User Reference#

This is the User Reference for the OSMnx package. If you are looking for an introduction to OSMnx, read the Getting Started guide.

This guide describes the usage of OSMnx’s public API. Every function can be accessed via ox.module_name.function_name() and many can also be accessed directly via ox.function_name() as a shortcut.

osmnx.bearing module#

Calculate graph edge bearings.

osmnx.bearing.add_edge_bearings(G, precision=None)#

Add compass bearing attributes to all graph edges.

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

Parameters:

G (networkx.MultiDiGraph) – unprojected graph
precision (int) – deprecated, do not use

Returns:

G – graph with edge bearing attributes

Return type:

networkx.MultiDiGraph

osmnx.bearing.calculate_bearing(lat1, lon1, lat2, lon2)#

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

Vectorized function to calculate initial bearings between two points’ coordinates or between arrays of points’ coordinates. Expects coordinates in decimal degrees. Bearing represents the clockwise angle in degrees between north and the geodesic line from (lat1, lon1) to (lat2, lon2).

Parameters:

lat1 (float or numpy.array of float) – first point’s latitude coordinate
lon1 (float or numpy.array of float) – first point’s longitude coordinate
lat2 (float or numpy.array of float) – second point’s latitude coordinate
lon2 (float or numpy.array of float) – second point’s longitude coordinate

Returns:

bearing – the bearing(s) in decimal degrees

Return type:

float or numpy.array of float

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

Calculate undirected graph’s orientation entropy.

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

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

Parameters:

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

Returns:

entropy – the graph’s orientation entropy

Return type:

float

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

Do not use: deprecated.

The plot_orientation function moved to the plot module. Calling it via the bearing module will raise an error in a future release.

Parameters:

Gu (networkx.MultiGraph) – deprecated, do not use
num_bins (int) – deprecated, do not use
min_length (float) – deprecated, do not use
weight (string) – deprecated, do not use
ax (matplotlib.axes.PolarAxesSubplot) – deprecated, do not use
figsize (tuple) – deprecated, do not use
area (bool) – deprecated, do not use
color (string) – deprecated, do not use
edgecolor (string) – deprecated, do not use
linewidth (float) – deprecated, do not use
alpha (float) – deprecated, do not use
title (string) – deprecated, do not use
title_y (float) – deprecated, do not use
title_font (dict) – deprecated, do not use
xtick_font (dict) – deprecated, do not use

Returns:

fig, ax – matplotlib figure, axis

Return type:

tuple

osmnx.distance module#

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

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

Add length attribute (in meters) to each edge.

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

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

Parameters:

G (networkx.MultiDiGraph) – unprojected, unsimplified input graph
precision (int) – deprecated, do not use
edges (tuple) – tuple of (u, v, k) tuples representing subset of edges to add length attributes to. if None, add lengths to all edges.

Returns:

G – graph with edge length attributes

Return type:

networkx.MultiDiGraph

osmnx.distance.euclidean(y1, x1, y2, x2)#

Calculate Euclidean distances between pairs of points.

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

Parameters:

y1 (float or numpy.array of float) – first point’s y coordinate
x1 (float or numpy.array of float) – first point’s x coordinate
y2 (float or numpy.array of float) – second point’s y coordinate
x2 (float or numpy.array of float) – second point’s x coordinate

Returns:

dist – distance from each (x1, y1) to each (x2, y2) in coordinates’ units

Return type:

float or numpy.array of float

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

Do not use, deprecated.

The euclidean_dist_vec function has been renamed euclidean. Calling euclidean_dist_vec will raise an error in a future release.

Parameters:

y1 (float or numpy.array of float) – first point’s y coordinate
x1 (float or numpy.array of float) – first point’s x coordinate
y2 (float or numpy.array of float) – second point’s y coordinate
x2 (float or numpy.array of float) – second point’s x coordinate

Returns:

dist – distance from each (x1, y1) to each (x2, y2) in coordinates’ units

Return type:

float or numpy.array of float

osmnx.distance.great_circle(lat1, lon1, lat2, lon2, earth_radius=6371009)#

Calculate great-circle distances between pairs of points.

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

Parameters:

lat1 (float or numpy.array of float) – first point’s latitude coordinate
lon1 (float or numpy.array of float) – first point’s longitude coordinate
lat2 (float or numpy.array of float) – second point’s latitude coordinate
lon2 (float or numpy.array of float) – second point’s longitude coordinate
earth_radius (float) – earth’s radius in units in which distance will be returned (default is meters)

Returns:

dist – distance from each (lat1, lon1) to each (lat2, lon2) in units of earth_radius

Return type:

float or numpy.array of float

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

Do not use, deprecated.

The great_circle_vec function has been renamed great_circle. Calling great_circle_vec will raise an error in a future release.

