Retrieving data from OpenStreetMap#

What is OpenStreetMap?#

OpenStreetMap (OSM) is a global collaborative (crowd-sourced) database and project that aims at creating a free editable map of the world containing of information about our environment. It contains data about streets, buildings, different services, and landuse, to mention but a few. The collected data is also basis for the map at


You can also sign up as a contributor if you want to add to the database and map or correct and improve existing data. Read more in the OpenStreetMap Wiki.

OSM has more than 8 million registered users who contribute around 4 million changes daily. Its database contains data that is described by more than 7 billion nodes (that make up lines, polygons and other objects).

While the most well-known side of OpenStreetMap is the map itself, that we have used as a background map, the project is much more than that. OSM’s data can be used for many other purposes such as routing, geocoding, education, and research. OSM is also widely used for humanitarian response, e.g., in crisis areas (e.g. after natural disasters) and for fostering economic development. Read more about humanitarian projects that use OSM data from the Humanitarian OpenStreetMap Team (HOTOSM) website.

Main tools in this lesson#


This week we will explore a Python package called OSMnx that can be used to retrieve street networks from OpenStreetMap, and construct, analyse, and visualise them. OSMnx can also fetch data about Points of Interest, such as restaurants, schools, and different kinds of services. The package also includes tools to find routes on a network downloaded from OpenStreetMap, and implements algorithms for finding shortest connections for walking, cycling, or driving.

To get an overview of the capabilities of the package, watch the introductory video given by the lead developer of the package, Prof. Geoff Boeing: “Meet the developer: Introduction to OSMnx package by Geoff Boeing”.

There is also a scientific article available describing the package:

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

This tutorial provides a practical overview of OSMnx functionalities, and has also inspired this AutoGIS lesson.


We will also use NetworkX to manipulate and analyse the street network data retrieved from OpenStreetMap. NetworkX is a Python package that can be used to create, manipulate, and study the structure, dynamics, and functions of complex networks.

Download and visualise OpenStreetMap data with OSMnx#

A useful feature of OSMnx is its easy-to-use tools to download OpenStreetMap data via the project’s OverPass API. In this section, we will learn how to download and visualise the street network and additional data from OpenStreetMap covering an area of interest.

Street network#

The osmnx.graph module downloads data to construct a routable road network graph, based on an user-defined area of interest. This area of interest can be specified, for instance, using a place name, a bounding box, or a polygon. Here, we will use a placename for fetching data covering the Kamppi area in Helsinki, Finland.

In the place name query, OSMnx uses the Nominatim Geocoding API. This means that place names should exist in the OpenStreetMap database (run a test search at or

We will read an OSM street network using OSMnx’s graph_from_place() function:

import osmnx

PLACE_NAME = "Kamppi, Helsinki, Finland"
graph = osmnx.graph_from_place(PLACE_NAME)

Check the data type of the graph:


What we have here is a networkx.MultiDiGraph object.

OSMnx’s graphs do not have a built-in method to plot them, but the package comes with a function to do so:

figure, ax = osmnx.plot_graph(graph)

Just as its GeoPandas and Pandas equivalents, osmnx.plot_graph() uses matplotlib. The function returns a (figure, axes) tuple, that can be used to modify the figure using all matplotlib functions we already got to know.

We can see that our graph contains nodes (the points) and edges (the lines) that connects those nodes to each other.

Convert a graph to GeoDataFrames#

The street network we just downloaded is a graph, more specifically a networkx.MultiDiGraph. Its main purpose is to represent the topological relationships between nodes and the links (edges) between them. Sometimes, it is more convenient to have the underlying geodata in geopandas.GeoDataFrames. OSMnx comes with a convenient function that converts a graph into two geo-data frames, one for nodes, and one for edges: osmnx.graph_to_gdfs().

