User reference¶
User reference for the OSMnx package.
This guide covers usage of all public modules and functions. Every function can be accessed via ox.module_name.function_name() and the vast majority of them can also be accessed directly via ox.function_name() as a shortcut. Only a few less-common functions are accessible only via ox.module_name.function_name().
osmnx.bearing module¶
Calculate graph edge bearings.
- osmnx.bearing.add_edge_bearings(G, precision=1)¶
Add compass bearing attributes to all graph edges.
Vectorized function to calculate (initial) bearing from origin node to destination node for each edge in a directed, unprojected graph then add these bearings as new edge attributes. Bearing represents angle in degrees (clockwise) between north and the geodesic line from the origin node to the destination node. Ignores self-loop edges as their bearings are undefined.
- Parameters
G (networkx.MultiDiGraph) – unprojected graph
precision (int) – decimal precision to round bearing
- Returns
G – graph with edge bearing attributes
- Return type
networkx.MultiDiGraph
- osmnx.bearing.calculate_bearing(lat1, lng1, lat2, lng2)¶
Calculate the compass bearing(s) between pairs of lat-lng points.
Vectorized function to calculate (initial) bearings between two points’ coordinates or between arrays of points’ coordinates. Expects coordinates in decimal degrees. Bearing represents angle in degrees (clockwise) between north and the geodesic line from point 1 to point 2.
- Parameters
lat1 (float or numpy.array of float) – first point’s latitude coordinate
lng1 (float or numpy.array of float) – first point’s longitude coordinate
lat2 (float or numpy.array of float) – second point’s latitude coordinate
lng2 (float or numpy.array of float) – second point’s longitude coordinate
- Returns
bearing – the bearing(s) in decimal degrees
- Return type
float or numpy.array of float
- osmnx.bearing.orientation_entropy(Gu, num_bins=36, min_length=0, weight=None)¶
Calculate undirected graph’s orientation entropy.
Orientation entropy is the entropy of its edges’ bidirectional bearings across evenly spaced bins. Ignores self-loop edges as their bearings are undefined.
- Parameters
Gu (networkx.MultiGraph) – undirected, unprojected graph with bearing attributes on each edge
num_bins (int) – number of bins; for example, if num_bins=36 is provided, then each bin will represent 10° around the compass
min_length (float) – ignore edges with length attributes less than min_length; useful to ignore the noise of many very short edges
weight (string) – if not None, weight edges’ bearings by this (non-null) edge attribute. for example, if “length” is provided, this will return 1 bearing observation per meter per street, which could result in a very large bearings array.
- Returns
entropy – the graph’s orientation entropy
- Return type
float
- osmnx.bearing.plot_orientation(Gu, num_bins=36, min_length=0, weight=None, ax=None, figsize=(5, 5), area=True, color='#003366', edgecolor='k', linewidth=0.5, alpha=0.7, title=None, title_y=1.05, title_font=None, xtick_font=None)¶
Plot a polar histogram of a spatial network’s bidirectional edge bearings.
Ignores self-loop edges as their bearings are undefined.
For more info see: Boeing, G. 2019. “Urban Spatial Order: Street Network Orientation, Configuration, and Entropy.” Applied Network Science, 4 (1), 67. https://doi.org/10.1007/s41109-019-0189-1
- Parameters
Gu (networkx.MultiGraph) – undirected, unprojected graph with bearing attributes on each edge
num_bins (int) – number of bins; for example, if num_bins=36 is provided, then each bin will represent 10° around the compass
min_length (float) – ignore edges with length attributes less than min_length
weight (string) – if not None, weight edges’ bearings by this (non-null) edge attribute
ax (matplotlib.axes.PolarAxesSubplot) – if not None, plot on this preexisting axis; must have projection=polar
figsize (tuple) – if ax is None, create new figure with size (width, height)
area (bool) – if True, set bar length so area is proportional to frequency, otherwise set bar length so height is proportional to frequency
color (string) – color of histogram bars
edgecolor (string) – color of histogram bar edges
linewidth (float) – width of histogram bar edges
alpha (float) – opacity of histogram bars
title (string) – title for plot
title_y (float) – y position to place title
title_font (dict) – the title’s fontdict to pass to matplotlib
xtick_font (dict) – the xtick labels’ fontdict to pass to matplotlib
- Returns
fig, ax – matplotlib figure, axis
- Return type
tuple
osmnx.distance module¶
Calculate distances and shortest paths and find nearest node/edge(s) to point(s).
- osmnx.distance.add_edge_lengths(G, precision=3, edges=None)¶
Add length attribute (in meters) to each edge.
Vectorized function to calculate great-circle distance between each edge’s incident nodes. Ensure graph is in unprojected coordinates, and unsimplified to get accurate distances.
Note: this function is run by all the graph.graph_from_x functions automatically to add length attributes to all edges. It calculates edge lengths as the great-circle distance from node u to node v. When OSMnx automatically runs this function upon graph creation, it does it before simplifying the graph: thus it calculates the straight-line lengths of edge segments that are themselves all straight. Only after simplification do edges take on a (potentially) curvilinear geometry. If you wish to calculate edge lengths later, you are calculating straight-line distances which necessarily ignore the curvilinear geometry. You only want to run this function on a graph with all straight edges (such as is the case with an unsimplified graph).
- Parameters
G (networkx.MultiDiGraph) – unprojected, unsimplified input graph
precision (int) – decimal precision to round lengths
edges (tuple) – tuple of (u, v, k) tuples representing subset of edges to add length attributes to. if None, add lengths to all edges.
- Returns
G – graph with edge length attributes
- Return type
networkx.MultiDiGraph
- osmnx.distance.euclidean_dist_vec(y1, x1, y2, x2)¶
Calculate Euclidean distances between pairs of points.
Vectorized function to calculate the Euclidean distance between two points’ coordinates or between arrays of points’ coordinates. For accurate results, use projected coordinates rather than decimal degrees.
- Parameters
y1 (float or numpy.array of float) – first point’s y coordinate
x1 (float or numpy.array of float) – first point’s x coordinate
y2 (float or numpy.array of float) – second point’s y coordinate
x2 (float or numpy.array of float) – second point’s x coordinate
- Returns
dist – distance from each (x1, y1) to each (x2, y2) in coordinates’ units
- Return type
float or numpy.array of float
- osmnx.distance.great_circle_vec(lat1, lng1, lat2, lng2, earth_radius=6371009)¶
Calculate great-circle distances between pairs of points.
Vectorized function to calculate the great-circle distance between two points’ coordinates or between arrays of points’ coordinates using the haversine formula. Expects coordinates in decimal degrees.
- Parameters
lat1 (float or numpy.array of float) – first point’s latitude coordinate
lng1 (float or numpy.array of float) – first point’s longitude coordinate
lat2 (float or numpy.array of float) – second point’s latitude coordinate
lng2 (float or numpy.array of float) – second point’s longitude coordinate
earth_radius (float) – earth’s radius in units in which distance will be returned (default is meters)
- Returns
dist – distance from each (lat1, lng1) to each (lat2, lng2) in units of earth_radius
- Return type
float or numpy.array of float
- osmnx.distance.k_shortest_paths(G, orig, dest, k, weight='length')¶
Solve k shortest paths from an origin node to a destination node.
