def _moran_loc_scatterplot(moran_loc,
zstandard=True,
p=None,
ax=None,
scatter_kwds=None,
fitline_kwds=None):
"""
Moran Scatterplot with option of coloring of Local Moran Statistics
Parameters
----------
moran_loc : esda.moran.Moran_Local instance
Values of Moran's I Local Autocorrelation Statistics
p : float, optional
If given, the p-value threshold for significance. Points will
be colored by significance. By default it will not be colored.
Default =None.
ax : Matplotlib Axes instance, optional
If given, the Moran plot will be created inside this axis.
Default =None.
scatter_kwds : keyword arguments, optional
Keywords used for creating and designing the scatter points.
Default =None.
fitline_kwds : keyword arguments, optional
Keywords used for creating and designing the moran fitline.
Default =None.
Returns
-------
fig : Matplotlib Figure instance
Moran Local scatterplot figure
ax : matplotlib Axes instance
Axes in which the figure is plotted
Examples
--------
Imports
>>> import matplotlib.pyplot as plt
>>> import geopandas as gpd
>>> from pysal.lib.weights.contiguity import Queen
>>> from pysal.lib import examples
>>> from pysal.explore.esda.moran import Moran_Local
>>> from pysal.viz.splot.esda import moran_scatterplot
Load data and calculate Moran Local statistics
>>> link = examples.get_path('Guerry.shp')
>>> gdf = gpd.read_file(link)
>>> y = gdf['Donatns'].values
>>> w = Queen.from_dataframe(gdf)
>>> w.transform = 'r'
>>> m = Moran_Local(y, w)
plot
>>> moran_scatterplot(m)
>>> plt.show()
customize plot
>>> moran_scatterplot(m, p=0.05,
... fitline_kwds=dict(color='#4393c3'))
>>> plt.show()
"""
# to set default as an empty dictionary that is later filled with defaults
if scatter_kwds is None:
scatter_kwds = dict()
if fitline_kwds is None:
fitline_kwds = dict()
if p is not None:
if not isinstance(moran_loc, Moran_Local):
raise ValueError("`moran_loc` is not a\n " +
"esda.moran.Moran_Local instance")
if 'color' in scatter_kwds or 'c' in scatter_kwds or 'cmap' in scatter_kwds:
warnings.warn(
'To change the color use cmap with a colormap of 5,\n' +
' color defines the LISA category')
# colors
spots = moran_hot_cold_spots(moran_loc, p)
hmap = colors.ListedColormap(
['#bababa', '#d7191c', '#abd9e9', '#2c7bb6', '#fdae61'])
# define customization
scatter_kwds.setdefault('alpha', 0.6)
scatter_kwds.setdefault('s', 40)
fitline_kwds.setdefault('alpha', 0.9)
# get fig and ax
fig, ax = _create_moran_fig_ax(ax, figsize=(7, 7))
# set labels
ax.set_xlabel('Attribute')
ax.set_ylabel('Spatial Lag')
ax.set_title('Moran Local Scatterplot')
# plot and set standards
if zstandard is True:
lag = lag_spatial(moran_loc.w, moran_loc.z)
fit = OLS(moran_loc.z[:, None], lag[:, None])
# v- and hlines
ax.axvline(0, alpha=0.5, color='k', linestyle='--')
ax.axhline(0, alpha=0.5, color='k', linestyle='--')
if p is not None:
fitline_kwds.setdefault('color', 'k')
scatter_kwds.setdefault('cmap', hmap)
scatter_kwds.setdefault('c', spots)
ax.plot(lag, fit.predy, **fitline_kwds)
ax.scatter(moran_loc.z, fit.predy, **scatter_kwds)
else:
scatter_kwds.setdefault('color', splot_colors['moran_base'])
fitline_kwds.setdefault('color', splot_colors['moran_fit'])
ax.plot(lag, fit.predy, **fitline_kwds)
ax.scatter(moran_loc.z, fit.predy, **scatter_kwds)
else:
lag = lag_spatial(moran_loc.w, moran_loc.y)
b, a = np.polyfit(moran_loc.y, lag, 1)
# dashed vert at mean of the attribute
ax.vlines(moran_loc.y.mean(),
lag.min(),
lag.max(),
alpha=0.5,
linestyle='--')
# dashed horizontal at mean of lagged attribute
ax.hlines(lag.mean(),
moran_loc.y.min(),
moran_loc.y.max(),
alpha=0.5,
linestyle='--')
if p is not None:
fitline_kwds.setdefault('color', 'k')
scatter_kwds.setdefault('cmap', hmap)
scatter_kwds.setdefault('c', spots)
ax.plot(moran_loc.y, a + b * moran_loc.y, **fitline_kwds)
ax.scatter(moran_loc.y, lag, **scatter_kwds)
else:
scatter_kwds.setdefault('c', splot_colors['moran_base'])
fitline_kwds.setdefault('color', splot_colors['moran_fit'])
ax.plot(moran_loc.y, a + b * moran_loc.y, **fitline_kwds)
ax.scatter(moran_loc.y, lag, **scatter_kwds)
return fig, ax
def _moran_global_scatterplot(moran,
zstandard=True,
ax=None,
scatter_kwds=None,
fitline_kwds=None):
"""
Global Moran's I Scatterplot.
Parameters
----------
moran : esda.moran.Moran instance
Values of Moran's I Global Autocorrelation Statistics
zstandard : bool, optional
If True, Moran Scatterplot will show z-standardized attribute and
spatial lag values. Default =True.
ax : Matplotlib Axes instance, optional
If given, the Moran plot will be created inside this axis.
