User reference¶

User reference for the OSMnx package.

This guide covers usage of all public modules and functions. Every function can be accessed via ox.module_name.function_name() and the vast majority of them can also be accessed directly via ox.function_name() as a shortcut. Only a few less-common functions are accessible only via ox.module_name.function_name().

osmnx.bearing module¶

Calculate graph edge bearings.

osmnx.bearing.add_edge_bearings(G, precision=1)¶

Add compass bearing attributes to all graph edges.

Vectorized function to calculate (initial) bearing from origin node to destination node for each edge in a directed, unprojected graph then add these bearings as new edge attributes. Bearing represents angle in degrees (clockwise) between north and the geodesic line from the origin node to the destination node. Ignores self-loop edges as their bearings are undefined.

Parameters:	G (networkx.MultiDiGraph) – unprojected graph precision (int) – decimal precision to round bearing
Returns:	G – graph with edge bearing attributes
Return type:	networkx.MultiDiGraph

osmnx.bearing.calculate_bearing(lat1, lng1, lat2, lng2)¶

Calculate the compass bearing(s) between pairs of lat-lng points.

Vectorized function to calculate (initial) bearings between two points’ coordinates or between arrays of points’ coordinates. Expects coordinates in decimal degrees. Bearing represents angle in degrees (clockwise) between north and the geodesic line from point 1 to point 2.

Parameters:	lat1 (float or numpy.array of float) – first point’s latitude coordinate lng1 (float or numpy.array of float) – first point’s longitude coordinate lat2 (float or numpy.array of float) – second point’s latitude coordinate lng2 (float or numpy.array of float) – second point’s longitude coordinate
Returns:	bearing – the bearing(s) in decimal degrees
Return type:	float or numpy.array of float

osmnx.bearing.orientation_entropy(Gu, num_bins=36, min_length=0, weight=None)¶

Calculate undirected graph’s orientation entropy.

Orientation entropy is the entropy of its edges’ bidirectional bearings across evenly spaced bins. Ignores self-loop edges as their bearings are undefined.

Parameters:	Gu (networkx.MultiGraph) – undirected, unprojected graph with bearing attributes on each edge num_bins (int) – number of bins; for example, if num_bins=36 is provided, then each bin will represent 10° around the compass min_length (float) – ignore edges with length attributes less than min_length; useful to ignore the noise of many very short edges weight (string) – if not None, weight edges’ bearings by this (non-null) edge attribute. for example, if “length” is provided, this will return 1 bearing observation per meter per street, which could result in a very large bearings array.
Returns:	entropy – the graph’s orientation entropy
Return type:	float

osmnx.bearing.plot_orientation(Gu, num_bins=36, min_length=0, weight=None, ax=None, figsize=(5, 5), area=True, color='#003366', edgecolor='k', linewidth=0.5, alpha=0.7, title=None, title_y=1.05, title_font=None, xtick_font=None)¶

Plot a polar histogram of a spatial network’s bidirectional edge bearings.

Ignores self-loop edges as their bearings are undefined.

For more info see: Boeing, G. 2019. “Urban Spatial Order: Street Network Orientation, Configuration, and Entropy.” Applied Network Science, 4 (1), 67. https://doi.org/10.1007/s41109-019-0189-1

Parameters:	Gu (networkx.MultiGraph) – undirected, unprojected graph with bearing attributes on each edge num_bins (int) – number of bins; for example, if num_bins=36 is provided, then each bin will represent 10° around the compass min_length (float) – ignore edges with length attributes less than min_length weight (string) – if not None, weight edges’ bearings by this (non-null) edge attribute ax (matplotlib.axes.PolarAxesSubplot) – if not None, plot on this preexisting axis; must have projection=polar figsize (tuple) – if ax is None, create new figure with size (width, height) area (bool) – if True, set bar length so area is proportional to frequency, otherwise set bar length so height is proportional to frequency color (string) – color of histogram bars edgecolor (string) – color of histogram bar edges linewidth (float) – width of histogram bar edges alpha (float) – opacity of histogram bars title (string) – title for plot title_y (float) – y position to place title title_font (dict) – the title’s fontdict to pass to matplotlib xtick_font (dict) – the xtick labels’ fontdict to pass to matplotlib
Returns:	fig, ax – matplotlib figure, axis
Return type:	tuple

osmnx.distance module¶

Calculate distances and shortest paths and find nearest node/edge(s) to point(s).

osmnx.distance.add_edge_lengths(G, precision=3, edges=None)¶

Add length attribute (in meters) to each edge.

Vectorized function to calculate great-circle distance between each edge’s incident nodes. Ensure graph is in unprojected coordinates, and unsimplified to get accurate distances.

Note: this function is run by all the graph.graph_from_x functions automatically to add length attributes to all edges. It calculates edge lengths as the great-circle distance from node u to node v. When OSMnx automatically runs this function upon graph creation, it does it before simplifying the graph: thus it calculates the straight-line lengths of edge segments that are themselves all straight. Only after simplification do edges take on a (potentially) curvilinear geometry. If you wish to calculate edge lengths later, you are calculating straight-line distances which necessarily ignore the curvilinear geometry. You only want to run this function on a graph with all straight edges (such as is the case with an unsimplified graph).

Parameters:	G (networkx.MultiDiGraph) – unprojected, unsimplified input graph precision (int) – decimal precision to round lengths edges (tuple) – tuple of (u, v, k) tuples representing subset of edges to add length attributes to. if None, add lengths to all edges.
Returns:	G – graph with edge length attributes
Return type:	networkx.MultiDiGraph

osmnx.distance.euclidean_dist_vec(y1, x1, y2, x2)¶

Calculate Euclidean distances between pairs of points.

Vectorized function to calculate the Euclidean distance between two points’ coordinates or between arrays of points’ coordinates. For accurate results, use projected coordinates rather than decimal degrees.

Parameters:	y1 (float or numpy.array of float) – first point’s y coordinate x1 (float or numpy.array of float) – first point’s x coordinate y2 (float or numpy.array of float) – second point’s y coordinate x2 (float or numpy.array of float) – second point’s x coordinate
Returns:	dist – distance from each (x1, y1) to each (x2, y2) in coordinates’ units
Return type:	float or numpy.array of float

osmnx.distance.great_circle_vec(lat1, lng1, lat2, lng2, earth_radius=6371009)¶

Calculate great-circle distances between pairs of points.

Vectorized function to calculate the great-circle distance between two points’ coordinates or between arrays of points’ coordinates using the haversine formula. Expects coordinates in decimal degrees.

Parameters:	lat1 (float or numpy.array of float) – first point’s latitude coordinate lng1 (float or numpy.array of float) – first point’s longitude coordinate lat2 (float or numpy.array of float) – second point’s latitude coordinate lng2 (float or numpy.array of float) – second point’s longitude coordinate earth_radius (float) – earth’s radius in units in which distance will be returned (default is meters)
Returns:	dist – distance from each (lat1, lng1) to each (lat2, lng2) in units of earth_radius
Return type:	float or numpy.array of float

osmnx.distance.k_shortest_paths(G, orig, dest, k, weight='length')¶

Solve k shortest paths from an origin node to a destination node.

Uses Yen’s algorithm. See also shortest_path to solve just the one shortest path.

Parameters:	G (networkx.MultiDiGraph) – input graph orig (int) – origin node ID dest (int) – destination node ID k (int) – number of shortest paths to solve weight (string) – edge attribute to minimize when solving shortest paths. default is edge length in meters.
Returns:	paths – a generator of k shortest paths ordered by total weight. each path is a list of node IDs.
Return type:	generator

osmnx.distance.nearest_edges(G, X, Y, interpolate=None, return_dist=False)¶

Find the nearest edge to a point or to each of several points.

If X and Y are single coordinate values, this will return the nearest edge to that point. If X and Y are lists of coordinate values, this will return the nearest edge to each point.

If interpolate is None, search for the nearest edge to each point, one at a time, using an R-tree and minimizing the euclidean distances from the point to the possible matches. For accuracy, use a projected graph and points. This method is precise and fast, particularly when searching for relatively few points compared to the graph’s size.

For an alternative method, use the interpolate argument to interpolate points along the edges and index them. If the graph is projected, this uses a k-d tree for euclidean nearest neighbor search, which requires that scipy is installed as an optional dependency. If graph is unprojected, this uses a ball tree for haversine nearest neighbor search, which requires that scikit-learn is installed as an optional dependency.

Parameters:	G (networkx.MultiDiGraph) – graph in which to find nearest edges X (float or list) – points’ x (longitude) coordinates, in same CRS/units as graph and containing no nulls Y (float or list) – points’ y (latitude) coordinates, in same CRS/units as graph and containing no nulls interpolate (float) – spacing distance between interpolated points, in same units as graph. smaller values generate more points. return_dist (bool) – optionally also return distance between points and nearest edges
Returns:	ne or (ne, dist) – nearest edges as (u, v, key) or optionally a tuple where dist contains distances between the points and their nearest edges
Return type:	tuple or list

osmnx.distance.nearest_nodes(G, X, Y, return_dist=False)¶

Find the nearest node to a point or to each of several points.

If X and Y are single coordinate values, this will return the nearest node to that point. If X and Y are lists of coordinate values, this will return the nearest node to each point.