Parameters:

lat1 (float or numpy.array of float) – first point’s latitude coordinate
lng1 (float or numpy.array of float) – first point’s longitude coordinate
lat2 (float or numpy.array of float) – second point’s latitude coordinate
lng2 (float or numpy.array of float) – second point’s longitude coordinate
earth_radius (float) – earth’s radius in units in which distance will be returned (default is meters)

Returns:

dist – distance from each (lat1, lng1) to each (lat2, lng2) in units of earth_radius

Return type:

float or numpy.array of float

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

Do not use, deprecated.

The k_shortest_paths function has moved to the routing module. Calling it via the distance module will raise an error in a future release.

Parameters:

G (networkx.MultiDiGraph) – input graph
orig (int) – origin node ID
dest (int) – destination node ID
k (int) – number of shortest paths to solve
weight (string) – edge attribute to minimize when solving shortest paths. default is edge length in meters.

Yields:

path (list) – a generator of k shortest paths ordered by total weight. each path is a list of node IDs.

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

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

If X and Y are single coordinate values, this will return the nearest edge to that point. If X and Y are lists of coordinate values, this will return the nearest edge to each point. This function uses an R-tree spatial index and minimizes the euclidean distance from each point to the possible matches. For accurate results, use a projected graph and points.

Parameters:

G (networkx.MultiDiGraph) – graph in which to find nearest edges
X (float or list) – points’ x (longitude) coordinates, in same CRS/units as graph and containing no nulls
Y (float or list) – points’ y (latitude) coordinates, in same CRS/units as graph and containing no nulls
interpolate (float) – deprecated, do not use
return_dist (bool) – optionally also return distance between points and nearest edges

Returns:

ne or (ne, dist) – nearest edges as (u, v, key) or optionally a tuple where dist contains distances between the points and their nearest edges

Return type:

tuple or list

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

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

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

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

Parameters:

G (networkx.MultiDiGraph) – graph in which to find nearest nodes
X (float or list) – points’ x (longitude) coordinates, in same CRS/units as graph and containing no nulls
Y (float or list) – points’ y (latitude) coordinates, in same CRS/units as graph and containing no nulls
return_dist (bool) – optionally also return distance between points and nearest nodes

Returns:

nn or (nn, dist) – nearest node IDs or optionally a tuple where dist contains distances between the points and their nearest nodes

Return type:

int/list or tuple

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

Do not use, deprecated.

The shortest_path function has moved to the routing module. Calling it via the distance module will raise an error in a future release.

Parameters:

G (networkx.MultiDiGraph) – input graph
orig (int or list) – origin node ID, or a list of origin node IDs
dest (int or list) – destination node ID, or a list of destination node IDs
weight (string) – edge attribute to minimize when solving shortest path
cpus (int) – how many CPU cores to use; if None, use all available

Returns:

path – list of node IDs constituting the shortest path, or, if orig and dest are lists, then a list of path lists

Return type:

list

osmnx.elevation module#

Add node elevations from raster files or web APIs, and calculate edge grades.

osmnx.elevation.add_edge_grades(G, add_absolute=True, precision=None)#

Add grade attribute to each graph edge.

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

See also the add_node_elevations_raster and add_node_elevations_google functions.

Parameters:

G (networkx.MultiDiGraph) – input graph with elevation node attribute
add_absolute (bool) – if True, also add absolute value of grade as grade_abs attribute
precision (int) – deprecated, do not use

Returns:

G – graph with edge grade (and optionally grade_abs) attributes

Return type:

networkx.MultiDiGraph

osmnx.elevation.add_node_elevations_google(G, api_key=None, max_locations_per_batch=350, pause=0, precision=None, url_template='https://maps.googleapis.com/maps/api/elevation/json?locations={}&key={}')#

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

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

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

Parameters:

G (networkx.MultiDiGraph) – input graph
api_key (string) – a valid API key, can be None if the API does not require a key
max_locations_per_batch (int) – max number of coordinate pairs to submit in each API call (if this is too high, the server will reject the request because its character limit exceeds the max allowed)
pause (float) – time to pause between API calls, which can be increased if you get rate limited
precision (int) – deprecated, do not use
url_template (string) – a URL string template for the API endpoint, containing exactly two parameters: locations and key; for example, for Open Topo Data: “https://api.opentopodata.org/v1/aster30m?locations={}&key={}”

Returns:

G – graph with node elevation attributes

Return type:

networkx.MultiDiGraph

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

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

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

osmnx.features module#

Download OpenStreetMap geospatial features’ geometries and attributes.

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

For more details, see https://wiki.openstreetmap.org/wiki/Map_features and https://wiki.openstreetmap.org/wiki/Elements

Refer to the Getting Started guide for usage limitations.

osmnx.features.features_from_address(address, tags, dist=1000)#

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

You can use the settings module to retrieve a snapshot of historical OSM data as of a certain date, or to configure the Overpass server timeout, memory allocation, and other custom settings.

