# 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)

gdf_edges.head()

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

# 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")

# 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"]

# 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")

# 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)

# 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

# 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)

# 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 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))

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