# osmnx package¶

This is the complete OSMnx internals reference, including private internal functions. If you are looking instead for the users’ reference guide to OSMnx’s public-facing API, you can find it here.

## osmnx.bearing module¶

Calculate graph edge bearings.

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

Add bearing attributes to all graph edges.

Calculate the compass bearing from origin node to destination node for each edge in the directed graph then add each bearing as a new edge attribute. Bearing represents angle in degrees (clockwise) between north and the direction from the origin node to the destination node.

Parameters: G (networkx.MultiDiGraph) – input graph precision (int) – decimal precision to round bearing G – graph with edge bearing attributes networkx.MultiDiGraph
`osmnx.bearing.``get_bearing`(origin_point, destination_point)

Calculate the bearing between two lat-lng points.

Each argument tuple should represent (lat, lng) as decimal degrees. Bearing represents angle in degrees (clockwise) between north and the direction from the origin point to the destination point.

Parameters: origin_point (tuple) – (lat, lng) destination_point (tuple) – (lat, lng) bearing – the compass bearing in decimal degrees from the origin point to the destination point float

## osmnx.distance module¶

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

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

Calculate Euclidean distances between points.

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

Parameters: y1 (float or np.array of float) – first point’s y coordinate x1 (float or np.array of float) – first point’s x coordinate y2 (float or np.array of float) – second point’s y coordinate x2 (float or np.array of float) – second point’s x coordinate dist – distance or array of distances from (x1, y1) to (x2, y2) in coordinates’ units float or np.array of float
`osmnx.distance.``get_nearest_edge`(G, point, return_geom=False, return_dist=False)

Find the nearest edge to a point by minimum Euclidean distance.

Parameters: G (networkx.MultiDiGraph) – input graph point (tuple) – the (lat, lng) or (y, x) point for which we will find the nearest edge in the graph return_geom (bool) – Optionally return the geometry of the nearest edge return_dist (bool) – Optionally return the distance in graph’s coordinates’ units between the point and the nearest edge Graph edge unique identifier as a tuple of (u, v, key). Or a tuple of (u, v, key, geom) if return_geom is True. Or a tuple of (u, v, key, dist) if return_dist is True. Or a tuple of (u, v, key, geom, dist) if return_geom and return_dist are True. tuple
`osmnx.distance.``get_nearest_edges`(G, X, Y, method=None, dist=0.0001)

Find the nearest edge to each point in a list of points.

Pass in points as separate lists of X and Y coordinates. The ‘kdtree’ method is by far the fastest with large data sets, but only finds approximate nearest edges if working in unprojected coordinates like lat-lng (it precisely finds the nearest edge if working in projected coordinates). The ‘balltree’ method is second fastest with large data sets, but it is precise if working in unprojected coordinates like lat-lng. As a rule of thumb, if you have a small graph just use method=None. If you have a large graph with lat-lng coordinates, use method=’balltree’. If you have a large graph with projected coordinates, use method=’kdtree’. Note that if you are working in units of lat-lng, the X vector corresponds to longitude and the Y vector corresponds to latitude. The method creates equally distanced points along the edges of the network. Then, these points are used in a kdTree or BallTree search to identify which is nearest. Note that this method will not give exact perpendicular point along the edge, but the smaller the dist parameter, the closer (but slower) the solution will be.

Parameters: G (networkx.MultiDiGraph) – input graph X (list-like) – the longitudes or x coordinates for which we will find the nearest edge in the graph. For projected graphs use the projected coordinates, usually in meters. Y (list-like) – the latitudes or y coordinates for which we will find the nearest edge in the graph. For projected graphs use the projected coordinates, usually in meters. method (string {None, 'kdtree', 'balltree'}) – Which method to use for finding nearest edge to each point. If None, we manually find each edge one at a time using get_nearest_edge. If ‘kdtree’ we use scipy.spatial.cKDTree for very fast euclidean search. Recommended for projected graphs. If ‘balltree’, we use sklearn.neighbors.BallTree for fast haversine search. Recommended for unprojected graphs. dist (float) – spacing length along edges. Units are the same as the graph’s geometries. The smaller the value, the more points are created. ne – array of edge IDs representing the edge nearest to each point in the passed-in list of points. Edge IDs are represented by u, v, key where u and v the node IDs of the nodes the edge links. np.array
`osmnx.distance.``get_nearest_node`(G, point, method='haversine', return_dist=False)

Find the nearest node to a point.

Return the graph node nearest to some (lat, lng) or (y, x) point and optionally the distance between the node and the point. This function can use either the haversine formula or Euclidean distance.

Parameters: G (networkx.MultiDiGraph) – input graph point (tuple) – The (lat, lng) or (y, x) point for which we will find the nearest node in the graph method (string {'haversine', 'euclidean'}) – Which method to use for calculating distances to find nearest node. If ‘haversine’, graph nodes’ coordinates must be in units of decimal degrees. If ‘euclidean’, graph nodes’ coordinates must be projected. return_dist (bool) – Optionally also return the distance (in meters if haversine, or graph node coordinate units if euclidean) between the point and the nearest node Nearest node ID or optionally a tuple of (node ID, dist), where dist is the distance (in meters if haversine, or graph node coordinate units if euclidean) between the point and nearest node int or tuple of (int, float)
`osmnx.distance.``get_nearest_nodes`(G, X, Y, method=None)

Find the nearest node to each point in a list of points.

Pass in points as separate lists of X and Y coordinates. The ‘kdtree’ method is by far the fastest with large data sets, but only finds approximate nearest nodes if working in unprojected coordinates like lat-lng (it precisely finds the nearest node if working in projected coordinates). The ‘balltree’ method is second fastest with large data sets but it is precise if working in unprojected coordinates like lat-lng.

Parameters: G (networkx.MultiDiGraph) – input graph X (list-like) – the longitudes or x coordinates for which we will find the nearest node in the graph Y (list-like) – the latitudes or y coordinates for which we will find the nearest node in the graph method (string {None, 'kdtree', 'balltree'}) – Which method to use for finding the nearest node to each point. If None, we manually find each node one at a time using utils.get_nearest_node and haversine. If ‘kdtree’ we use scipy.spatial.cKDTree for very fast euclidean search. If ‘balltree’, we use sklearn.neighbors.BallTree for fast haversine search. nn – array of node IDs representing the node nearest to each point in the passed-in list of points np.array
`osmnx.distance.``great_circle_vec`(lat1, lng1, lat2, lng2, earth_radius=6371009)

Calculate great-circle distances between 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 np.array of float) – first point’s latitude coordinate lng1 (float or np.array of float) – first point’s longitude coordinate lat2 (float or np.array of float) – second point’s latitude coordinate lng2 (float or np.array of float) – second point’s longitude coordinate earth_radius (int or float) – radius of earth in units in which distance will be returned (default is meters) dist – distance or array of distances from (lat1, lng1) to (lat2, lng2) in units of earth_radius float or np.array
`osmnx.distance.``k_shortest_paths`(G, orig, dest, k, weight='length')

Get k shortest paths from origin node to destination node.

Parameters: G (networkx.MultiDiGraph) – input graph orig (int) – origin node ID dest (int) – destination node ID k (int) – number of shortest paths to get weight (string) – edge attribute to minimize when solving shortest paths. default is edge length in meters. a generator of k shortest paths ordered by total weight. each path is a list of node IDs. generator
`osmnx.distance.``shortest_path`(G, orig, dest, weight='length')

Get shortest path from origin node to destination node.

