OSMnx: Python for street networks. Retrieve, model, analyze, and visualize street networks and other spatial data from OpenStreetMap.


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OSMnx is a Python package that lets you download geospatial data from OpenStreetMap and model, project, visualize, and analyze real-world street networks and any other geospatial geometries. You can download and model walkable, drivable, or bikeable urban networks with a single line of Python code then easily analyze and visualize them. You can just as easily download and work with other infrastructure types, amenities/points of interest, building footprints, elevation data, street bearings/orientations, and speed/travel time.

If you use OSMnx in your work, please cite the journal article.

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

Getting Started

How do I install OSMnx? See the installation instructions.

How do I use OSMnx? See the usage examples and tutorials in the examples repo.

How does this function work? Check out the documentation.

What can I do with OSMnx? Check out recent projects and blog posts that used OSMnx.

I have a usage question. Please ask it on StackOverflow.


OSMnx is built on top of GeoPandas, NetworkX, and matplotlib and interacts with OpenStreetMap's APIs to:

  • Download and model street networks or other networked infrastructure anywhere in the world with a single line of code
  • Download any other spatial geometries, place boundaries, building footprints, or points of interest as a GeoDataFrame
  • Download by city name, polygon, bounding box, or point/address + network distance
  • Download drivable, walkable, bikeable, or all street networks
  • Download node elevations and calculate edge grades (inclines)
  • Impute missing speeds and calculate graph edge travel times
  • Simplify and correct the network's topology to clean-up nodes and consolidate intersections
  • Fast map-matching of points, routes, or trajectories to nearest graph edges or nodes
  • Save networks to disk as shapefiles, GeoPackages, and GraphML
  • Save/load street network to/from a local .osm XML file
  • Conduct topological and spatial analyses to automatically calculate dozens of indicators
  • Calculate and visualize street bearings and orientations
  • Calculate and visualize shortest-path routes that minimize distance, travel time, elevation, etc
  • Visualize street networks as a static map or interactive Leaflet web map
  • Visualize travel distance and travel time with isoline and isochrone maps
  • Plot figure-ground diagrams of street networks and building footprints

All of these features are demonstrated in the examples repo and documented in the documentation. Feature development details are in the change log. Read the journal article for further technical details.

  • Extend clean_intersections to return a new graph, topologically equal to the input graph, but with clusters of nodes merged into a single centroid node.

    Extend clean_intersections to return a new graph, topologically equal to the input graph, but with clusters of nodes merged into a single centroid node.

    Hi @gboeing. I'm happy to put forward the first version of the new clean_intersections() method. The method now returns the simplified graph instead of just the intersection centroids.

    I've added comments to the code, which hopefully will be easy to follow, but I'm happy to write down the main steps of the algorithm. The method doesn't appear to be (too) slow, but there is probably room for improvement.

    There are a few nuances to look out for, e.g. when choosing between multiple geometries, but I think that the final decision was sensible.

    Finally, the simplified graph can end up having a lot of nodes of degree 2 (in its undirected version). Therefore, further simplification can be achieved by removing these nodes. I'm working on this, but maybe this should be a separate method.

    I'm looking forward to your input. Cheers, Pedro.

    opened by ppintosilva 41
  • New feature: Retrieving OSM POIs with osmnx?

    New feature: Retrieving OSM POIs with osmnx?


    first of all, awesome work with the package! Highly useful and needed.

    I was wondering if there would be interest to include separate functionalities to retrieve OpenStreetMap POIs with osmnx? I am currently starting to write such functionalities for my own project, and I think I will be using osmnx as a basis for fetching Points of interest from OpenStreetMap (such as restaurants etc.). I know that it is possible to work with other infrastructures than roads, but if I have understood correctly that function works only with graphs, not with point data such as POIs.

    If you think this would be interesting, I would be happy to help implementing these functions.

