pysal.viz.splot.esda.plot_moran_bv

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

Bivariate Moran’s I simulated reference distribution and scatterplot.

Parameters:
moran_bv : esda.moran.Moran_BV instance

Values of Bivariate Moran’s I Autocorrelation Statistics

scatter_kwds : keyword arguments, optional

Keywords used for creating and designing the scatter points. Default =None.

fitline_kwds : keyword arguments, optional

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

**kwargs : keyword arguments, optional

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

Returns:
fig : Matplotlib Figure instance

Bivariate moran scatterplot and 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_BV
>>> from pysal.viz.splot.esda import plot_moran_bv

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(moran_bv)
>>> plt.show()

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

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

customize plot

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

(png, hires.png, pdf)

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