OpenStreetMap

📖 Ahead of time…

This session is all about OpenStreetMap. To provide an overview of what the project is, whether you have never heard of it or you are somewhat familiar, the followring will set your mind “on course”:

  • The following short clip provides a general overview of what OpenStreetMap is

  • This recent piece contains several interesting points about how OpenStreetMap is currently being created and some of the implications this model may have.

  • Anderson et al. (2019) [ASP19] provides some of the academic underpinnings to the views expressed in Morrison’s piece

💻 Hands-on coding

import geopandas
import contextily
from IPython.display import GeoJSON

Since some of the query options we will discuss involve pre-defined extents, we will read the Madrid neighbourhoods dataset first:

Assuming you have the file locally on the path ../data/:

neis = geopandas.read_file("../data/neighbourhoods.geojson")

If you’re online, you can do:

neis = geopandas.read_file(
    "http://darribas.org/gds4ae/_downloads/44b4bc22c042386c2c0f8dc6685ef17c/neighbourhoods.geojson"
)

To make some of the examples below easy on OpenStreetMap servers, we will single out the smallest neighborhood:

areas = neis.to_crs(
    epsg=32630
).area

smallest = neis[areas == areas.min()]
smallest
neighbourhood neighbourhood_group geometry
98 Atalaya Ciudad Lineal MULTIPOLYGON (((-3.66195 40.46338, -3.66364 40...
ax = smallest.plot(
    facecolor="none", edgecolor="blue", linewidth=2
)
contextily.add_basemap(
    ax, 
    crs=smallest.crs, 
    source=contextily.providers.OpenStreetMap.Mapnik
);
../../_images/06-OpenStreetMap_12_0.png

osmnx

import osmnx as ox
ox.config(
    overpass_settings='[out:json][timeout:90][date:"2021-03-07T00:00:00Z"]'
)

Tip

Much of the methods covered here rely on the osmnx.geometries module. Check out its reference here

There are two broad areas to keep in mind when querying data on OpenStreetMap through osmnx:

  • The interface to specify the extent of the search

  • The nature of the entities being queried. Here, the interface relies entirely on OpenStreetMap’s tagging system. Given the distributed nature of the project, this is variable, but a good place to start is:

Generally, the interface we will follow involves the following:

received_entities = ox.geometries_from_XXX(
    <extent>, tags={<key>: True/<value(s)>}, ...
)

The <extent> can take several forms:

[i for i in dir(ox) if "geometries_from_" in i]
['geometries_from_address',
 'geometries_from_bbox',
 'geometries_from_place',
 'geometries_from_point',
 'geometries_from_polygon',
 'geometries_from_xml']

The tags follow the official feature spec.

Buildings

blgs = ox.geometries_from_polygon(
    smallest.squeeze().geometry, tags={"building": True}
)
blgs.plot();
../../_images/06-OpenStreetMap_25_0.png
blgs.info()
<class 'geopandas.geodataframe.GeoDataFrame'>
MultiIndex: 115 entries, ('way', 442595762) to ('way', 577690922)
Data columns (total 27 columns):
 #   Column            Non-Null Count  Dtype   
---  ------            --------------  -----   
 0   amenity           2 non-null      object  
 1   name              2 non-null      object  
 2   geometry          115 non-null    geometry
 3   nodes             115 non-null    object  
 4   building          115 non-null    object  
 5   addr:housenumber  21 non-null     object  
 6   addr:postcode     3 non-null      object  
 7   addr:street       9 non-null      object  
 8   denomination      1 non-null      object  
 9   phone             2 non-null      object  
 10  religion          1 non-null      object  
 11  source            1 non-null      object  
 12  source:date       1 non-null      object  
 13  url               1 non-null      object  
 14  wheelchair        1 non-null      object  
 15  building:levels   11 non-null     object  
 16  addr:city         8 non-null      object  
 17  addr:country      6 non-null      object  
 18  wikidata          1 non-null      object  
 19  website           1 non-null      object  
 20  country           1 non-null      object  
 21  diplomatic        1 non-null      object  
 22  name:en           1 non-null      object  
 23  name:fr           1 non-null      object  
 24  name:ko           1 non-null      object  
 25  office            1 non-null      object  
 26  target            1 non-null      object  
dtypes: geometry(1), object(26)
memory usage: 29.7+ KB
blgs.head()
amenity name geometry nodes building addr:housenumber addr:postcode addr:street denomination phone ... addr:country wikidata website country diplomatic name:en name:fr name:ko office target
element_type osmid
way 442595762 NaN NaN POLYGON ((-3.66377 40.46317, -3.66363 40.46322... [4402722774, 4402722775, 4402722776, 440272277... yes NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
442595763 NaN NaN POLYGON ((-3.66394 40.46346, -3.66415 40.46339... [4402722778, 4402722779, 4402722780, 440272278... yes NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
442595764 NaN NaN POLYGON ((-3.66379 40.46321, -3.66401 40.46314... [4402722782, 4402722783, 4402722784, 440272278... yes NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
442595765 NaN NaN POLYGON ((-3.66351 40.46356, -3.66294 40.46371... [4402722786, 4402722787, 4402722788, 440272278... yes NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
442596830 NaN NaN POLYGON ((-3.66293 40.46289, -3.66281 40.46294... [4402729658, 4402729659, 4402729660, 440272966... yes NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 27 columns

