00-osmnx-features-demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OSMnx features demo\n",
    "\n",
    "Author: [Geoff Boeing](https://geoffboeing.com/)\n",
    "\n",
    "Get street networks and other spatial data anywhere in the world from OpenStreetMap then analyze and visualize them.\n",
    "\n",
    "More info:\n",
    "\n",
    "  - [Overview of OSMnx](http://geoffboeing.com/2016/11/osmnx-python-street-networks/)\n",
    "  - [Documentation and install instructions](https://osmnx.readthedocs.io)\n",
    "  - [Examples, demos, tutorials](https://github.com/gboeing/osmnx-examples)\n",
    "  - [Journal article and citation info](http://geoffboeing.com/publications/osmnx-complex-street-networks/)\n",
    "  - [GitHub repo](https://github.com/gboeing/osmnx)\n",
    "  \n",
    "This notebook provides a quick tour of some of OSMnx's key features including how to:\n",
    "\n",
    "  - download/model street networks\n",
    "  - calculate stats\n",
    "  - visualize centrality\n",
    "  - impute speeds/travel times and calculate shortest paths\n",
    "  - attach and visualize elevation data and edge grades\n",
    "  - download/model other infrastructure types\n",
    "  - download points of interest data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.2'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import osmnx as ox\n",
    "\n",
    "%matplotlib inline\n",
    "ox.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with street networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_place\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnetwork_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'all_private'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msimplify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mretain_all\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtruncate_by_edge\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mwhich_result\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbuffer_dist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mclean_periphery\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcustom_filter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Create graph from OSM within the boundaries of some geocodable place(s).\n",
       "\n",
       "The query must be geocodable and OSM must have polygon boundaries for the\n",
       "geocode result. If OSM does not have a polygon for this place, you can\n",
       "instead get its street network using the graph_from_address function,\n",
       "which geocodes the place name to a point and gets the network within some\n",
       "distance of that point.\n",
       "\n",
       "If OSM does have polygon boundaries for this place but you're not finding\n",
       "it, try to vary the query string, pass in a structured query dict, or vary\n",
       "the which_result argument to use a different geocode result. If you know\n",
       "the OSM ID of the place, you can retrieve its boundary polygon using the\n",
       "geocode_to_gdf function, then pass it to the graph_from_polygon function.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "query : string or dict or list\n",
       "    the query or queries to geocode to get place boundary polygon(s)\n",
       "network_type : string {\"all_private\", \"all\", \"bike\", \"drive\", \"drive_service\", \"walk\"}\n",
       "    what type of street network to get if custom_filter is None\n",
       "simplify : bool\n",
       "    if True, simplify graph topology with the `simplify_graph` function\n",
       "retain_all : bool\n",
       "    if True, return the entire graph even if it is not connected.\n",
       "    otherwise, retain only the largest weakly connected component.\n",
       "truncate_by_edge : bool\n",
       "    if True, retain nodes outside boundary polygon if at least one of\n",
       "    node's neighbors is within the polygon\n",
       "which_result : int\n",
       "    which geocoding result to use. if None, auto-select the first\n",
       "    (Multi)Polygon or raise an error if OSM doesn't return one.\n",
       "buffer_dist : float\n",
       "    distance to buffer around the place geometry, in meters\n",
       "clean_periphery : bool\n",
       "    if True, buffer 500m to get a graph larger than requested, then\n",
       "    simplify, then truncate it to requested spatial boundaries\n",
       "custom_filter : string\n",
       "    a custom ways filter to be used instead of the network_type presets\n",
       "    e.g., '[\"power\"~\"line\"]' or '[\"highway\"~\"motorway|trunk\"]'. Also pass\n",
       "    in a network_type that is in settings.bidirectional_network_types if\n",
       "    you want graph to be fully bi-directional.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "G : networkx.MultiDiGraph\n",
       "\n",
       "Notes\n",
       "-----\n",
       "You can configure the Overpass server timeout, memory allocation, and\n",
       "other custom settings via the `settings` module. Very large query areas\n",
