def datasets():
sac1 = load_example("Sacramento1")
sac2 = load_example("Sacramento2")
sac1 = geopandas.read_file(sac1.get_path("sacramentot2.shp"))
sac2 = geopandas.read_file(sac2.get_path("SacramentoMSA2.shp"))
sac1["pct_poverty"] = sac1.POV_POP / sac1.POV_TOT
return sac1, sac2
def datasets():
sac1 = load_example("Sacramento1")
sac2 = load_example("Sacramento2")
sac1 = geopandas.read_file(sac1.get_path("sacramentot2.shp"))
sac2 = geopandas.read_file(sac2.get_path("SacramentoMSA2.shp"))
sac1["pct_poverty"] = sac1.POV_POP / sac1.POV_TOT
categories = ["cat", "dog", "donkey", "wombat", "capybara"]
sac1["animal"] = (categories * ((len(sac1) // len(categories)) + 1))[: len(sac1)]
return sac1, sac2
def datasets():
if not os.path.exists("nlcd_2011.tif"):
p = quilt3.Package.browse("rasters/nlcd", "s3://spatial-ucr")
p["nlcd_2011.tif"].fetch()
sac1 = load_example("Sacramento1")
sac2 = load_example("Sacramento2")
sac1 = geopandas.read_file(sac1.get_path("sacramentot2.shp"))
sac2 = geopandas.read_file(sac2.get_path("SacramentoMSA2.shp"))
return sac1, sac2
def datasets():
if not QUILTMISSING:
if not os.path.exists("nlcd_2011.tif"):
p = quilt3.Package.browse("rasters/nlcd", "s3://spatial-ucr")
p["nlcd_2011.tif"].fetch()
sac1 = load_example('Sacramento1')
sac2 = load_example('Sacramento2')
sac1 = geopandas.read_file(sac1.get_path("sacramentot2.shp"))
sac2 = geopandas.read_file(sac2.get_path("SacramentoMSA2.shp"))
sac1['pct_poverty'] = sac1.POV_POP / sac1.POV_TOT
return sac1, sac2
else:
pass
def test_Multi_Gini_Seg(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
groups_list = ['WHITE', 'BLACK', 'ASIAN', 'HISP']
df = s_map[groups_list]
index = MultiGini(df, groups_list)
np.testing.assert_almost_equal(index.statistic, 0.5456349992598081)
def test_Multi_Information_Theory(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
groups_list = ['WHITE', 'BLACK', 'ASIAN', 'HISP']
df = s_map[groups_list]
index = MultiInfoTheory(df, groups_list)
np.testing.assert_almost_equal(index.statistic, 0.1710160297858887)
def test_Relative_Clustering(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
s_map = s_map.to_crs(s_map.estimate_utm_crs())
df = s_map[['geometry', 'HISP', 'TOT_POP']]
index = RelativeClustering(df, 'HISP', 'TOT_POP')
np.testing.assert_almost_equal(index.statistic, 0.5172753899054523)
def test_Distance_Decay_Isolation(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
df = s_map[['geometry', 'HISP', 'TOT_POP']]
df = df.to_crs(df.estimate_utm_crs())
index = DistanceDecayIsolation(df, 'HISP', 'TOT_POP')
np.testing.assert_almost_equal(index.statistic, 0.20144710843904595)
def test_Multi_Dissim(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
groups_list = ['WHITE_', 'BLACK_', 'ASIAN_', 'HISP_']
df = s_map[groups_list]
index = MultiDissim(df, groups_list)
np.testing.assert_almost_equal(index.statistic, 0.41340872573177806)
def test_Multi_Multi_Normalized_Exposure(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
groups_list = ['WHITE', 'BLACK', 'ASIAN', 'HISP']
df = s_map[groups_list]
index = MultiNormExposure(df, groups_list)
np.testing.assert_almost_equal(index.statistic, 0.18821879029994157)
def test_Spatial_Prox_Prof(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
df = s_map[['geometry', 'HISP', 'TOT_POP']]
df = df.to_crs(df.estimate_utm_crs())
index = SpatialProxProf(df, 'HISP', 'TOT_POP')
np.testing.assert_almost_equal(index.statistic, 0.22847334404621394)
def test_Multi_Relative_Diversity(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
groups_list = ['WHITE_', 'BLACK_', 'ASIAN_', 'HISP_']
df = s_map[groups_list]
index = MultiRelativeDiversity(df, groups_list)
np.testing.assert_almost_equal(index.statistic, 0.15820019878220337)
def test_Multi_Squared_Coefficient_of_Variation(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