If the graph is projected, this uses a k-d tree for euclidean nearest neighbor search, which requires that scipy is installed as an optional dependency. If it is unprojected, this uses a ball tree for haversine nearest neighbor search, which requires that scikit-learn is installed as an optional dependency.

Parameters:	G (networkx.MultiDiGraph) – graph in which to find nearest nodes X (float or list) – points’ x (longitude) coordinates, in same CRS/units as graph and containing no nulls Y (float or list) – points’ y (latitude) coordinates, in same CRS/units as graph and containing no nulls return_dist (bool) – optionally also return distance between points and nearest nodes
Returns:	nn or (nn, dist) – nearest node IDs or optionally a tuple where dist contains distances between the points and their nearest nodes
Return type:	int/list or tuple

osmnx.distance.shortest_path(G, orig, dest, weight='length', cpus=1)¶

Solve shortest path from origin node(s) to destination node(s).

Uses Dijkstra’s algorithm. If orig and dest are single node IDs, this will return a list of the nodes constituting the shortest path between them. If orig and dest are lists of node IDs, this will return a list of lists of the nodes constituting the shortest path between each origin-destination pair. If a path cannot be solved, this will return None for that path. You can parallelize solving multiple paths with the cpus parameter, but be careful to not exceed your available RAM.

See also k_shortest_paths to solve multiple shortest paths between a single origin and destination. For additional functionality or different solver algorithms, use NetworkX directly.

Parameters:	G (networkx.MultiDiGraph) – input graph orig (int or list) – origin node ID, or a list of origin node IDs dest (int or list) – destination node ID, or a list of destination node IDs weight (string) – edge attribute to minimize when solving shortest path cpus (int) – how many CPU cores to use; if None, use all available
Returns:	path – list of node IDs constituting the shortest path, or, if orig and dest are lists, then a list of path lists
Return type:	list

osmnx.downloader module¶

Interact with the OSM APIs.

osmnx.downloader.nominatim_request(params, request_type='search', pause=1, error_pause=60)¶

Send a HTTP GET request to the Nominatim API and return JSON response.

Parameters:	params (OrderedDict) – key-value pairs of parameters request_type (string {"search", "reverse", "lookup"}) – which Nominatim API endpoint to query pause (int) – how long to pause before request, in seconds. per the nominatim usage policy: “an absolute maximum of 1 request per second” is allowed error_pause (int) – how long to pause in seconds before re-trying request if error
Returns:	response_json
Return type:	dict

osmnx.downloader.overpass_request(data, pause=None, error_pause=60)¶

Send a HTTP POST request to the Overpass API and return JSON response.

Parameters:	data (OrderedDict) – key-value pairs of parameters pause (int) – how long to pause in seconds before request, if None, will query API status endpoint to find when next slot is available error_pause (int) – how long to pause in seconds (in addition to pause) before re-trying request if error
Returns:	response_json
Return type:	dict

osmnx.elevation module¶

Get node elevations and calculate edge grades.

osmnx.elevation.add_edge_grades(G, add_absolute=True, precision=3)¶

Add grade attribute to each graph edge.

Vectorized function to calculate the directed grade (ie, rise over run) for each edge in the graph and add it to the edge as an attribute. Nodes must already have elevation attributes to use this function.

See also the add_node_elevations_raster and add_node_elevations_google functions.

Parameters:	G (networkx.MultiDiGraph) – input graph with elevation node attribute add_absolute (bool) – if True, also add absolute value of grade as grade_abs attribute precision (int) – decimal precision to round grade values
Returns:	G – graph with edge grade (and optionally grade_abs) attributes
Return type:	networkx.MultiDiGraph

osmnx.elevation.add_node_elevations_google(G, api_key, max_locations_per_batch=350, pause_duration=0, precision=3, url_template='https://maps.googleapis.com/maps/api/elevation/json?locations={}&key={}')¶

Add elevation (meters) attribute to each node using a web service.

By default, this uses the Google Maps Elevation API but you can optionally use an equivalent API with the same interface and response format, such as Open Topo Data. The Google Maps Elevation API requires an API key but other providers may not.

For a free local alternative see the add_node_elevations_raster function. See also the add_edge_grades function.

Parameters:	G (networkx.MultiDiGraph) – input graph api_key (string) – a valid API key max_locations_per_batch (int) – max number of coordinate pairs to submit in each API call (if this is too high, the server will reject the request because its character limit exceeds the max allowed) pause_duration (float) – time to pause between API calls, which can be increased if you get rate limited precision (int) – decimal precision to round elevation values url_template (string) – a URL string template for the API endpoint, containing exactly two parameters: locations and key; for example, for Open Topo Data: “https://api.opentopodata.org/v1/aster30m?locations={}&key={}”
Returns:	G – graph with node elevation attributes
Return type:	networkx.MultiDiGraph

osmnx.elevation.add_node_elevations_raster(G, filepath, band=1, cpus=None)¶

Add elevation attribute to each node from local raster file(s).

If filepath is a list of paths, this will generate a virtual raster composed of the files at those paths as an intermediate step.

osmnx.folium module¶

Create interactive Leaflet web maps of graphs and routes via folium.

osmnx.folium.plot_graph_folium(G, graph_map=None, popup_attribute=None, tiles='cartodbpositron', zoom=1, fit_bounds=True, **kwargs)¶

Plot a graph as an interactive Leaflet web map.

Note that anything larger than a small city can produce a large web map file that is slow to render in your browser.

Parameters:	G (networkx.MultiDiGraph) – input graph graph_map (folium.folium.Map) – if not None, plot the graph on this preexisting folium map object popup_attribute (string) – edge attribute to display in a pop-up when an edge is clicked tiles (string) – name of a folium tileset zoom (int) – initial zoom level for the map fit_bounds (bool) – if True, fit the map to the boundaries of the graph’s edges kwargs – keyword arguments to pass to folium.PolyLine(), see folium docs for options (for example color=”#333333”, weight=5, opacity=0.7)
Returns:
Return type:	folium.folium.Map

osmnx.folium.plot_route_folium(G, route, route_map=None, popup_attribute=None, tiles='cartodbpositron', zoom=1, fit_bounds=True, **kwargs)¶

Plot a route as an interactive Leaflet web map.

Parameters:	G (networkx.MultiDiGraph) – input graph route (list) – the route as a list of nodes route_map (folium.folium.Map) – if not None, plot the route on this preexisting folium map object popup_attribute (string) – edge attribute to display in a pop-up when an edge is clicked tiles (string) – name of a folium tileset zoom (int) – initial zoom level for the map fit_bounds (bool) – if True, fit the map to the boundaries of the route’s edges kwargs – keyword arguments to pass to folium.PolyLine(), see folium docs for options (for example color=”#cc0000”, weight=5, opacity=0.7)
Returns:
Return type:	folium.folium.Map

osmnx.geocoder module¶

Geocode queries and create GeoDataFrames of place boundaries.

osmnx.geocoder.geocode(query)¶

Geocode a query string to (lat, lng) with the Nominatim geocoder.

Parameters:	query (string) – the query string to geocode
Returns:	point – the (lat, lng) coordinates returned by the geocoder
Return type:	tuple

osmnx.geocoder.geocode_to_gdf(query, which_result=None, by_osmid=False, buffer_dist=None)¶

Retrieve place(s) by name or ID from the Nominatim API as a GeoDataFrame.

You can query by place name or OSM ID. If querying by place name, the query argument can be a string or structured dict, or a list of such strings/dicts to send to geocoder. You can instead query by OSM ID by setting by_osmid=True. In this case, geocode_to_gdf treats the query argument as an OSM ID (or list of OSM IDs) for Nominatim lookup rather than text search. OSM IDs must be prepended with their types: node (N), way (W), or relation (R), in accordance with the Nominatim format. For example, query=[“R2192363”, “N240109189”, “W427818536”].

If query argument is a list, then which_result should be either a single value or a list with the same length as query. The queries you provide must be resolvable to places in the Nominatim database. The resulting GeoDataFrame’s geometry column contains place boundaries if they exist in OpenStreetMap.

Parameters:	query (string or dict or list) – query string(s) or structured dict(s) to geocode which_result (int) – which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn’t return one. to get the top match regardless of geometry type, set which_result=1 by_osmid (bool) – if True, handle query as an OSM ID for lookup rather than text search buffer_dist (float) – distance to buffer around the place geometry, in meters
Returns:	gdf – a GeoDataFrame with one row for each query
Return type:	geopandas.GeoDataFrame

osmnx.geometries module¶

Download geospatial entities’ geometries and attributes from OpenStreetMap.

Retrieve points of interest, building footprints, or any other objects from OSM, including their geometries and attribute data, and construct a GeoDataFrame of them. You can use this module to query for nodes, ways, and relations (the latter of type “multipolygon” or “boundary” only) by passing a dictionary of desired tags/values.

osmnx.geometries.geometries_from_address(address, tags, dist=1000)¶

Create GeoDataFrame of OSM entities within some distance N, S, E, W of address.

Parameters:	address (string) – the address to geocode and use as the central point around which to get the geometries tags (dict) – Dict of tags used for finding objects in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop. dist (numeric) – distance in meters
Returns:	gdf
Return type:	geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module.

osmnx.geometries.geometries_from_bbox(north, south, east, west, tags)¶

Create a GeoDataFrame of OSM entities within a N, S, E, W bounding box.