For more details, see: https://wiki.openstreetmap.org/wiki/Map_features

Parameters:

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

Returns:

gdf

Return type:

geopandas.GeoDataFrame

osmnx.features.features_from_bbox(north, south, east, west, tags)#

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

You can use the settings module to retrieve a snapshot of historical OSM data as of a certain date, or to configure the Overpass server timeout, memory allocation, and other custom settings.

For more details, see: https://wiki.openstreetmap.org/wiki/Map_features

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 elements in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.

Returns:

gdf

Return type:

geopandas.GeoDataFrame

osmnx.features.features_from_place(query, tags, which_result=None, buffer_dist=None)#

Create GeoDataFrame of OSM features within boundaries of some 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 features within it using the features_from_address function, which geocodes the place name to a point and gets the features within some distance of that point.

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

You can use the settings module to retrieve a snapshot of historical OSM data as of a certain date, or to configure the Overpass server timeout, memory allocation, and other custom settings.

For more details, see: https://wiki.openstreetmap.org/wiki/Map_features

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 elements 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) – deprecated, do not use

Returns:

gdf

Return type:

geopandas.GeoDataFrame

osmnx.features.features_from_point(center_point, tags, dist=1000)#

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

You can use the settings module to retrieve a snapshot of historical OSM data as of a certain date, or to configure the Overpass server timeout, memory allocation, and other custom settings.

For more details, see: https://wiki.openstreetmap.org/wiki/Map_features

Parameters:

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

Returns:

gdf

Return type:

geopandas.GeoDataFrame

osmnx.features.features_from_polygon(polygon, tags)#

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

You can use the settings module to retrieve a snapshot of historical OSM data as of a certain date, or to configure the Overpass server timeout, memory allocation, and other custom settings.

For more details, see: https://wiki.openstreetmap.org/wiki/Map_features

Parameters:

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

Returns:

gdf

Return type:

geopandas.GeoDataFrame

osmnx.features.features_from_xml(filepath, polygon=None, tags=None)#

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

Because this function creates a GeoDataFrame of features 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, features_from_xml() will return features for all of the tagged elements in the file. If they are supplied they will be used to filter the final GeoDataFrame.

For more details, see: https://wiki.openstreetmap.org/wiki/Map_features

Parameters:

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

Returns:

gdf

Return type:

geopandas.GeoDataFrame

osmnx.geocoder module#

Geocode place names or addresses or retrieve OSM elements by place name or ID.

This module uses the Nominatim API’s “search” and “lookup” endpoints. For more details see https://wiki.openstreetmap.org/wiki/Elements and https://nominatim.org/.

osmnx.geocoder.geocode(query)#

Geocode place names or addresses to (lat, lon) with the Nominatim API.

This geocodes the query via the Nominatim “search” endpoint.

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

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

Retrieve OSM elements by place name or OSM ID with the Nominatim API.

If searching by place name, the query argument can be a string or structured dict, or a list of such strings/dicts to send to the geocoder. This uses the Nominatim “search” endpoint to geocode the place name to the best-matching OSM element, then returns that element and its attribute data.

You can instead query by OSM ID by passing by_osmid=True. This uses the Nominatim “lookup” endpoint to retrieve the OSM element with that ID. In this case, the function treats the query argument as an OSM ID (or list of OSM IDs), which must be prepended with their types: node (N), way (W), or relation (R) in accordance with the Nominatim API format. For example, query=[“R2192363”, “N240109189”, “W427818536”].

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

Parameters:

query (string or dict or list of strings/dicts) – query string(s) or structured dict(s) to geocode
which_result (int) – which search result to return. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn’t return one. to get the top match regardless of geometry type, set which_result=1. ignored if by_osmid=True.
by_osmid (bool) – if True, treat query as an OSM ID lookup rather than text search
buffer_dist (float) – deprecated, do not use

Returns:

gdf – a GeoDataFrame with one row for each query

Return type:

geopandas.GeoDataFrame

osmnx.graph module#

Download and create graphs from OpenStreetMap data.

This module uses filters to query the Overpass API: you can either specify a built-in network type or provide your own custom filter with Overpass QL.

Refer to the Getting Started guide for usage limitations.

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=None, custom_filter=None)#

Download and create a graph within some distance of an address.

You can use the settings module to retrieve a snapshot of historical OSM data as of a certain date, or to configure the Overpass server timeout, memory allocation, and other custom settings.

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 {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None
simplify (bool) – if True, simplify graph topology with the simplify_graph function
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
truncate_by_edge (bool) – if True, retain nodes outside bounding box if at least one of node’s neighbors is within the bounding box
return_coords (bool) – optionally also return the geocoded coordinates of the address
clean_periphery (bool) – deprecated, do not use
custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.