    Cheers, Henrikki

    P.S. FYI: I introduce osmnx also with the fully open GIS course (Lesson 7) that can be found here http://autogis.github.io.

    opened by HTenkanen 37
  • ValueError: Geometry must be a shapely Polygon or MultiPolygon

    ValueError: Geometry must be a shapely Polygon or MultiPolygon

    If we run this code:

    import osmnx as ox
    G = ox.graph_from_place('Baltimore, Maryland, USA', network_type='drive')

    We get this error:

    ValueError                                Traceback (most recent call last)
    <ipython-input-1-afb511cf7347> in <module>()
          1 import osmnx as ox
          2 ox.config(log_console=True)
    ----> 3 G = ox.graph_from_place('Baltimore, Maryland, USA', network_type='drive')
    C:\Anaconda\lib\site-packages\osmnx\osmnx.py in graph_from_place(query, network_type, simplify, retain_all, truncate_by_edge, name, which_result, buffer_dist, timeout, memory, max_query_area_size, clean_periphery)
       1815     # create graph using this polygon(s) geometry
       1816     G = graph_from_polygon(polygon, network_type=network_type, simplify=simplify, retain_all=retain_all, 
    -> 1817                            truncate_by_edge=truncate_by_edge, name=name, timeout=timeout, memory=memory, max_query_area_size=max_query_area_size, clean_periphery=clean_periphery)
       1819     log('graph_from_place() returning graph with {:,} nodes and {:,} edges'.format(len(G.nodes()), len(G.edges())))
    C:\Anaconda\lib\site-packages\osmnx\osmnx.py in graph_from_polygon(polygon, network_type, simplify, retain_all, truncate_by_edge, name, timeout, memory, max_query_area_size, clean_periphery)
       1730         raise ValueError('Shape does not have a valid geometry')
       1731     if not isinstance(polygon, (Polygon, MultiPolygon)):
    -> 1732         raise ValueError('Geometry must be a shapely Polygon or MultiPolygon')
       1734     if clean_periphery and simplify:
    ValueError: Geometry must be a shapely Polygon or MultiPolygon
    opened by gboeing 28
  • graph_from_place giving networkx NetworkXPointlessConcept Exception

    graph_from_place giving networkx NetworkXPointlessConcept Exception

    I am trying to follow the article on the link here (awesome blog btw).

    However, every time I try to get a graph from a place using graph_from_place I get the following error:

      File "/home/manuel/miniconda3/envs/test/lib/python3.5/site-packages/osmnx/osmnx.py", line 1815, in graph_from_place
        truncate_by_edge=truncate_by_edge, name=name, timeout=timeout, memory=memory, max_query_area_size=max_query_area_size, clean_periphery=clean_periphery)
      File "/home/manuel/miniconda3/envs/test/lib/python3.5/site-packages/osmnx/osmnx.py", line 1749, in graph_from_polygon
        G = truncate_graph_polygon(G_buffered, polygon, retain_all=retain_all, truncate_by_edge=truncate_by_edge)
      File "/home/manuel/miniconda3/envs/test/lib/python3.5/site-packages/osmnx/osmnx.py", line 1320, in truncate_graph_polygon
        G = get_largest_component(G)
      File "/home/manuel/miniconda3/envs/test/lib/python3.5/site-packages/osmnx/osmnx.py", line 1099, in get_largest_component
        if not nx.is_weakly_connected(G):
      File "<decorator-gen-62>", line 2, in is_weakly_connected
      File "/home/manuel/miniconda3/envs/test/lib/python3.5/site-packages/networkx/utils/decorators.py", line 68, in _not_implemented_for
        return f(*args,**kwargs)
      File "/home/manuel/miniconda3/envs/test/lib/python3.5/site-packages/networkx/algorithms/components/weakly_connected.py", line 178, in is_weakly_connected
        """Connectivity is undefined for the null graph.""")
    networkx.exception.NetworkXPointlessConcept: Connectivity i

    gdf_from_place works, as well as graph_from_point .

    I am using spatialindex=1.8.5

    The environment's packages:


    My machine's watermark:

    CPython 3.5.1
    IPython 4.2.0
    compiler   : GCC 4.4.7 20120313 (Red Hat 4.4.7-1)
    system     : Linux
    release    : 4.4.0-47-generic
    machine    : x86_64
    processor  : x86_64
    CPU cores  : 8
    interpreter: 64bit
    opened by manugarri 25
  • New geometries module - Work In Progress

    New geometries module - Work In Progress

    Resolves #478

    Work in progress on a new geometries module that combines the capabilities of the existing pois and footprints modules and introduces conversion of ways to LineStrings or Polygons as appropriate.


