splot.esda.plot_moran¶
-
splot.esda.
plot_moran
(moran, zstandard=True, aspect_equal=True, scatter_kwds=None, fitline_kwds=None, **kwargs)[source]¶ Global Moran’s I simulated reference distribution and scatterplot.
- Parameters
- moranesda.moran.Moran instance
Values of Moran’s I Global Autocorrelation Statistics
- zstandardbool, optional
If True, Moran Scatterplot will show z-standardized attribute and spatial lag values. Default =True.
- aspect_equalbool, optional
If True, Axes of Moran Scatterplot will show the same aspect or visual proportions.
- scatter_kwdskeyword arguments, optional
Keywords used for creating and designing the scatter points. Default =None.
- fitline_kwdskeyword arguments, optional
Keywords used for creating and designing the moran fitline and vertical fitline. Default =None.
- **kwargskeyword arguments, optional
Keywords used for creating and designing the figure, passed to seaborne.kdeplot.
- Returns
- figMatplotlib Figure instance
Moran scatterplot and 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
Load data and calculate weights
>>> link_to_data = examples.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(moran) >>> plt.show()
customize plot
>>> plot_moran(moran, zstandard=False, ... fitline_kwds=dict(color='#4393c3')) >>> plt.show()