notebooks/14-osmnx-to-igraph.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OSMnx + igraph\n",
    "\n",
    "Author: [Geoff Boeing](https://geoffboeing.com/)\n",
    "\n",
    "First install [igraph](https://igraph.org/python/) or run Jupyter from the [Docker container](https://github.com/gboeing/osmnx/tree/master/docker) (which already has it installed along with OSMnx and NetworkX).\n",
    "\n",
    "  - [Overview of OSMnx](http://geoffboeing.com/2016/11/osmnx-python-street-networks/)\n",
    "  - [GitHub repo](https://github.com/gboeing/osmnx)\n",
    "  - [Examples, demos, tutorials](https://github.com/gboeing/osmnx-examples)\n",
    "  - [Documentation](https://osmnx.readthedocs.io/en/stable/)\n",
    "  - [Journal article/citation](http://geoffboeing.com/publications/osmnx-complex-street-networks/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.14.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import igraph as ig\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import operator\n",
    "import osmnx as ox\n",
    "%matplotlib inline\n",
    "ox.config(use_cache=True, log_console=True)\n",
    "weight = 'length'\n",
    "\n",
    "ox.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create networkx graph\n",
    "G_nx = ox.graph_from_place('Piedmont, CA, USA', network_type='drive')\n",
    "G_nx = nx.relabel.convert_node_labels_to_integers(G_nx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.49 ms, sys: 0 ns, total: 2.49 ms\n",
      "Wall time: 2.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# convert networkx graph to igraph\n",
    "G_ig = ig.Graph(directed=True)\n",
    "G_ig.add_vertices(list(G_nx.nodes()))\n",
    "G_ig.add_edges(list(G_nx.edges()))\n",
    "G_ig.vs['osmid'] = list(nx.get_node_attributes(G_nx, 'osmid').values())\n",
    "G_ig.es[weight] = list(nx.get_edge_attributes(G_nx, weight).values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(G_nx.nodes()) == G_ig.vcount()\n",
    "assert len(G_nx.edges()) == G_ig.ecount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shortest paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = list(G_nx.nodes())[0]\n",
    "target = list(G_nx.nodes())[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 184 µs, sys: 9 µs, total: 193 µs\n",
      "Wall time: 197 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "path1 = G_ig.get_shortest_paths(v=source, to=target, weights=weight)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.32 ms, sys: 0 ns, total: 1.32 ms\n",
      "Wall time: 1.33 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "path2 = nx.shortest_path(G_nx, source, target, weight=weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert path1 == path2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 168 µs, sys: 9 µs, total: 177 µs\n",
      "Wall time: 180 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "path_length1 = G_ig.shortest_paths(source=source, target=target, weights=weight)[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.06 ms, sys: 53 µs, total: 1.11 ms\n",
      "Wall time: 1.12 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "path_length2 = nx.shortest_path_length(G_nx, source, target, weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert path_length1 == path_length2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Centrality analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 25.1 ms, sys: 0 ns, total: 25.1 ms\n",
      "Wall time: 50.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "closeness1 = G_ig.closeness(vertices=None, mode='ALL', cutoff=None, weights=weight, normalized=True)\n",
    "max_closeness1 = np.argmax(closeness1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 429 ms, sys: 0 ns, total: 429 ms\n",
      "Wall time: 429 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "closeness2 = nx.closeness_centrality(G_nx, distance=weight, wf_improved=True)\n",
    "max_closeness2 = max(closeness2.items(), key=operator.itemgetter(1))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert G_nx.nodes[max_closeness2]['osmid'] == G_ig.vs[max_closeness1]['osmid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (ox)",
   "language": "python",
   "name": "ox"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}