{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Pandana demo\n",
    "\n",
    "Sam Maurer, July 2020\n",
    "\n",
    "This notebook demonstrates the main features of the [Pandana](https://github.com/UDST/pandana/) library, a Python package for network analysis that uses [contraction hierarchies](https://en.wikipedia.org/wiki/Contraction_hierarchies) to calculate super-fast travel accessibility metrics and shortest paths.\n",
    "\n",
    "See full documentation here: http://udst.github.io/pandana/\n",
    "\n",
    "### Sections\n",
    "\n",
    "- [1. Loading data](#section1)\n",
    "- [2. Shortest paths](#section2)\n",
    "- [3. Proximity](#section3)\n",
    "- [4. Accessibility](#section4)\n",
    "- [5. Visualization](#section5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import pandana\n",
    "print(pandana.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppress scientific notation in the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '{:.2f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section1'></a>\n",
    "\n",
    "## 1. Loading data\n",
    "\n",
    "### Load street networks directly from Open Street Map\n",
    "\n",
    "This requires installing a Pandana extension called OSMnet.\n",
    "- `conda install osmnet` or  `pip install osmnet`\n",
    "\n",
    "You can use http://boundingbox.klokantech.com/ to get the coordinates of bounding boxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandana.loaders import osm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [pandana.loaders.osm.pdna_network_from_bbox()](http://udst.github.io/pandana/loaders.html#openstreetmap-api)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requesting network data within bounding box from Overpass API in 1 request(s)\n",
      "Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"motor|proposed|construction|abandoned|platform|raceway\"][\"foot\"!~\"no\"][\"pedestrians\"!~\"no\"](37.69800000,-122.51700000,37.81900000,-122.35400000);>;);out;'}\"\n",
      "Downloaded 23,350.9KB from www.overpass-api.de in 4.78 seconds\n",
      "Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 5.17 seconds\n",
      "Returning OSM data with 133,353 nodes and 34,346 ways...\n",
      "Edge node pairs completed. Took 39.16 seconds\n",
      "Returning processed graph with 41,623 nodes and 62,464 edges...\n",
      "Completed OSM data download and Pandana node and edge table creation in 46.39 seconds\n"
     ]
    }
   ],
   "source": [
    "network = osm.pdna_network_from_bbox(37.698, -122.517, 37.819, -122.354)  # San Francisco, CA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What does the network look like?\n",
    "\n",
    "Edges and nodes are visible as DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33242031</th>\n",
       "      <td>-122.42</td>\n",
       "      <td>37.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54763573</th>\n",
       "      <td>-122.40</td>\n",
       "      <td>37.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54891208</th>\n",
       "      <td>-122.39</td>\n",
       "      <td>37.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54891395</th>\n",
       "      <td>-122.39</td>\n",
       "      <td>37.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54891397</th>\n",
       "      <td>-122.39</td>\n",
       "      <td>37.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               x     y\n",
       "id                    \n",
       "33242031 -122.42 37.80\n",
       "54763573 -122.40 37.77\n",
       "54891208 -122.39 37.77\n",
       "54891395 -122.39 37.77\n",
       "54891397 -122.39 37.77"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.nodes_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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></th>\n",
       "      <th>from</th>\n",
       "      <th>to</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">33242031</th>\n",
       "      <th>65292112</th>\n",
       "      <td>33242031</td>\n",
       "      <td>65292112</td>\n",
       "      <td>71.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65292114</th>\n",
       "      <td>33242031</td>\n",
       "      <td>65292114</td>\n",
       "      <td>75.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">54763573</th>\n",
       "      <th>3532718877</th>\n",
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       "      <td>3532718877</td>\n",
       "      <td>85.64</td>\n",
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       "    <tr>\n",
       "      <th>7358092398</th>\n",
       "      <td>54763573</td>\n",
       "      <td>7358092398</td>\n",
       "      <td>161.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54891208</th>\n",
       "      <th>54891397</th>\n",
       "      <td>54891208</td>\n",
       "      <td>54891397</td>\n",