       "will use the utils_geo._consolidate_subdivide_geometry function to perform\n",
       "multiple queries: see that function's documentation for caveats.\n",
       "\u001b[0;31mFile:\u001b[0m      /opt/conda/lib/python3.10/site-packages/osmnx/graph.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?ox.graph_from_place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# download/model a street network for some city then visualize it\n",
    "G = ox.graph_from_place(\"Piedmont, California, USA\", network_type=\"drive\")\n",
    "fig, ax = ox.plot_graph(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OSMnx geocodes the query \"Piedmont, California, USA\" to retrieve the place boundaries of that city from the Nominatim API, retrieves the drivable street network data within those boundaries from the Overpass API, constructs a graph model, then simplifies/corrects its topology such that nodes represent intersections and dead-ends and edges represent the street segments linking them. All of this is discussed in detail in the documentation and these examples.\n",
    "\n",
    "OSMnx models all networks as NetworkX `MultiDiGraph` objects. You can convert to:\n",
    "  - undirected MultiGraphs\n",
    "  - DiGraphs without (possible) parallel edges\n",
    "  - GeoPandas node/edge GeoDataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert your MultiDiGraph to an undirected MultiGraph\n",
    "M = ox.utils_graph.get_undirected(G)\n",
    "\n",
    "# convert your MultiDiGraph to a DiGraph without parallel edges\n",
    "D = ox.utils_graph.get_digraph(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can convert your graph to node and edge GeoPandas GeoDataFrames\n",
    "gdf_nodes, gdf_edges = ox.graph_to_gdfs(G)\n",
    "gdf_nodes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_edges.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create a graph from node/edge GeoDataFrames, as long as gdf_nodes is indexed by osmid and gdf_edges is multi-indexed by u, v, key (following normal MultiDiGraph structure). This allows you to load graph node/edge shapefiles or GeoPackage layers as GeoDataFrames then convert to a MultiDiGraph for graph analytics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert node/edge GeoPandas GeoDataFrames to a NetworkX MultiDiGraph\n",
    "G2 = ox.graph_from_gdfs(gdf_nodes, gdf_edges, graph_attrs=G.graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic street network stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# what sized area does our network cover in square meters?\n",
    "G_proj = ox.project_graph(G)\n",
    "nodes_proj = ox.graph_to_gdfs(G_proj, edges=False)\n",
    "graph_area_m = nodes_proj.unary_union.convex_hull.area\n",
    "graph_area_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show some basic stats about the network\n",
    "ox.basic_stats(G_proj, area=graph_area_m, clean_int_tol=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "stats documentation: https://osmnx.readthedocs.io/en/stable/osmnx.html#module-osmnx.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save graph to disk as geopackage (for GIS) or graphml file (for gephi etc)\n",
    "ox.save_graph_geopackage(G, filepath=\"./data/mynetwork.gpkg\")\n",
    "ox.save_graphml(G, filepath=\"./data/mynetwork.graphml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize street centrality\n",
    "\n",
    "Here we plot the street network and color its edges (streets) by their relative closeness centrality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert graph to line graph so edges become nodes and vice versa\n",
    "edge_centrality = nx.closeness_centrality(nx.line_graph(G))\n",
    "nx.set_edge_attributes(G, edge_centrality, \"edge_centrality\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# color edges in original graph with closeness centralities from line graph\n",
    "ec = ox.plot.get_edge_colors_by_attr(G, \"edge_centrality\", cmap=\"inferno\")\n",
    "fig, ax = ox.plot_graph(G, edge_color=ec, edge_linewidth=2, node_size=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Routing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute missing edge speeds and calculate edge travel times with the speed module\n",
    "G = ox.speed.add_edge_speeds(G)\n",
    "G = ox.speed.add_edge_travel_times(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the nearest network nodes to two lat/lng points with the distance module\n",
    "orig = ox.distance.nearest_nodes(G, X=-122.245846, Y=37.828903)\n",
    "dest = ox.distance.nearest_nodes(G, X=-122.215006, Y=37.812303)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the shortest path between nodes, minimizing travel time, then plot it\n",
    "route = ox.shortest_path(G, orig, dest, weight=\"travel_time\")\n",
    "fig, ax = ox.plot_graph_route(G, route, node_size=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how long is our route in meters?\n",
    "edge_lengths = ox.utils_graph.get_route_edge_attributes(G, route, \"length\")\n",