nodes, edges = osmnx.graph_to_gdfs(graph)
y x street_count highway ref geometry
25216594 60.164794 24.921057 5 NaN NaN POINT (24.92106 60.16479)
25238874 60.163663 24.921029 4 NaN NaN POINT (24.92103 60.16366)
25238883 60.163452 24.921441 4 crossing NaN POINT (24.92144 60.16345)
25238933 60.161114 24.924529 3 NaN NaN POINT (24.92453 60.16111)
25238937 60.160860 24.925861 3 NaN NaN POINT (24.92586 60.16086)
osmid oneway lanes name highway maxspeed reversed length geometry junction width tunnel access service bridge
u v key
25216594 1372425721 0 23717777 True 2 Porkkalankatu primary 40 False 10.404 LINESTRING (24.92106 60.16479, 24.92087 60.16479) NaN NaN NaN NaN NaN NaN
1372425714 0 23856784 True 2 Mechelininkatu primary 40 False 40.885 LINESTRING (24.92106 60.16479, 24.92095 60.164... NaN NaN NaN NaN NaN NaN
25238874 336192701 0 29977177 True 3 Mechelininkatu primary 40 False 6.101 LINESTRING (24.92103 60.16366, 24.92104 60.16361) NaN NaN NaN NaN NaN NaN
1519889266 0 930820886 True 1 Itämerenkatu tertiary 30 False 10.885 LINESTRING (24.92103 60.16366, 24.92083 60.16366) NaN NaN NaN NaN NaN NaN
25238883 568147264 0 58077048 True 4 Mechelininkatu primary 40 False 15.388 LINESTRING (24.92144 60.16345, 24.92140 60.16359) NaN NaN NaN NaN NaN NaN

Nice! Now, as we can see, we have our graph as GeoDataFrames and we can plot them using the same functions and tools as we have used before.

Place polygon#

Let’s also plot the polygon that represents our area of interest (Kamppi, Helsinki). We can retrieve the polygon geometry using the [osmnx.geocode_to_gdf()]( function.

# Get place boundary related to the place name as a geodataframe
area = osmnx.geocode_to_gdf(PLACE_NAME)

As the name of the function already tells us, it returns a GeoDataFrame object based on the specified place name query. Let’s still verify the data type:

# Check the data type

Let’s also have a look at the data:

# Check data values
geometry bbox_north bbox_south bbox_east bbox_west place_id osm_type osm_id lat lon class type place_rank importance addresstype name display_name
0 POLYGON ((24.92064 60.16483, 24.92069 60.16447... 60.172075 60.160469 24.943453 24.920643 180300156 relation 184714 60.167626 24.931709 boundary administrative 20 0.430313 suburb Kamppi Kamppi, Southern major district, Helsinki, Hel...
# Plot the area:
<AxesSubplot: >

Building footprints#

Besides network data, OSMnx can also download any other data contained in the OpenStreetMap database. This includes, for instance, building footprints, and different points-of-interests (POIs). To download arbitrary geometries, filtered by OSM tags and a place name, use osmnx.geometries_from_place() [geometries is soon deprecated - Let’s already use features instead]. The tag to retrieve all buildings is building = yes.

buildings = osmnx.geometries_from_place(
    {"building": True},
ele geometry amenity operator wheelchair source access addr:housenumber addr:street addr:unit ... lippakioski toilets:disposal unisex covered area leisure ways type electrified nohousenumber
element_type osmid
node 11711721042 NaN POINT (24.92714 60.16420) NaN Nice Bike Oy NaN NaN NaN 46 Eerikinkatu NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
way 8035238 NaN POLYGON ((24.93563 60.17045, 24.93557 60.17054... NaN NaN NaN NaN NaN 22-24 Mannerheimintie NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
8042297 NaN POLYGON ((24.92938 60.16795, 24.92933 60.16797... NaN NaN NaN NaN NaN 2 Runeberginkatu NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
14797170 NaN POLYGON ((24.92427 60.16648, 24.92427 60.16650... NaN City of Helsinki NaN survey NaN 10 Lapinlahdenkatu NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
14797171 NaN POLYGON ((24.92390 60.16729, 24.92391 60.16731... NaN NaN NaN survey NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 121 columns

As you can see, there are several columns in buildings. Each column contains information about a specific tag that OpenStreetMap contributors have added. Each tag consists of a key (the column name), and a values (for example building=yes or building=school). Read more about tags and tagging practices in the OpenStreetMap wiki.