See also shortest_path to get just the one shortest path.
- Parameters
G (networkx.MultiDiGraph) – input graph
orig (int) – origin node ID
dest (int) – destination node ID
k (int) – number of shortest paths to solve
weight (string) – edge attribute to minimize when solving shortest paths. default is edge length in meters.
- Returns
paths – a generator of k shortest paths ordered by total weight. each path is a list of node IDs.
- Return type
generator
- osmnx.distance.nearest_edges(G, X, Y, interpolate=None, return_dist=False)¶
Find the nearest edge to a point or to each of several points.
If X and Y are single coordinate values, this will return the nearest edge to that point. If X and Y are lists of coordinate values, this will return the nearest edge to each point.
If interpolate is None, search for the nearest edge to each point, one at a time, using an r-tree and minimizing the euclidean distances from the point to the possible matches. For accuracy, use a projected graph and points. This method is precise and also fastest if searching for few points relative to the graph’s size.
For a faster method if searching for many points relative to the graph’s size, use the interpolate argument to interpolate points along the edges and index them. If the graph is projected, this uses a k-d tree for euclidean nearest neighbor search, which requires that scipy is installed as an optional dependency. If graph is unprojected, this uses a ball tree for haversine nearest neighbor search, which requires that scikit-learn is installed as an optional dependency.
- Parameters
G (networkx.MultiDiGraph) – graph in which to find nearest edges
X (float or list) – points’ x (longitude) coordinates, in same CRS/units as graph and containing no nulls
Y (float or list) – points’ y (latitude) coordinates, in same CRS/units as graph and containing no nulls
interpolate (float) – spacing distance between interpolated points, in same units as graph. smaller values generate more points.
return_dist (bool) – optionally also return distance between points and nearest edges
- Returns
ne or (ne, dist) – nearest edges as (u, v, key) or optionally a tuple where dist contains distances between the points and their nearest edges
- Return type
tuple or list
- osmnx.distance.nearest_nodes(G, X, Y, return_dist=False)¶
Find the nearest node to a point or to each of several points.
If X and Y are single coordinate values, this will return the nearest node to that point. If X and Y are lists of coordinate values, this will return the nearest node to each point.
If the graph is projected, this uses a k-d tree for euclidean nearest neighbor search, which requires that scipy is installed as an optional dependency. If it is unprojected, this uses a ball tree for haversine nearest neighbor search, which requires that scikit-learn is installed as an optional dependency.
- Parameters
G (networkx.MultiDiGraph) – graph in which to find nearest nodes
X (float or list) – points’ x (longitude) coordinates, in same CRS/units as graph and containing no nulls
Y (float or list) – points’ y (latitude) coordinates, in same CRS/units as graph and containing no nulls
return_dist (bool) – optionally also return distance between points and nearest nodes
- Returns
nn or (nn, dist) – nearest node IDs or optionally a tuple where dist contains distances between the points and their nearest nodes
- Return type
int/list or tuple
- osmnx.distance.shortest_path(G, orig, dest, weight='length', cpus=1)¶
Solve shortest path from origin node(s) to destination node(s).
If orig and dest are single node IDs, this will return a list of the nodes constituting the shortest path between them. If orig and dest are lists of node IDs, this will return a list of lists of the nodes constituting the shortest path between each origin-destination pair. If a path cannot be solved, this will return None for that path. You can parallelize solving multiple paths with the cpus parameter, but be careful to not exceed your available RAM.
See also k_shortest_paths to solve multiple shortest paths between a single origin and destination. For additional functionality or different solver algorithms, use NetworkX directly.
- Parameters
G (networkx.MultiDiGraph) – input graph
orig (int or list) – origin node ID, or a list of origin node IDs
dest (int or list) – destination node ID, or a list of destination node IDs
weight (string) – edge attribute to minimize when solving shortest path
cpus (int) – how many CPU cores to use; if None, use all available
- Returns
path – list of node IDs constituting the shortest path, or, if orig and dest are lists, then a list of path lists
- Return type
list
osmnx.downloader module¶
Interact with the OSM APIs.
- osmnx.downloader.nominatim_request(params, request_type='search', pause=1, error_pause=60)¶
Send a HTTP GET request to the Nominatim API and return JSON response.
- Parameters
params (OrderedDict) – key-value pairs of parameters
request_type (string {"search", "reverse", "lookup"}) – which Nominatim API endpoint to query
pause (int) – how long to pause before request, in seconds. per the nominatim usage policy: “an absolute maximum of 1 request per second” is allowed
error_pause (int) – how long to pause in seconds before re-trying request if error
- Returns
response_json
- Return type
dict
- osmnx.downloader.overpass_request(data, pause=None, error_pause=60)¶
Send a HTTP POST request to the Overpass API and return JSON response.
- Parameters
data (OrderedDict) – key-value pairs of parameters
pause (int) – how long to pause in seconds before request, if None, will query API status endpoint to find when next slot is available
error_pause (int) – how long to pause in seconds (in addition to pause) before re-trying request if error
- Returns
response_json
- Return type
dict
osmnx.elevation module¶
Get node elevations and calculate edge grades.
- osmnx.elevation.add_edge_grades(G, add_absolute=True, precision=3)¶
Add grade attribute to each graph edge.
Vectorized function to calculate the directed grade (ie, rise over run) for each edge in the graph and add it to the edge as an attribute. Nodes must already have elevation attributes to use this function.
See also the add_node_elevations_raster and add_node_elevations_google functions.
- Parameters
G (networkx.MultiDiGraph) – input graph with elevation node attribute
add_absolute (bool) – if True, also add absolute value of grade as grade_abs attribute
precision (int) – decimal precision to round grade values
- Returns
G – graph with edge grade (and optionally grade_abs) attributes
- Return type
networkx.MultiDiGraph
- osmnx.elevation.add_node_elevations_google(G, api_key, max_locations_per_batch=350, pause_duration=0, precision=3)¶
Add elevation (meters) attribute to each node using a web service.
This uses the Google Maps Elevation API and requires an API key. 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 Google Maps Elevation API key
max_locations_per_batch (int) – max number of coordinate pairs to submit in each API call (if this is too high, the server will reject the request because its character limit exceeds the max allowed)
pause_duration (float) – time to pause between API calls, which can be increased if you get rate limited
precision (int) – decimal precision to round elevation values
- 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.
See also the add_edge_grades function.
- Parameters
G (networkx.MultiDiGraph) – input graph, in same CRS as raster
filepath (string or pathlib.Path or list of strings/Paths) – path (or list of paths) to the raster file(s) to query
band (int) – which raster band to query
cpus (int) – how many CPU cores to use; if None, use all available
- Returns
G – graph with node elevation attributes
- Return type
networkx.MultiDiGraph
osmnx.folium module¶
Create interactive Leaflet web maps of graphs and routes via folium.
- osmnx.folium.plot_graph_folium(G, graph_map=None, popup_attribute=None, tiles='cartodbpositron', zoom=1, fit_bounds=True, **kwargs)¶
Plot a graph as an interactive Leaflet web map.
Note that anything larger than a small city can produce a large web map file that is slow to render in your browser.