Return type:

networkx.MultiDiGraph or optionally (networkx.MultiDiGraph, (lat, lon))

Notes

Very large query areas use the utils_geo._consolidate_subdivide_geometry function to automatically make multiple requests: see that function’s documentation for caveats.

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

Download and create a graph within some bounding box.

You can use the settings module to retrieve a snapshot of historical OSM data as of a certain date, or to configure the Overpass server timeout, memory allocation, and other custom settings.

Parameters:

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

Returns:

Return type:

networkx.MultiDiGraph

Notes

Very large query areas use the utils_geo._consolidate_subdivide_geometry function to automatically make multiple requests: see that function’s documentation for caveats.

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

Download and create a graph within the boundaries of some 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.

You can use the settings module to retrieve a snapshot of historical OSM data as of a certain date, or to configure the Overpass server timeout, memory allocation, and other custom settings.

Parameters:

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

Returns:

Return type:

networkx.MultiDiGraph

Notes

Very large query areas use the utils_geo._consolidate_subdivide_geometry function to automatically make multiple requests: see that function’s documentation for caveats.

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

Download and create a graph within some distance of a (lat, lon) point.

You can use the settings module to retrieve a snapshot of historical OSM data as of a certain date, or to configure the Overpass server timeout, memory allocation, and other custom settings.

Parameters:

center_point (tuple) – the (lat, lon) 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, {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None
simplify (bool) – if True, simplify graph topology with the simplify_graph function
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
truncate_by_edge (bool) – if True, retain nodes outside bounding box if at least one of node’s neighbors is within the bounding box
clean_periphery (bool,) – deprecated, do not use
custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.

Returns:

Return type:

networkx.MultiDiGraph

Notes

Very large query areas use the utils_geo._consolidate_subdivide_geometry function to automatically make multiple requests: see that function’s documentation for caveats.

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

Download and create a graph within the boundaries of a (multi)polygon.

You can use the settings module to retrieve a snapshot of historical OSM data as of a certain date, or to configure the Overpass server timeout, memory allocation, and other custom settings.

Parameters:

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

Returns:

Return type:

networkx.MultiDiGraph

Notes

Very large query areas use the utils_geo._consolidate_subdivide_geometry function to automatically make multiple requests: see that function’s documentation for caveats.

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

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

Do not load an XML file generated by OSMnx: this use case is not supported and may not behave as expected. To save/load graphs to/from disk for later use in OSMnx, use the io.save_graphml and io.load_graphml functions instead.

Parameters:

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

Returns:

Return type:

networkx.MultiDiGraph

osmnx.io module#

Serialize graphs to/from files on disk.

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

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

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

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

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

Parameters:

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

Returns:

Return type:

networkx.MultiDiGraph

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

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

Parameters:

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

Return type:

None

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

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

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

Parameters:

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

Return type:

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, api_version=0.6, precision=6)#

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

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

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

Example

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

Parameters:

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

Return type:

None

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

Save graph to disk as GraphML file.

Parameters:

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

Return type:

None

osmnx.plot module#

Visualize street networks, routes, orientations, and geospatial features.

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

Get n evenly-spaced colors from a matplotlib colormap.

Parameters:

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

Returns:

color_list

Return type:

list

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

Get colors based on edge attribute values.

Parameters:

G (networkx.MultiDiGraph) – input graph
attr (string) – name of a numerical edge attribute
num_bins (int) – if None, linearly map a color to each value. otherwise, assign values to this many bins then assign a color to each bin.
cmap (string) – name of a matplotlib colormap
start (float) – where to start in the colorspace
stop (float) – where to end in the colorspace
na_color (string) – what color to assign edges with missing attr values
equal_size (bool) – ignored if num_bins is None. if True, bin into equal-sized quantiles (requires unique bin edges). if False, bin into equal-spaced bins.

Returns:

edge_colors – series labels are edge IDs (u, v, key) and values are colors

Return type:

pandas.Series

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

Get colors based on node attribute values.

Parameters:

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

Returns:

node_colors – series labels are node IDs and values are colors

Return type:

pandas.Series

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

Plot a figure-ground diagram of a street network.

Parameters:

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

Returns:

fig, ax – matplotlib figure, axis

Return type:

tuple

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

Visualize a GeoDataFrame of geospatial features’ footprints.

Parameters:

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

Returns:

fig, ax – matplotlib figure, axis

Return type:

tuple

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

Visualize a graph.

Parameters:

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

Returns:

fig, ax – matplotlib figure, axis

Return type:

tuple

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

Visualize a route along a graph.

