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

Bivariate Moran’s I simulated reference distribution.

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.

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 seaborne.kdeplot.

fig : Matplotlib Figure instance

Bivariate moran reference distribution figure

ax : matplotlib Axes instance

Axes in which the figure is plotted



>>> 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_moran_bv_simulation(moran_bv)

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


customize plot

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

(png, hires.png, pdf)