- Parameters
G (networkx.MultiDiGraph) – input graph
graph_map (folium.folium.Map) – if not None, plot the graph on this preexisting folium map object
popup_attribute (string) – edge attribute to display in a pop-up when an edge is clicked
tiles (string) – name of a folium tileset
zoom (int) – initial zoom level for the map
fit_bounds (bool) – if True, fit the map to the boundaries of the graph’s edges
kwargs – keyword arguments to pass to folium.PolyLine(), see folium docs for options (for example color=”#333333”, weight=5, opacity=0.7)
- Return type
folium.folium.Map
- osmnx.folium.plot_route_folium(G, route, route_map=None, popup_attribute=None, tiles='cartodbpositron', zoom=1, fit_bounds=True, **kwargs)¶
Plot a route as an interactive Leaflet web map.
- Parameters
G (networkx.MultiDiGraph) – input graph
route (list) – the route as a list of nodes
route_map (folium.folium.Map) – if not None, plot the route on this preexisting folium map object
popup_attribute (string) – edge attribute to display in a pop-up when an edge is clicked
tiles (string) – name of a folium tileset
zoom (int) – initial zoom level for the map
fit_bounds (bool) – if True, fit the map to the boundaries of the route’s edges
kwargs – keyword arguments to pass to folium.PolyLine(), see folium docs for options (for example color=”#cc0000”, weight=5, opacity=0.7)
- Return type
folium.folium.Map
osmnx.geocoder module¶
Geocode queries and create GeoDataFrames of place boundaries.
- osmnx.geocoder.geocode(query)¶
Geocode a query string to (lat, lng) with the Nominatim geocoder.
- Parameters
query (string) – the query string to geocode
- Returns
point – the (lat, lng) coordinates returned by the geocoder
- Return type
tuple
- osmnx.geocoder.geocode_to_gdf(query, which_result=None, by_osmid=False, buffer_dist=None)¶
Retrieve place(s) by name or ID from the Nominatim API as a GeoDataFrame.
You can query by place name or OSM ID. If querying by place name, the query argument can be a string or structured dict, or a list of such strings/dicts to send to geocoder. You can instead query by OSM ID by setting by_osmid=True. In this case, geocode_to_gdf treats the query argument as an OSM ID (or list of OSM IDs) for Nominatim lookup rather than text search. OSM IDs must be prepended with their types: node (N), way (W), or relation (R), in accordance with the Nominatim format. For example, query=[“R2192363”, “N240109189”, “W427818536”].
If query argument is a list, then which_result should be either a single value or a list with the same length as query. The queries you provide must be resolvable to places in the Nominatim database. The resulting GeoDataFrame’s geometry column contains place boundaries if they exist in OpenStreetMap.
- Parameters
query (string or dict or list) – query string(s) or structured dict(s) to geocode
which_result (int) – which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn’t return one. to get the top match regardless of geometry type, set which_result=1
by_osmid (bool) – if True, handle query as an OSM ID for lookup rather than text search
buffer_dist (float) – distance to buffer around the place geometry, in meters
- Returns
gdf – a GeoDataFrame with one row for each query
- Return type
geopandas.GeoDataFrame
osmnx.geometries module¶
Download geospatial entities’ geometries and attributes from OpenStreetMap.
Retrieve points of interest, building footprints, or any other objects from OSM, including their geometries and attribute data, and construct a GeoDataFrame of them. You can use this module to query for nodes, ways, and relations (the latter of type “multipolygon” or “boundary” only) by passing a dictionary of desired tags/values.
- osmnx.geometries.geometries_from_address(address, tags, dist=1000)¶
Create GeoDataFrame of OSM entities within some distance N, S, E, W of address.
- Parameters
address (string) – the address to geocode and use as the central point around which to get the geometries
tags (dict) – Dict of tags used for finding objects in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.
dist (numeric) – distance in meters
- Returns
gdf
- Return type
geopandas.GeoDataFrame
Notes
You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module.
- osmnx.geometries.geometries_from_bbox(north, south, east, west, tags)¶
Create a GeoDataFrame of OSM entities within a N, S, E, W bounding box.
- Parameters
north (float) – northern latitude of bounding box
south (float) – southern latitude of bounding box
east (float) – eastern longitude of bounding box
west (float) – western longitude of bounding box
tags (dict) – Dict of tags used for finding objects in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.
- Returns
gdf
- Return type
geopandas.GeoDataFrame
Notes
You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module.
- osmnx.geometries.geometries_from_place(query, tags, which_result=None, buffer_dist=None)¶
Create GeoDataFrame of OSM entities within boundaries of geocodable place(s).
The query must be geocodable and OSM must have polygon boundaries for the geocode result. If OSM does not have a polygon for this place, you can instead get geometries within it using the geometries_from_address function, which geocodes the place name to a point and gets the geometries within some distance of that point.
If OSM does have polygon boundaries for this place but you’re not finding it, try to vary the query string, pass in a structured query dict, or vary the which_result argument to use a different geocode result. If you know the OSM ID of the place, you can retrieve its boundary polygon using the geocode_to_gdf function, then pass it to the geometries_from_polygon function.
- Parameters
query (string or dict or list) – the query or queries to geocode to get place boundary polygon(s)
tags (dict) – Dict of tags used for finding objects in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.
which_result (int) – which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn’t return one.
buffer_dist (float) – distance to buffer around the place geometry, in meters
- Returns
gdf
- Return type
geopandas.GeoDataFrame
Notes
You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module.
- osmnx.geometries.geometries_from_point(center_point, tags, dist=1000)¶
Create GeoDataFrame of OSM entities within some distance N, S, E, W of a point.
- Parameters
center_point (tuple) – the (lat, lng) center point around which to get the geometries
tags (dict) – Dict of tags used for finding objects in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.
dist (numeric) – distance in meters
- Returns
gdf
- Return type
geopandas.GeoDataFrame
Notes
You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module.
- osmnx.geometries.geometries_from_polygon(polygon, tags)¶
Create GeoDataFrame of OSM entities within boundaries of a (multi)polygon.
- Parameters
polygon (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – geographic boundaries to fetch geometries within
tags (dict) – Dict of tags used for finding objects in the selected area. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.
- Returns
gdf
- Return type
geopandas.GeoDataFrame
Notes
You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module.
- osmnx.geometries.geometries_from_xml(filepath, polygon=None, tags=None)¶
Create a GeoDataFrame of OSM entities in an OSM-formatted XML file.
Because this function creates a GeoDataFrame of geometries from an OSM-formatted XML file that has already been downloaded (i.e. no query is made to the Overpass API) the polygon and tags arguments are not required. If they are not supplied to the function, geometries_from_xml() will return geometries for all of the tagged elements in the file. If they are supplied they will be used to filter the final GeoDataFrame.
- Parameters
filepath (string or pathlib.Path) – path to file containing OSM XML data
polygon (shapely.geometry.Polygon) – optional geographic boundary to filter objects
tags (dict) – optional dict of tags for filtering objects from the XML. Results returned are the union, not intersection of each individual tag. Each result matches at least one given tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag. For example, tags = {‘building’: True} would return all building footprints in the area. tags = {‘amenity’:True, ‘landuse’:[‘retail’,’commercial’], ‘highway’:’bus_stop’} would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.
- Returns
gdf
- Return type
geopandas.GeoDataFrame
osmnx.graph module¶
Graph creation functions.