Parameters:

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

Returns:

fig, ax – matplotlib figure, axis

Return type:

tuple

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

Visualize several routes along a graph.

Parameters:

G (networkx.MultiDiGraph) – input graph
routes (list) – routes as a list of lists of node IDs
route_colors (string or list) – if string, 1 color for all routes. if list, the colors for each route.
route_linewidths (int or list) – if int, 1 linewidth for all routes. if list, the linewidth for each route.
pgr_kwargs – keyword arguments to pass to plot_graph_route

Returns:

fig, ax – matplotlib figure, axis

Return type:

tuple

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

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

Ignores self-loop edges as their bearings are undefined.

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

Parameters:

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

Returns:

fig, ax – matplotlib figure, axis

Return type:

tuple

osmnx.projection module#

Project a graph, GeoDataFrame, or geometry to a different CRS.

osmnx.projection.is_projected(crs)#

Determine if a coordinate reference system is projected or not.

Parameters:: crs (string or pyproj.CRS) – the identifier of the coordinate reference system, which can be anything accepted by pyproj.CRS.from_user_input() such as an authority string or a WKT string
Returns:: projected – True if crs is projected, otherwise False
Return type:: bool

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

Project a GeoDataFrame from its current CRS to another.

If to_latlong is True, this projects the GeoDataFrame to the CRS defined by settings.default_crs, otherwise it projects it to the CRS defined by to_crs. If to_crs is None, it projects it to the CRS of an appropriate UTM zone given gdf’s bounds.

Parameters:

gdf (geopandas.GeoDataFrame) – the GeoDataFrame to be projected
to_crs (string or pyproj.CRS) – if None, project to an appropriate UTM zone, otherwise project to this CRS
to_latlong (bool) – if True, project to settings.default_crs and ignore to_crs

Returns:

gdf_proj – the projected GeoDataFrame

Return type:

geopandas.GeoDataFrame

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

Project a Shapely geometry from its current CRS to another.

Parameters:

geometry (shapely geometry) – the geometry to be projected
crs (string or pyproj.CRS) – the initial CRS of geometry. if None, it will be set to settings.default_crs
to_crs (string or pyproj.CRS) – if None, project to an appropriate UTM zone, otherwise project to this CRS
to_latlong (bool) – if True, project to settings.default_crs and ignore to_crs

Returns:

geometry_proj, crs – the projected geometry and its new CRS

Return type:

tuple

osmnx.projection.project_graph(G, to_crs=None, to_latlong=False)#

Project a graph from its current CRS to another.

Parameters:

G (networkx.MultiDiGraph) – the graph to be projected
to_crs (string or pyproj.CRS) – if None, project to an appropriate UTM zone, otherwise project to this CRS
to_latlong (bool) – if True, project to settings.default_crs and ignore to_crs

Returns:

G_proj – the projected graph

Return type:

networkx.MultiDiGraph

osmnx.routing module#

Calculate weighted shortest paths between graph nodes.

osmnx.routing.k_shortest_paths(G, orig, dest, k, weight='length')#

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

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

Parameters:

G (networkx.MultiDiGraph) – input graph
orig (int) – origin node ID
dest (int) – destination node ID
k (int) – number of shortest paths to solve
weight (string) – edge attribute to minimize when solving shortest paths. default is edge length in meters.

Yields:

path (list) – a generator of k shortest paths ordered by total weight. each path is a list of node IDs.

osmnx.routing.shortest_path(G, orig, dest, weight='length', cpus=1)#

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

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

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

Parameters:

G (networkx.MultiDiGraph) – input graph
orig (int or list) – origin node ID, or a list of origin node IDs
dest (int or list) – destination node ID, or a list of destination node IDs
weight (string) – edge attribute to minimize when solving shortest path
cpus (int) – how many CPU cores to use; if None, use all available

Returns:

path – list of node IDs constituting the shortest path, or, if orig and dest are lists, then a list of path lists

Return type:

list

osmnx.settings module#

Global settings that can be configured by the user.