Parameters:	north (float) – northern latitude of bounding box south (float) – southern latitude of bounding box east (float) – eastern longitude of bounding box west (float) – western longitude of bounding box tags (dict) – Dict of tags used for finding objects in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.
Returns:	gdf
Return type:	geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module.

osmnx.geometries.geometries_from_place(query, tags, which_result=None, buffer_dist=None)¶

Create GeoDataFrame of OSM entities within boundaries of geocodable place(s).

The query must be geocodable and OSM must have polygon boundaries for the geocode result. If OSM does not have a polygon for this place, you can instead get geometries within it using the geometries_from_address function, which geocodes the place name to a point and gets the geometries within some distance of that point.

If OSM does have polygon boundaries for this place but you’re not finding it, try to vary the query string, pass in a structured query dict, or vary the which_result argument to use a different geocode result. If you know the OSM ID of the place, you can retrieve its boundary polygon using the geocode_to_gdf function, then pass it to the geometries_from_polygon function.

Parameters:	query (string or dict or list) – the query or queries to geocode to get place boundary polygon(s) tags (dict) – Dict of tags used for finding objects in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop. which_result (int) – which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn’t return one. buffer_dist (float) – distance to buffer around the place geometry, in meters
Returns:	gdf
Return type:	geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module.

osmnx.geometries.geometries_from_point(center_point, tags, dist=1000)¶

Create GeoDataFrame of OSM entities within some distance N, S, E, W of a point.

Parameters:	center_point (tuple) – the (lat, lng) center point around which to get the geometries tags (dict) – Dict of tags used for finding objects in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop. dist (numeric) – distance in meters
Returns:	gdf
Return type:	geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module.

osmnx.geometries.geometries_from_polygon(polygon, tags)¶

Create GeoDataFrame of OSM entities within boundaries of a (multi)polygon.

Parameters:	polygon (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – geographic boundaries to fetch geometries within tags (dict) – Dict of tags used for finding objects in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.
Returns:	gdf
Return type:	geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module.

osmnx.geometries.geometries_from_xml(filepath, polygon=None, tags=None)¶

Create a GeoDataFrame of OSM entities in an OSM-formatted XML file.

Because this function creates a GeoDataFrame of geometries from an OSM-formatted XML file that has already been downloaded (i.e. no query is made to the Overpass API) the polygon and tags arguments are not required. If they are not supplied to the function, geometries_from_xml() will return geometries for all of the tagged elements in the file. If they are supplied they will be used to filter the final GeoDataFrame.

Parameters:	filepath (string or pathlib.Path) – path to file containing OSM XML data polygon (shapely.geometry.Polygon) – optional geographic boundary to filter objects tags (dict) – optional dict of tags for filtering objects from the XML. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.
Returns:	gdf
Return type:	geopandas.GeoDataFrame

osmnx.graph module¶

Graph creation functions.

osmnx.graph.graph_from_address(address, dist=1000, dist_type='bbox', network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, return_coords=False, clean_periphery=True, custom_filter=None)¶

Create a graph from OSM within some distance of some address.

Parameters:	address (string) – the address to geocode and use as the central point around which to construct the graph dist (int) – retain only those nodes within this many meters of the center of the graph dist_type (string {"network", "bbox"}) – if “bbox”, retain only those nodes within a bounding box of the distance parameter. if “network”, retain only those nodes within some network distance from the center-most node (requires that scikit-learn is installed as an optional dependency). network_type (string {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None simplify (bool) – if True, simplify graph topology with the simplify_graph function retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component. truncate_by_edge (bool) – if True, retain nodes outside bounding box if at least one of node’s neighbors is within the bounding box return_coords (bool) – optionally also return the geocoded coordinates of the address clean_periphery (bool,) – if True, buffer 500m to get a graph larger than requested, then simplify, then truncate it to requested spatial boundaries custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway\|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.
Returns:
Return type:	networkx.MultiDiGraph or optionally (networkx.MultiDiGraph, (lat, lng))

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module. Very large query areas will use the utils_geo._consolidate_subdivide_geometry function to perform multiple queries: see that function’s documentation for caveats.

osmnx.graph.graph_from_bbox(north, south, east, west, network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, clean_periphery=True, custom_filter=None)¶

Create a graph from OSM within some bounding box.

Parameters:	north (float) – northern latitude of bounding box south (float) – southern latitude of bounding box east (float) – eastern longitude of bounding box west (float) – western longitude of bounding box network_type (string {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None simplify (bool) – if True, simplify graph topology with the simplify_graph function retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component. truncate_by_edge (bool) – if True, retain nodes outside bounding box if at least one of node’s neighbors is within the bounding box clean_periphery (bool) – if True, buffer 500m to get a graph larger than requested, then simplify, then truncate it to requested spatial boundaries custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway\|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.
Returns:	G
Return type:	networkx.MultiDiGraph

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module. Very large query areas will use the utils_geo._consolidate_subdivide_geometry function to perform multiple queries: see that function’s documentation for caveats.

osmnx.graph.graph_from_place(query, network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, which_result=None, buffer_dist=None, clean_periphery=True, custom_filter=None)¶

Create graph from OSM within the boundaries of some geocodable place(s).

The query must be geocodable and OSM must have polygon boundaries for the geocode result. If OSM does not have a polygon for this place, you can instead get its street network using the graph_from_address function, which geocodes the place name to a point and gets the network within some distance of that point.

If OSM does have polygon boundaries for this place but you’re not finding it, try to vary the query string, pass in a structured query dict, or vary the which_result argument to use a different geocode result. If you know the OSM ID of the place, you can retrieve its boundary polygon using the geocode_to_gdf function, then pass it to the graph_from_polygon function.

Parameters:	query (string or dict or list) – the query or queries to geocode to get place boundary polygon(s) network_type (string {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None simplify (bool) – if True, simplify graph topology with the simplify_graph function retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component. truncate_by_edge (bool) – if True, retain nodes outside boundary polygon if at least one of node’s neighbors is within the polygon which_result (int) – which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn’t return one. buffer_dist (float) – distance to buffer around the place geometry, in meters clean_periphery (bool) – if True, buffer 500m to get a graph larger than requested, then simplify, then truncate it to requested spatial boundaries custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway\|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.
Returns:	G
Return type:	networkx.MultiDiGraph

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module. Very large query areas will use the utils_geo._consolidate_subdivide_geometry function to perform multiple queries: see that function’s documentation for caveats.

osmnx.graph.graph_from_point(center_point, dist=1000, dist_type='bbox', network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, clean_periphery=True, custom_filter=None)¶

Create a graph from OSM within some distance of some (lat, lng) point.

Parameters:	center_point (tuple) – the (lat, lng) center point around which to construct the graph dist (int) – retain only those nodes within this many meters of the center of the graph, with distance determined according to dist_type argument dist_type (string {"network", "bbox"}) – if “bbox”, retain only those nodes within a bounding box of the distance parameter. if “network”, retain only those nodes within some network distance from the center-most node (requires that scikit-learn is installed as an optional dependency). network_type (string, {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None simplify (bool) – if True, simplify graph topology with the simplify_graph function retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component. truncate_by_edge (bool) – if True, retain nodes outside bounding box if at least one of node’s neighbors is within the bounding box clean_periphery (bool,) – if True, buffer 500m to get a graph larger than requested, then simplify, then truncate it to requested spatial boundaries custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway\|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.
Returns:	G
Return type:	networkx.MultiDiGraph

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module. Very large query areas will use the utils_geo._consolidate_subdivide_geometry function to perform multiple queries: see that function’s documentation for caveats.

osmnx.graph.graph_from_polygon(polygon, network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, clean_periphery=True, custom_filter=None)¶

Create a graph from OSM within the boundaries of some shapely polygon.

Parameters:	polygon (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – the shape to get network data within. coordinates should be in unprojected latitude-longitude degrees (EPSG:4326). network_type (string {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None simplify (bool) – if True, simplify graph topology with the simplify_graph function retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component. truncate_by_edge (bool) – if True, retain nodes outside boundary polygon if at least one of node’s neighbors is within the polygon clean_periphery (bool) – if True, buffer 500m to get a graph larger than requested, then simplify, then truncate it to requested spatial boundaries custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway\|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.
Returns:	G
Return type:	networkx.MultiDiGraph

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module. Very large query areas will use the utils_geo._consolidate_subdivide_geometry function to perform multiple queries: see that function’s documentation for caveats.

osmnx.graph.graph_from_xml(filepath, bidirectional=False, simplify=True, retain_all=False)¶

Create a graph from data in a .osm formatted XML file.

Parameters:	filepath (string or pathlib.Path) – path to file containing OSM XML data bidirectional (bool) – if True, create bi-directional edges for one-way streets simplify (bool) – if True, simplify graph topology with the simplify_graph function retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
Returns:	G
Return type:	networkx.MultiDiGraph

osmnx.io module¶

Serialize graphs to/from files on disk.

osmnx.io.load_graphml(filepath=None, graphml_str=None, node_dtypes=None, edge_dtypes=None, graph_dtypes=None)¶

Load an OSMnx-saved GraphML file from disk or GraphML string.