Index(['ele', 'geometry', 'amenity', 'operator', 'wheelchair', 'source',
       'access', 'addr:housenumber', 'addr:street', 'addr:unit',
       'lippakioski', 'toilets:disposal', 'unisex', 'covered', 'area',
       'leisure', 'ways', 'type', 'electrified', 'nohousenumber'],
      dtype='object', length=121)


Point-of-interest (POI) is a generic concept that describes point locations that represent places of interest. As osmnx.geometries_from_place() can download any geometry data contained in the OpenStreetMap database, it can also be used to download any kind of POI data. [geometries is soon deprecated - Let’s already use features instead]

In OpenStreetMap, many POIs are described using the amenity tag. We can, for example, retrieve all restaurant locations by querying amenity=restaurant.

restaurants = osmnx.geometries_from_place(
        "amenity": "restaurant"

As we can see, there are quite many restaurants in the area.

Let’s explore what kind of attributes we have in our restaurants GeoDataFrame:

# Available columns
array(['addr:city', 'addr:country', 'addr:housenumber', 'addr:postcode',
       'addr:street', 'amenity', 'cuisine', 'diet:halal', 'diet:kosher',
       'name', 'payment:credit_cards', 'payment:debit_cards', 'phone',
       'website', 'wheelchair', 'geometry', 'email', 'facebook',
       'indoor_seating', 'level', 'opening_hours', 'outdoor_seating',
       'short_name', 'start_date', 'toilets:wheelchair',
       'delivery:covid19', 'opening_hours:covid19', 'takeaway:covid19',
       'diet:vegetarian', 'fixme', 'name:fi', 'name:zh', 'payment:cash',
       'diet:vegan', 'disused:amenity', 'addr:housename',
       'access:covid19', 'drive_through:covid19', 'takeaway',
       'contact:facebook', 'contact:phone', 'note',
       'opening_hours:brunch', 'source', 'toilets', 'contact:website',
       'capacity', 'smoking', 'dog', 'operator', 'shop', 'check_date',
       'alt_name', 'contact:email', 'established', 'description',
       'diet:non-vegetarian', 'reservation', 'name:sv', 'drive_through',
       'internet_access', 'url', 'floor', 'brand', 'lunch', 'addr:state',
       'description:en', 'old_name', 'addr:unit', 'delivery', 'name:en',
       'highchair', 'contact:instagram', 'lunch:opening_hours',
       'website:en', 'branch', 'check_date:opening_hours',
       'check_date:diet:vegetarian', 'stars', 'wikidata', 'wikipedia',
       'description:covid19', 'lunch:buffet', 'operator:wikidata',
       'operator:wikipedia', 'addr:place', 'addr:floor', 'lunch:menu',
       'image', 'payment:mastercard', 'payment:visa', 'contact:tiktok',
       'brand:wikidata', 'level:ref', 'nodes', 'building'], dtype=object)

As you can see, there is quite a lot of (potential) information related to the amenities. Let’s subset the columns and inspect the data further. Can we extract all restaurants’ names, address, and opening hours?

# Select some useful cols and print
interesting_columns = [

# Print only selected cols
name opening_hours addr:city addr:country addr:housenumber addr:postcode addr:street
element_type osmid
node 60062502 Kabuki NaN Helsinki FI 12 00180 Lapinlahdenkatu
62965963 Restaurant & Bar Fusion Mo-Th 11-22; Fr-Sa 11-02; Su 12-20 NaN NaN NaN NaN NaN
76617692 Johan Ludvig NaN Helsinki FI NaN NaN NaN
76624339 Shinobi We-Th 17:00-23:00; Fr-Sa 16:00-24:00 Helsinki FI 38 00120 Albertinkatu
76624351 Pueblo NaN Helsinki FI NaN NaN Eerikinkatu
151006260 Ravintola China Mo-Fr 11:00-23:00; Sa-Su 12:00-23:00; PH off Helsinki FI 25 00100 Annankatu
151006483 Tony's deli + Street Bar NaN Helsinki FI 7 00120 Bulevardi
151006932 Haru Sushi Mo-Fr 11:00-21:00; Sa 12:00-21:00; Su 13:00-21:00 Helsinki FI 30 00120 Fredrikinkatu
151006967 Game Taste Cafe NaN Helsinki FI 21 NaN Lönnrotinkatu
151007074 Koto NaN Helsinki FI 22 00120 Lönnrotinkatu


if some of the information needs an update, head over to and edit the source data!