This function is a convenience wrapper around networkx.shortest_path. For more functionality or different algorithms, use networkx directly.

Parameters: G (networkx.MultiDiGraph) – input graph orig (int) – origin node ID dest (int) – destination node ID weight (string) – edge attribute to minimize when solving shortest path. default is edge length in meters. path – list of node IDs consituting the shortest path list

Interact with the OSM APIs.

class `osmnx.downloader.``_OSMContentHandler`

SAX content handler for OSM XML.

Used to build an Overpass-like response JSON object in self.object. For format notes, see http://wiki.openstreetmap.org/wiki/OSM_XML#OSM_XML_file_format_notes and http://overpass-api.de/output_formats.html#json

`endElement`(name)

Signals the end of an element in non-namespace mode.

The name parameter contains the name of the element type, just as with the startElement event.

`startElement`(name, attrs)

Signals the start of an element in non-namespace mode.

The name parameter contains the raw XML 1.0 name of the element type as a string and the attrs parameter holds an instance of the Attributes class containing the attributes of the element.

`osmnx.downloader.``_create_overpass_query`(polygon_coord_str, tags)

Create an overpass query string based on passed tags.

Parameters: polygon_coord_str (list) – list of lat lng coordinates tags (dict) – dict of tags used for finding geometry in the selected area query string
`osmnx.downloader.``_get_http_headers`(user_agent=None, referer=None, accept_language=None)

Update the default requests HTTP headers with OSMnx info.

Parameters: user_agent (string) – the user agent string, if None will set with OSMnx default referer (string) – the referer string, if None will set with OSMnx default accept_language (string) – make accept-language explicit e.g. for consistent nominatim result sorting headers dict
`osmnx.downloader.``_get_osm_filter`(network_type)

Create a filter to query OSM for the specified network type.

Parameters: network_type (string) – {‘walk’, ‘bike’, ‘drive’, ‘drive_service’, ‘all’, ‘all_private’} what type of street or other network to get string
`osmnx.downloader.``_get_pause`(recursive_delay=5, default_duration=60)

Get a pause duration from the Overpass API status endpoint.

Check the Overpass API status endpoint to determine how long to wait until next slot is available.

Parameters: recursive_delay (int) – how long to wait between recursive calls if the server is currently running a query default_duration (int) – if fatal error, fall back on returning this value pause int
`osmnx.downloader.``_make_overpass_polygon_coord_strs`(polygon)

Subdivide query polygon and return list of coordinate strings.

Project to utm, divide polygon up into sub-polygons if area exceeds a max size (in meters), project back to lat-lng, then get a list of polygon(s) exterior coordinates

Parameters: polygon (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – geographic boundaries to fetch the OSM geometries within polygon_coord_strs – list of exterior coordinate strings for smaller sub-divided polygons list
`osmnx.downloader.``_make_overpass_settings`()

Make settings string to send in Overpass query.

Returns: string
`osmnx.downloader.``_osm_geometry_download`(polygon, tags)

Note that if a polygon is passed-in, the query will be limited to the exterior ring only.

Parameters: polygon (shapely.geometry.Polygon) – geographic boundaries to fetch geometry within tags (dict) – dict of tags used for finding geometry in the selected area response_jsons – list of JSON responses from the Overpass server list
`osmnx.downloader.``_osm_net_download`(polygon, network_type, custom_filter)

Download OSM ways and nodes within some polygon from the Overpass API.

Parameters: polygon (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – geographic boundaries to fetch the street network within network_type (string) – what type of street network to get if custom_filter is not None custom_filter (string) – a custom network filter to be used instead of the network_type presets response_jsons list
`osmnx.downloader.``_osm_polygon_download`(query, limit=1, polygon_geojson=1)

Parameters: query (string or dict) – query string or structured query dict to geocode/download limit (int) – max number of results to return polygon_geojson (int) – request the boundary geometry polygon from the API, 0=no, 1=yes response_json dict
`osmnx.downloader.``_overpass_json_from_file`(filepath)

Read OSM XML from file and return Overpass-like JSON.

Parameters: filepath (string) – path to file containing OSM XML data OSMContentHandler object
`osmnx.downloader.``_retrieve_from_cache`(url, check_remark=False)

Retrieve a HTTP response JSON object from the cache, if it exists.

Parameters: url (string) – the URL of the request check_remark (string) – if True, only return filepath if cached response does not have a remark key indicating a server warning response_json – cached response for url if it exists in the cache, otherwise None dict
`osmnx.downloader.``_save_to_cache`(url, response_json, sc)

Save a HTTP response JSON object to a file in the cache folder.

Function calculates the checksum of url to generate the cache file’s name. If the request was sent to server via POST instead of GET, then URL should be a GET-style representation of request. Response is only saved to a cache file if settings.use_cache is True, response_json is not None, and sc = 200.

Users should always pass OrderedDicts instead of dicts of parameters into request functions, so the parameters remain in the same order each time, producing the same URL string, and thus the same hash. Otherwise the cache will eventually contain multiple saved responses for the same request because the URL’s parameters appeared in a different order each time.

Parameters: url (string) – the URL of the request response_json (dict) – the JSON response sc (int) – the response’s HTTP status code None
`osmnx.downloader.``_url_in_cache`(url)

Determine if a URL’s response exists in the cache.

Calculates the checksum of url to determine the cache file’s name.

Parameters: url (string) – the URL to look for in the cache filepath – path to cached response for url if it exists, otherwise None string
`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) – Type of Nominatim query. One of: search, reverse, or lookup 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 response_json 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 response_json dict

## osmnx.elevation module¶

Get node elevations and calculate edge grades.

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

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

Parameters: G (networkx.MultiDiGraph) – input graph add_absolute (bool) – if True, also add absolute value of grade as grade_abs attribute precision (int) – decimal precision to round grade values G – graph with edge grade (and optionally grade_abs) attributes networkx.MultiDiGraph
`osmnx.elevation.``add_node_elevations`(G, api_key, max_locations_per_batch=350, pause_duration=0.02, precision=3)

Add elevation (meters) attribute to each node.

Uses the Google Maps Elevation API by default, but you can configure this to a different provider via ox.config()

Parameters: G (networkx.MultiDiGraph) – input graph api_key (string) – your google maps elevation API key, or equivalent if using a different provider 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) pause_duration (float) – time to pause between API calls precision (int) – decimal precision to round elevation G – graph with node elevation attributes networkx.MultiDiGraph

## osmnx.folium module¶

Create leaflet web maps via folium.

`osmnx.folium.``_make_folium_polyline`(edge, edge_color, edge_width, edge_opacity, popup_attribute=None, **kwargs)

Turn row GeoDataFrame into a folium PolyLine with attributes.

Parameters: edge (GeoSeries) – a row from the gdf_edges GeoDataFrame edge_color (string) – color of the edge lines edge_width (numeric) – width of the edge lines edge_opacity (numeric) – opacity of the edge lines popup_attribute (string) – edge attribute to display in a pop-up when an edge is clicked, if None, no popup kwargs (dict) – Extra parameters passed through to folium pl folium.PolyLine
`osmnx.folium.``plot_graph_folium`(G, graph_map=None, popup_attribute=None, tiles='cartodbpositron', zoom=1, fit_bounds=True, edge_color='#333333', edge_width=5, edge_opacity=1, **kwargs)

Plot a graph on an interactive folium web map.