groups_list = ['WHITE', 'BLACK', 'ASIAN', 'HISP']
df = s_map[groups_list]
index = MultiSquaredCoefVar(df, groups_list)
np.testing.assert_almost_equal(index.statistic, 0.11875484641127525)
def test_Simpsons_Concentration(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
groups_list = ['WHITE', 'BLACK', 'ASIAN', 'HISP']
df = s_map[groups_list]
index = SimpsonsConcentration(df, groups_list)
np.testing.assert_almost_equal(index.statistic, 0.49182413151957904)
def test_moran_loc_bv_scatterplot():
guerry = examples.load_example('Guerry')
link_to_data = guerry.get_path('Guerry.shp')
gdf = gpd.read_file(link_to_data)
x = gdf['Suicids'].values
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calculate Univariate and Bivariate Moran
moran_loc = Moran_Local(y, w)
moran_loc_bv = Moran_Local_BV(x, y, w)
# try with p value so points are colored
fig, _ = _moran_loc_bv_scatterplot(moran_loc_bv)
plt.close(fig)
# try with p value and different figure size
fig, _ = _moran_loc_bv_scatterplot(moran_loc_bv,
p=0.05,
aspect_equal=False)
plt.close(fig)
assert_raises(ValueError, _moran_loc_bv_scatterplot, moran_loc, p=0.5)
assert_warns(UserWarning,
_moran_loc_bv_scatterplot,
moran_loc_bv,
p=0.5,
scatter_kwds=dict(c='r'))
def test_Decomposition(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
index1 = Dissim(s_map, 'HISP', 'TOT_POP')
index2 = Dissim(s_map, 'BLACK', 'TOT_POP')
res = DecomposeSegregation(index1,
index2,
counterfactual_approach="composition")
np.testing.assert_almost_equal(res.c_a, -0.16138819842911295)
np.testing.assert_almost_equal(res.c_s, -0.005104643275796905)
res.plot(plot_type='cdfs')
res.plot(plot_type='maps')
res = DecomposeSegregation(index1,
index2,
counterfactual_approach="share")
np.testing.assert_almost_equal(res.c_a, -0.1543828579279878)
np.testing.assert_almost_equal(res.c_s, -0.012109983776922045)
res.plot(plot_type='cdfs')
res.plot(plot_type='maps')
res = DecomposeSegregation(index1,
index2,
counterfactual_approach="dual_composition")
np.testing.assert_almost_equal(res.c_a, -0.16159526946235048)
np.testing.assert_almost_equal(res.c_s, -0.004897572242559378)
res.plot(plot_type='cdfs')
res.plot(plot_type='maps')
def test_Simpsons_Interaction(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
groups_list = ['WHITE', 'BLACK', 'ASIAN', 'HISP']
df = s_map[groups_list]
index = SimpsonsInteraction(df, groups_list)
np.testing.assert_almost_equal(index.statistic, 0.508175868480421)
def test_plot_local_autocorrelation():
columbus = examples.load_example('Columbus')
link_to_data = columbus.get_path('columbus.shp')
df = gpd.read_file(link_to_data)
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
fig, _ = plot_local_autocorrelation(moran_loc, df, 'HOVAL', p=0.05)
plt.close(fig)
# also test with quadrant and mask
fig, _ = plot_local_autocorrelation(moran_loc, df, 'HOVAL', p=0.05,
region_column='POLYID',
aspect_equal=False,
mask=['1', '2', '3'], quadrant=1)
plt.close(fig)
# also test with quadrant and mask
assert_raises(ValueError, plot_local_autocorrelation, moran_loc,
df, 'HOVAL', p=0.05, region_column='POLYID',
mask=['100', '200', '300'], quadrant=1)
def test_Multi_Local_Simpson_Interaction(self):
s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
df = s_map[groups_list]
index = MultiLocalSimpsonInteraction(df, groups_list)
np.testing.assert_almost_equal(index.statistics[0:10], np.array([0.15435993, 0.33391595, 0.49909747, 0.1299449 , 0.09805056,
0.13128178, 0.04447356, 0.0398933 , 0.03723054, 0.11758548]))
def test_plot_spatial_weights():
# get data
rio_grande_do_sul = examples.load_example('Rio Grande do Sul')
gdf = gpd.read_file(rio_grande_do_sul.get_path('43MUE250GC_SIR.shp'))
gdf.head()
# calculate weights
weights = Queen.from_dataframe(gdf)
# plot weights
fig, _ = plot_spatial_weights(weights, gdf)