all_onewaybool: Only use if specifically saving to .osm XML file with the save_graph_xml function. If True, forces all ways to be loaded as oneway ways, preserving the original order of nodes stored in the OSM way XML. This also retains original OSM string values for oneway attribute values, rather than converting them to a True/False bool. Default is False.
bidirectional_network_typeslist: Network types for which a fully bidirectional graph will be created. Default is [“walk”].
cache_folderstring or pathlib.Path: Path to folder in which to save/load HTTP response cache, if the use_cache setting equals True. Default is “./cache”.
cache_only_modebool: If True, download network data from Overpass then raise a CacheOnlyModeInterrupt error for user to catch. This prevents graph building from taking place and instead just saves OSM response data to cache. Useful for sequentially caching lots of raw data (as you can only query Overpass one request at a time) then using the local cache to quickly build many graphs simultaneously with multiprocessing. Default is False.
data_folderstring or pathlib.Path: Path to folder in which to save/load graph files by default. Default is “./data”.
default_accept_languagestring: HTTP header accept-language. Default is “en”.
default_accessstring: Default filter for OSM “access” key. Default is ‘[“access”!~”private”]’. Note that also filtering out “access=no” ways prevents including transit-only bridges (e.g., Tilikum Crossing) from appearing in drivable road network (e.g., ‘[“access”!~”private|no”]’). However, some drivable tollroads have “access=no” plus a “access:conditional” key to clarify when it is accessible, so we can’t filter out all “access=no” ways by default. Best to be permissive here then remove complicated combinations of tags programatically after the full graph is downloaded and constructed.
default_crsstring: Default coordinate reference system to set when creating graphs. Default is “epsg:4326”.
default_refererstring: HTTP header referer. Default is “OSMnx Python package (https://github.com/gboeing/osmnx)”.
default_user_agentstring: HTTP header user-agent. Default is “OSMnx Python package (https://github.com/gboeing/osmnx)”.
doh_url_templatestring: Endpoint to resolve DNS-over-HTTPS if local DNS resolution fails. Set to None to disable DoH, but see downloader._config_dns documentation for caveats. Default is: “https://8.8.8.8/resolve?name={hostname}”
imgs_folderstring or pathlib.Path: Path to folder in which to save plotted images by default. Default is “./images”.
log_filebool: If True, save log output to a file in logs_folder. Default is False.
log_filenamestring: Name of the log file, without file extension. Default is “osmnx”.
log_consolebool: If True, print log output to the console (terminal window). Default is False.
log_levelint: One of Python’s logger.level constants. Default is logging.INFO.
log_namestring: Name of the logger. Default is “OSMnx”.
logs_folderstring or pathlib.Path: Path to folder in which to save log files. Default is “./logs”.
max_query_area_sizeint: Maximum area for any part of the geometry in meters: any polygon bigger than this will get divided up for multiple queries to the API. Default is 2500000000.
memoryint: Overpass server memory allocation size for the query, in bytes. If None, server will use its default allocation size. Use with caution. Default is None.
nominatim_endpointstring: The base API url to use for Nominatim queries. Default is “https://nominatim.openstreetmap.org/”.
nominatim_keystring: Your Nominatim API key, if you are using an API instance that requires one. Default is None.
osm_xml_node_attrslist: Node attributes for saving .osm XML files with save_graph_xml function. Default is [“id”, “timestamp”, “uid”, “user”, “version”, “changeset”, “lat”, “lon”].
osm_xml_node_tagslist: Node tags for saving .osm XML files with save_graph_xml function. Default is [“highway”].
osm_xml_way_attrslist: Edge attributes for saving .osm XML files with save_graph_xml function. Default is [“id”, “timestamp”, “uid”, “user”, “version”, “changeset”].
osm_xml_way_tagslist: Edge tags for for saving .osm XML files with save_graph_xml function. Default is [“highway”, “lanes”, “maxspeed”, “name”, “oneway”].
overpass_endpointstring: The base API url to use for overpass queries. Default is “https://overpass-api.de/api”.
overpass_rate_limitbool: If True, check the Overpass server status endpoint for how long to pause before making request. Necessary if server uses slot management, but can be set to False if you are running your own overpass instance without rate limiting. Default is True.
overpass_settingsstring: Settings string for Overpass queries. Default is “[out:json][timeout:{timeout}]{maxsize}”. By default, the {timeout} and {maxsize} values are set dynamically by OSMnx when used. To query, for example, historical OSM data as of a certain date: ‘[out:json][timeout:90][date:”2019-10-28T19:20:00Z”]’. Use with caution.
requests_kwargsdict: Optional keyword args to pass to the requests package when connecting to APIs, for example to configure authentication or provide a path to a local certificate file. More info on options such as auth, cert, verify, and proxies can be found in the requests package advanced docs. Default is {}.
timeoutint: The timeout interval in seconds for HTTP requests, and (when applicable) for API to use while running the query. Default is 180.
use_cachebool: If True, cache HTTP responses locally instead of calling API repeatedly for the same request. Default is True.
useful_tags_nodelist: OSM “node” tags to add as graph node attributes, when present in the data retrieved from OSM. Default is [“ref”, “highway”].
useful_tags_waylist: OSM “way” tags to add as graph edge attributes, when present in the data retrieved from OSM. Default is [“bridge”, “tunnel”, “oneway”, “lanes”, “ref”, “name”, “highway”, “maxspeed”, “service”, “access”, “area”, “landuse”, “width”, “est_width”, “junction”].

osmnx.simplification module#

Simplify, correct, and consolidate network topology.

