splot.esda.plot_local_autocorrelation(moran_loc, gdf, attribute, p=0.05, region_column=None, mask=None, mask_color='#636363', quadrant=None, aspect_equal=True, legend=True, scheme='Quantiles', cmap='YlGnBu', figsize=(15, 4), scatter_kwds=None, fitline_kwds=None)[source]

Produce three-plot visualisation of Moran Scatteprlot, LISA cluster and Choropleth maps, with Local Moran region and quadrant masking

moran_locesda.moran.Moran_Local or Moran_Local_BV instance

Values of Moran’s Local Autocorrelation Statistic

gdfgeopandas dataframe

The Dataframe containing information to plot the two maps.


Column name of attribute which should be depicted in Choropleth map.

pfloat, optional

The p-value threshold for significance. Points and polygons will be colored by significance. Default = 0.05.

region_column: string, optional

Column name containing mask region of interest. Default = None

mask: str, optional

Identifier or name of the region to highlight. Default = None

mask_color: str, optional

Color of mask. Default = ‘#636363’

quadrantint, optional

Quadrant 1-4 in scatterplot masking values in LISA cluster and Choropleth maps. Default = None

aspect_equalbool, optional

If True, Axes of Moran Scatterplot will show the same aspect or visual proportions.

figsize: tuple, optional

W, h of figure. Default = (15,4)

legend: boolean, optional

If True, legend for maps will be depicted. Default = True

scheme: str, optional

Name of PySAL classifier to be used. Default = ‘Quantiles’

cmap: str, optional

Name of matplotlib colormap used for plotting the Choropleth. Default = ‘YlGnBu’

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 in the scatterplot. Default =None.

figMatplotlib figure instance

Moran Scatterplot, LISA cluster map and Choropleth.

axslist of Matplotlib axes

Lisat of Matplotlib axes plotted.



>>> import matplotlib.pyplot as plt
>>> from libpysal.weights.contiguity import Queen
>>> from libpysal import examples
>>> import geopandas as gpd
>>> from esda.moran import Moran_Local
>>> from splot.esda import plot_local_autocorrelation

Data preparation and analysis

>>> link = examples.get_path('Guerry.shp')
>>> gdf = gpd.read_file(link)
>>> y = gdf['Donatns'].values
>>> w = Queen.from_dataframe(gdf)
>>> w.transform = 'r'
>>> moran_loc = Moran_Local(y, w)

Plotting with quadrant mask and region mask

>>> fig = plot_local_autocorrelation(moran_loc, gdf, 'Donatns', p=0.05,
...                                  region_column='Dprtmnt',
...                                  mask=['Ain'], quadrant=1)
>>> plt.show()

(Source code)