This function converts node, edge, and graph-level attributes (serialized as strings) to their appropriate data types. These can be customized as needed by passing in dtypes arguments providing types or custom converter functions. For example, if you want to convert some attribute’s values to bool, consider using the built-in ox.io._convert_bool_string function to properly handle “True”/”False” string literals as True/False booleans: ox.load_graphml(fp, node_dtypes={my_attr: ox.io._convert_bool_string}).

If you manually configured the all_oneway=True setting, you may need to manually specify here that edge oneway attributes should be type str.

Note that you must pass one and only one of filepath or graphml_str. If passing graphml_str, you may need to decode the bytes read from your file before converting to string to pass to this function.

Parameters:	filepath (string or pathlib.Path) – path to the GraphML file graphml_str (string) – a valid and decoded string representation of a GraphML file’s contents node_dtypes (dict) – dict of node attribute names:types to convert values’ data types. the type can be a python type or a custom string converter function. edge_dtypes (dict) – dict of edge attribute names:types to convert values’ data types. the type can be a python type or a custom string converter function. graph_dtypes (dict) – dict of graph-level attribute names:types to convert values’ data types. the type can be a python type or a custom string converter function.
Returns:	G
Return type:	networkx.MultiDiGraph

osmnx.io.save_graph_geopackage(G, filepath=None, encoding='utf-8', directed=False)¶

Save graph nodes and edges to disk as layers in a GeoPackage file.

Parameters:	G (networkx.MultiDiGraph) – input graph filepath (string or pathlib.Path) – path to the GeoPackage file including extension. if None, use default data folder + graph.gpkg encoding (string) – the character encoding for the saved file directed (bool) – if False, save one edge for each undirected edge in the graph but retain original oneway and to/from information as edge attributes; if True, save one edge for each directed edge in the graph
Returns:
Return type:	None

osmnx.io.save_graph_shapefile(G, filepath=None, encoding='utf-8', directed=False)¶

Do not use: deprecated. Use the save_graph_geopackage function instead.

The Shapefile format is proprietary and outdated. Instead, use the superior GeoPackage file format via the save_graph_geopackage function. See http://switchfromshapefile.org/ for more information.

Parameters:	G (networkx.MultiDiGraph) – input graph filepath (string or pathlib.Path) – path to the shapefiles folder (no file extension). if None, use default data folder + graph_shapefile encoding (string) – the character encoding for the saved files directed (bool) – if False, save one edge for each undirected edge in the graph but retain original oneway and to/from information as edge attributes; if True, save one edge for each directed edge in the graph
Returns:
Return type:	None

osmnx.io.save_graphml(G, filepath=None, gephi=False, encoding='utf-8')¶

Save graph to disk as GraphML file.

Parameters:	G (networkx.MultiDiGraph) – input graph filepath (string or pathlib.Path) – path to the GraphML file including extension. if None, use default data folder + graph.graphml gephi (bool) – if True, give each edge a unique key/id to work around Gephi’s interpretation of the GraphML specification encoding (string) – the character encoding for the saved file
Returns:
Return type:	None

osmnx.osm_xml module¶

Read/write .osm formatted XML files.

osmnx.osm_xml.save_graph_xml(data, filepath=None, node_tags=['highway'], node_attrs=['id', 'timestamp', 'uid', 'user', 'version', 'changeset', 'lat', 'lon'], edge_tags=['highway', 'lanes', 'maxspeed', 'name', 'oneway'], edge_attrs=['id', 'timestamp', 'uid', 'user', 'version', 'changeset'], oneway=False, merge_edges=True, edge_tag_aggs=None)¶

Save graph to disk as an OSM-formatted XML .osm file.

This function exists only to allow serialization to the .osm file format for applications that require it, and has constraints to conform to that. As such, this function has a limited use case which does not include saving/loading graphs for subsequent OSMnx analysis. To save/load graphs to/from disk for later use in OSMnx, use the io.save_graphml and io.load_graphml functions instead. To load a graph from a .osm file that you have downloaded or generated elsewhere, use the graph.graph_from_xml function.

Note: for large networks this function can take a long time to run. Before using this function, make sure you configured OSMnx as described in the example below when you created the graph.

Example

>>> import osmnx as ox
>>> utn = ox.settings.useful_tags_node
>>> oxna = ox.settings.osm_xml_node_attrs
>>> oxnt = ox.settings.osm_xml_node_tags
>>> utw = ox.settings.useful_tags_way
>>> oxwa = ox.settings.osm_xml_way_attrs
>>> oxwt = ox.settings.osm_xml_way_tags
>>> utn = list(set(utn + oxna + oxnt))
>>> utw = list(set(utw + oxwa + oxwt))
>>> ox.settings.all_oneway = True
>>> ox.settings.useful_tags_node = utn
>>> ox.settings.useful_tags_way = utw
>>> G = ox.graph_from_place('Piedmont, CA, USA', network_type='drive')
>>> ox.save_graph_xml(G, filepath='./data/graph.osm')

Parameters:	data (networkx multi(di)graph OR a length 2 iterable of nodes/edges) – geopandas GeoDataFrames filepath (string or pathlib.Path) – path to the .osm file including extension. if None, use default data folder + graph.osm node_tags (list) – osm node tags to include in output OSM XML node_attrs (list) – osm node attributes to include in output OSM XML edge_tags (list) – osm way tags to include in output OSM XML edge_attrs (list) – osm way attributes to include in output OSM XML oneway (bool) – the default oneway value used to fill this tag where missing merge_edges (bool) – if True merges graph edges such that each OSM way has one entry and one entry only in the OSM XML. Otherwise, every OSM way will have a separate entry for each node pair it contains. edge_tag_aggs (list of length-2 string tuples) – useful only if merge_edges is True, this argument allows the user to specify edge attributes to aggregate such that the merged OSM way entry tags accurately represent the sum total of their component edge attributes. For example, if the user wants the OSM way to have a “length” attribute, the user must specify edge_tag_aggs=[(‘length’, ‘sum’)] in order to tell this method to aggregate the lengths of the individual component edges. Otherwise, the length attribute will simply reflect the length of the first edge associated with the way.
Returns:
Return type:	None

osmnx.plot module¶

Plot spatial geometries, street networks, and routes.

osmnx.plot.get_colors(n, cmap='viridis', start=0.0, stop=1.0, alpha=1.0, return_hex=False)¶

Get n evenly-spaced colors from a matplotlib colormap.

Parameters:	n (int) – number of colors cmap (string) – name of a matplotlib colormap start (float) – where to start in the colorspace stop (float) – where to end in the colorspace alpha (float) – opacity, the alpha channel for the RGBa colors return_hex (bool) – if True, convert RGBa colors to HTML-like hexadecimal RGB strings. if False, return colors as (R, G, B, alpha) tuples.
Returns:	color_list
Return type:	list

osmnx.plot.get_edge_colors_by_attr(G, attr, num_bins=None, cmap='viridis', start=0, stop=1, na_color='none', equal_size=False)¶

Get colors based on edge attribute values.

Parameters:	G (networkx.MultiDiGraph) – input graph attr (string) – name of a numerical edge attribute num_bins (int) – if None, linearly map a color to each value. otherwise, assign values to this many bins then assign a color to each bin. cmap (string) – name of a matplotlib colormap start (float) – where to start in the colorspace stop (float) – where to end in the colorspace na_color (string) – what color to assign edges with missing attr values equal_size (bool) – ignored if num_bins is None. if True, bin into equal-sized quantiles (requires unique bin edges). if False, bin into equal-spaced bins.
Returns:	edge_colors – series labels are edge IDs (u, v, key) and values are colors
Return type:	pandas.Series

osmnx.plot.get_node_colors_by_attr(G, attr, num_bins=None, cmap='viridis', start=0, stop=1, na_color='none', equal_size=False)¶

Get colors based on node attribute values.

Parameters:	G (networkx.MultiDiGraph) – input graph attr (string) – name of a numerical node attribute num_bins (int) – if None, linearly map a color to each value. otherwise, assign values to this many bins then assign a color to each bin. cmap (string) – name of a matplotlib colormap start (float) – where to start in the colorspace stop (float) – where to end in the colorspace na_color (string) – what color to assign nodes with missing attr values equal_size (bool) – ignored if num_bins is None. if True, bin into equal-sized quantiles (requires unique bin edges). if False, bin into equal-spaced bins.
Returns:	node_colors – series labels are node IDs and values are colors
Return type:	pandas.Series

osmnx.plot.plot_figure_ground(G=None, address=None, point=None, dist=805, network_type='drive_service', street_widths=None, default_width=4, figsize=(8, 8), edge_color='w', smooth_joints=True, **pg_kwargs)¶

Plot a figure-ground diagram of a street network.

Parameters:	G (networkx.MultiDiGraph) – input graph, must be unprojected address (string) – address to geocode as the center point if G is not passed in point (tuple) – center point if address and G are not passed in dist (numeric) – how many meters to extend north, south, east, west from center point network_type (string) – what type of street network to get street_widths (dict) – dict keys are street types and values are widths to plot in pixels default_width (numeric) – fallback width in pixels for any street type not in street_widths figsize (numeric) – (width, height) of figure, should be equal edge_color (string) – color of the edges’ lines smooth_joints (bool) – if True, plot nodes same width as streets to smooth line joints and prevent cracks between them from showing pg_kwargs – keyword arguments to pass to plot_graph
Returns:	fig, ax – matplotlib figure, axis
Return type:	tuple

osmnx.plot.plot_footprints(gdf, ax=None, figsize=(8, 8), color='orange', edge_color='none', edge_linewidth=0, alpha=None, bgcolor='#111111', bbox=None, save=False, show=True, close=False, filepath=None, dpi=600)¶

Plot a GeoDataFrame of geospatial entities’ footprints.