    # New geometries module
    MK = ox.gdf_from_point((52.0430,-0.7590), dist=1000, tags={'highway':True, 'landuse':True, 'building':True})
    # Presentation code
    MK.to_crs(epsg=27700, inplace=True)
    fig,ax = plt.subplots(figsize=(16,16))
    MK[MK['landuse'].notna()].plot(ax=ax, column='landuse', cmap='tab20b', legend=True)
    MK[MK['highway'].notna()].plot(ax=ax, color='white')
    MK[MK['building'].notna()].plot(ax=ax, color='dimgrey', zorder=3)


    opened by AtelierLibre 24
  • Add scheduled Overpass and Nominatim API tests

    Add scheduled Overpass and Nominatim API tests

    Adds tests for OpenStreetMap APIs designed to run on a schedule under AWS Lambda.

    This is a stub and I’m looking for feedback on the approach. osm_api_tests.py is outside the normal project hierarchy, and gets deployed to Lambda as a standalone Python 3 script. It’s possible but slightly annoying to include 3rd party packages on Lambda, so I’m using only standard library functionality. If merged, the AWS deployment details will need to be updated.

    Will close https://github.com/gboeing/osmnx/issues/107.

    opened by migurski 24
  • add geometries module

    add geometries module

    This PR consolidates testing and revisions to the work done by @AtelierLibre in #542. Closes #478.

    It also incorporates the changes in #552 including:

    • renames all the geometries.gdf_from_x functions -> geometries.geometries_from_x
    • makes polygon_features module private
    • deprecates the footprints module/functions with a user warning
    • deprecates the pois module/functions with a user warning
    • cleans up comments and docstrings
    • autodocs the geometries module

    Plus some refactoring/generalizing of code in the truncate and utils_geo modules to make pre-existing code more general for use in both graph construction as well as geometries GeoDataFrame construction.

    opened by gboeing 23
  • Feature: Closest Road to a certain location's coordinates

    Feature: Closest Road to a certain location's coordinates

    I'm using osmnx and networkx for calculating shortest routes in Dubai and I'm having an issue when finding the closest road to a tracking location's coordinates. Even if I use coordinates that are right on a specific highway, sometimes it is matched to a close road due to the fact that I use the osmnx.get_nearest_node to find the closest road. This function will search for the closest graph node in the OSM map to that specific location. Due to the fact that for certain highways you have very large distances between consective node, the closest node might not be on the same road on which the tracking location was recorded. It would be nice if there was a method osmnx.get_nearest_edge that mapped each location to the closest road.

    Here is an example with 2 orig_coordinates that were recorded on the same highway but the second one is matched to a node on a different road. I recommend using jupyter notebooks to visualize the problem on the map

    import networkx as nx
    import osmnx as ox
    import requests
    from matplotlib import pyplot as plt
    import matplotlib.cm as cm
    import matplotlib.colors as colors
    ox.config(use_cache=True, log_console=True)
    # get a graph for some city
    SMALL_MARINA = [25.09, 25.06, 55.16, 55.11]
    G2 = ox.graph_from_bbox(
    fig2, ax2 = ox.plot_graph(G2, fig_height=20, fig_width=20)
    # get the nearest network node to each point
    good_orig_node = ox.get_nearest_node(G2, (25.070661, 55.137911), method='euclidean')
    bad_orig_node = ox.get_nearest_node(G2, (25.071764, 55.138978), method='euclidean')
    dest_node = ox.get_nearest_node(G2, (25.079223, 55.146241), method='euclidean')
    # find the route between these nodes then plot it
    route = nx.shortest_path(G2, good_orig_node, dest_node, weight='length')
    fig, ax = ox.plot_graph_route(G2, route, fig_height=20, fig_width=20)
    # find the route between these nodes then plot it
    route = nx.shortest_path(G2, bad_orig_node, dest_node, weight='length')
    fig, ax = ox.plot_graph_route(G2, route, fig_height=20, fig_width=20)

    Coordinate(South-West) is matched to the right road

    screen shot 2018-08-24 at 17 07 42

    Coordinate(South-West) is matched to the wrong road

    screen shot 2018-08-24 at 17 07 59 enhancement 
    opened by miskolc 22
  • Are the pois module and footprints module redundant?

    Are the pois module and footprints module redundant?

    Over time, the pois and footprints modules have expanded their functionality and grown more similar to each other. I believe they are approximately redundant now and should be merged together to simplify the codebase.