Default =None.
scatter_kwds : keyword arguments, optional
Keywords used for creating and designing the scatter points.
Default =None.
fitline_kwds : keyword arguments, optional
Keywords used for creating and designing the moran fitline.
Default =None.
Returns
-------
fig : Matplotlib Figure instance
Moran scatterplot figure
ax : matplotlib Axes instance
Axes in which the figure is plotted
Examples
--------
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
>>> from pysal.viz.splot.esda import moran_scatterplot
Load data and calculate weights
>>> 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'
Calculate Global Moran
>>> moran = Moran(y, w)
plot
>>> moran_scatterplot(moran)
>>> plt.show()
customize plot
>>> fig, ax = moran_scatterplot(moran, zstandard=False,
... fitline_kwds=dict(color='#4393c3'))
>>> ax.set_xlabel('Donations')
>>> plt.show()
"""
# to set default as an empty dictionary that is later filled with defaults
if scatter_kwds is None:
scatter_kwds = dict()
if fitline_kwds is None:
fitline_kwds = dict()
# define customization defaults
scatter_kwds.setdefault('alpha', 0.6)
scatter_kwds.setdefault('color', splot_colors['moran_base'])
scatter_kwds.setdefault('s', 40)
fitline_kwds.setdefault('alpha', 0.9)
fitline_kwds.setdefault('color', splot_colors['moran_fit'])
# get fig and ax
fig, ax = _create_moran_fig_ax(ax, figsize=(7, 7))
# set labels
ax.set_xlabel('Attribute')
ax.set_ylabel('Spatial Lag')
ax.set_title('Moran Scatterplot' + ' (' + str(round(moran.I, 2)) + ')')
# plot and set standards
if zstandard is True:
lag = lag_spatial(moran.w, moran.z)
fit = OLS(moran.z[:, None], lag[:, None])
# plot
ax.scatter(moran.z, lag, **scatter_kwds)
ax.plot(lag, fit.predy, **fitline_kwds)
# v- and hlines
ax.axvline(0, alpha=0.5, color='k', linestyle='--')
ax.axhline(0, alpha=0.5, color='k', linestyle='--')
else:
lag = lag_spatial(moran.w, moran.y)
b, a = np.polyfit(moran.y, lag, 1)
# plot
ax.scatter(moran.y, lag, **scatter_kwds)
ax.plot(moran.y, a + b * moran.y, **fitline_kwds)
# dashed vert at mean of the attribute
ax.vlines(moran.y.mean(),
lag.min(),
lag.max(),
alpha=0.5,
linestyle='--')
# dashed horizontal at mean of lagged attribute
ax.hlines(lag.mean(),
moran.y.min(),
moran.y.max(),
alpha=0.5,
linestyle='--')
return fig, ax
def _moran_bv_scatterplot(moran_bv,
ax=None,
scatter_kwds=None,
fitline_kwds=None):
"""
Bivariate Moran Scatterplot.
Parameters
----------
moran_bv : esda.moran.Moran_BV instance
Values of Bivariate Moran's I Autocorrelation Statistics
ax : Matplotlib Axes instance, optional
If given, the Moran plot will be created inside this axis.
Default =None.
scatter_kwds : keyword arguments, optional
Keywords used for creating and designing the scatter points.
Default =None.
fitline_kwds : keyword arguments, optional
Keywords used for creating and designing the moran fitline.
Default =None.
Returns
-------
fig : Matplotlib Figure instance
Bivariate moran scatterplot figure
ax : matplotlib Axes instance
Axes in which the figure is plotted
Examples
--------
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 moran_scatterplot
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
>>> moran_scatterplot(moran_bv)
>>> plt.show()
customize plot
>>> moran_scatterplot(moran_bv,
... fitline_kwds=dict(color='#4393c3'))
>>> plt.show()
"""
# to set default as an empty dictionary that is later filled with defaults
if scatter_kwds is None:
scatter_kwds = dict()
if fitline_kwds is None:
fitline_kwds = dict()
# define customization
scatter_kwds.setdefault('alpha', 0.6)
scatter_kwds.setdefault('color', splot_colors['moran_base'])
scatter_kwds.setdefault('s', 40)
fitline_kwds.setdefault('alpha', 0.9)
fitline_kwds.setdefault('color', splot_colors['moran_fit'])
# get fig and ax
fig, ax = _create_moran_fig_ax(ax, figsize=(7, 7))
# set labels
ax.set_xlabel('Attribute X')
ax.set_ylabel('Spatial Lag of Y')
ax.set_title('Bivariate Moran Scatterplot' + ' (' +
str(round(moran_bv.I, 2)) + ')')
# plot and set standards
lag = lag_spatial(moran_bv.w, moran_bv.zy)
fit = OLS(moran_bv.zy[:, None], lag[:, None])
# plot
ax.scatter(moran_bv.zx, lag, **scatter_kwds)
ax.plot(lag, fit.predy, **fitline_kwds)
# v- and hlines
ax.axvline(0, alpha=0.5, color='k', linestyle='--')
ax.axhline(0, alpha=0.5, color='k', linestyle='--')
return fig, ax
import numpy as np
import pysal.lib
from pysal.model.spreg import OLS
db = pysal.lib.io.open(pysal.lib.examples.get_path('columbus.dbf'), 'r')
hoval = db.by_col("HOVAL")
y = np.array(hoval)
y.shape = (len(hoval), 1)
X = []
X.append(db.by_col("INC"))
X.append(db.by_col("CRIME"))
X = np.array(X).T
ols = OLS(y,
X,
name_y='home value',
name_x=['income', 'crime'],
name_ds='columbus',
white_test=True)
print(ols.summary)