       "      <td>101.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         from          to  distance\n",
       "33242031 65292112    33242031    65292112     71.49\n",
       "         65292114    33242031    65292114     75.13\n",
       "54763573 3532718877  54763573  3532718877     85.64\n",
       "         7358092398  54763573  7358092398    161.06\n",
       "54891208 54891397    54891208    54891397    101.15"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.edges_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving and reloading networks\n",
    "\n",
    "You can't directly save a Pandana network object, but you can easily recreate it from the nodes and edges.\n",
    "- [pandana.Network()](http://udst.github.io/pandana/network.html#pandana.network.Network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.nodes_df.to_csv('nodes.csv')\n",
    "network.edges_df.to_csv('edges.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = pd.read_csv('nodes.csv', index_col=0)\n",
    "edges = pd.read_csv('edges.csv', index_col=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = pandana.Network(nodes['x'], nodes['y'], \n",
    "                          edges['from'], edges['to'], edges[['distance']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section2'></a>\n",
    "\n",
    "## 2. Shortest paths\n",
    "\n",
    "This functionality was added in Pandana v0.5.\n",
    "\n",
    "### Load some restaurant locations\n",
    "\n",
    "Here we'll load the locations of restaurants listed on Open Street Map (using the same OSMnet extension as above), and then calculate some shortest paths between them.\n",
    "- [pandana.loaders.osm.node_query()](http://udst.github.io/pandana/loaders.html#pandana.loaders.osm.node_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "restaurants = osm.node_query(\n",
    "    37.698, -122.517, 37.819, -122.354, tags='\"amenity\"=\"restaurant\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose two at random:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>amenity</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>name</th>\n",
       "      <th>wheelchair</th>\n",
       "      <th>addr:city</th>\n",
       "      <th>addr:country</th>\n",
       "      <th>addr:housenumber</th>\n",
       "      <th>addr:postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>height</th>\n",
       "      <th>cocktails</th>\n",
       "      <th>music</th>\n",
       "      <th>payment:american_express</th>\n",
       "      <th>changing_table</th>\n",
       "      <th>changing_table:count</th>\n",
       "      <th>changing_table:location</th>\n",
       "      <th>bar</th>\n",
       "      <th>brewery</th>\n",
       "      <th>strapline</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7044895254</th>\n",
       "      <td>37.76</td>\n",
       "      <td>-122.42</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>greek</td>\n",
       "      <td>Souvla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>758</td>\n",
       "      <td>94110</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4945080616</th>\n",
       "      <td>37.75</td>\n",
       "      <td>-122.42</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>mexican</td>\n",
       "      <td>Chavita's Mexican Restaurant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3006</td>\n",
       "      <td>94110</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             lat     lon     amenity  cuisine                          name  \\\n",
       "id                                                                            \n",
       "7044895254 37.76 -122.42  restaurant    greek                        Souvla   \n",
       "4945080616 37.75 -122.42  restaurant  mexican  Chavita's Mexican Restaurant   \n",
       "\n",
       "           wheelchair      addr:city addr:country addr:housenumber  \\\n",
       "id                                                                   \n",
       "7044895254        NaN  San Francisco          NaN              758   \n",
       "4945080616        NaN  San Francisco          NaN             3006   \n",
       "\n",
       "           addr:postcode  ... height cocktails music payment:american_express  \\\n",
       "id                        ...                                                   \n",
       "7044895254         94110  ...    NaN       NaN   NaN                      NaN   \n",
       "4945080616         94110  ...    NaN       NaN   NaN                      NaN   \n",
       "\n",
       "           changing_table changing_table:count changing_table:location  bar  \\\n",
       "id                                                                            \n",
       "7044895254            NaN                  NaN                     NaN  NaN   \n",
       "4945080616            NaN                  NaN                     NaN  NaN   \n",