If you want to visit the entity online, you can do so at:

https://www.openstreetmap.org/<unique_id>

Challenge

Extract the building footprints for the Sol neighbourhood in neis

Other polygons

park = ox.geometries_from_place(
    "Parque El Retiro, Madrid", tags={"leisure": "park"}
)
ax = park.plot(
    facecolor="none", edgecolor="blue", linewidth=2
)
contextily.add_basemap(
    ax, 
    crs=smallest.crs, 
    source=contextily.providers.OpenStreetMap.Mapnik
);
../../_images/06-OpenStreetMap_32_0.png

Points of interest

Bars around Atocha station:

bars = ox.geometries_from_address(
    "Madrid Puerta de Atocha", tags={"amenity": "bar"}, dist=1500
)

We can quickly explore with GeoJSON:

bars.explore()
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And stores within Malasaña:

shops = ox.geometries_from_address(
    "Malasaña, Madrid, Spain", # Boundary to search within
    tags={
        "shop": True,
        "landuse": ["retail", "commercial"],
        "building": "retail"
    },
    dist=1000
)

We use geometries_from_place for delineated areas (“polygonal entities”):

cs = ox.geometries_from_place(
    "Madrid, Spain",
    tags={"amenity": "charging_station"}
)
cs.explore()
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Similarly, we can work with location data. For example, searches around a given point:

bakeries = ox.geometries_from_point(
    (40.418881103417675, -3.6920446157455444),
    tags={"shop": "bakery", "craft": "bakery"},
    dist=500
)
GeoJSON(bakeries.__geo_interface__)
<IPython.display.GeoJSON object>

Challenge

  • How many music shops does OSM record within 750 metres of Puerta de Alcalá?

  • Are there more restaurants or clothing shops within the polygon that represents the Pacífico neighbourhood in neis table?

Streets

Street data can be obtained as another type of entity, as above; or as a graph object.

Geo-tables

centro = ox.geometries_from_polygon(
    neis.query("neighbourhood == 'Sol'").squeeze().geometry,
    tags={"highway": True}
)

We can get a quick peak into what is returned (grey), compared to the region we used for the query:

ax = neis.query(
    "neighbourhood == 'Sol'"
).plot(color="k")
centro.plot(
    ax=ax, 
    color="0.5", 
    linewidth=0.2, 
    markersize=0.5
);
../../_images/06-OpenStreetMap_51_0.png

This however will return all sorts of things:

centro.geometry
element_type  osmid    
node          21734214                             POINT (-3.70427 40.41662)
              21734250                             POINT (-3.70802 40.41612)
              21734252                             POINT (-3.70847 40.41677)
              21968134                             POINT (-3.69945 40.41786)
              21968197                             POINT (-3.70054 40.41645)
                                                 ...                        
way           907553665    LINESTRING (-3.70686 40.41380, -3.70719 40.41369)
              909056211    LINESTRING (-3.70705 40.42021, -3.70680 40.42020)
relation      5662178      POLYGON ((-3.70948 40.41551, -3.70952 40.41563...
              7424032      POLYGON ((-3.70243 40.41716, -3.70242 40.41714...
              8765884      POLYGON ((-3.70636 40.41475, -3.70635 40.41481...
Name: geometry, Length: 609, dtype: geometry

Spatial graphs

This returns clean, processed graph objects for the street network:

[i for i in dir(ox) if "graph_from_" in i]
['graph_from_address',
 'graph_from_bbox',
 'graph_from_gdfs',
 'graph_from_place',
 'graph_from_point',
 'graph_from_polygon',
 'graph_from_xml']
centro_gr = ox.graph_from_polygon(
    neis.query("neighbourhood == 'Sol'").squeeze().geometry,
)

Note

For more on graph representations of street networks, see block 07

centro_gr
<networkx.classes.multidigraph.MultiDiGraph at 0x7fe97aab7dc0>

And to visualise it:

[i for i in dir(ox) if "plot_graph" in i]
['plot_graph', 'plot_graph_folium', 'plot_graph_route', 'plot_graph_routes']
ox.plot_figure_ground(centro_gr);
../../_images/06-OpenStreetMap_62_0.png
ox.plot_graph_folium(centro_gr)
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