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

Global Moran’s I simulated reference distribution.

moranesda.moran.Moran instance

Values of Moran’s I Global Autocorrelation Statistics

aspect_equalbool, optional

If True, Axes of Moran Scatterplot will show the same aspect or visual proportions.

axMatplotlib Axes instance, optional

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

fitline_kwdskeyword arguments, optional

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

**kwargskeyword arguments, optional

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

figMatplotlib Figure instance

Simulated reference distribution figure

axmatplotlib Axes instance

Axes in which the figure is plotted



>>> import matplotlib.pyplot as plt
>>> from libpysal.weights.contiguity import Queen
>>> from libpysal import examples
>>> import geopandas as gpd
>>> from esda.moran import Moran
>>> from 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_moran_simulation(moran)
>>> plt.show()

customize plot

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

(Source code)