       "\n",
       "           brewery strapline  \n",
       "id                            \n",
       "7044895254     NaN       NaN  \n",
       "4945080616     NaN       NaN  \n",
       "\n",
       "[2 rows x 117 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = restaurants.sample(2)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the shortest route between them\n",
    "\n",
    "First, identify the nearest node to each restaurant.\n",
    "- [network.get_node_ids()](http://udst.github.io/pandana/network.html#pandana.network.Network.get_node_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5443111232, 4021550352])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes = network.get_node_ids(res.lon, res.lat).values\n",
    "nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then get the routing between the nodes.\n",
    "- [network.shortest_path()](http://udst.github.io/pandana/network.html#pandana.network.Network.shortest_path)\n",
    "- [network.shortest_path_length()](http://udst.github.io/pandana/network.html#pandana.network.Network.shortest_path_length)\n",
    "- [network.shortest_path_lengths()](http://udst.github.io/pandana/network.html#pandana.network.Network.shortest_path_lengths)\n",
    "\n",
    "Note that these are only *approximately* the shortest path. Pandana uses a heuristic called [contraction hierarchies](https://en.wikipedia.org/wiki/Contraction_hierarchies) to prioritize trunk routes, similar to e.g. Google Maps. This dramatically improves routing performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5443111232,   65293593, 4179475362,   65319652, 3863544424,\n",
       "       4179475361,   65319650,   65298401,   65298403,   65302196,\n",
       "         65345534, 5443374295,   65345537, 5443374302,   65322314,\n",
       "         65288852,   65288851,   65288850, 4353708389, 5443374273,\n",
       "       4021550352])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.shortest_path(nodes[0], nodes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1515.406"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.shortest_path_length(nodes[0], nodes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This network's distance units are meters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate many shortest paths at once\n",
    "\n",
    "Pandana can generate several million shortest paths in less than a minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "restaurant_nodes = network.get_node_ids(restaurants.lon, restaurants.lat).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "origs = [o for o in restaurant_nodes for d in restaurant_nodes]\n",
    "dests = [d for o in restaurant_nodes for d in restaurant_nodes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 21s, sys: 541 ms, total: 2min 22s\n",
      "Wall time: 20.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "distances = network.shortest_path_lengths(origs, dests)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the distance from each restaurant to each other restaurant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   2822400.00\n",
       "mean      14898.82\n",
       "std      208988.95\n",
       "min           0.00\n",
       "25%        2446.33\n",
       "50%        4356.32\n",
       "75%        6552.76\n",
       "max     4294967.29\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(distances).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section3'></a>\n",
    "\n",
    "## 3. Proximity\n",
    "\n",
    "### Find the closest restaurants to each node\n",
    "\n",
    "To do a network-wide calculation like this, we first need to formally add the restaurants into the network.\n",
    "- [network.set_pois()](http://udst.github.io/pandana/network.html#pandana.network.Network.set_pois)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.set_pois(category = 'restaurants',\n",
    "                 maxdist = 1000,\n",
    "                 maxitems = 3,\n",
    "                 x_col = restaurants.lon, \n",
    "                 y_col = restaurants.lat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(The `maxdist` and `maxitems` parameters are the maximum distance and item counts you anticipate using in proximity searches, so that Pandana can optimize the caching.)\n",
    "\n",
    "Now we can run the query.\n",
    " - [network.nearest_pois()](http://udst.github.io/pandana/network.html#pandana.network.Network.nearest_pois)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>poi1</th>\n",
       "      <th>poi2</th>\n",
       "      <th>poi3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33242031</th>\n",
       "      <td>273.17</td>\n",
       "      <td>275.24</td>\n",
       "      <td>290.37</td>\n",
       "      <td>3609348594.00</td>\n",
       "      <td>1478520956.00</td>\n",