Parameters:	gdf (geopandas.GeoDataFrame) – GeoDataFrame of footprints (shapely Polygons and MultiPolygons) ax (axis) – if not None, plot on this preexisting axis figsize (tuple) – if ax is None, create new figure with size (width, height) color (string) – color of the footprints edge_color (string) – color of the edge of the footprints edge_linewidth (float) – width of the edge of the footprints alpha (float) – opacity of the footprints bgcolor (string) – background color of the plot bbox (tuple) – bounding box as (north, south, east, west). if None, will calculate from the spatial extents of the geometries in gdf save (bool) – if True, save the figure to disk at filepath show (bool) – if True, call pyplot.show() to show the figure close (bool) – if True, call pyplot.close() to close the figure filepath (string) – if save is True, the path to the file. file format determined from extension. if None, use settings.imgs_folder/image.png dpi (int) – if save is True, the resolution of saved file
Returns:	fig, ax – matplotlib figure, axis
Return type:	tuple

osmnx.plot.plot_graph(G, ax=None, figsize=(8, 8), bgcolor='#111111', node_color='w', node_size=15, node_alpha=None, node_edgecolor='none', node_zorder=1, edge_color='#999999', edge_linewidth=1, edge_alpha=None, show=True, close=False, save=False, filepath=None, dpi=300, bbox=None)¶

Plot a graph.

Parameters:	G (networkx.MultiDiGraph) – input graph ax (matplotlib axis) – if not None, plot on this preexisting axis figsize (tuple) – if ax is None, create new figure with size (width, height) bgcolor (string) – background color of plot node_color (string or list) – color(s) of the nodes node_size (int) – size of the nodes: if 0, then skip plotting the nodes node_alpha (float) – opacity of the nodes, note: if you passed RGBA values to node_color, set node_alpha=None to use the alpha channel in node_color node_edgecolor (string) – color of the nodes’ markers’ borders node_zorder (int) – zorder to plot nodes: edges are always 1, so set node_zorder=0 to plot nodes below edges edge_color (string or list) – color(s) of the edges’ lines edge_linewidth (float) – width of the edges’ lines: if 0, then skip plotting the edges edge_alpha (float) – opacity of the edges, note: if you passed RGBA values to edge_color, set edge_alpha=None to use the alpha channel in edge_color show (bool) – if True, call pyplot.show() to show the figure close (bool) – if True, call pyplot.close() to close the figure save (bool) – if True, save the figure to disk at filepath filepath (string) – if save is True, the path to the file. file format determined from extension. if None, use settings.imgs_folder/image.png dpi (int) – if save is True, the resolution of saved file bbox (tuple) – bounding box as (north, south, east, west). if None, will calculate from spatial extents of plotted geometries.
Returns:	fig, ax – matplotlib figure, axis
Return type:	tuple

osmnx.plot.plot_graph_route(G, route, route_color='r', route_linewidth=4, route_alpha=0.5, orig_dest_size=100, ax=None, **pg_kwargs)¶

Plot a route along a graph.

Parameters:	G (networkx.MultiDiGraph) – input graph route (list) – route as a list of node IDs route_color (string) – color of the route route_linewidth (int) – width of the route line route_alpha (float) – opacity of the route line orig_dest_size (int) – size of the origin and destination nodes ax (matplotlib axis) – if not None, plot route on this preexisting axis instead of creating a new fig, ax and drawing the underlying graph pg_kwargs – keyword arguments to pass to plot_graph
Returns:	fig, ax – matplotlib figure, axis
Return type:	tuple

osmnx.plot.plot_graph_routes(G, routes, route_colors='r', route_linewidths=4, **pgr_kwargs)¶

Plot several routes along a graph.

Parameters:	G (networkx.MultiDiGraph) – input graph routes (list) – routes as a list of lists of node IDs route_colors (string or list) – if string, 1 color for all routes. if list, the colors for each route. route_linewidths (int or list) – if int, 1 linewidth for all routes. if list, the linewidth for each route. pgr_kwargs – keyword arguments to pass to plot_graph_route
Returns:	fig, ax – matplotlib figure, axis
Return type:	tuple

osmnx.projection module¶

Project spatial geometries and spatial networks.

osmnx.projection.is_projected(crs)¶

Determine if a coordinate reference system is projected or not.

This is a convenience wrapper around the pyproj.CRS.is_projected function.

Parameters:	crs (string or pyproj.CRS) – the coordinate reference system
Returns:	projected – True if crs is projected, otherwise False
Return type:	bool

osmnx.projection.project_gdf(gdf, to_crs=None, to_latlong=False)¶

Project a GeoDataFrame from its current CRS to another.

If to_crs is None, project to the UTM CRS for the UTM zone in which the GeoDataFrame’s centroid lies. Otherwise project to the CRS defined by to_crs. The simple UTM zone calculation in this function works well for most latitudes, but may not work for some extreme northern locations like Svalbard or far northern Norway.

Parameters:	gdf (geopandas.GeoDataFrame) – the GeoDataFrame to be projected to_crs (string or pyproj.CRS) – if None, project to UTM zone in which gdf’s centroid lies, otherwise project to this CRS to_latlong (bool) – if True, project to settings.default_crs and ignore to_crs
Returns:	gdf_proj – the projected GeoDataFrame
Return type:	geopandas.GeoDataFrame

osmnx.projection.project_geometry(geometry, crs=None, to_crs=None, to_latlong=False)¶

Project a shapely geometry from its current CRS to another.

If to_crs is None, project to the UTM CRS for the UTM zone in which the geometry’s centroid lies. Otherwise project to the CRS defined by to_crs.

Parameters:	geometry (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – the geometry to project crs (string or pyproj.CRS) – the starting CRS of the passed-in geometry. if None, it will be set to settings.default_crs to_crs (string or pyproj.CRS) – if None, project to UTM zone in which geometry’s centroid lies, otherwise project to this CRS to_latlong (bool) – if True, project to settings.default_crs and ignore to_crs
Returns:	geometry_proj, crs – the projected geometry and its new CRS
Return type:	tuple

osmnx.projection.project_graph(G, to_crs=None)¶

Project graph from its current CRS to another.

If to_crs is None, project the graph to the UTM CRS for the UTM zone in which the graph’s centroid lies. Otherwise, project the graph to the CRS defined by to_crs.

Parameters:	G (networkx.MultiDiGraph) – the graph to be projected to_crs (string or pyproj.CRS) – if None, project graph to UTM zone in which graph centroid lies, otherwise project graph to this CRS
Returns:	G_proj – the projected graph
Return type:	networkx.MultiDiGraph

osmnx.settings module¶

Global settings that can be configured by the user.

