def test_union_no_index(self):
# explicitly ignore indicies
dfB = overlay(self.polydf, self.polydf2, how="union", use_sindex=False)
self.assertEquals(dfB.shape, self.union_shape)
# remove indicies from df
self.polydf._sindex = None
self.polydf2._sindex = None
dfC = overlay(self.polydf, self.polydf2, how="union")
self.assertEquals(dfC.shape, self.union_shape)
def test_union_no_index(self):
# explicitly ignore indicies
dfB = overlay(self.polydf, self.polydf2, how="union", use_sindex=False)
self.assertEquals(dfB.shape, self.union_shape)
# remove indicies from df
self.polydf._sindex = None
self.polydf2._sindex = None
dfC = overlay(self.polydf, self.polydf2, how="union")
self.assertEquals(dfC.shape, self.union_shape)
def merge_precinct_bg(pre_df, bg_df, yr_name):
"""Merge block group boundaries with precinct. (Might take a few minutes.)
Args:
pre_df (geoDataFrame): precinct
bg_df (geoDataFrame): block groups
yr_name (str): year
Returns:
DataFrame: merged block group boundaries and precinct.
"""
print('working on intersection for year {}'.format(yr_name))
newdf = overlay(pre_df, bg_df, how="intersection")
# intersection has both precinct and block group IDs.
#newdf.head()
# create a field with the area, will later divide by the total precinct area
newdf['intersect_area'] = newdf.geometry.area
# drop the unneeded columns to clean up
try:
cols_to_drop = [
'BLKGRPCE10', 'COUNTYFP10', 'FUNCSTAT10', 'INTPTLAT10',
'INTPTLON10', 'MTFCC10', 'NAMELSAD10', 'STATEFP10'
]
newdf = newdf.drop(cols_to_drop, axis=1)
except ValueError:
print('cols not present')
print(newdf.columns)
print('New df has {} precincts'.format(len(
pre_df.precname.unique()))) # just checking how many precincts.
return (newdf)
def weights_between_shape_and_xy(shapes, stacked_spatiotemporal):
x, y = zip(*stacked_spatiotemporal.xy.values)
grid_gdf = (gpd.GeoSeries(
gpd.points_from_xy(x=x, y=y, crs=stacked_spatiotemporal.crs),
crs=shapes.crs).buffer(
infer_resolution(stacked_spatiotemporal.unstack("xy")) /
2).envelope)
index_gdf = gpd.GeoDataFrame(geometry=grid_gdf,
data={"xy": stacked_spatiotemporal.xy},
crs=shapes.crs)
overlaid = overlay(index_gdf, shapes, how="intersection")
overlaid["area"] = overlaid.area
overlaid["weight"] = overlaid.groupby("shape_id").area.transform(
lambda area: area / area.sum())
weights = (
overlaid.set_index(
["xy", "shape_id"]).loc[:, "weight"].to_xarray().reindex_like(
stacked_spatiotemporal) # add all xy's even without overlay
.fillna(0) # xy's without overlay have 0 weight
)
return weights
def test_duplicate_column_name(self):
polydf2r = self.polydf2.rename(columns={"value2": "Shape_Area"})
df = overlay(self.polydf, polydf2r, how="union")
self.assertTrue("Shape_Area_2" in df.columns and "Shape_Area" in df.columns)
def test_difference(self):
df = overlay(self.polydf, self.polydf2, how="difference")
self.assertEquals(df.shape, (86, 7))
def test_duplicate_column_name(self):
polydf2r = self.polydf2.rename(columns={'value2': 'Shape_Area'})
df = overlay(self.polydf, polydf2r, how="union")
self.assertTrue('Shape_Area_2' in df.columns
and 'Shape_Area' in df.columns)
def test_identity(self):
df = overlay(self.polydf, self.polydf2, how="identity")
self.assertEquals(df.shape, (154, 7))
def test_symmetric_difference(self):
df = overlay(self.polydf, self.polydf2, how="symmetric_difference")
self.assertEquals(df.shape, (122, 7))
def test_union(self):
df = overlay(self.polydf, self.polydf2, how="union")
self.assertTrue(type(df) is GeoDataFrame)
self.assertEquals(df.shape, self.union_shape)
self.assertTrue('value1' in df.columns and 'Shape_Area' in df.columns)
def test_intersection(self):
df = overlay(self.polydf, self.polydf2, how="intersection")
self.assertIsNotNone(df['BoroName'][0])
self.assertEquals(df.shape, (68, 7))
########################################################################################################################
# filter 1
Gent = data[data['NAAM'] == 'Gent']
print(Gent)
print(Gent.values)
print(Gent.describe())
# filter 2
Grote_gem = data[data['OPPERVL'] > 100000000]
# plot
base = Grote_gem.plot(color='red', edgecolor='none')
Gent.plot(ax=base, color='green', edgecolor='none')
data.plot(ax=base, color='none', edgecolor='grey')
data2.plot(ax=base, color='none', edgecolor='black')
plt.show()
Gent = data[data['NAAM'] == 'Gent']
invloedsfeer = gpd.GeoDataFrame({'geometry': Gent.buffer(distance=20000)
}) # overlay works only for GeoDataFrames
from geopandas.tools import overlay
invloed_bel = overlay(invloedsfeer, data, how='intersection')
base = invloedsfeer.plot(color='yellow')
invloed_bel.plot(ax=base, color='red')
Gent.plot(ax=base, color='none', edgecolor='black')
data2.plot(ax=base, color='none', edgecolor='black')
plt.show()
def test_union(self):
df = overlay(self.polydf, self.polydf2, how="union")
self.assertTrue(type(df) is GeoDataFrame)
self.assertEquals(df.shape, self.union_shape)
self.assertTrue('value1' in df.columns and 'Shape_Area' in df.columns)
def test_duplicate_column_name(self):
polydf2r = self.polydf2.rename(columns={'value2': 'Shape_Area'})
df = overlay(self.polydf, polydf2r, how="union")
self.assertTrue('Shape_Area_2' in df.columns and 'Shape_Area' in df.columns)
def test_difference(self):
df = overlay(self.polydf, self.polydf2, how="difference")
self.assertEquals(df.shape, (86, 7))
def test_symmetric_difference(self):
df = overlay(self.polydf, self.polydf2, how="symmetric_difference")
self.assertEquals(df.shape, (122, 7))
def test_identity(self):
df = overlay(self.polydf, self.polydf2, how="identity")
self.assertEquals(df.shape, (154, 7))
def test_intersection(self):
df = overlay(self.polydf, self.polydf2, how="intersection")
self.assertIsNotNone(df['BoroName'][0])
self.assertEquals(df.shape, (68, 7))