splot.esda.lisa_cluster¶

splot.esda.lisa_cluster(moran_loc, gdf, p=0.05, ax=None, legend=True, legend_kwds=None, **kwargs)[source]¶

Create a LISA Cluster map

Parameters

moran_locesda.moran.Moran_Local or Moran_Local_BV instance: Values of Moran’s Local Autocorrelation Statistic
gdfgeopandas dataframe instance: The Dataframe containing information to plot. Note that gdf will be modified, so calling functions should use a copy of the user provided gdf. (either using gdf.assign() or gdf.copy())
pfloat, optional: The p-value threshold for significance. Points will be colored by significance.
axmatplotlib Axes instance, optional: Axes in which to plot the figure in multiple Axes layout. Default = None
legendboolean, optional: If True, legend for maps will be depicted. Default = True
legend_kwdsdict, optional: Dictionary to control legend formatting options. Example: legend_kwds={'loc': 'upper left', 'bbox_to_anchor': (0.92, 1.05)} Default = None
**kwargskeyword arguments, optional: Keywords designing and passed to geopandas.GeoDataFrame.plot().

Returns

figmatplotlip Figure instance: Figure of LISA cluster map
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_Local
>>> from splot.esda import lisa_cluster

Data preparation and statistical 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

>>> fig = lisa_cluster(moran_loc, gdf)
>>> plt.show()

(Source code)