.. ipython:: python :suppress: import geopandas import matplotlib.pyplot as plt plt.close('all') Set-Operations with Overlay ============================ When working with multiple spatial datasets -- especially multiple *polygon* or *line* datasets -- users often wish to create new shapes based on places where those datasets overlap (or don't overlap). These manipulations are often referred using the language of sets -- intersections, unions, and differences. These types of operations are made available in the *geopandas* library through the ``overlay`` function. The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the DataFrame level, not on individual geometries, and the properties from both are retained. In effect, for every shape in the first GeoDataFrame, this operation is executed against every other shape in the other GeoDataFrame: .. image:: _static/overlay_operations.png **Source: QGIS Documentation** (Note to users familiar with the *shapely* library: ``overlay`` can be thought of as offering versions of the standard *shapely* set-operations that deal with the complexities of applying set operations to two *GeoSeries*. The standard *shapely* set-operations are also available as ``GeoSeries`` methods.) The different Overlay operations -------------------------------- First, we create some example data: .. ipython:: python from shapely.geometry import Polygon polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]), Polygon([(2,2), (4,2), (4,4), (2,4)])]) polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]), Polygon([(3,3), (5,3), (5,5), (3,5)])]) df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1':[1,2]}) df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2':[1,2]}) These two GeoDataFrames have some overlapping areas: .. ipython:: python ax = df1.plot(color='red'); @savefig overlay_example.png width=5in df2.plot(ax=ax, color='green', alpha=0.5); We illustrate the different overlay modes with the above example. The ``overlay`` function will determine the set of all individual geometries from overlaying the two input GeoDataFrames. This result covers the area covered by the two input GeoDataFrames, and also preserves all unique regions defined by the combined boundaries of the two GeoDataFrames. When using ``how='union'``, all those possible geometries are returned: .. ipython:: python res_union = geopandas.overlay(df1, df2, how='union') res_union ax = res_union.plot(alpha=0.5, cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_union.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); The other ``how`` operations will return different subsets of those geometries. With ``how='intersection'``, it returns only those geometries that are contained by both GeoDataFrames: .. ipython:: python res_intersection = geopandas.overlay(df1, df2, how='intersection') res_intersection ax = res_intersection.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_intersection.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); ``how='symmetric_difference'`` is the opposite of ``'intersection'`` and returns the geometries that are only part of one of the GeoDataFrames but not of both: .. ipython:: python res_symdiff = geopandas.overlay(df1, df2, how='symmetric_difference') res_symdiff ax = res_symdiff.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_symdiff.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); To obtain the geometries that are part of ``df1`` but are not contained in ``df2``, you can use ``how='difference'``: .. ipython:: python res_difference = geopandas.overlay(df1, df2, how='difference') res_difference ax = res_difference.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_difference.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); Finally, with ``how='identity'``, the result consists of the surface of ``df1``, but with the geometries obtained from overlaying ``df1`` with ``df2``: .. ipython:: python res_identity = geopandas.overlay(df1, df2, how='identity') res_identity ax = res_identity.plot(cmap='tab10') df1.plot(ax=ax, facecolor='none', edgecolor='k'); @savefig overlay_example_identity.png width=5in df2.plot(ax=ax, facecolor='none', edgecolor='k'); Overlay Countries Example ------------------------- First, we load the countries and cities example datasets and select : .. ipython:: python world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) capitals = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities')) # Select South Amarica and some columns countries = world[world['continent'] == "South America"] countries = countries[['geometry', 'name']] # Project to crs that uses meters as distance measure countries = countries.to_crs('+init=epsg:3395') capitals = capitals.to_crs('+init=epsg:3395') To illustrate the ``overlay`` function, consider the following case in which one wishes to identify the "core" portion of each country -- defined as areas within 500km of a capital -- using a ``GeoDataFrame`` of countries and a ``GeoDataFrame`` of capitals. .. ipython:: python # Look at countries: @savefig world_basic.png width=5in countries.plot(); # Now buffer cities to find area within 500km. # Check CRS -- World Mercator, units of meters. capitals.crs # make 500km buffer capitals['geometry']= capitals.buffer(500000) @savefig capital_buffers.png width=5in capitals.plot(); To select only the portion of countries within 500km of a capital, we specify the ``how`` option to be "intersect", which creates a new set of polygons where these two layers overlap: .. ipython:: python country_cores = geopandas.overlay(countries, capitals, how='intersection') @savefig country_cores.png width=5in country_cores.plot(alpha=0.5, edgecolor='k', cmap='tab10'); Changing the "how" option allows for different types of overlay operations. For example, if we were interested in the portions of countries *far* from capitals (the peripheries), we would compute the difference of the two. .. ipython:: python country_peripheries = geopandas.overlay(countries, capitals, how='difference') @savefig country_peripheries.png width=5in country_peripheries.plot(alpha=0.5, edgecolor='k', cmap='tab10'); .. ipython:: python :suppress: import matplotlib.pyplot as plt plt.close('all') More Examples ------------- A larger set of examples of the use of ``overlay`` can be found `here `_ .. toctree:: :maxdepth: 2