[9321067326, 9853696637, 9853696643, 985369664...</td>\n",
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       "      <td>[3940030173, 3279917893, 185488060, 3066065025...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>3940030173</td>\n",
       "      <td>5605082613</td>\n",
       "      <td>202736.143</td>\n",
       "      <td>[3940030173, 3279917893, 185488060, 3066065025...</td>\n",
       "      <td>[3940030173, 3279917893, 185488060, 3066065025...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>3940030173</td>\n",
       "      <td>332722599</td>\n",
       "      <td>156649.451</td>\n",
       "      <td>[3940030173, 3279917893, 185488060, 3066065025...</td>\n",
       "      <td>[3940030173, 3279917893, 185488060, 3066065025...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>3940030173</td>\n",
       "      <td>3874140238</td>\n",
       "      <td>40593.641</td>\n",
       "      <td>[3940030173, 3279917893, 185488060, 3066065025...</td>\n",
       "      <td>[3940030173, 3279917893, 185488060, 3066065025...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>3940030173</td>\n",
       "      <td>1951202736</td>\n",
       "      <td>230680.055</td>\n",
       "      <td>[3940030173, 3279917893, 185488060, 3066065025...</td>\n",
       "      <td>[3940030173, 3279917893, 185488060, 3066065025...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          origs       dests  path_length  \\\n",
       "0    9321067326  3934113197   104878.939   \n",
       "1    9321067326   990276754   134144.451   \n",
       "2    9321067326  2390780609    52229.198   \n",
       "3    9321067326  1049251210   155783.352   \n",
       "4    9321067326  5326294638   108997.806   \n",
       "..          ...         ...          ...   \n",
       "495  3940030173   312587257   173658.173   \n",
       "496  3940030173  5605082613   202736.143   \n",
       "497  3940030173   332722599   156649.451   \n",
       "498  3940030173  3874140238    40593.641   \n",
       "499  3940030173  1951202736   230680.055   \n",
       "\n",
       "                                         path_lengthOX  \\\n",
       "0    [9321067326, 9854494186, 9321065684, 932106567...   \n",
       "1    [9321067326, 9854494186, 9321065684, 932106567...   \n",
       "2    [9321067326, 9854494186, 9321065684, 932106567...   \n",
       "3    [9321067326, 9854494186, 9321065684, 932106567...   \n",
       "4    [9321067326, 9853696637, 9853696643, 985369664...   \n",
       "..                                                 ...   \n",
       "495  [3940030173, 3279917893, 185488060, 3066065025...   \n",
       "496  [3940030173, 3279917893, 185488060, 3066065025...   \n",
       "497  [3940030173, 3279917893, 185488060, 3066065025...   \n",
       "498  [3940030173, 3279917893, 185488060, 3066065025...   \n",
       "499  [3940030173, 3279917893, 185488060, 3066065025...   \n",
       "\n",
       "                                               path_OX  \n",
       "0    [9321067326, 9854494186, 9321065684, 932106567...  \n",
       "1    [9321067326, 9854494186, 9321065684, 932106567...  \n",
       "2    [9321067326, 9854494186, 9321065684, 932106567...  \n",
       "3    [9321067326, 9854494186, 9321065684, 932106567...  \n",
       "4    [9321067326, 9853696637, 9853696643, 985369664...  \n",
       "..                                                 ...  \n",
       "495  [3940030173, 3279917893, 185488060, 3066065025...  \n",
       "496  [3940030173, 3279917893, 185488060, 3066065025...  \n",
       "497  [3940030173, 3279917893, 185488060, 3066065025...  \n",
       "498  [3940030173, 3279917893, 185488060, 3066065025...  \n",
       "499  [3940030173, 3279917893, 185488060, 3066065025...  \n",
       "\n",
       "[500 rows x 5 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distances in meters\n",
    "sampl_df"
   ]
  }
 ],
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