splot.esda.plot_moran_bv_simulation¶

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.

Parameters

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.

Returns

figMatplotlib Figure instance: Bivariate moran reference distribution figure
axmatplotlib Axes instance: Axes in which the figure is plotted

Examples

Imports

>>> 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

>>> 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)