    The following methods are approximately equivalent:

    import osmnx as ox
    ox.config(use_cache=True, log_console=True)
    place = 'Emeryville, CA, USA'
    # use pois module
    po = ox.pois_from_place(place, tags={'building':True})
    po.shape #(1117, 73)
    # use footprints module
    fp = ox.footprints_from_place(place)
    fp.shape #(1127, 55)

    Minor differences would have to be reconciled between these modules (especially the detailed handling of relations in footprints) to account for the differences between the resulting GeoDataFrames.

    enhancement help wanted 
    opened by gboeing 21
  • Certificate verify failed

    Certificate verify failed

    Hello all,

    I am new here thus excuse me if I am asking something simple.

    I have been trying to get osmnx going with that: import osmnx as ox city = ox.gdf_from_place('Berkeley, CA') ox.plot_shape(ox.project_gdf(city))

    But I am getting this output error all the time: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:590)

    Please could you suggest any solutions cause the ones I have seen so far are not working.

    Thanks a lot.

    opened by tetekos 20
  • Add node elevations from DEM files

    Add node elevations from DEM files

    Refactor of #343 to use demquery.


    • I split my code into a new Python package on PyPI.
    • Since learning about Virtual Rasters, the code to find elevations is an order of magnitude simpler. I don't do any joining of nearby rasters; GDAL does all of that.
    • I discovered that interp2d gives those warnings only when the grid is not the expected size for the type of interpolation: 3x3 for linear interpolation, 5x5 for cubic interpolation, and 7x7 for quintic interpolation. So I removed the option to manually choose the size of the interpolation region. If interp_kind is linear, it creates a 3x3 grid.
    • Checks for nodata values; the virtual raster can have regions with no data that are inside the file's bounds, if the provided files didn't create an exact rectangle. My package raises an error if it sees those values in the interpolation region.
    • I'm working on writing tests in the demquery package. ~~I'm having issues with rasterio working on Travis CI~~ the osmnx .travis.yml was quite helpful.
    • Do you have plans to deprecate Python 2.7 and 3.4? Python 3.4 is already end of life, and 2.7 is in a month. I made demquery 3.5+ compatible, but haven't tested Python 2.7.
    • Right now, demquery is only on PyPI, and I think that's why the current Travis build is failing.


    Download both the files USGS_NED_13_n33w117_IMG USGS_NED_13_n34w117_IMG 450MB extracted each

    import osmnx as ox
    west = -116.66587829589844
    south = 32.90120475753773
    north = 33.094201303793194
    east = -116.46263122558594
    G = ox.graph_from_bbox(north=north, south=south, west=west, east=east)
    dem_paths = ['USGS_NED_13_n33w117_IMG.img', 'USGS_NED_13_n34w117_IMG.img']
    # interp_kind can be None, linear, cubic, or quintic
    G = add_node_elevations_from_dem(G, dem_paths, interp_kind=None)
    G = add_edge_grades(G)
    opened by kylebarron 19
  • Streamline _create_overpass_query method

    Streamline _create_overpass_query method

    Hello, first time contributor here

    Is your feature proposal related to a problem?

    Yes. The _create_overpass_query method creates a larger than necessary query string. In it are three main issues that cause a significant amount of redundancy: 1) each tag-value pair is explicitly called one by one, 2) each tag-value is called three times (node, way, relation), and 3) the polygon coordinate string is replicated for every tag-value pair.

    Describe the solution you'd like to propose

    The following changes address the above three deficiencies:

    1. multiple values for the same tag should be combined together into a single component, as per Overpass documentation https://dev.overpass-api.de/overpass-doc/en/criteria/per_tag.html Example: way[highway~"^(trunk|primary|secondary|tertiary)$"]

    2. The node, way, and relation trifecta can be replaced with the single query statement nwr as per Overpass issue https://github.com/drolbr/Overpass-API/issues/453#issuecomment-378718323

    3. The polygon can be globalized and referenced multiple times, rather than explicitly sending the same coordinate sequence for each query component, as per Overpass documentation https://dev.overpass-api.de/overpass-doc/en/criteria/union.html Example: nwr({{bbox}})->.all; ( nwr.all[amenity=atm]; nwr.all[atm=yes]; );

    A clear and concise description of what you want to change in OSMnx and how you plan to implement it.