Parks and green areas#

Let’s try to fetch all public parks in the Kamppi area. In OpenStreetMap, parks hould be tagged as leisure = park. Smaller green areas (puistikot) are sometimes also tagged landuse = grass. We can combine multiple tags in one data query.

parks = osmnx.geometries_from_place(
        "leisure": "park",
        "landuse": "grass",
geometry access source addr:city nodes leisure name name:fi name:sv hoitoluokitus_viheralue wikidata wikimedia_commons wikipedia landuse alt_name loc_name area ways name:en type
element_type osmid
way 8042256 POLYGON ((24.93566 60.17132, 24.93566 60.17130... NaN NaN NaN [292719496, 1001543836, 1037987967, 1001544060... park Pikkuparlamentin puisto Pikkuparlamentin puisto Lilla parlamentets park NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
8042613 POLYGON ((24.93701 60.16947, 24.93627 60.16919... NaN NaN NaN [552965718, 293390264, 295056669, 256264975, 1... park Simonpuistikko Simonpuistikko Simonsskvären NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
15218362 POLYGON ((24.92330 60.16499, 24.92323 60.16500... NaN survey NaN [150532954, 150532964, 150532958, 150532959, 2... park Työmiehenpuistikko Työmiehenpuistikko Arbetarparken A2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
15218739 POLYGON ((24.92741 60.16575, 24.92741 60.16574... NaN NaN NaN [1876856069, 1876856056, 1876856052, 187685606... park Lastenlehto Lastenlehto Barnslunden A2 Q18660505 Category:Lastenlehto Park fi:Lastenlehto NaN NaN NaN NaN NaN NaN NaN
15223911 POLYGON ((24.93126 60.16589, 24.93075 60.16624... NaN NaN NaN [1008235303, 1008235126, 1008235240, 100823522... park Lapinlahden puistikko Lapinlahden puistikko Lappviksskvären A2 Q18660481 NaN fi:Lapinlahden puistikko NaN NaN NaN NaN NaN NaN NaN
<AxesSubplot: >

Plotting the data#

Let’s create a map out of the streets, buildings, restaurants, and the area polygon.

import matplotlib
figure, ax = matplotlib.pyplot.subplots(figsize=(12,8))

# Plot the footprint
area.plot(ax=ax, facecolor="black")

# Plot parks
parks.plot(ax=ax, facecolor="green")

# Plot street ‘edges’
edges.plot(ax=ax, linewidth=1, edgecolor="dimgray")

# Plot buildings
buildings.plot(ax=ax, facecolor="silver", alpha=0.7)

# Plot restaurants
restaurants.plot(ax=ax, color="yellow", alpha=0.7, markersize=10)
<AxesSubplot: >

Cool! Now we have a map where we have plotted the restaurants, buildings, streets and the boundaries of the selected region of ‘Kamppi’ in Helsinki. And all of this required only a few lines of code. Pretty neat!

Check your understanding

Retrieve OpenStreetMap data from some other area! Download these elements using OSMnx functions from your area of interest:

  • Extent of the area using geocode_to_gdf()

  • Street network using graph_from_place(), and convert to geo-data frame using graph_to_gdfs()

  • Building footprints (and other geometries) using geometries_from_place() and appropriate tags.

Note, the larger the area you choose, the longer it takes to retrieve data from the API!

# Specify the name that is used to seach for the data. Check that the place
# name is valid from
# Get street network
# Get building footprints
# Plot the data

Advanced reading#

To analyse OpenStreetMap data over large areas, it is often more efficient and meaningful to download the data all at once, instead of separate queries to the API. Such data dumps from OpenStreetMap are available in various file formats, OSM Protocolbuffer Binary Format (PBF) being one of them. Data extracts covering whole countries and continents are available, for instance, at

Pyrosm is a Python package for reading OpenStreetMap data from PBF files into geopandas.GeoDataFrames. Pyrosm makes it easy to extract road networks, buildings, Points of Interest (POI), landuse, natural elements, administrative boundaries and much more - similar to OSMnx, but taylored to analyses of large areas. While OSMnx reads the data from the Overpass API, pyrosm reads the data from a local PBF file.

Read more about fetching and using pbf files as a source for analysing OpenStreetMap data in Python from the pyrosm documentation.