splot.esda.plot_moran_simulation¶
- splot.esda.plot_moran_simulation(moran, aspect_equal=True, ax=None, fitline_kwds=None, **kwargs)[source]¶
Global Moran’s I simulated reference distribution.
- Parameters
- 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.
- Returns
- figMatplotlib Figure instance
Simulated 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 >>> from splot.esda import plot_moran_simulation
Load data and calculate weights
>>> guerry = examples.load_example('Guerry') >>> link_to_data = guerry.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
>>> plot_moran_simulation(moran) >>> plt.show()
(Source code, png, hires.png, pdf)
customize plot
>>> plot_moran_simulation(moran, fitline_kwds=dict(color='#4393c3')) >>> plt.show()