def _moran_loc_from_rose_calc(rose):
"""
Calculate esda.moran.Moran_Local values from pysal.explore.giddy.rose object
"""
old_state = np.random.get_state()
moran_locy = Moran_Local(rose.Y[:, 0], rose.w)
np.random.set_state(old_state)
moran_locx = Moran_Local(rose.Y[:, 1], rose.w)
np.random.set_state(old_state)
return moran_locy, moran_locx
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 get_lisa(gdf, indicator, w):
'''
This function takes a year and a variable
and returns the all the quadrants for local moran analysis
...
Args:
'''
gdf = gdf.copy()
quadrant_labels = {1: 'HH',
2: 'HL',
3: 'LL',
4: 'LH'
}
moran = Moran(gdf[indicator].values, w)
moran_loc = Moran_Local(gdf[indicator].values, w, transformation="r")
significant = pd.Series(moran_loc.p_sim < 0.05)
quadrant = pd.Series(moran_loc.q)
quadrant = quadrant.replace(quadrant_labels)
quadrant[~significant] = 'Non-significant'
gdf['lisa_cluster'] = quadrant
return gdf
def test_lisa_cluster():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
fig, _ = lisa_cluster(moran_loc, df)
plt.close(fig)
def test_moran_loc_scatterplot():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
# try with p value so points are colored
fig, _ = _moran_loc_scatterplot(moran_loc, p=0.05)
plt.close(fig)
# try with p value and different figure size
fig, _ = _moran_loc_scatterplot(moran_loc,
p=0.05,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
def test_moran_loc_scatterplot():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
x = df['INC'].values
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
moran_bv = Moran_BV(x, y, w)
# try without p value
fig, _ = _moran_loc_scatterplot(moran_loc)
plt.close(fig)
# try with p value and different figure size
fig, _ = _moran_loc_scatterplot(moran_loc,
p=0.05,
aspect_equal=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
# try with p value and zstandard=False
fig, _ = _moran_loc_scatterplot(moran_loc,
p=0.05,
zstandard=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
# try without p value and zstandard=False
fig, _ = _moran_loc_scatterplot(moran_loc,
zstandard=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
assert_raises(ValueError, _moran_loc_scatterplot, moran_bv, p=0.5)
assert_warns(UserWarning,
_moran_loc_scatterplot,
moran_loc,
p=0.5,
scatter_kwds=dict(c='#4393c3'))
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, 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 get_lisa_legacy(path, indicator, w, grid_id):
'''
This function takes a year and a variable
and returns the all the quadrants for local moran analysis
...
Args:
'''
carto = gpd.read_file(path)
moran = Moran(carto[indicator].values, w)
moran_loc = Moran_Local(carto[indicator].values, w, transformation="r",
permutations=99)
carto['significant'] = moran_loc.p_sim < 0.05
carto['quadrant'] = moran_loc.q
return {'gdf': carto,
'moran': moran,
'value': moran.I,
'significance': moran.p_sim,
'local': moran_loc}
def test_plot_local_autocorrelation():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
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',
mask=['1', '2', '3'],
quadrant=1)
plt.close(fig)
# Imports
import matplotlib.pyplot as plt
from pysal.lib.weights.contiguity import Queen
from pysal.lib import examples
import geopandas as gpd
from pysal.explore.esda.moran import Moran_Local
from pysal.viz.splot.esda import plot_local_autocorrelation
# Data preparation and analysis
link = examples.get_path('Guerry.shp')
gdf = gpd.read_file(link)
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
# Plotting with quadrant mask and region mask
fig = plot_local_autocorrelation(moran_loc, gdf, 'Donatns', p=0.05,
region_column='Dprtmnt',
mask=['Ain'], quadrant=1)
plt.show()