pysal.viz.splot.esda.plot_moran_simulation

pysal.viz.splot.esda.plot_moran_simulation(moran, ax=None, fitline_kwds=None, **kwargs)[source]

Global Moran’s I simulated reference distribution.

Parameters:
moran : esda.moran.Moran instance

Values of Moran’s I Global Autocorrelation Statistics

ax : Matplotlib Axes instance, optional

If given, the Moran plot will be created inside this axis. Default =None.

fitline_kwds : keyword arguments, optional

Keywords used for creating and designing the vertical moran fitline. Default =None.

**kwargs : keyword arguments, optional

Keywords used for creating and designing the figure, passed to seaborn.kdeplot.

Returns:
fig : Matplotlib Figure instance

Simulated reference distribution 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 plot_moran_simulation

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

>>> plot_moran_simulation(moran)
>>> plt.show()

(Source code, png, hires.png, pdf)

../_images/pysal-viz-splot-esda-plot_moran_simulation-1_00_00.png

customize plot

>>> plot_moran_simulation(moran, fitline_kwds=dict(color='#4393c3'))
>>> plt.show()

(png, hires.png, pdf)

../_images/pysal-viz-splot-esda-plot_moran_simulation-1_01_00.png