def test_plot_spatial_weights():
# get data
gdf = gpd.read_file(examples.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 = pysal.lib.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_plot_moran_simulation():
# Load data and apply statistical analysis
link_to_data = examples.get_path('Guerry.shp')
gdf = gpd.read_file(link_to_data)
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calc Global Moran
w = Queen.from_dataframe(gdf)
moran = Moran(y, w)
# plot
fig, _ = plot_moran_simulation(moran)
plt.close(fig)
# customize
fig, _ = plot_moran_simulation(moran, fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
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_plot_choropleth():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
w = Queen.from_dataframe(df)
w.transform = 'r'
TOOLS = "tap,help"
fig = plot_choropleth(df, 'HOVAL', title='columbus',
reverse_colors=True, tools=TOOLS)
def test_moran_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 = esda.moran.Moran_Local(y, w)
fig = moran_scatterplot(moran_loc, p=0.05)
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 = esda.moran.Moran_Local(y, w)
fig = plot_local_autocorrelation(moran_loc, df, 'HOVAL')
def test_moran_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 = esda.moran.Moran_Local(y, w)
fig = moran_scatterplot(moran_loc, p=0.05)
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 = esda.moran.Moran_Local(y, w)
fig = plot_local_autocorrelation(moran_loc, df, 'HOVAL')
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_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 = esda.moran.Moran_Local(y, w)
TOOLS = "tap,reset,help"
fig = lisa_cluster(moran_loc, df, p=0.05, tools=TOOLS)
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 = esda.moran.Moran_Local(y, w)
TOOLS = "tap,reset,help"
fig = lisa_cluster(moran_loc, df, p=0.05, tools=TOOLS)
def test_plot_choropleth():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
w = Queen.from_dataframe(df)
w.transform = 'r'
TOOLS = "tap,help"
fig = plot_choropleth(df,
'HOVAL',
title='columbus',
reverse_colors=True,
tools=TOOLS)
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_plot_moran_bv():
# Load data and calculate weights
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_bv = Moran_BV(x, y, w)
# plot
fig, _ = plot_moran_bv(moran_bv)
plt.close(fig)
# customize plot
fig, _ = plot_moran_bv(moran_bv, fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
def test_plot_spatial_weights():
# get data
gdf = gpd.read_file(examples.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 = pysal.lib.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)
def test_moran_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_bv = Moran_BV(x, y, w)
# plot
fig, _ = _moran_bv_scatterplot(moran_bv)
plt.close(fig)
# customize plot
fig, _ = _moran_bv_scatterplot(moran_bv,
aspect_equal=False,
fitline_kwds=dict(color='#4393c3'))
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_plot_spatial_weights():
# get data
gdf = gpd.read_file(examples.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 = pysal.lib.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)
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 _data_generation():
# get csv and shp
shp_link = examples.get_path('us48.shp')
df = gpd.read_file(shp_link)
income_table = pd.read_csv(examples.get_path("usjoin.csv"))
# calculate relative values
for year in range(1969, 2010):
income_table[str(year) + '_rel'] = (income_table[str(year)] /
income_table[str(year)].mean())
# merge
gdf = df.merge(income_table, left_on='STATE_NAME', right_on='Name')
# retrieve spatial weights and data for two points in time
w = Queen.from_dataframe(gdf)
w.transform = 'r'
y1 = gdf['1969_rel'].values
y2 = gdf['2000_rel'].values
#calculate rose Object
Y = np.array([y1, y2]).T
rose = Rose(Y, w, k=5)
return gdf, y1, rose
def _data_generation():
# get csv and shp
shp_link = examples.get_path('us48.shp')
df = gpd.read_file(shp_link)
income_table = pd.read_csv(examples.get_path("usjoin.csv"))
# calculate relative values
for year in range(1969, 2010):
income_table[str(year) + '_rel'] = (
income_table[str(year)] / income_table[str(year)].mean())
# merge
gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name')
# retrieve spatial weights and data for two points in time
w = Queen.from_dataframe(gdf)
w.transform = 'r'
y1 = gdf['1969_rel'].values
y2 = gdf['2000_rel'].values
#calculate rose Object
Y = np.array([y1, y2]).T
rose = Rose(Y, w, k=5)
return gdf, y1, rose
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 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_BV
from pysal.viz.splot.esda import plot_moran_bv_simulation
# Load data and calculate weights
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_bv = Moran_BV(x, y, w)
# plot
plot_moran_bv_simulation(moran_bv)
plt.show()
# customize plot
plot_moran_bv_simulation(moran_bv,
fitline_kwds=dict(color='#4393c3'))
# Imports
from pysal.lib.weights.contiguity import Queen
import geopandas as gpd
import pysal.lib
from pysal.lib import examples
import matplotlib.pyplot as plt
from pysal.viz.splot.libpysal import plot_spatial_weights
# Data preparation and statistical analysis
gdf = gpd.read_file(examples.get_path('map_RS_BR.shp'))
weights = Queen.from_dataframe(gdf)
wnp = pysal.lib.weights.util.nonplanar_neighbors(weights, gdf)
# Plot weights
plot_spatial_weights(weights, gdf)
plt.show()
# Plot corrected weights
plot_spatial_weights(wnp, gdf)
plt.show()