OSMnx features demo

Get street networks and other spatial data anywhere in the world from OpenStreetMap then analyze and visualize them.

More info:

This notebook provides a quick tour of some of OSMnx's key features including how to:

download/model street networks
calculate stats
visualize centrality
impute speeds/travel times and calculate shortest paths
attach and visualize elevation data and edge grades
download/model other infrastructure types
download points of interest data

import networkx as nx import osmnx as ox %matplotlib inline ox.config(log_console=True) ox.__version__

Working with street networks

# download/model a street network for some city then visualize it G = ox.graph_from_place("Shenyang, Liaoning, China", network_type="drive") fig, ax = ox.plot_graph(G)

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.

OSMnx models all networks as NetworkX MultiDiGraph objects. You can convert to:

undirected MultiGraphs
DiGraphs without (possible) parallel edges
GeoPandas node/edge GeoDataFrames

# convert your MultiDiGraph to an undirected MultiGraph M = ox.get_undirected(G) # convert your MultiDiGraph to a DiGraph without parallel edges D = ox.get_digraph(G)

# you can convert your graph to node and edge GeoPandas GeoDataFrames gdf_nodes, gdf_edges = ox.graph_to_gdfs(G) gdf_nodes.head()

gdf_edges.head()

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.

# convert node/edge GeoPandas GeoDataFrames to a NetworkX MultiDiGraph G2 = ox.graph_from_gdfs(gdf_nodes, gdf_edges, graph_attrs=G.graph)

Basic street network stats

# what sized area does our network cover in square meters? G_proj = ox.project_graph(G) nodes_proj = ox.graph_to_gdfs(G_proj, edges=False) graph_area_m = nodes_proj.unary_union.convex_hull.area graph_area_m

# show some basic stats about the network ox.basic_stats(G_proj, area=graph_area_m, clean_intersects=True, circuity_dist="euclidean")

stats documentation: https://osmnx.readthedocs.io/en/stable/osmnx.html#module-osmnx.stats

# see more stats (mostly topological stuff) with extended_stats more_stats = ox.extended_stats( G, ecc=True, bc=True, cc=True ) # use arguments to turn other toplogical analyses on/off for key in sorted(more_stats.keys()): print(key)

more_stats["radius"]

The radius (i.e., minimum eccentricity) of this street network is approximately 2.5 kilometers.

# save graph to disk as geopackage (for GIS) or graphml file (for gephi etc) ox.save_graph_geopackage(G, filepath="./data/mynetwork.gpkg") ox.save_graphml(G, filepath="./data/mynetwork.graphml")

Visualize street centrality

Here we plot the street network and color its edges (streets) by their relative closeness centrality.

# convert graph to line graph so edges become nodes and vice versa edge_centrality = nx.closeness_centrality(nx.line_graph(G)) nx.set_edge_attributes(G, edge_centrality, "edge_centrality")

# color edges in original graph with closeness centralities from line graph ec = ox.plot.get_edge_colors_by_attr(G, "edge_centrality", cmap="inferno") fig, ax = ox.plot_graph(G, edge_color=ec, edge_linewidth=2, node_size=0)

Routing

# impute missing edge speeds then calculate edge travel times G = ox.add_edge_speeds(G) G = ox.add_edge_travel_times(G)

# get the nearest network nodes to two lat/lng points orig = ox.get_nearest_node(G, (37.828903, -122.245846)) dest = ox.get_nearest_node(G, (37.812303, -122.215006))

# find the shortest path between these nodes, minimizing travel time, then plot it route = ox.shortest_path(G, orig, dest, weight="travel_time") fig, ax = ox.plot_graph_route(G, route, node_size=0)

# how long is our route in meters? edge_lengths = ox.utils_graph.get_route_edge_attributes(G, route, "length") sum(edge_lengths)

# how far is it between these two nodes as the crow flies (haversine)? ox.distance.great_circle_vec( G.nodes[orig]["y"], G.nodes[orig]["x"], G.nodes[dest]["y"], G.nodes[dest]["x"] )

# add elevation to nodes automatically, calculate edge grades, plot network # you need a google elevation api key to run this cell! try: from keys import google_elevation_api_key G = ox.add_node_elevations(G, api_key=google_elevation_api_key) G = ox.add_edge_grades(G) nc = ox.plot.get_node_colors_by_attr(G, "elevation", cmap="plasma") fig, ax = ox.plot_graph(G, node_color=nc, node_size=20, edge_linewidth=2, edge_color="#333") except ImportError: pass

Nodes are colored from lowest elevation (dark blue) to highest (bright yellow).

Example: create elevation-based impedance functions to route around hills.

Get networks other ways

make queries less ambiguous to help the geocoder out, if it's not finding what you're looking for

# you can make query an unambiguous dict to help the geocoder find it place = {"city": "San Francisco", "state": "California", "country": "USA"} G = ox.graph_from_place(place, network_type="drive", truncate_by_edge=True) fig, ax = ox.plot_graph(G, figsize=(10, 10), node_size=0, edge_color="y", edge_linewidth=0.2)

# you can get networks anywhere in the world G = ox.graph_from_place("Sinalunga, Italy", network_type="all") fig, ax = ox.plot_graph(G, node_size=0, edge_linewidth=0.5)

# or get network by address, coordinates, bounding box, or any custom polygon # ...useful when OSM just doesn't already have a polygon for the place you want wurster_hall = (37.870605, -122.254830) one_mile = 1609 # meters G = ox.graph_from_point(wurster_hall, dist=one_mile, network_type="drive") fig, ax = ox.plot_graph(G, node_size=0)

Examples of getting networks by coordinates, bounding box, or any custom polygon shape.

Get other networked infrastructure types

...like rail or electric grids or even the canals of Venice and Amsterdam, using the custom_filter parameter: see more examples.

# get NY subway rail network G = ox.graph_from_place( "New York City, New York", retain_all=False, truncate_by_edge=True, simplify=True, custom_filter='["railway"~"subway"]', ) fig, ax = ox.plot_graph(G, node_size=0, edge_color="w", edge_linewidth=0.2)

Get any geospatial entities' geometries and attributes

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. For more usage examples of downloading geospatial objects from OSM, see this notebook.

# get all building footprints in some neighborhood place = "Bunker Hill, Los Angeles, California" tags = {"building": True} gdf = ox.geometries_from_place(place, tags) gdf.shape

fig, ax = ox.plot_footprints(gdf, figsize=(3, 3))

See the other notebooks for more examples of visualization with OSMnx.

# get all parks and bus stops in some neighborhood tags = {"leisure": "park", "highway": "bus_stop"} gdf = ox.geometries_from_place(place, tags) gdf.shape

OSMnx features demo

Working with street networks

Basic street network stats

Visualize street centrality

Routing

Get networks other ways

Get other networked infrastructure types

Get any geospatial entities' geometries and attributes

Recommended on Deepnote

Stock market analysis

10 ways to create NumPy arrays

Wide Residual Networks