- osmnx.graph.graph_from_address(address, dist=1000, dist_type='bbox', network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, return_coords=False, clean_periphery=True, custom_filter=None)¶
Create a graph from OSM within some distance of some address.
- Parameters
address (string) – the address to geocode and use as the central point around which to construct the graph
dist (int) – retain only those nodes within this many meters of the center of the graph
dist_type (string {"network", "bbox"}) – if “bbox”, retain only those nodes within a bounding box of the distance parameter. if “network”, retain only those nodes within some network distance from the center-most node (requires that scikit-learn is installed as an optional dependency).
network_type (string {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None
simplify (bool) – if True, simplify graph topology with the simplify_graph function
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
truncate_by_edge (bool) – if True, retain nodes outside bounding box if at least one of node’s neighbors is within the bounding box
return_coords (bool) – optionally also return the geocoded coordinates of the address
clean_periphery (bool,) – if True, buffer 500m to get a graph larger than requested, then simplify, then truncate it to requested spatial boundaries
custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.
- Return type
networkx.MultiDiGraph or optionally (networkx.MultiDiGraph, (lat, lng))
Notes
You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module. Very large query areas will use the utils_geo._consolidate_subdivide_geometry function to perform multiple queries: see that function’s documentation for caveats.
- osmnx.graph.graph_from_bbox(north, south, east, west, network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, clean_periphery=True, custom_filter=None)¶
Create a graph from OSM within some bounding box.
- Parameters
north (float) – northern latitude of bounding box
south (float) – southern latitude of bounding box
east (float) – eastern longitude of bounding box
west (float) – western longitude of bounding box
network_type (string {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None
simplify (bool) – if True, simplify graph topology with the simplify_graph function
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
truncate_by_edge (bool) – if True, retain nodes outside bounding box if at least one of node’s neighbors is within the bounding box
clean_periphery (bool) – if True, buffer 500m to get a graph larger than requested, then simplify, then truncate it to requested spatial boundaries
custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.
- Returns
G
- Return type
networkx.MultiDiGraph
Notes
You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module. Very large query areas will use the utils_geo._consolidate_subdivide_geometry function to perform multiple queries: see that function’s documentation for caveats.
- osmnx.graph.graph_from_place(query, network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, which_result=None, buffer_dist=None, clean_periphery=True, custom_filter=None)¶
Create graph from OSM within the boundaries of some geocodable place(s).
The query must be geocodable and OSM must have polygon boundaries for the geocode result. If OSM does not have a polygon for this place, you can instead get its street network using the graph_from_address function, which geocodes the place name to a point and gets the network within some distance of that point.
If OSM does have polygon boundaries for this place but you’re not finding it, try to vary the query string, pass in a structured query dict, or vary the which_result argument to use a different geocode result. If you know the OSM ID of the place, you can retrieve its boundary polygon using the geocode_to_gdf function, then pass it to the graph_from_polygon function.
- Parameters
query (string or dict or list) – the query or queries to geocode to get place boundary polygon(s)
network_type (string {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None
simplify (bool) – if True, simplify graph topology with the simplify_graph function
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
truncate_by_edge (bool) – if True, retain nodes outside boundary polygon if at least one of node’s neighbors is within the polygon
which_result (int) – which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn’t return one.
buffer_dist (float) – distance to buffer around the place geometry, in meters
clean_periphery (bool) – if True, buffer 500m to get a graph larger than requested, then simplify, then truncate it to requested spatial boundaries
custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.
- Returns
G
- Return type
networkx.MultiDiGraph
Notes
You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module. Very large query areas will use the utils_geo._consolidate_subdivide_geometry function to perform multiple queries: see that function’s documentation for caveats.
- osmnx.graph.graph_from_point(center_point, dist=1000, dist_type='bbox', network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, clean_periphery=True, custom_filter=None)¶
Create a graph from OSM within some distance of some (lat, lng) point.
- Parameters
center_point (tuple) – the (lat, lng) center point around which to construct the graph
dist (int) – retain only those nodes within this many meters of the center of the graph, with distance determined according to dist_type argument
dist_type (string {"network", "bbox"}) – if “bbox”, retain only those nodes within a bounding box of the distance parameter. if “network”, retain only those nodes within some network distance from the center-most node (requires that scikit-learn is installed as an optional dependency).
network_type (string, {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None
simplify (bool) – if True, simplify graph topology with the simplify_graph function
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
truncate_by_edge (bool) – if True, retain nodes outside bounding box if at least one of node’s neighbors is within the bounding box
clean_periphery (bool,) – if True, buffer 500m to get a graph larger than requested, then simplify, then truncate it to requested spatial boundaries
custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.
- Returns
G
- Return type
networkx.MultiDiGraph
Notes
You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module. Very large query areas will use the utils_geo._consolidate_subdivide_geometry function to perform multiple queries: see that function’s documentation for caveats.
- osmnx.graph.graph_from_polygon(polygon, network_type='all_private', simplify=True, retain_all=False, truncate_by_edge=False, clean_periphery=True, custom_filter=None)¶
Create a graph from OSM within the boundaries of some shapely polygon.
- Parameters
polygon (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – the shape to get network data within. coordinates should be in unprojected latitude-longitude degrees (EPSG:4326).
network_type (string {"all_private", "all", "bike", "drive", "drive_service", "walk"}) – what type of street network to get if custom_filter is None
simplify (bool) – if True, simplify graph topology with the simplify_graph function
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
truncate_by_edge (bool) – if True, retain nodes outside boundary polygon if at least one of node’s neighbors is within the polygon
clean_periphery (bool) – if True, buffer 500m to get a graph larger than requested, then simplify, then truncate it to requested spatial boundaries
custom_filter (string) – a custom ways filter to be used instead of the network_type presets e.g., ‘[“power”~”line”]’ or ‘[“highway”~”motorway|trunk”]’. Also pass in a network_type that is in settings.bidirectional_network_types if you want graph to be fully bi-directional.
- Returns
G
- Return type
networkx.MultiDiGraph
Notes
You can configure the Overpass server timeout, memory allocation, and other custom settings via the settings module. Very large query areas will use the utils_geo._consolidate_subdivide_geometry function to perform multiple queries: see that function’s documentation for caveats.
- osmnx.graph.graph_from_xml(filepath, bidirectional=False, simplify=True, retain_all=False)¶
Create a graph from data in a .osm formatted XML file.
- Parameters
filepath (string or pathlib.Path) – path to file containing OSM XML data
bidirectional (bool) – if True, create bi-directional edges for one-way streets
simplify (bool) – if True, simplify graph topology with the simplify_graph function
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
- Returns
G
- Return type
networkx.MultiDiGraph
osmnx.io module¶
Serialize graphs to/from files on disk.
- osmnx.io.load_graphml(filepath=None, graphml_str=None, node_dtypes=None, edge_dtypes=None, graph_dtypes=None)¶
Load an OSMnx-saved GraphML file from disk or GraphML string.
This function converts node, edge, and graph-level attributes (serialized as strings) to their appropriate data types. These can be customized as needed by passing in dtypes arguments providing types or custom converter functions. For example, if you want to convert some attribute’s values to bool, consider using the built-in ox.io._convert_bool_string function to properly handle “True”/”False” string literals as True/False booleans: ox.load_graphml(fp, node_dtypes={my_attr: ox.io._convert_bool_string}).