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

Consolidate intersections comprising clusters of nearby nodes.

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

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

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

Parameters:

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

Returns:

if rebuild_graph=True, returns MultiDiGraph with consolidated intersections and reconnected edge geometries. if rebuild_graph=False, returns GeoSeries of shapely Points representing the centroids of street intersections

Return type:

networkx.MultiDiGraph or geopandas.GeoSeries

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

Simplify a graph’s topology by removing interstitial nodes.

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

Parameters:

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

Returns:

G – topologically simplified graph, with a new geometry attribute on each simplified edge

Return type:

networkx.MultiDiGraph

osmnx.speed module#

Calculate graph edge speeds and travel times.

osmnx.speed.add_edge_speeds(G, hwy_speeds=None, fallback=None, precision=None, agg=numpy.mean)#

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

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

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

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

Parameters:

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

Returns:

G – graph with speed_kph attributes on all edges

Return type:

networkx.MultiDiGraph

osmnx.speed.add_edge_travel_times(G, precision=None)#

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) – deprecated, do not use

Returns:

G – graph with travel_time attributes on all edges

Return type:

networkx.MultiDiGraph

osmnx.stats module#

Calculate geometric and topological network measures.

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

You can use NetworkX directly for additional topological network measures.

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

Calculate basic descriptive geometric and topological measures of a graph.

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

Parameters:

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

Returns:

stats –

dictionary containing the following keys

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

Return type:

dict

osmnx.stats.circuity_avg(Gu)#

Calculate average street circuity using edges of undirected graph.

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

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

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

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

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

Parameters:

G (networkx.MultiDiGraph) – input graph
nodes (list) – which node IDs to get counts for. if None, use all graph nodes, otherwise calculate counts only for these node IDs

Returns:

streets_per_node – counts of how many physical streets connect to each node, with keys = node ids and values = counts

Return type:

dict

osmnx.stats.edge_length_total(G)#

Calculate graph’s total edge length.

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

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

Count the intersections in a graph.

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

Parameters:

G (networkx.MultiDiGraph) – input graph
min_streets (int) – a node must have at least min_streets incident on them to count as an intersection

Returns:

count – count of intersections in graph

Return type:

int

osmnx.stats.self_loop_proportion(Gu)#

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

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

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

osmnx.stats.street_length_total(Gu)#

Calculate graph’s total street segment length.

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

osmnx.stats.street_segment_count(Gu)#

Count the street segments in a graph.

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

osmnx.stats.streets_per_node(G)#

Count streets (undirected edges) incident on each node.

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

osmnx.stats.streets_per_node_avg(G)#

Calculate graph’s average count of streets per node.

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

osmnx.stats.streets_per_node_counts(G)#

Calculate streets-per-node counts.

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

osmnx.stats.streets_per_node_proportions(G)#

Calculate streets-per-node proportions.

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

osmnx.truncate module#

Truncate graph by distance, bounding box, or polygon.

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

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

Parameters:

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

Returns:

G – the truncated graph

Return type:

networkx.MultiDiGraph

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

Remove every node farther than some network distance from source_node.

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

Parameters:

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

Returns:

G – the truncated graph

Return type:

networkx.MultiDiGraph

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

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

Parameters:

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

Returns:

G – the truncated graph

Return type:

networkx.MultiDiGraph

osmnx.utils module#

General utility functions.

osmnx.utils.citation(style='bibtex')#

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

Parameters:: style (string {"apa", "bibtex", "ieee"}) – citation format, either APA or BibTeX or IEEE
Return type:: None

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

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

Parameters:

all_oneway (bool) – deprecated
bidirectional_network_types (list) – deprecated
cache_folder (string or pathlib.Path) – deprecated
data_folder (string or pathlib.Path) – deprecated
cache_only_mode (bool) – deprecated
default_accept_language (string) – deprecated
default_access (string) – deprecated
default_crs (string) – deprecated
default_referer (string) – deprecated
default_user_agent (string) – deprecated
imgs_folder (string or pathlib.Path) – deprecated
log_file (bool) – deprecated
log_filename (string) – deprecated
log_console (bool) – deprecated
log_level (int) – deprecated
log_name (string) – deprecated
logs_folder (string or pathlib.Path) – deprecated
max_query_area_size (int) – deprecated
memory (int) – deprecated
nominatim_endpoint (string) – deprecated
nominatim_key (string) – deprecated
osm_xml_node_attrs (list) – deprecated
osm_xml_node_tags (list) – deprecated
osm_xml_way_attrs (list) – deprecated
osm_xml_way_tags (list) – deprecated
overpass_endpoint (string) – deprecated
overpass_rate_limit (bool) – deprecated
overpass_settings (string) – deprecated
requests_kwargs (dict) – deprecated
timeout (int) – deprecated
use_cache (bool) – deprecated
useful_tags_node (list) – deprecated
useful_tags_way (list) – deprecated

Return type:

None

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

Write a message to the logger.