all_oneway : bool: If True, forces all ways to be loaded as oneway ways, preserving the original order of nodes stored in the OSM way XML. This also retains original OSM string values for oneway attribute values, rather than converting them to a True/False bool. Only use if specifically saving to .osm XML file with the save_graph_xml function. Default is False.
bidirectional_network_types : list: Network types for which a fully bidirectional graph will be created. Default is [“walk”].
cache_folder : string or pathlib.Path: Path to folder in which to save/load HTTP response cache. Default is “./cache”.
cache_only_mode : bool: If True, download network data from Overpass then raise a CacheOnlyModeInterrupt error for user to catch. This prevents graph building from taking place and instead just saves OSM response data to cache. Useful for sequentially caching lots of raw data (as you can only query Overpass one request at a time) then using the local cache to quickly build many graphs simultaneously with multiprocessing. Default is False.
data_folder : string or pathlib.Path: Path to folder in which to save/load graph files by default. Default is “./data”.
default_accept_language : string: HTTP header accept-language. Default is “en”.
default_access : string: Default filter for OSM “access” key. Default is ‘[“access”!~”private”]’. Note that also filtering out “access=no” ways prevents including transit-only bridges (e.g., Tilikum Crossing) from appearing in drivable road network (e.g., ‘[“access”!~”private|no”]’). However, some drivable tollroads have “access=no” plus a “access:conditional” key to clarify when it is accessible, so we can’t filter out all “access=no” ways by default. Best to be permissive here then remove complicated combinations of tags programatically after the full graph is downloaded and constructed.
default_crs : string: Default coordinate reference system to set when creating graphs. Default is “epsg:4326”.
default_referer : string: HTTP header referer. Default is “OSMnx Python package (https://github.com/gboeing/osmnx)”.
default_user_agent : string: HTTP header user-agent. Default is “OSMnx Python package (https://github.com/gboeing/osmnx)”.
imgs_folder : string or pathlib.Path: Path to folder in which to save plotted images by default. Default is “./images”.
log_file : bool: If True, save log output to a file in logs_folder. Default is False.
log_filename : string: Name of the log file, without file extension. Default is “osmnx”.
log_console : bool: If True, print log output to the console (terminal window). Default is False.
log_level : int: One of Python’s logger.level constants. Default is logging.INFO.
log_name : string: Name of the logger. Default is “OSMnx”.
logs_folder : string or pathlib.Path: Path to folder in which to save log files. Default is “./logs”.
max_query_area_size : int: Maximum area for any part of the geometry in meters: any polygon bigger than this will get divided up for multiple queries to the API. Default is 2500000000.
memory : int: Overpass server memory allocation size for the query, in bytes. If None, server will use its default allocation size. Use with caution. Default is None.
nominatim_endpoint : string: The base API url to use for Nominatim queries. Default is “https://nominatim.openstreetmap.org/”.
nominatim_key : string: Your Nominatim API key, if you are using an API instance that requires one. Default is None.
osm_xml_node_attrs : list: Node attributes for saving .osm XML files with save_graph_xml function. Default is [“id”, “timestamp”, “uid”, “user”, “version”, “changeset”, “lat”, “lon”].
osm_xml_node_tags : list: Node tags for saving .osm XML files with save_graph_xml function. Default is [“highway”].
osm_xml_way_attrs : list: Edge attributes for saving .osm XML files with save_graph_xml function. Default is [“id”, “timestamp”, “uid”, “user”, “version”, “changeset”].
osm_xml_way_tags : list: Edge tags for for saving .osm XML files with save_graph_xml function. Default is [“highway”, “lanes”, “maxspeed”, “name”, “oneway”].
overpass_endpoint : string: The base API url to use for overpass queries. Default is “https://overpass-api.de/api”.
overpass_rate_limit : bool: If True, check the Overpass server status endpoint for how long to pause before making request. Necessary if server uses slot management, but can be set to False if you are running your own overpass instance without rate limiting. Default is True.
overpass_settings : string: Settings string for Overpass queries. Default is “[out:json][timeout:{timeout}]{maxsize}”. By default, the {timeout} and {maxsize} values are set dynamically by OSMnx when used. To query, for example, historical OSM data as of a certain date: ‘[out:json][timeout:90][date:”2019-10-28T19:20:00Z”]’. Use with caution.
requests_kwargs : dict: Optional keyword args to pass to the requests package when connecting to APIs, for example to configure authentication or provide a path to a local certificate file. More info on options such as auth, cert, verify, and proxies can be found in the requests package advanced docs. Default is {}.
timeout : int: The timeout interval in seconds for the HTTP request and for API to use while running the query. Default is 180.
use_cache : bool: If True, cache HTTP responses locally instead of calling API repeatedly for the same request. Default is True.
useful_tags_node : list: OSM “node” tags to add as graph node attributes, when present in the data retrieved from OSM. Default is [“ref”, “highway”].
useful_tags_way : list: OSM “way” tags to add as graph edge attributes, when present in the data retrieved from OSM. Default is [“bridge”, “tunnel”, “oneway”, “lanes”, “ref”, “name”, “highway”, “maxspeed”, “service”, “access”, “area”, “landuse”, “width”, “est_width”, “junction”].

osmnx.simplification module¶

Simplify, correct, and consolidate network topology.

osmnx.simplification.consolidate_intersections(G, tolerance=10, rebuild_graph=True, dead_ends=False, reconnect_edges=True)¶

Consolidate intersections comprising clusters of nearby nodes.

Merges nearby nodes and returns either their centroids or a rebuilt graph with consolidated intersections and reconnected edge geometries. The tolerance argument should be adjusted to approximately match street design standards in the specific street network, and you should always use a projected graph to work in meaningful and consistent units like meters. Note the tolerance represents a per-node buffering radius: for example, to consolidate nodes within 10 meters of each other, use tolerance=5.

When rebuild_graph=False, it uses a purely geometrical (and relatively fast) algorithm to identify “geometrically close” nodes, merge them, and return just the merged intersections’ centroids. When rebuild_graph=True, it uses a topological (and slower but more accurate) algorithm to identify “topologically close” nodes, merge them, then rebuild/return the graph. Returned graph’s node IDs represent clusters rather than osmids. Refer to nodes’ osmid_original attributes for original osmids. If multiple nodes were merged together, the osmid_original attribute is a list of merged nodes’ osmids.

Divided roads are often represented by separate centerline edges. The intersection of two divided roads thus creates 4 nodes, representing where each edge intersects a perpendicular edge. These 4 nodes represent a single intersection in the real world. A similar situation occurs with roundabouts and traffic circles. This function consolidates nearby nodes by buffering them to an arbitrary distance, merging overlapping buffers, and taking their centroid.

Parameters:	G (networkx.MultiDiGraph) – a projected graph tolerance (float) – nodes are buffered to this distance (in graph’s geometry’s units) and subsequent overlaps are dissolved into a single node rebuild_graph (bool) – if True, consolidate the nodes topologically, rebuild the graph, and return as networkx.MultiDiGraph. if False, consolidate the nodes geometrically and return the consolidated node points as geopandas.GeoSeries dead_ends (bool) – if False, discard dead-end nodes to return only street-intersection points reconnect_edges (bool) – ignored if rebuild_graph is not True. if True, reconnect edges and their geometries in rebuilt graph to the consolidated nodes and update edge length attributes; if False, returned graph has no edges (which is faster if you just need topologically consolidated intersection counts).
Returns:	if rebuild_graph=True, returns MultiDiGraph with consolidated intersections and reconnected edge geometries. if rebuild_graph=False, returns GeoSeries of shapely Points representing the centroids of street intersections
Return type:	networkx.MultiDiGraph or geopandas.GeoSeries

osmnx.simplification.simplify_graph(G, strict=True, remove_rings=True, track_merged=False)¶

Simplify a graph’s topology by removing interstitial nodes.

Simplifies graph topology by removing all nodes that are not intersections or dead-ends. Create an edge directly between the end points that encapsulate them, but retain the geometry of the original edges, saved as a new geometry attribute on the new edge. Note that only simplified edges receive a geometry attribute. Some of the resulting consolidated edges may comprise multiple OSM ways, and if so, their multiple attribute values are stored as a list. Optionally, the simplified edges can receive a merged_edges attribute that contains a list of all the (u, v) node pairs that were merged together.

Parameters:	G (networkx.MultiDiGraph) – input graph strict (bool) – if False, allow nodes to be end points even if they fail all other rules but have incident edges with different OSM IDs. Lets you keep nodes at elbow two-way intersections, but sometimes individual blocks have multiple OSM IDs within them too. remove_rings (bool) – if True, remove isolated self-contained rings that have no endpoints track_merged (bool) – if True, add merged_edges attribute on simplified edges, containing a list of all the (u, v) node pairs that were merged together
Returns:	G – topologically simplified graph, with a new geometry attribute on each simplified edge
Return type:	networkx.MultiDiGraph

osmnx.speed module¶

Calculate graph edge speeds and travel times.

osmnx.speed.add_edge_speeds(G, hwy_speeds=None, fallback=None, precision=1, agg=<sphinx.ext.autodoc.importer._MockObject object>)¶

Add edge speeds (km per hour) to graph as new speed_kph edge attributes.

By default, this imputes free-flow travel speeds for all edges via the mean maxspeed value of the edges of each highway type. For highway types in the graph that have no maxspeed value on any edge, it assigns the mean of all maxspeed values in graph.

This default mean-imputation can obviously be imprecise, and the user can override it by passing in hwy_speeds and/or fallback arguments that correspond to local speed limit standards. The user can also specify a different aggregation function (such as the median) to impute missing values from the observed values.

If edge maxspeed attribute has “mph” in it, value will automatically be converted from miles per hour to km per hour. Any other speed units should be manually converted to km per hour prior to running this function, otherwise there could be unexpected results. If “mph” does not appear in the edge’s maxspeed attribute string, then function assumes kph, per OSM guidelines: https://wiki.openstreetmap.org/wiki/Map_Features/Units

Parameters:	G (networkx.MultiDiGraph) – input graph hwy_speeds (dict) – dict keys = OSM highway types and values = typical speeds (km per hour) to assign to edges of that highway type for any edges missing speed data. Any edges with highway type not in hwy_speeds will be assigned the mean preexisting speed value of all edges of that highway type. fallback (numeric) – default speed value (km per hour) to assign to edges whose highway type did not appear in hwy_speeds and had no preexisting speed values on any edge precision (int) – decimal precision to round speed_kph agg (function) – aggregation function to impute missing values from observed values. the default is numpy.mean, but you might also consider for example numpy.median, numpy.nanmedian, or your own custom function
Returns:	G – graph with speed_kph attributes on all edges
Return type:	networkx.MultiDiGraph

osmnx.speed.add_edge_travel_times(G, precision=1)¶

Add edge travel time (seconds) to graph as new travel_time edge attributes.

Calculates free-flow travel time along each edge, based on length and speed_kph attributes. Note: run add_edge_speeds first to generate the speed_kph attribute. All edges must have length and speed_kph attributes and all their values must be non-null.

Parameters:	G (networkx.MultiDiGraph) – input graph precision (int) – decimal precision to round travel_time
Returns:	G – graph with travel_time attributes on all edges
Return type:	networkx.MultiDiGraph

osmnx.stats module¶

Calculate geometric and topological network measures.

This module defines streets as the edges in an undirected representation of the graph. Using undirected graph edges prevents double-counting bidirectional edges of a two-way street, but may double-count a divided road’s separate centerlines with different end point nodes. If clean_periphery=True when the graph was created (which is the default parameterization), then you will get accurate node degrees (and in turn streets-per-node counts) even at the periphery of the graph.

