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