    Replace the guts of the _create_overpass_query method to take advantage of the above. On an example query of mine the query size shrank from 20,625 bytes to 770 bytes (see attached files pre.txt and post.txt). osmnx lint and unit tests all pass in my forked copy of the repo.

    pre.txt post.txt

    Provide a complete minimal working example of code to demonstrate your use case. Add any additional code snippets, context, or screenshots about the feature proposal to demonstrate its importance and feasibility.

    def _create_overpass_query(polygon_coord_str, tags):
        Create an overpass query string based on passed tags.
        polygon_coord_str : list
            list of lat lng coordinates
        tags : dict
            dict of tags used for finding elements in the selected area
        query : string
        # create overpass settings string
        overpass_settings = _make_overpass_settings()
        # make sure every value in dict is bool, str, or list of str
        error_msg = "tags must be a dict with values of bool, str, or list of str"
        if not isinstance(tags, dict):  # pragma: no cover
            raise TypeError(error_msg)
        # make sure every value in dict is bool, str, or list of str
        error_msg = "tags must be a dict with values of bool, str, or list of str"
        if not isinstance(tags, dict):  # pragma: no cover
            raise TypeError(error_msg)
        # add node/way/relation query components one at a time
        components = []
        for key, value in tags.items():
            if isinstance(value, bool) and value:
                # if bool (ie, True) just pass the key, no value
                tag_str = f"[{key}]"
            elif isinstance(value, str):
                tag_str = f"[{key}='{value}']"
            elif isinstance(value, list):
                if not all(isinstance(s, str) for s in value):  # pragma: no cover
                    raise TypeError(error_msg)
                if len(value) == 1:
                    tag_str = f"[{key}='{value[0]}']"
                    tag_str = "[{0}~'^({1})$']".format(key, "|".join(value))
            else:  # pragma: no cover
                raise TypeError(error_msg)
        # finalize query and return
        components = "".join(components)
        query = f"{overpass_settings};nwr(poly:'{polygon_coord_str}')->.all;({components});(._;>;);out;"
        return query
    opened by derinwalters 13
  • ARM Docker Image

    ARM Docker Image

    Is your feature proposal related to a problem? Yes

    When I use the docker image on my ARM based machine it ... is ... very ... slow.

    Describe the solution you'd like to propose Build the image for ARM architecture

    Maybe you can build cross platform from your workstation and continue to manually build-push the images to docker hub?

    Might require some changes to the build pipeline - which I see in https://github.com/gboeing/osmnx/pulls you're already looking into - whereby a remote pipeline is used that is capable of building for both.

    Describe alternatives you've considered

    I've built an ARM version myself and I use that. So, I'm happy, but maybe there are others out there? I'm on ARM so checking out the repo and running docker build was enough.

    Additional context

    I sent you a https://twitter.com/alushtom/status/1557747027286564864 in case this isn't the best place.

    Something like the below would build cross platofrm

    docker buildx build --platform 'linux/amd64,linux/arm64,linux/arm/v7,linux/arm/v8' -t gboeing/osmnx .'

    Some more info on that command https://www.docker.com/blog/faster-multi-platform-builds-dockerfile-cross-compilation-guide/

    opened by tomharvey 8
  • Include Point geometries in folium module to plot nodes interactively

    Include Point geometries in folium module to plot nodes interactively

    Hi @gboeing! I'm working on a streamlit project where users can get bbox coordinates by clicking on a web map. The main goal is to let them create their own street networks and get some relevant stats to study the mobility in the target area.

    The folium module looks great to add some interactivity to the analysis but I faced that it only supports LineStrings geometries. And since I do not want to only describe the graph with edges but also with nodes, I was looking for some extra methods to allow plotting them based on their connectivity attributes.

    Specifically, I needed to render Point geometries on a Leaflet web map and get them colored by some attribute (betweenness centrality, degree centrality, node degree, etc) with the cmap bar legend/reference. Something like this:


    Below I share you the code and some examples:

    1. build a Point geoms gdf and add node colors based on some attribute
    2. create a circle marker map based on color attributes
    3. some examples can be found here

    If you find this could be an interesting feature to add in the folium.py module we can have some further discussions about the CircleMarker map implementation.


    opened by PyMap 2
Geoff Boeing
Urban planning professor at USC: urban analytics, street networks, rental markets, data science.
Geoff Boeing
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