plt.close(fig)
# calculate nonplanar_joins
wnp = libpysal.weights.util.nonplanar_neighbors(weights, gdf)
# plot new joins
fig2, _ = plot_spatial_weights(wnp, gdf)
plt.close(fig2)
#customize
fig3, _ = plot_spatial_weights(wnp, gdf, nonplanar_edge_kws=dict(color='#4393c3'))
plt.close(fig3)
# plot in existing figure
fig4, axs = plt.subplots(1,3)
plot_spatial_weights(wnp, gdf, ax=axs[0])
plt.close(fig4)
# uses a column as the index for spatial weights object
weights_index = Queen.from_dataframe(gdf, idVariable="CD_GEOCMU")
fig, _ = plot_spatial_weights(weights_index, gdf, indexed_on="CD_GEOCMU")
plt.close(fig)
def test_h3fy_diff_crs():
sac1 = load_example("Sacramento1")
sac1 = geopandas.read_file(sac1.get_path("sacramentot2.shp"))
sac1 = sac1.to_crs(32710)
sac_hex = h3fy(sac1)
assert sac_hex.shape == (364, 1)
assert sac_hex.crs.to_string() == "EPSG:32710"
def test_Modified_Gini(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
df = s_map[['geometry', 'HISP', 'TOT_POP']]
index = ModifiedGini(df, 'HISP', 'TOT_POP', seed=1234, backend='loky')
np.testing.assert_almost_equal(index.statistic,
0.4217844443896344,
decimal=3)
def test_h3fy_nocrs():
sac1 = load_example("Sacramento1")
sac1 = geopandas.read_file(sac1.get_path("sacramentot2.shp"))
sac1.crs = None
try:
sac_hex = h3fy(sac1, return_geoms=True)
except ValueError:
pass
def test_Boundary_Spatial_Dissim(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
df = s_map[['geometry', 'HISP', 'TOT_POP']]
index = BoundarySpatialDissim(df, 'HISP', 'TOT_POP')
np.testing.assert_almost_equal(index.statistic,
0.2638936888653678,
decimal=2)
def test_Modified_Gini_Seg(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
df = s_map[['geometry', 'HISP_', 'TOT_POP']]
np.random.seed(1234)
index = ModifiedGiniSeg(df, 'HISP_', 'TOT_POP')
np.testing.assert_almost_equal(index.statistic,
0.4217844443896344,
decimal=3)
def test_Bias_Corrected_Dissim(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
df = s_map[['geometry', 'HISP', 'TOT_POP']]
np.random.seed(1234)
index = BiasCorrectedDissim(df, 'HISP', 'TOT_POP')
np.testing.assert_almost_equal(index.statistic,
0.32136474449360836,
decimal=3)
def datasets():
if not QUILTMISSING:
if not os.path.exists("nlcd_2011.tif"):
p = quilt3.Package.browse("rasters/nlcd", "s3://spatial-ucr")
p["nlcd_2011.tif"].fetch()
sac1 = load_example("Sacramento1")
sac2 = load_example("Sacramento2")
sac1 = geopandas.read_file(sac1.get_path("sacramentot2.shp"))
sac2 = geopandas.read_file(sac2.get_path("SacramentoMSA2.shp"))
sac1["pct_poverty"] = sac1.POV_POP / sac1.POV_TOT
categories = ["cat", "dog", "donkey", "wombat", "capybara"]
sac1["animal"] = (categories *
((len(sac1) // len(categories)) + 1))[:len(sac1)]
return sac1, sac2
else:
pass
def test_Modified_Dissim(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
df = s_map[['geometry', 'HISP', 'TOT_POP']]
np.random.seed(1234)
index = ModifiedDissim(df, 'HISP', 'TOT_POP')
np.testing.assert_almost_equal(index.statistic,
0.31075891224250635,
decimal=3)
def setUp(self):
south = load_example('South')
db = libpysal.io.open(south.get_path("south.dbf"), 'r')
self.y_name = "HR90"
self.y = np.array(db.by_col(self.y_name))
self.y.shape = (len(self.y), 1)
self.x_names = ["RD90", "PS90", "UE90", "DV90"]
self.x = np.array([db.by_col(var) for var in self.x_names]).T
self.w = Queen.from_shapefile(south.get_path("south.shp"))
self.w.transform = 'r'
def test_Multi_Location_Quotient(self):
s_map = gpd.read_file(
load_example("Sacramento1").get_path("sacramentot2.shp"))
groups_list = ['WHITE', 'BLACK', 'ASIAN', 'HISP']
df = s_map[groups_list]
index = MultiLocationQuotient(df, groups_list)
np.testing.assert_almost_equal(
index.statistics[0:3, 0:3],
np.array([[1.36543221, 0.07478049, 0.16245651],
[1.18002164, 0., 0.14836683],
[0.68072696, 0.03534425, 0.]]))