def test_moran_loc_bv_scatterplot():
link_to_data = examples.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_moran_loc_bv_scatterplot():
link_to_data = examples.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 Bivariate Moran
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)
plt.close(fig)
def test_moran_scatterplot():
gdf = _test_data()
x = gdf['Suicids'].values
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calculate `esda.moran` Objects
moran = Moran(y, w)
moran_bv = Moran_BV(y, x, w)
moran_loc = Moran_Local(y, w)
moran_loc_bv = Moran_Local_BV(y, x, w)
# try with p value so points are colored or warnings apply
fig, _ = moran_scatterplot(moran, p=0.05, aspect_equal=False)
plt.close(fig)
fig, _ = moran_scatterplot(moran_loc, p=0.05)
plt.close(fig)
fig, _ = moran_scatterplot(moran_bv, p=0.05)
plt.close(fig)
fig, _ = moran_scatterplot(moran_loc_bv, p=0.05)
plt.close(fig)
def test_moran_scatterplot():
link_to_data = examples.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 `esda.moran` Objects
moran = Moran(y, w)
moran_bv = Moran_BV(y, x, w)
moran_loc = Moran_Local(y, w)
moran_loc_bv = Moran_Local_BV(y, x, w)
# try with p value so points are colored or warnings apply
fig, _ = moran_scatterplot(moran, p=0.05)
plt.close(fig)
fig, _ = moran_scatterplot(moran_loc, p=0.05)
plt.close(fig)
fig, _ = moran_scatterplot(moran_bv, p=0.05)
plt.close(fig)
fig, _ = moran_scatterplot(moran_loc_bv, p=0.05)
plt.close(fig)
def test_moran_loc_bv_scatterplot():
gdf = _test_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)
raises(ValueError, _moran_loc_bv_scatterplot, moran_loc, p=0.5)
warns(UserWarning, _moran_loc_bv_scatterplot, moran_loc_bv, p=0.5,
scatter_kwds=dict(c='r'))
distGeo170['dist'] = np.array(distGeo170['DIST_CODE'].values).astype(int)
# distGeo170 = gpd.read_file("data/geo2_gh2010.gpkg")
# distGeo170['dist'] = np.array(distGeo170['DIST2010'].values).astype(int)
x = pd.read_csv('data/data_fromR.csv')
distGeo170 = pd.merge(distGeo170,
x,
left_on='dist',
right_on='dist',
how='outer')
w = weights.Queen.from_dataframe(distGeo170)
for tempYear in [2010, 2013, 2017]:
tempVar1 = 'sachet_usage_' + str(tempYear)
tempVar2 = 'HH_DENSITY_' + str(tempYear)
moran_loc_bv = Moran_Local_BV(distGeo170[tempVar1], distGeo170[tempVar2],
w)
distGeo170['p_sim_' + str(tempYear)] = moran_loc_bv.p_sim
distGeo170['q_' + str(tempYear)] = moran_loc_bv.q
dist_geo_170_lisa = distGeo170.copy()
dist_geo_170_lisa.to_file('data/dist_geo_170_updated.gpkg',
driver='GPKG',
index=True)
## Spatial autocorrelation of sachet drinking consuming
## regions in 2010, 2013 and 2017
## Calculate spatial autocorrelation for the consumption rate of sachet water
w = weights.Queen.from_dataframe(distGeo170)
w.transform = 'r'
moran_I = []
def moran_local_bv(dataset):
x = dataset['AVG_SUICIDE_RATE'].values
y = dataset['AVG_DISEASE_RATE'].values
moran_bv = Moran_Local_BV(y, x, weights)
return moran_bv