Note that anything larger than a small city can take a long time to plot and create a large web map file that is very slow to load as JavaScript.

Parameters: G (networkx.MultiDiGraph) – input graph graph_map (folium.folium.Map or folium.FeatureGroup) – 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 route’s edges edge_color (string) – color of the edge lines edge_width (numeric) – width of the edge lines edge_opacity (numeric) – opacity of the edge lines kwargs (dict) – Extra keyword arguments passed through to folium graph_map folium.folium.Map
`osmnx.folium.``plot_route_folium`(G, route, route_map=None, popup_attribute=None, tiles='cartodbpositron', zoom=1, fit_bounds=True, route_color='#cc0000', route_width=5, route_opacity=1, **kwargs)

Plot a route on an interactive folium 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 route_color (string) – color of the route’s line route_width (numeric) – width of the route’s line route_opacity (numeric) – opacity of the route lines kwargs (dict) – Extra parameters passed through to folium route_map folium.folium.Map

## osmnx.footprints module¶

Deprecated: use the new geometries module instead.

`osmnx.footprints.``footprints_from_address`(address, dist=1000, footprint_type='building', retain_invalid=False)

Get footprints within some distance N, S, E, W of an address.

Parameters: address (string) – the address to geocode to a lat-lng point dist (numeric) – distance in meters footprint_type (string) – type of footprint to be downloaded. OSM tag key e.g. ‘building’, ‘landuse’, ‘place’, etc. retain_invalid (bool) – deprecated, is ignored geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`osmnx.footprints.``footprints_from_place`(place, footprint_type='building', retain_invalid=False, which_result=None)

Get footprints within the boundaries of some place.

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 footprints using the footprints_from_address function, which geocodes the place name to a point and gets the footprints within some distance of that point.

Parameters: place (string) – the query to geocode to get place boundary polygon footprint_type (string) – type of footprint to be downloaded. OSM tag key e.g. ‘building’, ‘landuse’, ‘place’, etc. retain_invalid (bool) – deprecated, is ignored 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. geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`osmnx.footprints.``footprints_from_point`(point, dist=1000, footprint_type='building', retain_invalid=False)

Get footprints within some distance N, S, E, W of a lat-lng point.

Parameters: point (tuple) – a lat-lng point dist (numeric) – distance in meters footprint_type (string) – type of footprint to be downloaded. OSM tag key e.g. ‘building’, ‘landuse’, ‘place’, etc. retain_invalid (bool) – deprecated, is ignored geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`osmnx.footprints.``footprints_from_polygon`(polygon, footprint_type='building', retain_invalid=False)

Get footprints within some polygon.

Parameters: polygon (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – the shape to get data within. coordinates should be in units of latitude-longitude degrees. footprint_type (string) – type of footprint to be downloaded. OSM tag key e.g. ‘building’, ‘landuse’, ‘place’, etc. retain_invalid (bool) – deprecated, is ignored geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

## osmnx.geocoder module¶

Geocode queries and create GeoDataFrames of place boundaries.

`osmnx.geocoder.``_geocode_query_to_gdf`(query, which_result)

Geocode a single place query to a GeoDataFrame.

Parameters: query (string or dict) – query string or structured dict 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. gdf – a GeoDataFrame with one row containing the result of geocoding geopandas.GeoDataFrame
`osmnx.geocoder.``_get_first_polygon`(results, query)

Choose first result with geometry type multi/polygon from list of results.

Parameters: results (list) – list of results from downloader._osm_polygon_download query (str) – the query string or structured dict that was geocoded result – the chosen result dict
`osmnx.geocoder.``geocode`(query)

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

Parameters: query (string) – the query string to geocode point – the (lat, lng) coordinates returned by the geocoder tuple
`osmnx.geocoder.``geocode_to_gdf`(query, which_result=None, buffer_dist=None)

Geocode a query or queries to a GeoDataFrame with the Nominatim geocoder.

Geometry column contains place boundaries if they exist in OpenStreetMap. Query can be a string or dict, or a list of strings/dicts to send to the geocoder. If query is a list, then which_result should be either a single value or a list of the same length as query.

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. buffer_dist (float) – distance to buffer around the place geometry, in meters gdf – a GeoDataFrame with one row for each query geopandas.GeoDataFrame

## osmnx.geometries module¶

Retrieve points of interest, building footprints, or any other objects from OSM, including their geometries and attribute data, and construct a GeoDataFrame of them.

`osmnx.geometries.``_assemble_multipolygon_component_polygons`(element, geometries)

Assemble a MultiPolygon from its component LineStrings and Polygons.

The OSM wiki suggests an algorithm for assembling multipolygon geometries https://wiki.openstreetmap.org/wiki/Relation:multipolygon/Algorithm. This method takes a simpler approach relying on the accurate tagging of component ways with ‘inner’ and ‘outer’ roles as required on this page https://wiki.openstreetmap.org/wiki/Relation:multipolygon.

Parameters: element (dict) – element type “relation” from overpass response JSON geometries (dict) – dict containing all linestrings and polygons generated from OSM ways geometry – a single MultiPolygon object shapely.geometry.MultiPolygon
`osmnx.geometries.``_buffer_invalid_geometries`(gdf)

Buffer any invalid geometries remaining in the GeoDataFrame.

Invalid geometries in the GeoDataFrame (which may accurately reproduce invalid geometries in OpenStreetMap) can cause the filtering to the query polygon and other subsequent geometric operations to fail. This function logs the ids of the invalid geometries and applies a buffer of zero to try to make them valid.

Note: the resulting geometries may differ from the originals - please check them against OpenStreetMap

Parameters: gdf (geopandas.GeoDataFrame) – a GeoDataFrame with possibly invalid geometries gdf – the GeoDataFrame with .buffer(0) applied to invalid geometries geopandas.GeoDataFrame
`osmnx.geometries.``_create_gdf`(response_jsons, polygon, tags)

Parse JSON responses from the Overpass API to a GeoDataFrame.

Note: the polygon and tags arguments can both be None and the GeoDataFrame will still be created but it won’t be filtered at the end i.e. the final GeoDataFrame will contain all tagged geometries in the response_jsons.

Parameters: response_jsons (list) – list of JSON responses from from the Overpass API polygon (shapely.geometry.Polygon) – geographic boundary used for filtering the final GeoDataFrame tags (dict) – dict of tags used for filtering the final GeoDataFrame gdf – GeoDataFrame of geometries and their associated tags geopandas.GeoDataFrame
`osmnx.geometries.``_filter_gdf_by_polygon_and_tags`(gdf, polygon, tags)

Filter the GeoDataFrame to the requested bounding polygon and tags.

Filters GeoDataFrame to query polygon and tags. Removes columns of all NaNs (that held values only in rows removed by the filters). Resets the index of GeoDataFrame, writing it into a new column called ‘unique_id’.