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

Parameters:

message (string) – the message to log
level (int) – one of Python’s logger.level constants
name (string) – name of the logger
filename (string) – name of the log file, without file extension

Return type:

None

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

Return current local timestamp as a string.

Parameters:

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

Returns:

ts – local timestamp string

Return type:

string

osmnx.utils_geo module#

Geospatial utility functions.

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

Create a bounding box from a (lat, lon) 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, lon) center point to create the bounding box around
dist (int) – bounding box distance in meters from the center point
project_utm (bool) – if True, return bounding box as UTM-projected coordinates
return_crs (bool) – if True, and project_utm=True, return the projected CRS too

Returns:

(north, south, east, west) or (north, south, east, west, crs_proj)

Return type:

tuple

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

Convert bounding box coordinates to shapely Polygon.

Parameters:

north (float) – northern coordinate
south (float) – southern coordinate
east (float) – eastern coordinate
west (float) – western coordinate

Return type:

shapely.geometry.Polygon

osmnx.utils_geo.interpolate_points(geom, dist)#

Interpolate evenly spaced points along a LineString.

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

Parameters:

geom (shapely.geometry.LineString) – a LineString geometry
dist (float) – spacing distance between interpolated points, in same units as geom. smaller values generate more points.

Yields:

point (tuple of floats) – a generator of (x, y) tuples of the interpolated points’ coordinates

osmnx.utils_geo.round_geometry_coords(geom, precision)#

Do not use: deprecated.

Parameters:

geom (shapely.geometry.geometry {Point, MultiPoint, LineString, MultiLineString, Polygon, MultiPolygon}) – deprecated, do not use
precision (int) – deprecated, do not use

Return type:

shapely.geometry.geometry

osmnx.utils_geo.sample_points(G, n)#

Randomly sample points constrained to a spatial graph.

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

Parameters:

G (networkx.MultiGraph) – graph to sample points from; should be undirected (to not oversample bidirectional edges) and projected (for accurate point interpolation)
n (int) – how many points to sample

Returns:

points – the sampled points, multi-indexed by (u, v, key) of the edge from which each point was drawn

Return type:

geopandas.GeoSeries

osmnx.utils_graph module#

Graph utility functions.

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

Convert MultiDiGraph to DiGraph.

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

Parameters:

G (networkx.MultiDiGraph) – input graph
weight (string) – attribute value to minimize when choosing between parallel edges

Return type:

networkx.DiGraph

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

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

Parameters:

G (networkx.MultiDiGraph) – input graph
strongly (bool) – if True, return the largest strongly instead of weakly connected component

Returns:

G – the largest connected component subgraph of the original graph

Return type:

networkx.MultiDiGraph

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

Do not use: deprecated.

Use the route_to_gdf function instead.

Parameters:

G (networkx.MultiDiGraph) – deprecated
route (list) – deprecated
attribute (string) – deprecated
minimize_key (string) – deprecated
retrieve_default (Callable[Tuple[Any, Any], Any]) – deprecated

Returns:

attribute_values – deprecated

Return type:

list

osmnx.utils_graph.get_undirected(G)#

Convert MultiDiGraph to undirected MultiGraph.

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

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

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

Convert node and edge GeoDataFrames to a MultiDiGraph.

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

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

Parameters:

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

Returns:

Return type:

networkx.MultiDiGraph

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

Convert a MultiDiGraph to node and/or edge GeoDataFrames.

This function is the inverse of graph_from_gdfs.

Parameters:

G (networkx.MultiDiGraph) – input graph
nodes (bool) – if True, convert graph nodes to a GeoDataFrame and return it
edges (bool) – if True, convert graph edges to a GeoDataFrame and return it
node_geometry (bool) – if True, create a geometry column from node x and y attributes
fill_edge_geometry (bool) – if True, fill in missing edge geometry fields using nodes u and v

Returns:

gdf_nodes or gdf_edges or tuple of (gdf_nodes, gdf_edges). gdf_nodes is indexed by osmid and gdf_edges is multi-indexed by u, v, key following normal MultiDiGraph structure.

Return type:

geopandas.GeoDataFrame or tuple

osmnx.utils_graph.remove_isolated_nodes(G)#

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

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

osmnx.utils_graph.route_to_gdf(G, route, weight='length')#

Return a GeoDataFrame of the edges in a path, in order.

Parameters:

G (networkx.MultiDiGraph) – input graph
route (list) – list of node IDs constituting the path
weight (string) – if there are parallel edges between two nodes, choose lowest weight

Returns:

gdf_edges – GeoDataFrame of the edges

Return type:

geopandas.GeoDataFrame