You can use NetworkX directly for additional topological network measures.

osmnx.stats.basic_stats(G, area=None, clean_int_tol=None)¶

Calculate basic descriptive geometric and topological measures of a graph.

Density measures are only calculated if area is provided and clean intersection measures are only calculated if clean_int_tol is provided.

Parameters:

G (networkx.MultiDiGraph) – input graph
area (float) – if not None, calculate density measures and use this area value (in square meters) as the denominator
clean_int_tol (float) – if not None, calculate consolidated intersections count (and density, if area is also provided) and use this tolerance value; refer to the simplification.consolidate_intersections function documentation for details

Returns:

stats –

dictionary containing the following attributes

circuity_avg - see circuity_avg function documentation
clean_intersection_count - see clean_intersection_count function documentation
clean_intersection_density_km - clean_intersection_count per sq km
edge_density_km - edge_length_total per sq km
edge_length_avg - edge_length_total / m
edge_length_total - see edge_length_total function documentation
intersection_count - see intersection_count function documentation
intersection_density_km - intersection_count per sq km
k_avg - graph’s average node degree (in-degree and out-degree)
m - count of edges in graph
n - count of nodes in graph
node_density_km - n per sq km
self_loop_proportion - see self_loop_proportion function documentation
street_density_km - street_length_total per sq km
street_length_avg - street_length_total / street_segment_count
street_length_total - see street_length_total function documentation
street_segment_count - see street_segment_count function documentation
streets_per_node_avg - see streets_per_node_avg function documentation
streets_per_node_counts - see streets_per_node_counts function documentation
streets_per_node_proportions - see streets_per_node_proportions function documentation

Return type:

dict

osmnx.stats.circuity_avg(Gu)¶

Calculate average street circuity using edges of undirected graph.

Circuity is the sum of edge lengths divided by the sum of straight-line distances between edge endpoints. Calculates straight-line distance as euclidean distance if projected or great-circle distance if unprojected.

Parameters:	Gu (networkx.MultiGraph) – undirected input graph
Returns:	circuity_avg – the graph’s average undirected edge circuity
Return type:	float

osmnx.stats.count_streets_per_node(G, nodes=None)¶

Count how many physical street segments connect to each node in a graph.

This function uses an undirected representation of the graph and special handling of self-loops to accurately count physical streets rather than directed edges. Note: this function is automatically run by all the graph.graph_from_x functions prior to truncating the graph to the requested boundaries, to add accurate street_count attributes to each node even if some of its neighbors are outside the requested graph boundaries.

Parameters:	G (networkx.MultiDiGraph) – input graph nodes (list) – which node IDs to get counts for. if None, use all graph nodes, otherwise calculate counts only for these node IDs
Returns:	streets_per_node – counts of how many physical streets connect to each node, with keys = node ids and values = counts
Return type:	dict

osmnx.stats.edge_length_total(G)¶

Calculate graph’s total edge length.

Parameters:	G (networkx.MultiDiGraph) – input graph
Returns:	length – total length (meters) of edges in graph
Return type:	float

osmnx.stats.intersection_count(G=None, min_streets=2)¶

Count the intersections in a graph.

Intersections are defined as nodes with at least min_streets number of streets incident on them.

Parameters:	G (networkx.MultiDiGraph) – input graph min_streets (int) – a node must have at least min_streets incident on them to count as an intersection
Returns:	count – count of intersections in graph
Return type:	int

osmnx.stats.self_loop_proportion(Gu)¶

Calculate percent of edges that are self-loops in a graph.

A self-loop is defined as an edge from node u to node v where u==v.

Parameters:	Gu (networkx.MultiGraph) – undirected input graph
Returns:	proportion – proportion of graph edges that are self-loops
Return type:	float

osmnx.stats.street_length_total(Gu)¶

Calculate graph’s total street segment length.

Parameters:	Gu (networkx.MultiGraph) – undirected input graph
Returns:	length – total length (meters) of streets in graph
Return type:	float

osmnx.stats.street_segment_count(Gu)¶

Count the street segments in a graph.

Parameters:	Gu (networkx.MultiGraph) – undirected input graph
Returns:	count – count of street segments in graph
Return type:	int

osmnx.stats.streets_per_node(G)¶

Count streets (undirected edges) incident on each node.

Parameters:	G (networkx.MultiDiGraph) – input graph
Returns:	spn – dictionary with node ID keys and street count values
Return type:	dict

osmnx.stats.streets_per_node_avg(G)¶

Calculate graph’s average count of streets per node.

Parameters:	G (networkx.MultiDiGraph) – input graph
Returns:	spna – average count of streets per node
Return type:	float

osmnx.stats.streets_per_node_counts(G)¶

Calculate streets-per-node counts.

Parameters:	G (networkx.MultiDiGraph) – input graph
Returns:	spnc – dictionary keyed by count of streets incident on each node, and with values of how many nodes in the graph have this count
Return type:	dict

osmnx.stats.streets_per_node_proportions(G)¶

Calculate streets-per-node proportions.

Parameters:	G (networkx.MultiDiGraph) – input graph
Returns:	spnp – dictionary keyed by count of streets incident on each node, and with values of what proportion of nodes in the graph have this count
Return type:	dict

osmnx.truncate module¶

Truncate graph by distance, bounding box, or polygon.

osmnx.truncate.truncate_graph_bbox(G, north, south, east, west, truncate_by_edge=False, retain_all=False, quadrat_width=0.05, min_num=3)¶

Remove every node in graph that falls outside a bounding box.

Parameters:	G (networkx.MultiDiGraph) – input graph north (float) – northern latitude of bounding box south (float) – southern latitude of bounding box east (float) – eastern longitude of bounding box west (float) – western longitude of bounding box truncate_by_edge (bool) – if True, retain nodes outside bounding box if at least one of node’s neighbors is within the bounding box retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component. quadrat_width (numeric) – passed on to intersect_index_quadrats: the linear length (in degrees) of the quadrats with which to cut up the geometry (default = 0.05, approx 4km at NYC’s latitude) min_num (int) – passed on to intersect_index_quadrats: the minimum number of linear quadrat lines (e.g., min_num=3 would produce a quadrat grid of 4 squares)
Returns:	G – the truncated graph
Return type:	networkx.MultiDiGraph

osmnx.truncate.truncate_graph_dist(G, source_node, max_dist=1000, weight='length', retain_all=False)¶

Remove every node farther than some network distance from source_node.

This function can be slow for large graphs, as it must calculate shortest path distances between source_node and every other graph node.

Parameters:	G (networkx.MultiDiGraph) – input graph source_node (int) – the node in the graph from which to measure network distances to other nodes max_dist (int) – remove every node in the graph greater than this distance from the source_node (along the network) weight (string) – how to weight the graph when measuring distance (default ‘length’ is how many meters long the edge is) retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
Returns:	G – the truncated graph
Return type:	networkx.MultiDiGraph

osmnx.truncate.truncate_graph_polygon(G, polygon, retain_all=False, truncate_by_edge=False, quadrat_width=0.05, min_num=3)¶

Remove every node in graph that falls outside a (Multi)Polygon.

Parameters:	G (networkx.MultiDiGraph) – input graph polygon (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – only retain nodes in graph that lie within this geometry retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component. truncate_by_edge (bool) – if True, retain nodes outside boundary polygon if at least one of node’s neighbors is within the polygon quadrat_width (numeric) – passed on to intersect_index_quadrats: the linear length (in degrees) of the quadrats with which to cut up the geometry (default = 0.05, approx 4km at NYC’s latitude) min_num (int) – passed on to intersect_index_quadrats: the minimum number of linear quadrat lines (e.g., min_num=3 would produce a quadrat grid of 4 squares)
Returns:	G – the truncated graph
Return type:	networkx.MultiDiGraph

osmnx.utils module¶

General utility functions.

osmnx.utils.citation()¶

Print the OSMnx package’s citation information.

Boeing, G. 2017. OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks. Computers, Environment and Urban Systems, 65, 126-139. https://doi.org/10.1016/j.compenvurbsys.2017.05.004

Returns:
Return type:	None

osmnx.utils.config(all_oneway=False, bidirectional_network_types=['walk'], cache_folder='./cache', cache_only_mode=False, data_folder='./data', default_accept_language='en', default_access='["access"!~"private"]', default_crs='epsg:4326', default_referer='OSMnx Python package (https://github.com/gboeing/osmnx)', default_user_agent='OSMnx Python package (https://github.com/gboeing/osmnx)', imgs_folder='./images', log_console=False, log_file=False, log_filename='osmnx', log_level=20, log_name='OSMnx', logs_folder='./logs', max_query_area_size=2500000000, memory=None, nominatim_endpoint='https://nominatim.openstreetmap.org/', nominatim_key=None, osm_xml_node_attrs=['id', 'timestamp', 'uid', 'user', 'version', 'changeset', 'lat', 'lon'], osm_xml_node_tags=['highway'], osm_xml_way_attrs=['id', 'timestamp', 'uid', 'user', 'version', 'changeset'], osm_xml_way_tags=['highway', 'lanes', 'maxspeed', 'name', 'oneway'], overpass_endpoint='https://overpass-api.de/api', overpass_rate_limit=True, overpass_settings='[out:json][timeout:{timeout}]{maxsize}', requests_kwargs={}, timeout=180, use_cache=True, useful_tags_node=['ref', 'highway'], useful_tags_way=['bridge', 'tunnel', 'oneway', 'lanes', 'ref', 'name', 'highway', 'maxspeed', 'service', 'access', 'area', 'landuse', 'width', 'est_width', 'junction'])¶

Do not use: deprecated. Use the settings module directly.