Parameters: gdf (geopandas.GeoDataFrame) – the GeoDataFrame to filter polygon (shapely.geometry.Polygon) – polygon defining the boundary of the requested area tags (dict) – the tags requested gdf – final filtered GeoDataFrame geopandas.GeoDataFrame
`osmnx.geometries.``_is_closed_way_a_polygon`(element, polygon_features={'aeroway': {'polygon': 'blocklist', 'values': ['taxiway']}, 'amenity': {'polygon': 'all'}, 'area': {'polygon': 'all'}, 'area:highway': {'polygon': 'all'}, 'barrier': {'polygon': 'passlist', 'values': ['city_wall', 'ditch', 'hedge', 'retaining_wall', 'spikes']}, 'boundary': {'polygon': 'all'}, 'building': {'polygon': 'all'}, 'building:part': {'polygon': 'all'}, 'craft': {'polygon': 'all'}, 'golf': {'polygon': 'all'}, 'highway': {'polygon': 'passlist', 'values': ['services', 'rest_area', 'escape', 'elevator']}, 'historic': {'polygon': 'all'}, 'indoor': {'polygon': 'all'}, 'landuse': {'polygon': 'all'}, 'leisure': {'polygon': 'all'}, 'man_made': {'polygon': 'blocklist', 'values': ['cutline', 'embankment', 'pipeline']}, 'military': {'polygon': 'all'}, 'natural': {'polygon': 'blocklist', 'values': ['coastline', 'cliff', 'ridge', 'arete', 'tree_row']}, 'office': {'polygon': 'all'}, 'place': {'polygon': 'all'}, 'power': {'polygon': 'passlist', 'values': ['plant', 'substation', 'generator', 'transformer']}, 'public_transport': {'polygon': 'all'}, 'railway': {'polygon': 'passlist', 'values': ['station', 'turntable', 'roundhouse', 'platform']}, 'ruins': {'polygon': 'all'}, 'shop': {'polygon': 'all'}, 'tourism': {'polygon': 'all'}, 'waterway': {'polygon': 'passlist', 'values': ['riverbank', 'dock', 'boatyard', 'dam']}})

Determine whether a closed OSM way represents a Polygon, not a LineString.

Closed OSM ways may represent LineStrings (e.g. a roundabout or hedge round a field) or Polygons (e.g. a building footprint or land use area) depending on the tags applied to them.

The starting assumption is that it is not a polygon, however any polygon type tagging will return a polygon unless explicitly tagged with area:no.

It is possible for a single closed OSM way to have both LineString and Polygon type tags (e.g. both barrier=fence and landuse=agricultural). OSMnx will return a single Polygon for elements tagged in this way. For more information see: https://wiki.openstreetmap.org/wiki/One_feature,_one_OSM_element)

Parameters: element (dict) – closed element type “way” from overpass response JSON polygon_features (dict) – dict of tag keys with associated values and blocklist/passlist is_polygon – True if the tags are for a polygon type geometry bool
`osmnx.geometries.``_parse_node_to_coords`(element)

Parse coordinates from a node in the overpass response.

The coords are only used to create LineStrings and Polygons.

Parameters: element (dict) – element type “node” from overpass response JSON coords – dict of latitude/longitude coordinates dict
`osmnx.geometries.``_parse_node_to_point`(element)

Parse point from a tagged node in the overpass response.

The points are geometries in their own right.

Parameters: element (dict) – element type “node” from overpass response JSON point – dict of OSM ID, OSM element type, tags and geometry dict
`osmnx.geometries.``_parse_relation_to_multipolygon`(element, geometries)

Parse multipolygon from OSM relation (type:MultiPolygon).

Parameters: element (dict) – element type “relation” from overpass response JSON geometries (dict) – dict containing all linestrings and polygons generated from OSM ways multipolygon – dict of tags and geometry for a single multipolygon dict
`osmnx.geometries.``_parse_way_to_linestring_or_polygon`(element, coords, polygon_features={'aeroway': {'polygon': 'blocklist', 'values': ['taxiway']}, 'amenity': {'polygon': 'all'}, 'area': {'polygon': 'all'}, 'area:highway': {'polygon': 'all'}, 'barrier': {'polygon': 'passlist', 'values': ['city_wall', 'ditch', 'hedge', 'retaining_wall', 'spikes']}, 'boundary': {'polygon': 'all'}, 'building': {'polygon': 'all'}, 'building:part': {'polygon': 'all'}, 'craft': {'polygon': 'all'}, 'golf': {'polygon': 'all'}, 'highway': {'polygon': 'passlist', 'values': ['services', 'rest_area', 'escape', 'elevator']}, 'historic': {'polygon': 'all'}, 'indoor': {'polygon': 'all'}, 'landuse': {'polygon': 'all'}, 'leisure': {'polygon': 'all'}, 'man_made': {'polygon': 'blocklist', 'values': ['cutline', 'embankment', 'pipeline']}, 'military': {'polygon': 'all'}, 'natural': {'polygon': 'blocklist', 'values': ['coastline', 'cliff', 'ridge', 'arete', 'tree_row']}, 'office': {'polygon': 'all'}, 'place': {'polygon': 'all'}, 'power': {'polygon': 'passlist', 'values': ['plant', 'substation', 'generator', 'transformer']}, 'public_transport': {'polygon': 'all'}, 'railway': {'polygon': 'passlist', 'values': ['station', 'turntable', 'roundhouse', 'platform']}, 'ruins': {'polygon': 'all'}, 'shop': {'polygon': 'all'}, 'tourism': {'polygon': 'all'}, 'waterway': {'polygon': 'passlist', 'values': ['riverbank', 'dock', 'boatyard', 'dam']}})

Parse open LineString, closed LineString or Polygon from OSM ‘way’.

Parameters: element (dict) – element type “way” from overpass response JSON coords (dict) – dict of node IDs and their latitude/longitude coordinates polygon_features (dict) – dict for determining whether closed ways are LineStrings or Polygons linestring_or_polygon – dict of OSM ID, OSM element type, nodes, tags and geometry dict
`osmnx.geometries.``_subtract_inner_polygons_from_outer_polygons`(element, outer_polygons, inner_polygons)

Subtract inner polygons from outer polygons.

Creates a Polygon or MultiPolygon with holes.

Parameters: element (dict) – element type “relation” from overpass response JSON outer_polygons (list) – list of outer polygons that are part of a multipolygon inner_polygons (list) – list of inner polygons that are part of a multipolygon geometry – a single Polygon or MultiPolygon shapely.geometry.Polygon or shapely.geometry.MultiPolygon
`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 gdf geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`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. gdf geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

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

Create a GeoDataFrame of OSM entities within the boundaries of a place.

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 gdf geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`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 gdf geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`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. gdf geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`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) – 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. gdf geopandas.GeoDataFrame

## osmnx.graph module¶

Graph creation functions.

`osmnx.graph.``_add_paths`(G, paths, bidirectional=False)

Add a list of paths to the graph as edges.

Parameters: G (networkx.MultiDiGraph) – graph to add paths to paths (list) – list of paths’ tag:value attribute data dicts bidirectional (bool) – if True, create bi-directional edges for one-way streets None
`osmnx.graph.``_convert_node`(element)

Convert an OSM node element into the format for a networkx node.

Parameters: element (dict) – an OSM node element node dict
`osmnx.graph.``_convert_path`(element)

Convert an OSM way element into the format for a networkx path.

Parameters: element (dict) – an OSM way element path dict
`osmnx.graph.``_create_graph`(response_jsons, retain_all=False, bidirectional=False)

Create a networkx MultiDiGraph from Overpass API responses.

Add length in meters (great-circle distance between endpoints) to all of the graph’s (unsimplified, straight-line) edges via add_edge_lengths.

