import warnings from functools import reduce import numpy as np import pandas as pd from geopandas import GeoDataFrame, GeoSeries from geopandas._compat import PANDAS_GE_30 from geopandas.array import ( LINE_GEOM_TYPES, POINT_GEOM_TYPES, POLYGON_GEOM_TYPES, _check_crs, _crs_mismatch_warn, ) def _ensure_geometry_column(df): """Ensure that the geometry column is called 'geometry'. If another column with that name exists, it will be dropped. """ if not df._geometry_column_name == "geometry": if PANDAS_GE_30: if "geometry" in df.columns: df = df.drop("geometry", axis=1) df = df.rename_geometry("geometry") else: if "geometry" in df.columns: df.drop("geometry", axis=1, inplace=True) df.rename_geometry("geometry", inplace=True) return df def _overlay_intersection(df1, df2): """Overlay Intersection operation used in overlay function.""" # Spatial Index to create intersections idx1, idx2 = df2.sindex.query(df1.geometry, predicate="intersects", sort=True) # Create pairs of geometries in both dataframes to be intersected if idx1.size > 0 and idx2.size > 0: left = df1.geometry.take(idx1) left.reset_index(drop=True, inplace=True) right = df2.geometry.take(idx2) right.reset_index(drop=True, inplace=True) intersections = left.intersection(right) poly_ix = intersections.geom_type.isin(POLYGON_GEOM_TYPES) intersections.loc[poly_ix] = intersections[poly_ix].make_valid() # only keep actual intersecting geometries pairs_intersect = pd.DataFrame({"__idx1": idx1, "__idx2": idx2}) geom_intersect = intersections # merge data for intersecting geometries df1 = df1.reset_index(drop=True) df2 = df2.reset_index(drop=True) dfinter = pairs_intersect.merge( df1.drop(df1._geometry_column_name, axis=1), left_on="__idx1", right_index=True, ) dfinter = dfinter.merge( df2.drop(df2._geometry_column_name, axis=1), left_on="__idx2", right_index=True, suffixes=("_1", "_2"), ) return GeoDataFrame(dfinter, geometry=geom_intersect, crs=df1.crs) else: result = df1.iloc[:0].merge( df2.iloc[:0].drop(df2.geometry.name, axis=1), left_index=True, right_index=True, suffixes=("_1", "_2"), ) result["__idx1"] = np.nan result["__idx2"] = np.nan return result[ result.columns.drop(df1.geometry.name).tolist() + [df1.geometry.name] ] def _overlay_difference(df1, df2): """Overlay Difference operation used in overlay function.""" # spatial index query to find intersections idx1, idx2 = df2.sindex.query(df1.geometry, predicate="intersects", sort=True) idx1_unique, idx1_unique_indices = np.unique(idx1, return_index=True) idx2_split = np.split(idx2, idx1_unique_indices[1:]) sidx = [ idx2_split.pop(0) if idx in idx1_unique else [] for idx in range(df1.geometry.size) ] # Create differences new_g = [] for geom, neighbours in zip(df1.geometry, sidx): new = reduce( lambda x, y: x.difference(y), [geom] + list(df2.geometry.iloc[neighbours]) ) new_g.append(new) differences = GeoSeries(new_g, index=df1.index, crs=df1.crs) poly_ix = differences.geom_type.isin(POLYGON_GEOM_TYPES) differences.loc[poly_ix] = differences[poly_ix].make_valid() geom_diff = differences[~differences.is_empty].copy() dfdiff = df1[~differences.is_empty].copy() dfdiff[dfdiff._geometry_column_name] = geom_diff return dfdiff def _overlay_identity(df1, df2): """Overlay Identity operation used in overlay function.""" dfintersection = _overlay_intersection(df1, df2) dfdifference = _overlay_difference(df1, df2) dfdifference = _ensure_geometry_column(dfdifference) # Columns that were suffixed in dfintersection need to be suffixed in dfdifference # as well so they can be matched properly in concat. new_columns = [ col if col in dfintersection.columns else f"{col}_1" for col in dfdifference.columns ] dfdifference.columns = new_columns # Now we can concatenate the two dataframes result = pd.concat([dfintersection, dfdifference], ignore_index=True, sort=False) # keep geometry column last columns = list(dfintersection.columns) columns.remove("geometry") columns.append("geometry") return result.reindex(columns=columns) def _overlay_symmetric_diff(df1, df2): """Overlay Symmetric Difference operation used in overlay function.""" dfdiff1 = _overlay_difference(df1, df2) dfdiff2 = _overlay_difference(df2, df1) dfdiff1["__idx1"] = range(len(dfdiff1)) dfdiff2["__idx2"] = range(len(dfdiff2)) dfdiff1["__idx2"] = np.nan dfdiff2["__idx1"] = np.nan # ensure geometry name (otherwise merge goes wrong) dfdiff1 = _ensure_geometry_column(dfdiff1) dfdiff2 = _ensure_geometry_column(dfdiff2) # combine both 'difference' dataframes dfsym = dfdiff1.merge( dfdiff2, on=["__idx1", "__idx2"], how="outer", suffixes=("_1", "_2") ) geometry = dfsym.geometry_1.copy() geometry.name = "geometry" # https://github.com/pandas-dev/pandas/issues/26468 use loc for now geometry.loc[dfsym.geometry_1.isnull()] = dfsym.loc[ dfsym.geometry_1.isnull(), "geometry_2" ] dfsym.drop(["geometry_1", "geometry_2"], axis=1, inplace=True) dfsym.reset_index(drop=True, inplace=True) dfsym = GeoDataFrame(dfsym, geometry=geometry, crs=df1.crs) return dfsym def _overlay_union(df1, df2): """Overlay Union operation used in overlay function.""" dfinter = _overlay_intersection(df1, df2) dfsym = _overlay_symmetric_diff(df1, df2) dfunion = pd.concat([dfinter, dfsym], ignore_index=True, sort=False) # keep geometry column last columns = list(dfunion.columns) columns.remove("geometry") columns.append("geometry") return dfunion.reindex(columns=columns) def overlay(df1, df2, how="intersection", keep_geom_type=None, make_valid=True): """Perform spatial overlay between two GeoDataFrames. Currently only supports data GeoDataFrames with uniform geometry types, i.e. containing only (Multi)Polygons, or only (Multi)Points, or a combination of (Multi)LineString and LinearRing shapes. Implements several methods that are all effectively subsets of the union. See the User Guide page :doc:`../../user_guide/set_operations` for details. Parameters ---------- df1 : GeoDataFrame df2 : GeoDataFrame how : string Method of spatial overlay: 'intersection', 'union', 'identity', 'symmetric_difference' or 'difference'. keep_geom_type : bool If True, return only geometries of the same geometry type as df1 has, if False, return all resulting geometries. Default is None, which will set keep_geom_type to True but warn upon dropping geometries. make_valid : bool, default True If True, any invalid input geometries are corrected with a call to make_valid(), if False, a `ValueError` is raised if any input geometries are invalid. Returns ------- df : GeoDataFrame GeoDataFrame with new set of polygons and attributes resulting from the overlay Examples -------- >>> 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_data':[1,2]}) >>> df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2_data':[1,2]}) >>> geopandas.overlay(df1, df2, how='union') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2.0 1.0 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2.0 2.0 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) 3 1.0 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 4 2.0 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 5 NaN 1.0 MULTIPOLYGON (((2 3, 2 2, 1 2, 1 3, 2 3)), ((3... 6 NaN 2.0 POLYGON ((3 5, 5 5, 5 3, 4 3, 4 4, 3 4, 3 5)) >>> geopandas.overlay(df1, df2, how='intersection') df1_data df2_data geometry 0 1 1 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2 1 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2 2 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) >>> geopandas.overlay(df1, df2, how='symmetric_difference') df1_data df2_data geometry 0 1.0 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 1 2.0 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 2 NaN 1.0 MULTIPOLYGON (((2 3, 2 2, 1 2, 1 3, 2 3)), ((3... 3 NaN 2.0 POLYGON ((3 5, 5 5, 5 3, 4 3, 4 4, 3 4, 3 5)) >>> geopandas.overlay(df1, df2, how='difference') geometry df1_data 0 POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 1 1 MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 2 >>> geopandas.overlay(df1, df2, how='identity') df1_data df2_data geometry 0 1 1.0 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2 1.0 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2 2.0 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) 3 1 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 4 2 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... See Also -------- sjoin : spatial join GeoDataFrame.overlay : equivalent method Notes ----- Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ # Allowed operations allowed_hows = [ "intersection", "union", "identity", "symmetric_difference", "difference", # aka erase ] # Error Messages if how not in allowed_hows: raise ValueError(f"`how` was '{how}' but is expected to be in {allowed_hows}") if isinstance(df1, GeoSeries) or isinstance(df2, GeoSeries): raise NotImplementedError( "overlay currently only implemented for GeoDataFrames" ) if not _check_crs(df1, df2): _crs_mismatch_warn(df1, df2, stacklevel=3) if keep_geom_type is None: keep_geom_type = True keep_geom_type_warning = True else: keep_geom_type_warning = False for i, df in enumerate([df1, df2]): poly_check = df.geom_type.isin(POLYGON_GEOM_TYPES).any() lines_check = df.geom_type.isin(LINE_GEOM_TYPES).any() points_check = df.geom_type.isin(POINT_GEOM_TYPES).any() if sum([poly_check, lines_check, points_check]) > 1: raise NotImplementedError(f"df{i + 1} contains mixed geometry types.") if how == "intersection": box_gdf1 = df1.total_bounds box_gdf2 = df2.total_bounds if not ( ((box_gdf1[0] <= box_gdf2[2]) and (box_gdf2[0] <= box_gdf1[2])) and ((box_gdf1[1] <= box_gdf2[3]) and (box_gdf2[1] <= box_gdf1[3])) ): result = df1.iloc[:0].merge( df2.iloc[:0].drop(df2.geometry.name, axis=1), left_index=True, right_index=True, suffixes=("_1", "_2"), ) return result[ result.columns.drop(df1.geometry.name).tolist() + [df1.geometry.name] ] # Computations def _make_valid(df): df = df.copy() if df.geom_type.isin(POLYGON_GEOM_TYPES).all(): mask = ~df.geometry.is_valid col = df._geometry_column_name if make_valid: df.loc[mask, col] = df.loc[mask, col].make_valid() # Extract only the input geometry type, as make_valid may change it if mask.any(): df = _collection_extract( df, geom_type="Polygon", keep_geom_type_warning=False ) elif mask.any(): raise ValueError( "You have passed make_valid=False along with " f"{mask.sum()} invalid input geometries. " "Use make_valid=True or make sure that all geometries " "are valid before using overlay." ) return df # Determine the geometry type before make_valid, as make_valid may change it if keep_geom_type: geom_type = df1.geom_type.iloc[0] df1 = _make_valid(df1) df2 = _make_valid(df2) with warnings.catch_warnings(): # CRS checked above, suppress array-level warning warnings.filterwarnings("ignore", message="CRS mismatch between the CRS") if how == "difference": result = _overlay_difference(df1, df2) elif how == "intersection": result = _overlay_intersection(df1, df2) elif how == "symmetric_difference": result = _overlay_symmetric_diff(df1, df2) elif how == "union": result = _overlay_union(df1, df2) elif how == "identity": result = _overlay_identity(df1, df2) if how in ["intersection", "symmetric_difference", "union", "identity"]: result.drop(["__idx1", "__idx2"], axis=1, inplace=True) if keep_geom_type: result = _collection_extract(result, geom_type, keep_geom_type_warning) result.reset_index(drop=True, inplace=True) return result def _collection_extract(df, geom_type, keep_geom_type_warning): # Check input if geom_type in POLYGON_GEOM_TYPES: geom_types = POLYGON_GEOM_TYPES elif geom_type in LINE_GEOM_TYPES: geom_types = LINE_GEOM_TYPES elif geom_type in POINT_GEOM_TYPES: geom_types = POINT_GEOM_TYPES else: raise TypeError(f"`geom_type` does not support {geom_type}.") result = df.copy() # First we filter the geometry types inside GeometryCollections objects # (e.g. GeometryCollection([polygon, point]) -> polygon) # we do this separately on only the relevant rows, as this is an expensive # operation (an expensive no-op for geometry types other than collections) is_collection = result.geom_type == "GeometryCollection" if is_collection.any(): geom_col = result._geometry_column_name collections = result.loc[is_collection, [geom_col]] exploded = collections.reset_index(drop=True).explode(index_parts=True) exploded = exploded.reset_index(level=0) orig_num_geoms_exploded = exploded.shape[0] exploded.loc[~exploded.geom_type.isin(geom_types), geom_col] = None num_dropped_collection = ( orig_num_geoms_exploded - exploded.geometry.isna().sum() ) # level_0 created with above reset_index operation # and represents the original geometry collections # TODO avoiding dissolve to call union_all in this case could further # improve performance (we only need to collect geometries in their # respective Multi version) dissolved = exploded.dissolve(by="level_0") result.loc[is_collection, geom_col] = dissolved[geom_col].values else: num_dropped_collection = 0 # Now we filter all geometries (in theory we don't need to do this # again for the rows handled above for GeometryCollections, but filtering # them out is probably more expensive as simply including them when this # is typically about only a few rows) orig_num_geoms = result.shape[0] result = result.loc[result.geom_type.isin(geom_types)] num_dropped = orig_num_geoms - result.shape[0] if (num_dropped > 0 or num_dropped_collection > 0) and keep_geom_type_warning: warnings.warn( "`keep_geom_type=True` in overlay resulted in " f"{num_dropped + num_dropped_collection} dropped geometries of different " "geometry types than df1 has. Set `keep_geom_type=False` to retain all " "geometries", UserWarning, stacklevel=2, ) return result