What is the units of distance_col and max_distance in geopandas sjoin_nearest?

Question

geopandas.sjoin_nearest takes parameters max_distance and distance_col. What is the units of the distances / how do I interpret them? Is it degrees?

https://geopandas.org/en/stable/docs/reference/api/geopandas.sjoin_nearest.html#geopandas.sjoin_nearest

Answer 1

While geopandas provides utilities for converting between coordinate systems (e.g. to_crs), most operations in geopandas ignore the projection information. Spatial operations such as distance, area, buffer, etc. are done in whatever units the geometries are in. If your geometries are in meters, these will be in meters. If they're in degrees, they'll be in degrees.

For example, let's take a look at the natural earth dataset. You can see that the geometry column is in lat/lon coordinates by just looking at the values:

In [1]: import geopandas as gpd

In [2]: gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

In [3]: gdf
Out[3]:
       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...
2       603253         Africa                 W. Sahara    ESH       906.5  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3     35623680  North America                    Canada    CAN   1674000.0  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4    326625791  North America  United States of America    USA  18560000.0  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...
..         ...            ...                       ...    ...         ...                                                ...
172    7111024         Europe                    Serbia    SRB    101800.0  POLYGON ((18.82982 45.90887, 18.82984 45.90888...
173     642550         Europe                Montenegro    MNE     10610.0  POLYGON ((20.07070 42.58863, 19.80161 42.50009...
174    1895250         Europe                    Kosovo    -99     18490.0  POLYGON ((20.59025 41.85541, 20.52295 42.21787...
175    1218208  North America       Trinidad and Tobago    TTO     43570.0  POLYGON ((-61.68000 10.76000, -61.10500 10.890...
176   13026129         Africa                  S. Sudan    SSD     20880.0  POLYGON ((30.83385 3.50917, 29.95350 4.17370, ...

[177 rows x 6 columns]

Specifically, it's in WGS84 (aka EPSG:4326). The units are degrees:

In [4]: gdf.crs
Out[4]:
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

If we call the area property, geopandas will issue a warning, but it will still calculate the area for us. The total area of the earth comes out to 21,497 degrees^2, which roughly 1/3 of 180*360:

In [6]: gdf.area.sum()
<ipython-input-6-10238de14784>:1: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  gdf.area.sum()
Out[6]: 21496.990987992736

If we instead use an equal area projection, we'll get something much closer to the land area of the earth, in m^2:

In [10]: gdf.to_crs('+proj=cea').area.sum() / 1e3 / 1e3 / 1e6
Out[10]: 147.36326937311017