    "round(sum(edge_lengths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how far is it between these two nodes as the crow flies?\n",
    "# use OSMnx's vectorized great-circle distance (haversine) function\n",
    "orig_x = G.nodes[orig][\"x\"]\n",
    "orig_y = G.nodes[orig][\"y\"]\n",
    "dest_x = G.nodes[dest][\"x\"]\n",
    "dest_y = G.nodes[dest][\"y\"]\n",
    "round(ox.distance.great_circle_vec(orig_y, orig_x, dest_y, dest_x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add elevation attributes to your graph's nodes automatically with the `elevation` module, using either local raster files or the Google Maps Elevation API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add elevation to nodes automatically, calculate edge grades, plot network\n",
    "# you need a google elevation api key to run this cell!\n",
    "try:\n",
    "    from keys import google_elevation_api_key\n",
    "\n",
    "    G = ox.elevation.add_node_elevations_google(G, api_key=google_elevation_api_key)\n",
    "    G = ox.elevation.add_edge_grades(G)\n",
    "    nc = ox.plot.get_node_colors_by_attr(G, \"elevation\", cmap=\"plasma\")\n",
    "    fig, ax = ox.plot_graph(G, node_color=nc, node_size=20, edge_linewidth=2, edge_color=\"#333\")\n",
    "except ImportError:\n",
    "    print(\"You need a google_elevation_api_key to run this cell.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nodes are colored from lowest elevation (dark blue) to highest (bright yellow).\n",
    "\n",
    "Example: create [elevation-based](12-node-elevations-edge-grades.ipynb) impedance functions to route around hills."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get networks other ways\n",
    "\n",
    "make queries less ambiguous to help the geocoder out, if it's not finding what you're looking for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can make query an unambiguous dict to help the geocoder find it\n",
    "place = {\"city\": \"San Francisco\", \"state\": \"California\", \"country\": \"USA\"}\n",
    "G = ox.graph_from_place(place, network_type=\"drive\", truncate_by_edge=True)\n",
    "fig, ax = ox.plot_graph(G, figsize=(10, 10), node_size=0, edge_color=\"y\", edge_linewidth=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can get networks anywhere in the world\n",
    "G = ox.graph_from_place(\"Sinalunga, Italy\", network_type=\"all\")\n",
    "fig, ax = ox.plot_graph(G, node_size=0, edge_linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# or get network by address, coordinates, bounding box, or any custom polygon\n",
    "# ...useful when OSM just doesn't already have a polygon for the place you want\n",
    "wurster_hall = (37.870605, -122.254830)\n",
    "one_mile = 1609  # meters\n",
    "G = ox.graph_from_point(wurster_hall, dist=one_mile, network_type=\"drive\")\n",
    "fig, ax = ox.plot_graph(G, node_size=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examples of [getting networks](01-overview-osmnx.ipynb) by coordinates, bounding box, or any custom polygon shape."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get other networked infrastructure types\n",
    "\n",
    "...like rail or electric grids or even the canals of Venice and Amsterdam, using the `custom_filter` parameter: [see more examples](08-custom-filters-infrastructure.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get NY subway rail network\n",
    "G = ox.graph_from_place(\n",
    "    \"New York, New York, USA\",\n",
    "    retain_all=False,\n",
    "    truncate_by_edge=True,\n",
    "    simplify=True,\n",
    "    custom_filter='[\"railway\"~\"subway\"]',\n",
    ")\n",
    "\n",
    "fig, ax = ox.plot_graph(G, node_size=0, edge_color=\"w\", edge_linewidth=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get any geospatial entities' geometries and attributes\n",
    "\n",
    "Use the `geometries` module to download entities, such as local amenities, points of interest, or building footprints, and turn them into a GeoDataFrame: [see docs](https://osmnx.readthedocs.io/en/stable/osmnx.html#module-osmnx.geometries). For more usage examples of downloading geospatial objects from OSM, see [this notebook](16-download-osm-geospatial-entities.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all building footprints in some neighborhood\n",
    "place = \"Civic Center, Los Angeles, California\"\n",
    "tags = {\"building\": True}\n",
    "gdf = ox.geometries_from_place(place, tags)\n",
    "gdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = ox.plot_footprints(gdf, figsize=(3, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the other notebooks for more examples of visualization with OSMnx."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all parks and bus stops in some neighborhood\n",
    "tags = {\"leisure\": \"park\", \"highway\": \"bus_stop\"}\n",
    "gdf = ox.geometries_from_place(place, tags)\n",
    "gdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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