If you manually configured the all_oneway=True setting, you may need to manually specify here that edge oneway attributes should be type str.
Note that you must pass one and only one of filepath or graphml_str. If passing graphml_str, you may need to decode the bytes read from your file before converting to string to pass to this function.
- Parameters
filepath (string or pathlib.Path) – path to the GraphML file
graphml_str (string) – a valid and decoded string representation of a GraphML file’s contents
node_dtypes (dict) – dict of node attribute names:types to convert values’ data types. the type can be a python type or a custom string converter function.
edge_dtypes (dict) – dict of edge attribute names:types to convert values’ data types. the type can be a python type or a custom string converter function.
graph_dtypes (dict) – dict of graph-level attribute names:types to convert values’ data types. the type can be a python type or a custom string converter function.
- Returns
G
- Return type
networkx.MultiDiGraph
- osmnx.io.save_graph_geopackage(G, filepath=None, encoding='utf-8', directed=False)¶
Save graph nodes and edges to disk as layers in a GeoPackage file.
- Parameters
G (networkx.MultiDiGraph) – input graph
filepath (string or pathlib.Path) – path to the GeoPackage file including extension. if None, use default data folder + graph.gpkg
encoding (string) – the character encoding for the saved file
directed (bool) – if False, save one edge for each undirected edge in the graph but retain original oneway and to/from information as edge attributes; if True, save one edge for each directed edge in the graph
- Return type
None
- osmnx.io.save_graph_shapefile(G, filepath=None, encoding='utf-8', directed=False)¶
Save graph nodes and edges to disk as ESRI shapefiles.
The shapefile format is proprietary and outdated. Whenever possible, you should use the superior GeoPackage file format instead via the save_graph_geopackage function.
- 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_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.osm_xml module¶
Read/write .osm formatted XML files.
- osmnx.osm_xml.save_graph_xml(data, filepath=None, node_tags=['highway'], node_attrs=['id', 'timestamp', 'uid', 'user', 'version', 'changeset', 'lat', 'lon'], edge_tags=['highway', 'lanes', 'maxspeed', 'name', 'oneway'], edge_attrs=['id', 'timestamp', 'uid', 'user', 'version', 'changeset'], oneway=False, merge_edges=True, edge_tag_aggs=None)¶
Save graph to disk as an OSM-formatted XML .osm file.
This function exists only to allow serialization to the .osm file format for applications that require it, and has constraints to conform to that. To save/load full-featured OSMnx graphs to/from disk for later use, use the io.save_graphml and io.load_graphml functions instead. To load a graph from a .osm file, 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.
- Return type
None
osmnx.plot module¶
Plot spatial geometries, street networks, and routes.
- osmnx.plot.get_colors(n, cmap='viridis', start=0.0, stop=1.0, alpha=1.0, return_hex=False)¶
Get n evenly-spaced colors from a matplotlib colormap.
- Parameters
n (int) – number of colors
cmap (string) – name of a matplotlib colormap
start (float) – where to start in the colorspace
stop (float) – where to end in the colorspace
alpha (float) – opacity, the alpha channel for the RGBa colors
return_hex (bool) – if True, convert RGBa colors to HTML-like hexadecimal RGB strings. if False, return colors as (R, G, B, alpha) tuples.
- Returns
color_list
- Return type
list
- osmnx.plot.get_edge_colors_by_attr(G, attr, num_bins=None, cmap='viridis', start=0, stop=1, na_color='none', equal_size=False)¶
Get colors based on edge attribute values.
- Parameters
G (networkx.MultiDiGraph) – input graph
attr (string) – name of a numerical edge attribute
num_bins (int) – if None, linearly map a color to each value. otherwise, assign values to this many bins then assign a color to each bin.
cmap (string) – name of a matplotlib colormap
start (float) – where to start in the colorspace
stop (float) – where to end in the colorspace
na_color (string) – what color to assign edges with missing attr values
equal_size (bool) – ignored if num_bins is None. if True, bin into equal-sized quantiles (requires unique bin edges). if False, bin into equal-spaced bins.
- Returns
edge_colors – series labels are edge IDs (u, v, key) and values are colors
- Return type
pandas.Series
- osmnx.plot.get_node_colors_by_attr(G, attr, num_bins=None, cmap='viridis', start=0, stop=1, na_color='none', equal_size=False)¶
Get colors based on node attribute values.
- Parameters
G (networkx.MultiDiGraph) – input graph
attr (string) – name of a numerical node attribute
num_bins (int) – if None, linearly map a color to each value. otherwise, assign values to this many bins then assign a color to each bin.
cmap (string) – name of a matplotlib colormap
start (float) – where to start in the colorspace
stop (float) – where to end in the colorspace
na_color (string) – what color to assign nodes with missing attr values
equal_size (bool) – ignored if num_bins is None. if True, bin into equal-sized quantiles (requires unique bin edges). if False, bin into equal-spaced bins.
- Returns
node_colors – series labels are node IDs and values are colors
- Return type
pandas.Series
- osmnx.plot.plot_figure_ground(G=None, address=None, point=None, dist=805, network_type='drive_service', street_widths=None, default_width=4, figsize=(8, 8), edge_color='w', smooth_joints=True, **pg_kwargs)¶
Plot a figure-ground diagram of a street network.
- Parameters
G (networkx.MultiDiGraph) – input graph, must be unprojected
address (string) – address to geocode as the center point if G is not passed in
point (tuple) – center point if address and G are not passed in
dist (numeric) – how many meters to extend north, south, east, west from center point
network_type (string) – what type of street network to get
street_widths (dict) – dict keys are street types and values are widths to plot in pixels
default_width (numeric) – fallback width in pixels for any street type not in street_widths
figsize (numeric) – (width, height) of figure, should be equal
edge_color (string) – color of the edges’ lines
smooth_joints (bool) – if True, plot nodes same width as streets to smooth line joints and prevent cracks between them from showing
pg_kwargs – keyword arguments to pass to plot_graph
- Returns
fig, ax – matplotlib figure, axis
- Return type
tuple
- osmnx.plot.plot_footprints(gdf, ax=None, figsize=(8, 8), color='orange', edge_color='none', edge_linewidth=0, alpha=None, bgcolor='#111111', bbox=None, save=False, show=True, close=False, filepath=None, dpi=600)¶
Plot a GeoDataFrame of geospatial entities’ footprints.
- Parameters
gdf (geopandas.GeoDataFrame) – GeoDataFrame of footprints (shapely Polygons and MultiPolygons)
ax (axis) – if not None, plot on this preexisting axis
figsize (tuple) – if ax is None, create new figure with size (width, height)
color (string) – color of the footprints
edge_color (string) – color of the edge of the footprints
edge_linewidth (float) – width of the edge of the footprints
alpha (float) – opacity of the footprints
bgcolor (string) – background color of the plot
bbox (tuple) – bounding box as (north, south, east, west). if None, will calculate from the spatial extents of the geometries in gdf
save (bool) – if True, save the figure to disk at filepath
show (bool) – if True, call pyplot.show() to show the figure
close (bool) – if True, call pyplot.close() to close the figure
filepath (string) – if save is True, the path to the file. file format determined from extension. if None, use settings.imgs_folder/image.png
dpi (int) – if save is True, the resolution of saved file
- Returns
fig, ax – matplotlib figure, axis
- Return type
tuple
- osmnx.plot.plot_graph(G, ax=None, figsize=(8, 8), bgcolor='#111111', node_color='w', node_size=15, node_alpha=None, node_edgecolor='none', node_zorder=1, edge_color='#999999', edge_linewidth=1, edge_alpha=None, show=True, close=False, save=False, filepath=None, dpi=300, bbox=None)¶
Plot a graph.