Parameters: response_jsons (list) – list of dicts of JSON responses from from the Overpass API retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component. bidirectional (bool) – if True, create bi-directional edges for one-way streets G networkx.MultiDiGraph
`osmnx.graph.``_is_path_one_way`(path, bidirectional, oneway_values)

Determine if a path of nodes allows travel in only one direction.

Parameters: path (dict) – a path’s tag:value attribute data bidirectional (bool) – whether this is a bi-directional network type oneway_values (set) – the values OSM uses in its ‘oneway’ tag to denote True bool
`osmnx.graph.``_is_path_reversed`(path, reversed_values)

Determine if the order of nodes in a path should be reversed.

Parameters: path (dict) – a path’s tag:value attribute data reversed_values (set) – the values OSM uses in its ‘oneway’ tag to denote travel can only occur in the opposite direction of the node order bool
`osmnx.graph.``_parse_nodes_paths`(response_json)

Construct dicts of nodes and paths from an Overpass response.

Parameters: response_json (dict) – JSON response from the Overpass API nodes, paths – dicts’ keys = osmid and values = dict of attributes tuple of dicts
`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. network_type (string) – what type of street network to get if custom_filter is None. One of ‘walk’, ‘bike’, ‘drive’, ‘drive_service’, ‘all’, or ‘all_private’. simplify (bool) – if True, simplify the 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 network 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. networkx.MultiDiGraph or optionally (networkx.MultiDiGraph, (lat, lng))

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`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) – what type of street network to get if custom_filter is None. One of ‘walk’, ‘bike’, ‘drive’, ‘drive_service’, ‘all’, or ‘all_private’. simplify (bool) – if True, simplify the 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 network 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. G networkx.MultiDiGraph

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`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. Alternatively, you might try to vary the which_result parameter to use a different geocode result. For example, the first geocode result (ie, the default) might resolve to a point geometry, but the second geocode result for this query might resolve to a polygon, in which case you can use graph_from_place with which_result=2. which_result=None will auto-select the first multi/polygon among the geocoding results.

Parameters: query (string or dict or list) – the query or queries to geocode to get place boundary polygon(s) network_type (string) – what type of street network to get if custom_filter is None. One of ‘walk’, ‘bike’, ‘drive’, ‘drive_service’, ‘all’, or ‘all_private’. simplify (bool) – if True, simplify the 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 network 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. G networkx.MultiDiGraph

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`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. network_type (string) – what type of street network to get if custom_filter is None. One of ‘walk’, ‘bike’, ‘drive’, ‘drive_service’, ‘all’, or ‘all_private’. simplify (bool) – if True, simplify the 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 network 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. G networkx.MultiDiGraph

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`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 units of latitude-longitude degrees. network_type (string) – what type of street network to get if custom_filter is None. One of ‘walk’, ‘bike’, ‘drive’, ‘drive_service’, ‘all’, or ‘all_private’. simplify (bool) – if True, simplify the 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 network 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. G networkx.MultiDiGraph

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

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

Create a graph from data in an OSM-formatted XML file.

Parameters: filepath (string) – path to file containing OSM XML data bidirectional (bool) – if True, create bi-directional edges for one-way streets simplify (bool) – if True, simplify the 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. G networkx.MultiDiGraph

## osmnx.io module¶

Serialize graphs to/from files on disk.

`osmnx.io.``_append_edges_xml_tree`(root, gdf_edges, edge_attrs, edge_tags, edge_tag_aggs, merge_edges)

Append edges to an XML tree.

Parameters: root (ElementTree.Element) – xml tree gdf_edges (geopandas.GeoDataFrame) – GeoDataFrame of graph edges edge_attrs (list) – osm way attributes to include in output OSM XML edge_tags (list) – osm way tags to include in output OSM XML 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. 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. root – xml tree with edges appended ElementTree.Element
`osmnx.io.``_append_nodes_xml_tree`(root, gdf_nodes, node_attrs, node_tags)

Append nodes to an XML tree.

Parameters: root (ElementTree.Element) – xml tree gdf_nodes (geopandas.GeoDataFrame) – GeoDataFrame of graph nodes node_attrs (list) – osm way attributes to include in output OSM XML node_tags (list) – osm way tags to include in output OSM XML root – xml tree with nodes appended ElementTree.Element
`osmnx.io.``_convert_edge_attr_types`(G, node_type)

Convert graph edges’ attributes’ types from string to numeric.

Parameters: G (networkx.MultiDiGraph) – input graph node_type (type) – convert osmid to this type G networkx.MultiDiGraph
`osmnx.io.``_convert_node_attr_types`(G, node_type)

Convert graph nodes’ attributes’ types from string to numeric.

Parameters: G (networkx.MultiDiGraph) – input graph node_type (type) – convert node ID (osmid) to this type G networkx.MultiDiGraph
`osmnx.io.``_get_unique_nodes_ordered_from_way`(df_way_edges)

Recover original node order from df of edges associated w/ single OSM way.

Parameters: df_way_edges (pandas.DataFrame) – Dataframe containing columns ‘u’ and ‘v’ corresponding to origin/destination nodes. unique_ordered_nodes – An ordered list of unique node IDs. Note: If the edges do not all connect (e.g. [(1, 2), (2,3), (10, 11), (11, 12), (12, 13)]), then this method will return only those nodes associated with the largest component of connected edges, even if subsequent connected chunks are contain more total nodes. This is done to ensure a proper topological representation of nodes in the XML way records because if there are unconnected components, the sorting algorithm cannot recover their original order. We would not likely ever encounter this kind of disconnected structure of nodes within a given way, but it is not explicitly forbidden in the OSM XML design schema. list
`osmnx.io.``_stringify_nonnumeric_cols`(gdf)

Make every non-numeric GeoDataFrame column (besides geometry) a string.

This allows proper serializing via Fiona of GeoDataFrames with mixed types such as strings and ints in the same column.

Parameters: gdf (geopandas.GeoDataFrame) – gdf to stringify non-numeric columns of gdf – gdf with non-numeric columns stringified geopandas.GeoDataFrame
`osmnx.io.``load_graphml`(filepath, node_type=<class 'int'>)

Load an OSMnx-saved GraphML file from disk.

Converts the node/edge attributes to appropriate data types.

Parameters: filepath (string) – path to the GraphML file node_type (type) – convert node ids to this data type G networkx.MultiDiGraph
`osmnx.io.``save_graph_geopackage`(G, filepath=None, encoding='utf-8')

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

Parameters: G (networkx.MultiDiGraph) – input graph filepath (string) – path to the GeoPackage file including extension. if None, use default data folder + graph.gpkg encoding (string) – the character encoding for the saved file None
`osmnx.io.``save_graph_shapefile`(G, filepath=None, encoding='utf-8')

Save graph nodes and edges to disk as ESRI shapefiles.

The shapefile format is proprietary and outdated. Whenever possible, you should use the superior GeoPackage file format instead, for instance, via the save_graph_geopackage function.

Parameters: G (networkx.MultiDiGraph) – input graph filepath (string) – 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 None
`osmnx.io.``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. To save/load full-featured OSMnx graphs to/from disk for later use, use the save_graphml and load_graphml functions instead.

