geopandas.sjoin_nearest(left_df: GeoDataFrame, right_df: GeoDataFrame, how: str = 'inner', max_distance: Optional[float] = None, lsuffix: str = 'left', rsuffix: str = 'right', distance_col: Optional[str] = None) GeoDataFrame[source]#

Spatial join of two GeoDataFrames based on the distance between their geometries.

Results will include multiple output records for a single input record where there are multiple equidistant nearest or intersected neighbors.

Distance is calculated in CRS units and can be returned using the distance_col parameter.

See the User Guide page for more details.

left_df, right_dfGeoDataFrames
howstring, default ‘inner’

The type of join:

  • ‘left’: use keys from left_df; retain only left_df geometry column

  • ‘right’: use keys from right_df; retain only right_df geometry column

  • ‘inner’: use intersection of keys from both dfs; retain only left_df geometry column

max_distancefloat, default None

Maximum distance within which to query for nearest geometry. Must be greater than 0. The max_distance used to search for nearest items in the tree may have a significant impact on performance by reducing the number of input geometries that are evaluated for nearest items in the tree.

lsuffixstring, default ‘left’

Suffix to apply to overlapping column names (left GeoDataFrame).

rsuffixstring, default ‘right’

Suffix to apply to overlapping column names (right GeoDataFrame).

distance_colstring, default None

If set, save the distances computed between matching geometries under a column of this name in the joined GeoDataFrame.

See also


binary predicate joins


equivalent method


Since this join relies on distances, results will be inaccurate if your geometries are in a geographic CRS.

Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account.


>>> countries = geopandas.read_file(geopandas.datasets.get_path("naturalearth_lowres"))
>>> cities = geopandas.read_file(geopandas.datasets.get_path("naturalearth_cities"))
>>> countries.head(2).name  
    pop_est      continent                      name iso_a3  gdp_md_est                                           geometry
0     920938        Oceania                      Fiji    FJI      8374.0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1   53950935         Africa                  Tanzania    TZA    150600.0  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
>>> cities.head(2).name  
        name                   geometry
0  Vatican City  POINT (12.45339 41.90328)
1    San Marino  POINT (12.44177 43.93610)
>>> cities_w_country_data = geopandas.sjoin_nearest(cities, countries)
>>> cities_w_country_data[['name_left', 'name_right']].head(2)  
        name_left                   geometry  index_right   pop_est continent name_right iso_a3  gdp_md_est
0    Vatican City  POINT (12.45339 41.90328)          141  62137802    Europe      Italy    ITA   2221000.0
1      San Marino  POINT (12.44177 43.93610)          141  62137802    Europe      Italy    ITA   2221000.0

To include the distances:

>>> cities_w_country_data = geopandas.sjoin_nearest(cities, countries, distance_col="distances")
>>> cities_w_country_data[["name_left", "name_right", "distances"]].head(2)  
        name_left name_right distances
0    Vatican City      Italy       0.0
1      San Marino      Italy       0.0

In the following example, we get multiple cities for Italy because all results are equidistant (in this case zero because they intersect). In fact, we get 3 results in total:

>>> countries_w_city_data = geopandas.sjoin_nearest(cities, countries, distance_col="distances", how="right")
>>> italy_results = countries_w_city_data[countries_w_city_data["name_left"] == "Italy"]
>>> italy_results  
     name_x        name_y
141  Vatican City  Italy
141    San Marino  Italy
141          Rome  Italy