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 _check_crs, _crs_mismatch_warn
def _ensure_geometry_column(df):
"""
Helper function to ensure 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", "MultiPolygon"])
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", "MultiPolygon"])
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_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 1.0 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...
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(
"`how` was '{0}' but is expected to be in {1}".format(how, 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
polys = ["Polygon", "MultiPolygon"]
lines = ["LineString", "MultiLineString", "LinearRing"]
points = ["Point", "MultiPoint"]
for i, df in enumerate([df1, df2]):
poly_check = df.geom_type.isin(polys).any()
lines_check = df.geom_type.isin(lines).any()
points_check = df.geom_type.isin(points).any()
if sum([poly_check, lines_check, points_check]) > 1:
raise NotImplementedError(
"df{} contains mixed geometry types.".format(i + 1)
)
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(polys).all():
mask = ~df.geometry.is_valid
col = df._geometry_column_name
if make_valid:
df.loc[mask, col] = df.loc[mask, col].make_valid()
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
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":
dfunion = _overlay_union(df1, df2)
result = dfunion[dfunion["__idx1"].notnull()].copy()
if how in ["intersection", "symmetric_difference", "union", "identity"]:
result.drop(["__idx1", "__idx2"], axis=1, inplace=True)
if keep_geom_type:
geom_type = df1.geom_type.iloc[0]
# 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[[geom_col]][is_collection]
exploded = collections.reset_index(drop=True).explode(index_parts=True)
exploded = exploded.reset_index(level=0)
orig_num_geoms_exploded = exploded.shape[0]
if geom_type in polys:
exploded.loc[~exploded.geom_type.isin(polys), geom_col] = None
elif geom_type in lines:
exploded.loc[~exploded.geom_type.isin(lines), geom_col] = None
elif geom_type in points:
exploded.loc[~exploded.geom_type.isin(points), geom_col] = None
else:
raise TypeError(
"`keep_geom_type` does not support {}.".format(geom_type)
)
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]
if geom_type in polys:
result = result.loc[result.geom_type.isin(polys)]
elif geom_type in lines:
result = result.loc[result.geom_type.isin(lines)]
elif geom_type in points:
result = result.loc[result.geom_type.isin(points)]
else:
raise TypeError("`keep_geom_type` does not support {}.".format(geom_type))
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 {} dropped "
"geometries of different geometry types than df1 has. "
"Set `keep_geom_type=False` to retain all "
"geometries".format(num_dropped + num_dropped_collection),
UserWarning,
stacklevel=2,
)
result.reset_index(drop=True, inplace=True)
return result