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

Bivariate Moran’s I simulated reference distribution.

moran_bvesda.moran.Moran_BV instance

Values of Bivariate Moran’s I Autocorrelation Statistics

axMatplotlib Axes instance, optional

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

aspect_equalbool, optional

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

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 seaborne.kdeplot.

figMatplotlib Figure instance

Bivariate moran 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_BV
>>> from 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_moran_bv_simulation(moran_bv)
>>> plt.show()

customize plot

>>> plot_moran_bv_simulation(moran_bv,
    ...                      fitline_kwds=dict(color='#4393c3'))
>>> plt.show()

(Source code)