       "      <td>1478520961.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54763573</th>\n",
       "      <td>282.32</td>\n",
       "      <td>433.90</td>\n",
       "      <td>472.55</td>\n",
       "      <td>1771741233.00</td>\n",
       "      <td>6252387628.00</td>\n",
       "      <td>2657227486.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54891208</th>\n",
       "      <td>226.17</td>\n",
       "      <td>317.33</td>\n",
       "      <td>339.41</td>\n",
       "      <td>2453599646.00</td>\n",
       "      <td>2453600264.00</td>\n",
       "      <td>5512661856.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54891395</th>\n",
       "      <td>266.57</td>\n",
       "      <td>362.67</td>\n",
       "      <td>391.63</td>\n",
       "      <td>2453599646.00</td>\n",
       "      <td>2453600264.00</td>\n",
       "      <td>6146447085.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54891397</th>\n",
       "      <td>125.03</td>\n",
       "      <td>216.18</td>\n",
       "      <td>398.20</td>\n",
       "      <td>2453599646.00</td>\n",
       "      <td>2453600264.00</td>\n",
       "      <td>6124173112.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              1      2      3          poi1          poi2          poi3\n",
       "id                                                                     \n",
       "33242031 273.17 275.24 290.37 3609348594.00 1478520956.00 1478520961.00\n",
       "54763573 282.32 433.90 472.55 1771741233.00 6252387628.00 2657227486.00\n",
       "54891208 226.17 317.33 339.41 2453599646.00 2453600264.00 5512661856.00\n",
       "54891395 266.57 362.67 391.63 2453599646.00 2453600264.00 6146447085.00\n",
       "54891397 125.03 216.18 398.20 2453599646.00 2453600264.00 6124173112.00"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = network.nearest_pois(distance = 1000,\n",
    "                               category = 'restaurants',\n",
    "                               num_pois = 3,\n",
    "                               include_poi_ids = True)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the distances (in meters) and IDs of the three closest restaurants to each network node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section4'></a>\n",
    "\n",
    "## 4. Accessibility\n",
    "\n",
    "### How many restaurants are within 500 meters of each node?\n",
    "\n",
    "Pandana calls this kind of calculation an *aggregation*. It requires passing a list of network nodes and associated values. \n",
    "\n",
    "In this case, the \"value\" is just presence of a restaurant, but it could also be characteristics like square footage of a building or income of a household.\n",
    "\n",
    "- [network.get_node_ids()](http://udst.github.io/pandana/network.html#pandana.network.Network.get_node_ids)\n",
    "- [network.set()](http://udst.github.io/pandana/network.html#pandana.network.Network.set_pois)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "restaurant_nodes = network.get_node_ids(restaurants.lon, restaurants.lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.set(restaurant_nodes, \n",
    "            name = 'restaurants')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can run the query.\n",
    "\n",
    "- [network.aggregate()](http://udst.github.io/pandana/network.html#pandana.network.Network.aggregate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   41623.00\n",
       "mean       11.74\n",
       "std        19.71\n",
       "min         0.00\n",
       "25%         0.00\n",
       "50%         3.00\n",
       "75%        15.00\n",
       "max       146.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accessibility = network.aggregate(distance = 500,\n",
    "                                  type = 'count',\n",
    "                                  name = 'restaurants')\n",
    "accessibility.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you can also calculate means, sums, percentiles, and other metrics, as well as applying linear or exponential \"decay\" to more distant values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section5'></a>\n",
    "\n",
    "## 5. Visualization\n",
    "\n",
    "Pandana's built-in `plot` function uses an older Matplotlib extension called Basemap that's now difficult to install. So here we'll just use Matplotlib directly.\n",
    "\n",
    "### Mapping restaurant accessibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.4\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "print(matplotlib.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "plt.title('San Francisco: Restaurants within 500m')\n",
    "plt.scatter(network.nodes_df.x, network.nodes_df.y, \n",
    "            c=accessibility, s=1, cmap='YlOrRd', \n",
    "            norm=matplotlib.colors.LogNorm())\n",
    "cb = plt.colorbar()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}