Parameters:	all_oneway (bool) – deprecated bidirectional_network_types (list) – deprecated cache_folder (string or pathlib.Path) – deprecated data_folder (string or pathlib.Path) – deprecated cache_only_mode (bool) – deprecated default_accept_language (string) – deprecated default_access (string) – deprecated default_crs (string) – deprecated default_referer (string) – deprecated default_user_agent (string) – deprecated imgs_folder (string or pathlib.Path) – deprecated log_file (bool) – deprecated log_filename (string) – deprecated log_console (bool) – deprecated log_level (int) – deprecated log_name (string) – deprecated logs_folder (string or pathlib.Path) – deprecated max_query_area_size (int) – deprecated memory (int) – deprecated nominatim_endpoint (string) – deprecated nominatim_key (string) – deprecated osm_xml_node_attrs (list) – deprecated osm_xml_node_tags (list) – deprecated osm_xml_way_attrs (list) – deprecated osm_xml_way_tags (list) – deprecated overpass_endpoint (string) – deprecated overpass_rate_limit (bool) – deprecated overpass_settings (string) – deprecated requests_kwargs (dict) – deprecated timeout (int) – deprecated use_cache (bool) – deprecated useful_tags_node (list) – deprecated useful_tags_way (list) – deprecated
Returns:
Return type:	None

osmnx.utils.log(message, level=None, name=None, filename=None)¶

Write a message to the logger.

This logs to file and/or prints to the console (terminal), depending on the current configuration of settings.log_file and settings.log_console.

Parameters:	message (string) – the message to log level (int) – one of Python’s logger.level constants name (string) – name of the logger filename (string) – name of the log file, without file extension
Returns:
Return type:	None

osmnx.utils.ts(style='datetime', template=None)¶

Get current timestamp as string.

Parameters:	style (string {"datetime", "date", "time"}) – format the timestamp with this built-in template template (string) – if not None, format the timestamp with this template instead of one of the built-in styles
Returns:	ts – the string timestamp
Return type:	string

osmnx.utils_geo module¶

Geospatial utility functions.

osmnx.utils_geo.bbox_from_point(point, dist=1000, project_utm=False, return_crs=False)¶

Create a bounding box from a (lat, lng) center point.

Create a bounding box some distance in each direction (north, south, east, and west) from the center point and optionally project it.

Parameters:	point (tuple) – the (lat, lng) center point to create the bounding box around dist (int) – bounding box distance in meters from the center point project_utm (bool) – if True, return bounding box as UTM-projected coordinates return_crs (bool) – if True, and project_utm=True, return the projected CRS too
Returns:	(north, south, east, west) or (north, south, east, west, crs_proj)
Return type:	tuple

osmnx.utils_geo.bbox_to_poly(north, south, east, west)¶

Convert bounding box coordinates to shapely Polygon.

Parameters:	north (float) – northern coordinate south (float) – southern coordinate east (float) – eastern coordinate west (float) – western coordinate
Returns:
Return type:	shapely.geometry.Polygon

osmnx.utils_geo.interpolate_points(geom, dist)¶

Interpolate evenly spaced points along a LineString.

The spacing is approximate because the LineString’s length may not be evenly divisible by it.

Parameters:	geom (shapely.geometry.LineString) – a LineString geometry dist (float) – spacing distance between interpolated points, in same units as geom. smaller values generate more points.
Yields:	points (generator) – a generator of (x, y) tuples of the interpolated points’ coordinates

osmnx.utils_geo.round_geometry_coords(geom, precision)¶

Round the coordinates of a shapely geometry to some decimal precision.

Parameters:	geom (shapely.geometry.geometry {Point, MultiPoint, LineString, MultiLineString, Polygon, MultiPolygon}) – the geometry to round the coordinates of precision (int) – decimal precision to round coordinates to
Returns:
Return type:	shapely.geometry.geometry

osmnx.utils_geo.sample_points(G, n)¶

Randomly sample points constrained to a spatial graph.

This generates a graph-constrained uniform random sample of points. Unlike typical spatially uniform random sampling, this method accounts for the graph’s geometry. And unlike equal-length edge segmenting, this method guarantees uniform randomness.

Parameters:	G (networkx.MultiGraph) – graph to sample points from; should be undirected (to not oversample bidirectional edges) and projected (for accurate point interpolation) n (int) – how many points to sample
Returns:	points – the sampled points, multi-indexed by (u, v, key) of the edge from which each point was drawn
Return type:	geopandas.GeoSeries

osmnx.utils_graph module¶

Graph utility functions.

osmnx.utils_graph.get_digraph(G, weight='length')¶

Convert MultiDiGraph to DiGraph.

Chooses between parallel edges by minimizing weight attribute value. Note: see also get_undirected to convert MultiDiGraph to MultiGraph.

Parameters:	G (networkx.MultiDiGraph) – input graph weight (string) – attribute value to minimize when choosing between parallel edges
Returns:
Return type:	networkx.DiGraph

osmnx.utils_graph.get_largest_component(G, strongly=False)¶

Get subgraph of G’s largest weakly/strongly connected component.

Parameters:	G (networkx.MultiDiGraph) – input graph strongly (bool) – if True, return the largest strongly instead of weakly connected component
Returns:	G – the largest connected component subgraph of the original graph
Return type:	networkx.MultiDiGraph

osmnx.utils_graph.get_route_edge_attributes(G, route, attribute=None, minimize_key='length', retrieve_default=None)¶

Get a list of attribute values for each edge in a path.

Parameters:	G (networkx.MultiDiGraph) – input graph route (list) – list of nodes IDs constituting the path attribute (string) – the name of the attribute to get the value of for each edge. If None, the complete data dict is returned for each edge. minimize_key (string) – if there are parallel edges between two nodes, select the one with the lowest value of minimize_key retrieve_default (Callable[Tuple[Any, Any], Any]) – function called with the edge nodes as parameters to retrieve a default value, if the edge does not contain the given attribute (otherwise a KeyError is raised)
Returns:	attribute_values – list of edge attribute values
Return type:	list

osmnx.utils_graph.get_undirected(G)¶

Convert MultiDiGraph to undirected MultiGraph.

Maintains parallel edges only if their geometries differ. Note: see also get_digraph to convert MultiDiGraph to DiGraph.

Parameters:	G (networkx.MultiDiGraph) – input graph
Returns:
Return type:	networkx.MultiGraph

osmnx.utils_graph.graph_from_gdfs(gdf_nodes, gdf_edges, graph_attrs=None)¶

Convert node and edge GeoDataFrames to a MultiDiGraph.

This function is the inverse of graph_to_gdfs and is designed to work in conjunction with it.

However, you can convert arbitrary node and edge GeoDataFrames as long as 1) gdf_nodes is uniquely indexed by osmid, 2) gdf_nodes contains x and y coordinate columns representing node geometries, 3) gdf_edges is uniquely multi-indexed by u, v, key (following normal MultiDiGraph structure). This allows you to load any node/edge shapefiles or GeoPackage layers as GeoDataFrames then convert them to a MultiDiGraph for graph analysis. Note that any geometry attribute on gdf_nodes is discarded since x and y provide the necessary node geometry information instead.

Parameters:	gdf_nodes (geopandas.GeoDataFrame) – GeoDataFrame of graph nodes uniquely indexed by osmid gdf_edges (geopandas.GeoDataFrame) – GeoDataFrame of graph edges uniquely multi-indexed by u, v, key graph_attrs (dict) – the new G.graph attribute dict. if None, use crs from gdf_edges as the only graph-level attribute (gdf_edges must have crs attribute set)
Returns:	G
Return type:	networkx.MultiDiGraph

osmnx.utils_graph.graph_to_gdfs(G, nodes=True, edges=True, node_geometry=True, fill_edge_geometry=True)¶

Convert a MultiDiGraph to node and/or edge GeoDataFrames.

This function is the inverse of graph_from_gdfs.

Parameters:	G (networkx.MultiDiGraph) – input graph nodes (bool) – if True, convert graph nodes to a GeoDataFrame and return it edges (bool) – if True, convert graph edges to a GeoDataFrame and return it node_geometry (bool) – if True, create a geometry column from node x and y attributes fill_edge_geometry (bool) – if True, fill in missing edge geometry fields using nodes u and v
Returns:	gdf_nodes or gdf_edges or tuple of (gdf_nodes, gdf_edges). gdf_nodes is indexed by osmid and gdf_edges is multi-indexed by u, v, key following normal MultiDiGraph structure.
Return type:	geopandas.GeoDataFrame or tuple

osmnx.utils_graph.remove_isolated_nodes(G)¶

Remove from a graph all nodes that have no incident edges.

Parameters:	G (networkx.MultiDiGraph) – graph from which to remove isolated nodes
Returns:	G – graph with all isolated nodes removed
Return type:	networkx.MultiDiGraph