- Parameters
G (networkx.MultiDiGraph) – input graph
ax (matplotlib axis) – if not None, plot on this preexisting axis
figsize (tuple) – if ax is None, create new figure with size (width, height)
bgcolor (string) – background color of plot
node_color (string or list) – color(s) of the nodes
node_size (int) – size of the nodes: if 0, then skip plotting the nodes
node_alpha (float) – opacity of the nodes, note: if you passed RGBA values to node_color, set node_alpha=None to use the alpha channel in node_color
node_edgecolor (string) – color of the nodes’ markers’ borders
node_zorder (int) – zorder to plot nodes: edges are always 1, so set node_zorder=0 to plot nodes below edges
edge_color (string or list) – color(s) of the edges’ lines
edge_linewidth (float) – width of the edges’ lines: if 0, then skip plotting the edges
edge_alpha (float) – opacity of the edges, note: if you passed RGBA values to edge_color, set edge_alpha=None to use the alpha channel in edge_color
show (bool) – if True, call pyplot.show() to show the figure
close (bool) – if True, call pyplot.close() to close the figure
save (bool) – if True, save the figure to disk at filepath
filepath (string) – if save is True, the path to the file. file format determined from extension. if None, use settings.imgs_folder/image.png
dpi (int) – if save is True, the resolution of saved file
bbox (tuple) – bounding box as (north, south, east, west). if None, will calculate from spatial extents of plotted geometries.
- Returns
fig, ax – matplotlib figure, axis
- Return type
tuple
- osmnx.plot.plot_graph_route(G, route, route_color='r', route_linewidth=4, route_alpha=0.5, orig_dest_size=100, ax=None, **pg_kwargs)¶
Plot a route along a graph.
- Parameters
G (networkx.MultiDiGraph) – input graph
route (list) – route as a list of node IDs
route_color (string) – color of the route
route_linewidth (int) – width of the route line
route_alpha (float) – opacity of the route line
orig_dest_size (int) – size of the origin and destination nodes
ax (matplotlib axis) – if not None, plot route on this preexisting axis instead of creating a new fig, ax and drawing the underlying graph
pg_kwargs – keyword arguments to pass to plot_graph
- Returns
fig, ax – matplotlib figure, axis
- Return type
tuple
- osmnx.plot.plot_graph_routes(G, routes, route_colors='r', route_linewidths=4, **pgr_kwargs)¶
Plot several routes along a graph.
- Parameters
G (networkx.MultiDiGraph) – input graph
routes (list) – routes as a list of lists of node IDs
route_colors (string or list) – if string, 1 color for all routes. if list, the colors for each route.
route_linewidths (int or list) – if int, 1 linewidth for all routes. if list, the linewidth for each route.
pgr_kwargs – keyword arguments to pass to plot_graph_route
- Returns
fig, ax – matplotlib figure, axis
- Return type
tuple
osmnx.projection module¶
Project spatial geometries and spatial networks.
- osmnx.projection.is_projected(crs)¶
Determine if a coordinate reference system is projected or not.
This is a convenience wrapper around the pyproj.CRS.is_projected function.
- Parameters
crs (string or pyproj.CRS) – the coordinate reference system
- Returns
projected – True if crs is projected, otherwise False
- Return type
bool
- osmnx.projection.project_gdf(gdf, to_crs=None, to_latlong=False)¶
Project a GeoDataFrame from its current CRS to another.
If to_crs is None, project to the UTM CRS for the UTM zone in which the GeoDataFrame’s centroid lies. Otherwise project to the CRS defined by to_crs. The simple UTM zone calculation in this function works well for most latitudes, but may not work for some extreme northern locations like Svalbard or far northern Norway.
- Parameters
gdf (geopandas.GeoDataFrame) – the GeoDataFrame to be projected
to_crs (string or pyproj.CRS) – if None, project to UTM zone in which gdf’s centroid lies, otherwise project to this CRS
to_latlong (bool) – if True, project to settings.default_crs and ignore to_crs
- Returns
gdf_proj – the projected GeoDataFrame
- Return type
geopandas.GeoDataFrame
- osmnx.projection.project_geometry(geometry, crs=None, to_crs=None, to_latlong=False)¶
Project a shapely geometry from its current CRS to another.
If to_crs is None, project to the UTM CRS for the UTM zone in which the geometry’s centroid lies. Otherwise project to the CRS defined by to_crs.
- Parameters
geometry (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – the geometry to project
crs (string or pyproj.CRS) – the starting CRS of the passed-in geometry. if None, it will be set to settings.default_crs
to_crs (string or pyproj.CRS) – if None, project to UTM zone in which geometry’s centroid lies, otherwise project to this CRS
to_latlong (bool) – if True, project to settings.default_crs and ignore to_crs
- Returns
geometry_proj, crs – the projected geometry and its new CRS
- Return type
tuple
- osmnx.projection.project_graph(G, to_crs=None)¶
Project graph from its current CRS to another.
If to_crs is None, project the graph to the UTM CRS for the UTM zone in which the graph’s centroid lies. Otherwise, project the graph to the CRS defined by to_crs.
- Parameters
G (networkx.MultiDiGraph) – the graph to be projected
to_crs (string or pyproj.CRS) – if None, project graph to UTM zone in which graph centroid lies, otherwise project graph to this CRS
- Returns
G_proj – the projected graph
- Return type
networkx.MultiDiGraph
osmnx.settings module¶
Global settings that can be configured by the user.
- all_onewaybool
If True, forces all ways to be loaded as oneway ways, preserving the original order of nodes stored in the OSM way XML. This also retains original OSM string values for oneway attribute values, rather than converting them to a True/False bool. Only use if specifically saving to .osm XML file with the save_graph_xml function. Default is False.
- bidirectional_network_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. 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)”.
- 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 the HTTP request and 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)¶
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.
- 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
- Returns
G – topologically simplified graph, with a new geometry attribute on each simplified edge
- Return type
networkx.MultiDiGraph
osmnx.speed module¶
Calculate graph edge speeds and travel times.
- osmnx.speed.add_edge_speeds(G, hwy_speeds=None, fallback=None, precision=1, agg=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) – decimal precision to round speed_kph
agg (function) – aggregation function to impute missing values from observed values. the default is numpy.mean, but you might also consider for example numpy.median, numpy.nanmedian, or your own custom function
- Returns
G – graph with speed_kph attributes on all edges
- Return type
networkx.MultiDiGraph
- osmnx.speed.add_edge_travel_times(G, precision=1)¶
Add edge travel time (seconds) to graph as new travel_time edge attributes.
Calculates free-flow travel time along each edge, based on length and speed_kph attributes. Note: run add_edge_speeds first to generate the speed_kph attribute. All edges must have length and speed_kph attributes and all their values must be non-null.