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.config(all_oneway=True, useful_tags_node=utn, useful_tags_way=utw)
>>> G = ox.graph_from_place('Piedmont, CA, USA', network_type='drive')
>>> ox.save_graph_xml(G, filepath='./data/graph1.osm')
```
Parameters: data (networkx multi(di)graph OR a length 2 iterable of nodes/edges) – geopandas GeoDataFrames filepath (string) – 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. 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) – 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 to work around Gephi’s restrictive interpretation of the GraphML specification encoding (string) – the character encoding for the saved file None

## osmnx.plot module¶

Plot spatial geometries, street networks, and routes.

`osmnx.plot.``_config_ax`(ax, crs, bbox, padding)

Configure axis for display.

Parameters: ax (matplotlib axis) – the axis containing the plot crs (dict or string or pyproj.CRS) – the CRS of the plotted geometries bbox (tuple) – bounding box as (north, south, east, west) padding (float) – relative padding to add around the plot’s bbox ax – the configured/styled axis matplotlib axis
`osmnx.plot.``_get_colors_by_value`(vals, num_bins, cmap, start, stop, na_color, equal_size)

Map colors to the values in a series.

Parameters: vals (pandas.Series) – series labels are node/edge IDs and values are attribute values 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 to missing 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. color_series – series labels are node/edge IDs and values are colors pandas.Series
`osmnx.plot.``_save_and_show`(fig, ax, save=False, show=True, close=True, filepath=None, dpi=300)

Save a figure to disk and/or show it, as specified by args.

Parameters: fig (figure) – matplotlib figure ax (axis) – matplotlib axis 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 fig, ax – matplotlib figure, axis tuple
`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. color_list 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. edge_colors – series labels are edge IDs (u, v, k) and values are colors 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. node_colors – series labels are node IDs and values are colors 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 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 fig, ax – matplotlib figure, axis tuple
`osmnx.plot.``plot_footprints`(gdf, ax=None, figsize=(8, 8), color='orange', 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 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 fig, ax – matplotlib figure, axis 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. fig, ax – matplotlib figure, axis 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 fig, ax – matplotlib figure, axis tuple
`osmnx.plot.``plot_graph_routes`(G, routes, route_colors='r', **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. pgr_kwargs – keyword arguments to pass to plot_graph_route fig, ax – matplotlib figure, axis tuple

## osmnx.pois module¶

Deprecated: use the new geometries module instead.

`osmnx.pois.``pois_from_address`(address, tags, dist=1000)

Get point of interests (POIs) within some distance N, S, E, W of address.

Parameters: address (string) – the address to geocode to a lat-lng point tags (dict) – Dict of tags used for finding POIs from the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one tag given. The dict keys should be OSM tags, (e.g., amenity, 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 = {‘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 gdf geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`osmnx.pois.``pois_from_place`(place, tags, which_result=None)

Get points of interest (POIs) within the boundaries of some place.

Parameters: place (string) – the query to geocode to get place boundary polygon tags (dict) – Dict of tags used for finding POIs from the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one tag given. The dict keys should be OSM tags, (e.g., amenity, 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 = {‘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. gdf geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`osmnx.pois.``pois_from_point`(point, tags, dist=1000)

Get point of interests (POIs) within some distance N, S, E, W of a point.

Parameters: point (tuple) – a (lat, lng) point tags (dict) – Dict of tags used for finding POIs from the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one tag given. The dict keys should be OSM tags, (e.g., amenity, 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 = {‘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 gdf geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

`osmnx.pois.``pois_from_polygon`(polygon, tags)

Get point of interests (POIs) within some polygon.

Parameters: polygon (shapely.geometry.Polygon) – geographic boundaries to fetch POIs within tags (dict) – Dict of tags used for finding POIs from the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one tag given. The dict keys should be OSM tags, (e.g., amenity, 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 = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop. gdf geopandas.GeoDataFrame

Notes

You can configure the Overpass server timeout, memory allocation, and other custom settings via ox.config().

## osmnx.projection module¶

Project spatial geometries and street networks.

`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 (dict or 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 gdf_proj – the projected GeoDataFrame 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 (dict or string or pyproj.CRS) – the starting CRS of the passed-in geometry. if None, it will be set to settings.default_crs to_crs (dict or 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 geometry_proj, crs – the projected geometry and its new CRS 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 (dict or string or pyproj.CRS) – if None, project graph to UTM zone in which graph centroid lies, otherwise project graph to this CRS G_proj – the projected graph networkx.MultiDiGraph

## osmnx.settings module¶

Global settings, can be configured by user with utils.config().

## osmnx.simplification module¶

Simplify, correct, and consolidate network topology.

`osmnx.simplification.``_build_path`(G, endpoint, endpoint_successor, endpoints)

Build a path of nodes from one endpoint node to next endpoint node.

Parameters: G (networkx.MultiDiGraph) – input graph endpoint (int) – the endpoint node from which to start the path endpoint_successor (int) – the successor of endpoint through which the path to the next endpoint will be built endpoints (set) – the set of all nodes in the graph that are endpoints path – the first and last items in the resulting path list are endpoint nodes, and all other items are interstitial nodes that can be removed subsequently list
`osmnx.simplification.``_consolidate_intersections_rebuild_graph`(G, tolerance=10, reconnect_edges=True)

Consolidate intersections comprising clusters of nearby nodes.

Merge nodes and return 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.

Returned graph’s node IDs represent clusters rather than osmids. Refer to nodes’ osmid attributes for original osmids. If multiple nodes were merged together, the osmid attribute is a list of merged nodes’ osmids.

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 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). H – a rebuilt graph with consolidated intersections and reconnected edge geometries networkx.MultiDiGraph
`osmnx.simplification.``_get_paths_to_simplify`(G, strict=True)

Generate all the paths to be simplified between endpoint nodes.

The path is ordered from the first endpoint, through the interstitial nodes, to the second endpoint.

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 edges with different OSM IDs path_to_simplify (list)
`osmnx.simplification.``_is_endpoint`(G, node, strict=True)

Is node a true endpoint of an edge.

Return True if the node is a “real” endpoint of an edge in the network, otherwise False. OSM data includes lots of nodes that exist only as points to help streets bend around curves. An end point is a node that either: 1) is its own neighbor, ie, it self-loops. 2) or, has no incoming edges or no outgoing edges, ie, all its incident edges point inward or all its incident edges point outward. 3) or, it does not have exactly two neighbors and degree of 2 or 4. 4) or, if strict mode is false, if its edges have different OSM IDs.

Parameters: G (networkx.MultiDiGraph) – input graph node (int) – the node to examine strict (bool) – if False, allow nodes to be end points even if they fail all other rules but have edges with different OSM IDs bool
`osmnx.simplification.``_is_simplified`(G)

Parameters: G (networkx.MultiDiGraph) – input graph bool
`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.

