Aggregation with dissolve

Spatial data are often more granular than we need. For example, we might have data on sub-national units, but we’re actually interested in studying patterns at the level of countries.

In a non-spatial setting, when all we need are summary statistics of the data, we aggregate our data using the groupby function. But for spatial data, we sometimes also need to aggregate geometric features. In the geopandas library, we can aggregate geometric features using the dissolve function.

dissolve can be thought of as doing three things: (a) it dissolves all the geometries within a given group together into a single geometric feature (using the unary_union method), and (b) it aggregates all the rows of data in a group using groupby.aggregate(), and (c) it combines those two results.

dissolve Example

Suppose we are interested in studying continents, but we only have country-level data like the country dataset included in geopandas. We can easily convert this to a continent-level dataset.

First, let’s look at the most simple case where we just want continent shapes and names. By default, dissolve will pass 'first' to groupby.aggregate.

In [1]: world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))

In [2]: world = world[['continent', 'geometry']]

In [3]: continents = world.dissolve(by='continent')

In [4]: continents.plot();

In [5]: continents.head()
Out[5]: 
                                                        geometry
continent                                                       
Africa         (POLYGON ((49.54351891459575 -12.4698328589405...
Antarctica     (POLYGON ((-159.2081835601977 -79.497059421708...
Asia           (POLYGON ((120.7156087586305 -10.2395813940878...
Europe         (POLYGON ((-52.55642473001839 2.50470530843705...
North America  (POLYGON ((-61.68000000000001 10.76, -61.105 1...
_images/continents1.png

If we are interested in aggregate populations, however, we can pass different functions to the dissolve method to aggregate populations using the aggfunc = argument:

In [6]: world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))

In [7]: world = world[['continent', 'geometry', 'pop_est']]

In [8]: continents = world.dissolve(by='continent', aggfunc='sum')

In [9]: continents.plot(column = 'pop_est', scheme='quantiles', cmap='YlOrRd');

In [10]: continents.head()
Out[10]: 
                                                        geometry       pop_est
continent                                                                     
Africa         (POLYGON ((49.54351891459575 -12.4698328589405...  9.932819e+08
Antarctica     (POLYGON ((-159.2081835601977 -79.497059421708...  3.802000e+03
Asia           (POLYGON ((120.7156087586305 -10.2395813940878...  4.085853e+09
Europe         (POLYGON ((-52.55642473001839 2.50470530843705...  7.281312e+08
North America  (POLYGON ((-61.68000000000001 10.76, -61.105 1...  5.393510e+08
_images/continents2.png

Dissolve Arguments

The aggfunc = argument defaults to ‘first’ which means that the first row of attributes values found in the dissolve routine will be assigned to the resultant dissolved geodataframe. However it also accepts other summary statistic options as allowed by pandas.groupby() including:

  • ‘first’
  • ‘last’
  • ‘min’
  • ‘max’
  • ‘sum’
  • ‘mean’
  • ‘median’