- Parameters
G (networkx.MultiDiGraph) – input graph
precision (int) – decimal precision to round travel_time
- Returns
G – graph with travel_time attributes on all edges
- Return type
networkx.MultiDiGraph
osmnx.stats module¶
Calculate geometric and topological network measures.
This module defines streets as the edges in an undirected representation of the graph. Using undirected graph edges prevents double-counting bidirectional edges of a two-way street, but may double-count a divided road’s separate centerlines with different end point nodes. If clean_periphery=True when the graph was created (which is the default parameterization), then you will get accurate node degrees (and in turn streets-per-node counts) even at the periphery of the graph.
You can use NetworkX directly for additional topological network measures.
- osmnx.stats.basic_stats(G, area=None, clean_int_tol=None)¶
Calculate basic descriptive geometric and topological measures of a graph.
Density measures are only calculated if area is provided and clean intersection measures are only calculated if clean_int_tol is provided.
- Parameters
G (networkx.MultiDiGraph) – input graph
area (float) – if not None, calculate density measures and use this area value (in square meters) as the denominator
clean_int_tol (float) – if not None, calculate consolidated intersections count (and density, if area is also provided) and use this tolerance value; refer to the simplification.consolidate_intersections function documentation for details
- Returns
stats –
- dictionary containing the following attributes
circuity_avg - see circuity_avg function documentation
clean_intersection_count - see clean_intersection_count function documentation
clean_intersection_density_km - clean_intersection_count per sq km
edge_density_km - edge_length_total per sq km
edge_length_avg - edge_length_total / m
edge_length_total - see edge_length_total function documentation
intersection_count - see intersection_count function documentation
intersection_density_km - intersection_count per sq km
k_avg - graph’s average node degree (in-degree and out-degree)
m - count of edges in graph
n - count of nodes in graph
node_density_km - n per sq km
self_loop_proportion - see self_loop_proportion function documentation
street_density_km - street_length_total per sq km
street_length_avg - street_length_total / street_segment_count
street_length_total - see street_length_total function documentation
street_segment_count - see street_segment_count function documentation
streets_per_node_avg - see streets_per_node_avg function documentation
streets_per_node_counts - see streets_per_node_counts function documentation
streets_per_node_proportions - see streets_per_node_proportions function documentation
- Return type
dict
- osmnx.stats.circuity_avg(Gu)¶
Calculate average street circuity using edges of undirected graph.
Circuity is the sum of edge lengths divided by the sum of straight-line distances between edge endpoints. Calculates straight-line distance as euclidean distance if projected or great-circle distance if unprojected.
- Parameters
Gu (networkx.MultiGraph) – undirected input graph
- Returns
circuity_avg – the graph’s average undirected edge circuity
- Return type
float
- osmnx.stats.count_streets_per_node(G, nodes=None)¶
Count how many physical street segments connect to each node in a graph.
This function uses an undirected representation of the graph and special handling of self-loops to accurately count physical streets rather than directed edges. Note: this function is automatically run by all the graph.graph_from_x functions prior to truncating the graph to the requested boundaries, to add accurate street_count attributes to each node even if some of its neighbors are outside the requested graph boundaries.
- Parameters
G (networkx.MultiDiGraph) – input graph
nodes (list) – which node IDs to get counts for. if None, use all graph nodes, otherwise calculate counts only for these node IDs
- Returns
streets_per_node – counts of how many physical streets connect to each node, with keys = node ids and values = counts
- Return type
dict
- osmnx.stats.edge_length_total(G)¶
Calculate graph’s total edge length.
- Parameters
G (networkx.MultiDiGraph) – input graph
- Returns
length – total length (meters) of edges in graph
- Return type
float
- osmnx.stats.intersection_count(G=None, min_streets=2)¶
Count the intersections in a graph.
Intersections are defined as nodes with at least min_streets number of streets incident on them.
- Parameters
G (networkx.MultiDiGraph) – input graph
min_streets (int) – a node must have at least min_streets incident on them to count as an intersection
- Returns
count – count of intersections in graph
- Return type
int
- osmnx.stats.self_loop_proportion(Gu)¶
Calculate percent of edges that are self-loops in a graph.
A self-loop is defined as an edge from node u to node v where u==v.
- Parameters
Gu (networkx.MultiGraph) – undirected input graph
- Returns
proportion – proportion of graph edges that are self-loops
- Return type
float
- osmnx.stats.street_length_total(Gu)¶
Calculate graph’s total street segment length.
- Parameters
Gu (networkx.MultiGraph) – undirected input graph
- Returns
length – total length (meters) of streets in graph
- Return type
float
- osmnx.stats.street_segment_count(Gu)¶
Count the street segments in a graph.
- Parameters
Gu (networkx.MultiGraph) – undirected input graph
- Returns
count – count of street segments in graph
- Return type
int
- osmnx.stats.streets_per_node(G)¶
Count streets (undirected edges) incident on each node.
- Parameters
G (networkx.MultiDiGraph) – input graph
- Returns
spn – dictionary with node ID keys and street count values
- Return type
dict
- osmnx.stats.streets_per_node_avg(G)¶
Calculate graph’s average count of streets per node.
- Parameters
G (networkx.MultiDiGraph) – input graph
- Returns
spna – average count of streets per node
- Return type
float
- osmnx.stats.streets_per_node_counts(G)¶
Calculate streets-per-node counts.
- Parameters
G (networkx.MultiDiGraph) – input graph
- Returns
spnc – dictionary keyed by count of streets incident on each node, and with values of how many nodes in the graph have this count
- Return type
dict
- osmnx.stats.streets_per_node_proportions(G)¶
Calculate streets-per-node proportions.
- Parameters
G (networkx.MultiDiGraph) – input graph
- Returns
spnp – dictionary keyed by count of streets incident on each node, and with values of what proportion of nodes in the graph have this count
- Return type
dict
osmnx.truncate module¶
Truncate graph by distance, bounding box, or polygon.
- osmnx.truncate.truncate_graph_bbox(G, north, south, east, west, truncate_by_edge=False, retain_all=False, quadrat_width=0.05, min_num=3)¶
Remove every node in graph that falls outside a bounding box.
- Parameters
G (networkx.MultiDiGraph) – input graph
north (float) – northern latitude of bounding box
south (float) – southern latitude of bounding box
east (float) – eastern longitude of bounding box
west (float) – western longitude of bounding box
truncate_by_edge (bool) – if True, retain nodes outside bounding box if at least one of node’s neighbors is within the bounding box
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
quadrat_width (numeric) – passed on to intersect_index_quadrats: the linear length (in degrees) of the quadrats with which to cut up the geometry (default = 0.05, approx 4km at NYC’s latitude)
min_num (int) – passed on to intersect_index_quadrats: the minimum number of linear quadrat lines (e.g., min_num=3 would produce a quadrat grid of 4 squares)
- Returns
G – the truncated graph
- Return type
networkx.MultiDiGraph
- osmnx.truncate.truncate_graph_dist(G, source_node, max_dist=1000, weight='length', retain_all=False)¶
Remove every node farther than some network distance from source_node.
This function can be slow for large graphs, as it must calculate shortest path distances between source_node and every other graph node.