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 attributes for original osmids. If multiple nodes were merged together, the osmid 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). 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 networkx.MultiDiGraph or geopandas.GeoSeries
`osmnx.simplification.``simplify_graph`(G, strict=True, remove_rings=True)

Simplify a graph’s topology by removing interstitial nodes.

Simplify 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 an attribute in new edge. Some of the resulting consolidated edges may comprise multiple OSM ways, and if so, their multiple attribute values are stored as a list.

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 G – topologically simplified graph networkx.MultiDiGraph

## osmnx.speed module¶

Calculate graph edge speeds and travel times.

`osmnx.speed.``_clean_maxspeed`(value, convert_mph=True)

Clean a maxspeed string and convert mph to kph if necessary.

Parameters: value (string) – an OSM way maxspeed value convert_mph (bool) – if True, convert mph to kph value_clean string
`osmnx.speed.``_collapse_multiple_maxspeed_values`(value)

Collapse a list of maxspeed values into its mean value.

Parameters: value (list or string) – an OSM way maxspeed value, or a list of them mean_value – an integer representation of the mean value in the list, converted to kph if original value was in mph. int
`osmnx.speed.``add_edge_speeds`(G, hwy_speeds=None, fallback=None, precision=1)

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

Imputes free-flow travel speeds for all edges based on mean maxspeed value of edges, per highway type. For highway types in graph that have no maxspeed value on any edge, function assigns the mean of all maxspeed values in graph.

This mean-imputation can obviously be imprecise, and the caller can override it by passing in hwy_speeds and/or fallback arguments that correspond to local speed limit standards.

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 G – graph with speed_kph attributes on all edges 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 G – graph with travel_time attributes on all edges networkx.MultiDiGraph

## osmnx.stats module¶

Calculate graph-theoretic network measures.

`osmnx.stats.``basic_stats`(G, area=None, clean_intersects=False, tolerance=15, circuity_dist='gc')

Calculate basic descriptive metric and topological stats for a graph.

For an unprojected lat-lng graph, tolerance and graph units should be in degrees, and circuity_dist should be ‘gc’. For a projected graph, tolerance and graph units should be in meters (or similar) and circuity_dist should be ‘euclidean’.

Parameters: G (networkx.MultiDiGraph) – input graph area (numeric) – the land area of this study site, in square meters. must be greater than 0. if None, will skip all density-based metrics. clean_intersects (bool) – if True, calculate consolidated intersections count (and density, if area is provided) via consolidate_intersections function tolerance (numeric) – tolerance value passed along if clean_intersects=True, see consolidate_intersections function documentation for details and usage circuity_dist (string) – ‘gc’ or ‘euclidean’, how to calculate straight-line distances for circuity measurement; use former for lat-lng networks and latter for projected networks stats – dictionary of network measures containing the following elements (some keys may not be present, based on the arguments passed into the function): n = number of nodes in the graph m = number of edges in the graph k_avg = average node degree of the graph intersection_count = number of intersections in graph, that is, nodes with >1 street emanating from them streets_per_node_avg = how many streets (edges in the undirected representation of the graph) emanate from each node (ie, intersection or dead-end) on average (mean) streets_per_node_counts = dict, with keys of number of streets emanating from the node, and values of number of nodes with this count streets_per_node_proportion = dict, same as previous, but as a proportion of the total, rather than counts edge_length_total = sum of all edge lengths in the graph, in meters edge_length_avg = mean edge length in the graph, in meters street_length_total = sum of all edges in the undirected representation of the graph street_length_avg = mean edge length in the undirected representation of the graph, in meters street_segments_count = number of edges in the undirected representation of the graph node_density_km = n divided by area in square kilometers intersection_density_km = intersection_count divided by area in square kilometers edge_density_km = edge_length_total divided by area in square kilometers street_density_km = street_length_total divided by area in square kilometers circuity_avg = edge_length_total divided by the sum of the great circle distances between the nodes of each edge self_loop_proportion = proportion of edges that have a single node as its two endpoints (ie, the edge links nodes u and v, and u==v) clean_intersection_count = number of intersections in street network, merging complex ones into single points clean_intersection_density_km = clean_intersection_count divided by area in square kilometers dict
`osmnx.stats.``extended_stats`(G, connectivity=False, anc=False, ecc=False, bc=False, cc=False)

Calculate extended topological stats and metrics for a graph.

Many of these algorithms have an inherently high time complexity. Global topological analysis of large complex networks is extremely time consuming and may exhaust computer memory. Consider using function arguments to not run metrics that require computation of a full matrix of paths if they will not be needed.

Parameters: G (networkx.MultiDiGraph) – input graph connectivity (bool) – if True, calculate node and edge connectivity anc (bool) – if True, calculate average node connectivity ecc (bool) – if True, calculate shortest paths, eccentricity, and topological metrics that use eccentricity bc (bool) – if True, calculate node betweenness centrality cc (bool) – if True, calculate node closeness centrality stats – dictionary of network measures containing the following elements (some only calculated/returned optionally, based on passed parameters): avg_neighbor_degree avg_neighbor_degree_avg avg_weighted_neighbor_degree avg_weighted_neighbor_degree_avg degree_centrality degree_centrality_avg clustering_coefficient clustering_coefficient_avg clustering_coefficient_weighted clustering_coefficient_weighted_avg pagerank pagerank_max_node pagerank_max pagerank_min_node pagerank_min node_connectivity node_connectivity_avg edge_connectivity eccentricity diameter radius center periphery closeness_centrality closeness_centrality_avg betweenness_centrality betweenness_centrality_avg 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) G – the truncated graph 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. G – the truncated graph 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) G – the truncated graph networkx.MultiDiGraph

## osmnx.utils module¶

General utility functions.

`osmnx.utils.``_get_logger`(level=None, name=None, filename=None)

Create a logger or return the current one if already instantiated.

Parameters: level (int) – one of the logger.level constants name (string) – name of the logger filename (string) – name of the log file logger logging.logger
`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: None
`osmnx.utils.``config`(data_folder='data', logs_folder='logs', imgs_folder='images', cache_folder='cache', use_cache=False, log_file=False, log_console=False, log_level=20, log_name='osmnx', log_filename='osmnx', useful_tags_node=['ref', 'highway'], useful_tags_way=['bridge', 'tunnel', 'oneway', 'lanes', 'ref', 'name', 'highway', 'maxspeed', 'service', 'access', 'area', 'landuse', 'width', 'est_width', 'junction'], 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_settings='[out:json][timeout:{timeout}]{maxsize}', timeout=180, memory=None, max_query_area_size=2500000000, default_access='["access"!~"private"]', default_crs='epsg:4326', default_user_agent='OSMnx Python package (https://github.com/gboeing/osmnx)', default_referer='OSMnx Python package (https://github.com/gboeing/osmnx)', default_accept_language='en', nominatim_endpoint='https://nominatim.openstreetmap.org/', nominatim_key=None, overpass_endpoint='http://overpass-api.de/api', all_oneway=False, elevation_provider='google')

Configure OSMnx by setting the default global settings’ values.

Any parameters not passed by the caller are set to their original default values.

`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 the logger.level constants name (string) – name of the logger filename (string) – name of the log file None
`osmnx.utils.``ts`(style='datetime', template=None)

Get current timestamp as string.

Parameters: style (string) – format the timestamp with this built-in template. must be one of {‘datetime’, ‘date’, ‘time’} template (string) – if not None, format the timestamp with this template instead of one of the built-in styles ts – the string timestamp string

