The "spatial join" operation

Working with Geospatial Data in Python

Dani Arribas-Bel

Geographic Data Science Lab (University of Liverpool)

Spatial relationships I

Spatial relationships II

Which cities are located within Brazil?

brazil = countries.loc[22, 'geometry']
cities[cities.within(brazil)]

               name                                       geometry
169        Brasília  POINT (-47.91799814700306 -15.78139437287899)
238  Rio de Janeiro  POINT (-43.22696665284366 -22.92307731561596)
239       São Paulo  POINT (-46.62696583905523 -23.55673372837896)

But what if we want to know for each city in which country it is located?

The Spatial Join

SPATIAL JOIN = transferring attributes from one layer to another based on their spatial relationship

The spatial join with GeoPandas

joined = geopandas.sjoin(cities,
                         countries[['name', 'geometry']],
                         op="within")

joined.head()

        name_left                                     geometry name_right
0    Vatican City  POINT (12.45338654497177 41.90328217996012)      Italy
1      San Marino    POINT (12.44177015780014 43.936095834768)      Italy
226          Rome    POINT (12.481312562874 41.89790148509894)      Italy
2           Vaduz  POINT (9.516669472907267 47.13372377429357)    Austria
212        Vienna  POINT (16.36469309674374 48.20196113681686)    Austria

Let's practice!

Working with Geospatial Data in Python