python
import geopandas import matplotlib orig = matplotlib.rcParams['figure.figsize'] matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]]
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.
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
.
python
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) world = world[['continent', 'geometry']] continents = world.dissolve(by='continent')
@savefig continents1.png continents.plot();
continents.head()
If we are interested in aggregate populations, however, we can pass different functions to the dissolve
method to aggregate populations using the aggfunc =
argument:
python
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) world = world[['continent', 'geometry', 'pop_est']] continents = world.dissolve(by='continent', aggfunc='sum')
@savefig continents2.png continents.plot(column = 'pop_est', scheme='quantiles', cmap='YlOrRd');
continents.head()
python
matplotlib.rcParams['figure.figsize'] = orig
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'