## osmnx.utils_geo module¶

Geospatial utility functions.

`osmnx.utils_geo.``_consolidate_subdivide_geometry`(geometry, max_query_area_size=None)

Consolidate and subdivide some geometry.

Consolidate a geometry into a convex hull, then subdivide it into smaller sub-polygons if its area exceeds max size (in geometry’s units). Configure the max size via max_query_area_size in the settings module.

Parameters: geometry (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – the geometry to consolidate and subdivide 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 API (default 50km x 50km). if None, use settings.max_query_area_size geometry shapely.geometry.Polygon or shapely.geometry.MultiPolygon
`osmnx.utils_geo.``_get_polygons_coordinates`(geometry)

Extract exterior coordinates from polygon(s) to pass to OSM.

Ignore the interior (“holes”) coordinates.

Parameters: geometry (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – the geometry to extract exterior coordinates from polygon_coord_strs list
`osmnx.utils_geo.``_intersect_index_quadrats`(geometries, polygon, quadrat_width=0.05, min_num=3)

Identify geometries that intersect a (multi)polygon.

Use an r-tree spatial index and cut the polygon up into smaller sub-polygons for r-tree acceleration.

Parameters: geometries (geopandas.GeoSeries) – the geometries to intersect with the polygon polygon (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – the polygon to intersect with the geometries quadrat_width (numeric) – the linear length (in units the polygon is in) of the quadrats with which to cut up the polygon (default = 0.05 degrees, approx 4km at NYC’s latitude) min_num (int) – the minimum number of linear quadrat lines (e.g., min_num=3 would produce a quadrat grid of 4 squares) geoms_in_poly – index labels of geometries that intersected polygon set
`osmnx.utils_geo.``_quadrat_cut_geometry`(geometry, quadrat_width, min_num=3)

Split a Polygon or MultiPolygon up into sub-polygons of a specified size.

Parameters: geometry (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – the geometry to split up into smaller sub-polygons quadrat_width (numeric) – the linear width of the quadrats with which to cut up the geometry (in the units the geometry is in) min_num (int) – the minimum number of linear quadrat lines (e.g., min_num=3 would produce a quadrat grid of 4 squares) geometry shapely.geometry.MultiPolygon
`osmnx.utils_geo.``_round_linestring_coords`(ls, precision)

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

Parameters: ls (shapely.geometry.LineString) – the LineString to round the coordinates of precision (int) – decimal precision to round coordinates to shapely.geometry.LineString
`osmnx.utils_geo.``_round_multilinestring_coords`(mls, precision)

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

Parameters: mls (shapely.geometry.MultiLineString) – the MultiLineString to round the coordinates of precision (int) – decimal precision to round coordinates to shapely.geometry.MultiLineString
`osmnx.utils_geo.``_round_multipoint_coords`(mpt, precision)

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

Parameters: mpt (shapely.geometry.MultiPoint) – the MultiPoint to round the coordinates of precision (int) – decimal precision to round coordinates to shapely.geometry.MultiPoint
`osmnx.utils_geo.``_round_multipolygon_coords`(mp, precision)

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

Parameters: mp (shapely.geometry.MultiPolygon) – the MultiPolygon to round the coordinates of precision (int) – decimal precision to round coordinates to shapely.geometry.MultiPolygon
`osmnx.utils_geo.``_round_point_coords`(pt, precision)

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

Parameters: pt (shapely.geometry.Point) – the Point to round the coordinates of precision (int) – decimal precision to round coordinates to shapely.geometry.Point
`osmnx.utils_geo.``_round_polygon_coords`(p, precision)

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

Parameters: p (shapely.geometry.Polygon) – the polygon to round the coordinates of precision (int) – decimal precision to round coordinates to new_poly – the polygon with rounded coordinates shapely.geometry.Polygon
`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 (north, south, east, west) or (north, south, east, west, crs_proj) 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 shapely.geometry.Polygon
`osmnx.utils_geo.``redistribute_vertices`(geom, dist)

Redistribute the vertices on a projected LineString or MultiLineString.

The distance argument is only approximate since the total distance of the linestring may not be a multiple of the preferred distance. This function works on only (Multi)LineString geometry types.

Parameters: geom (shapely.geometry.LineString or shapely.geometry.MultiLineString) – a Shapely geometry (should be projected) dist (float) – spacing length along edges. Units are same as the geom: degrees for unprojected geometries and meters for projected geometries. The smaller the dist value, the more points are created. the redistributed vertices as a list if geom is a LineString or MultiLineString if geom is a MultiLineString list or shapely.geometry.MultiLineString
`osmnx.utils_geo.``round_geometry_coords`(shape, precision)

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

Parameters: shape (shapely.geometry.geometry) – the geometry to round the coordinates of; must be one of {Point, MultiPoint, LineString, MultiLineString, Polygon, MultiPolygon} precision (int) – decimal precision to round coordinates to shapely.geometry.geometry

## osmnx.utils_graph module¶

Graph utility functions.

`osmnx.utils_graph.``_is_duplicate_edge`(data, data_other)

Check if two edge data dicts are the same based on OSM ID and geometry.

Parameters: data (dict) – the first edge’s data data_other (dict) – the second edge’s data is_dupe bool
`osmnx.utils_graph.``_is_same_geometry`(ls1, ls2)

Check if LineString geometries in two edges are the same.

Check both normal and reversed order of constituent points.

Parameters: ls1 (shapely.geometry.LineString) – the first edge’s geometry ls2 (shapely.geometry.LineString) – the second edge’s geometry bool
`osmnx.utils_graph.``_update_edge_keys`(G)

Update keys of edges that share u, v with other edge but differ in geometry.

For example, two one-way streets from u to v that bow away from each other as separate streets, rather than opposite direction edges of a single street.

Parameters: G (networkx.MultiDiGraph) – input graph G networkx.MultiDiGraph
`osmnx.utils_graph.``add_edge_lengths`(G, precision=3)

Add length (meters) attribute to each edge.

Calculated via great-circle distance between each edge’s incident nodes, so ensure graph is in unprojected coordinates.

Parameters: G (networkx.MultiDiGraph) – input graph precision (int) – decimal precision to round lengths G – graph with edge length attributes networkx.MultiDiGraph
`osmnx.utils_graph.``count_streets_per_node`(G, nodes=None)

Count how many street segments emanate from each node in this graph.

If nodes is passed, then only count the nodes in the graph with those IDs.

Parameters: G (networkx.MultiDiGraph) – input graph nodes (iterable) – the set of node IDs to get counts for streets_per_node – counts of how many streets emanate from each node with keys=node id and values=count dict
`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 networkx.DiGraph
`osmnx.utils_graph.``get_largest_component`(G, strongly=False)

Get subgraph of MultiDiGraph’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 G – the largest connected component subgraph of the original graph 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) attribute_values – list of edge attribute values list
`osmnx.utils_graph.``get_undirected`(G)

Convert MultiDiGraph to 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 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.

Parameters: gdf_nodes (geopandas.GeoDataFrame) – GeoDataFrame of graph nodes gdf_edges (geopandas.GeoDataFrame) – GeoDataFrame of graph edges, must have crs attribute set graph_attrs (dict) – the new G.graph attribute dict; if None, add crs as the only graph-level attribute G networkx.MultiDiGraph
`osmnx.utils_graph.``graph_to_gdfs`(G, nodes=True, edges=True, node_geometry=True, fill_edge_geometry=True)

Convert a graph 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 data fill_edge_geometry (bool) – if True, fill in missing edge geometry fields using nodes u and v gdf_nodes or gdf_edges or tuple of (gdf_nodes, gdf_edges) geopandas.GeoDataFrame or tuple
`osmnx.utils_graph.``induce_subgraph`(G, node_subset)

Induce a subgraph of G: deprecated, do not use.

Parameters: G (networkx.MultiDiGraph) – input graph node_subset (list-like) – the subset of nodes to induce a subgraph of G the subgraph of G induced by node_subset networkx.MultiDiGraph
`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 G – graph with all isolated nodes removed networkx.MultiDiGraph