- Parameters
G (networkx.MultiDiGraph) – input graph
source_node (int) – the node in the graph from which to measure network distances to other nodes
max_dist (int) – remove every node in the graph greater than this distance from the source_node (along the network)
weight (string) – how to weight the graph when measuring distance (default ‘length’ is how many meters long the edge is)
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
- Returns
G – the truncated graph
- Return type
networkx.MultiDiGraph
- osmnx.truncate.truncate_graph_polygon(G, polygon, retain_all=False, truncate_by_edge=False, quadrat_width=0.05, min_num=3)¶
Remove every node in graph that falls outside a (Multi)Polygon.
- Parameters
G (networkx.MultiDiGraph) – input graph
polygon (shapely.geometry.Polygon or shapely.geometry.MultiPolygon) – only retain nodes in graph that lie within this geometry
retain_all (bool) – if True, return the entire graph even if it is not connected. otherwise, retain only the largest weakly connected component.
truncate_by_edge (bool) – if True, retain nodes outside boundary polygon if at least one of node’s neighbors is within the polygon
quadrat_width (numeric) – passed on to intersect_index_quadrats: the linear length (in degrees) of the quadrats with which to cut up the geometry (default = 0.05, approx 4km at NYC’s latitude)
min_num (int) – passed on to intersect_index_quadrats: the minimum number of linear quadrat lines (e.g., min_num=3 would produce a quadrat grid of 4 squares)
- Returns
G – the truncated graph
- Return type
networkx.MultiDiGraph
osmnx.utils module¶
General utility functions.
- osmnx.utils.citation()¶
Print the OSMnx package’s citation information.
Boeing, G. 2017. OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks. Computers, Environment and Urban Systems, 65, 126-139. https://doi.org/10.1016/j.compenvurbsys.2017.05.004
- 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)¶
Get current timestamp as string.
- Parameters
style (string {"datetime", "date", "time"}) – format the timestamp with this built-in template
template (string) – if not None, format the timestamp with this template instead of one of the built-in styles
- Returns
ts – the string timestamp
- Return type
string
osmnx.utils_geo module¶
Geospatial utility functions.
- osmnx.utils_geo.bbox_from_point(point, dist=1000, project_utm=False, return_crs=False)¶
Create a bounding box from a (lat, lng) center point.
Create a bounding box some distance in each direction (north, south, east, and west) from the center point and optionally project it.
- Parameters
point (tuple) – the (lat, lng) center point to create the bounding box around
dist (int) – bounding box distance in meters from the center point
project_utm (bool) – if True, return bounding box as UTM-projected coordinates
return_crs (bool) – if True, and project_utm=True, return the projected CRS too
- Returns
(north, south, east, west) or (north, south, east, west, crs_proj)
- Return type
tuple
- osmnx.utils_geo.bbox_to_poly(north, south, east, west)¶
Convert bounding box coordinates to shapely Polygon.
- Parameters
north (float) – northern coordinate
south (float) – southern coordinate
east (float) – eastern coordinate
west (float) – western coordinate
- Return type
shapely.geometry.Polygon
- osmnx.utils_geo.interpolate_points(geom, dist)¶
Interpolate evenly spaced points along a LineString.
The spacing is approximate because the LineString’s length may not be evenly divisible by it.
- Parameters
geom (shapely.geometry.LineString) – a LineString geometry
dist (float) – spacing distance between interpolated points, in same units as geom. smaller values generate more points.
- Yields
points (generator) – a generator of (x, y) tuples of the interpolated points’ coordinates
- osmnx.utils_geo.round_geometry_coords(geom, precision)¶
Round the coordinates of a shapely geometry to some decimal precision.
- Parameters
geom (shapely.geometry.geometry {Point, MultiPoint, LineString, MultiLineString, Polygon, MultiPolygon}) – the geometry to round the coordinates of
precision (int) – decimal precision to round coordinates to
- 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)¶
Get a list of attribute values for each edge in a path.
- Parameters
G (networkx.MultiDiGraph) – input graph
route (list) – list of nodes IDs constituting the path
attribute (string) – the name of the attribute to get the value of for each edge. If None, the complete data dict is returned for each edge.
minimize_key (string) – if there are parallel edges between two nodes, select the one with the lowest value of minimize_key
retrieve_default (Callable[Tuple[Any, Any], Any]) – function called with the edge nodes as parameters to retrieve a default value, if the edge does not contain the given attribute (otherwise a KeyError is raised)
- Returns
attribute_values – list of edge attribute values
- Return type
list
- osmnx.utils_graph.get_undirected(G)¶
Convert MultiDiGraph to undirected MultiGraph.
Maintains parallel edges only if their geometries differ. Note: see also get_digraph to convert MultiDiGraph to DiGraph.
- Parameters
G (networkx.MultiDiGraph) – input graph
- Return type
networkx.MultiGraph
- osmnx.utils_graph.graph_from_gdfs(gdf_nodes, gdf_edges, graph_attrs=None)¶
Convert node and edge GeoDataFrames to a MultiDiGraph.
This function is the inverse of graph_to_gdfs and is designed to work in conjunction with it.
However, you can convert arbitrary node and edge GeoDataFrames as long as 1) gdf_nodes is uniquely indexed by osmid, 2) gdf_nodes contains x and y coordinate columns representing node geometries, 3) gdf_edges is uniquely multi-indexed by u, v, key (following normal MultiDiGraph structure). This allows you to load any node/edge shapefiles or GeoPackage layers as GeoDataFrames then convert them to a MultiDiGraph for graph analysis. Note that any geometry attribute on gdf_nodes is discarded since x and y provide the necessary node geometry information instead.
- Parameters
gdf_nodes (geopandas.GeoDataFrame) – GeoDataFrame of graph nodes uniquely indexed by osmid
gdf_edges (geopandas.GeoDataFrame) – GeoDataFrame of graph edges uniquely multi-indexed by u, v, key
graph_attrs (dict) – the new G.graph attribute dict. if None, use crs from gdf_edges as the only graph-level attribute (gdf_edges must have crs attribute set)
- Returns
G
- Return type
networkx.MultiDiGraph
- osmnx.utils_graph.graph_to_gdfs(G, nodes=True, edges=True, node_geometry=True, fill_edge_geometry=True)¶
Convert a MultiDiGraph to node and/or edge GeoDataFrames.
This function is the inverse of graph_from_gdfs.
- Parameters
G (networkx.MultiDiGraph) – input graph
nodes (bool) – if True, convert graph nodes to a GeoDataFrame and return it
edges (bool) – if True, convert graph edges to a GeoDataFrame and return it
node_geometry (bool) – if True, create a geometry column from node x and y attributes
fill_edge_geometry (bool) – if True, fill in missing edge geometry fields using nodes u and v
- Returns
gdf_nodes or gdf_edges or tuple of (gdf_nodes, gdf_edges). gdf_nodes is indexed by osmid and gdf_edges is multi-indexed by u, v, key following normal MultiDiGraph structure.
- Return type
geopandas.GeoDataFrame or tuple
- osmnx.utils_graph.remove_isolated_nodes(G)¶
Remove from a graph all nodes that have no incident edges.
- Parameters
G (networkx.MultiDiGraph) – graph from which to remove isolated nodes
- Returns
G – graph with all isolated nodes removed
- Return type
networkx.MultiDiGraph