splot.esda.moran_scatterplot¶
-
splot.esda.
moran_scatterplot
(moran, zstandard=True, p=None, aspect_equal=True, ax=None, scatter_kwds=None, fitline_kwds=None)[source]¶ Moran Scatterplot
- Parameters
- moranesda.moran instance
Values of Moran’s I Global, Bivariate and Local Autocorrelation Statistics
- zstandardbool, optional
If True, Moran Scatterplot will show z-standardized attribute and spatial lag values. Default =True.
- pfloat, optional
If given, the p-value threshold for significance for Local Autocorrelation analysis. Points will be colored by significance. By default it will not be colored. Default =None.
- aspect_equalbool, optional
If True, Axes will show the same aspect or visual proportions for Moran Scatterplot.
- axMatplotlib Axes instance, optional
If given, the Moran plot will be created inside this axis. Default =None.
- 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. Default =None.
- Returns
- figMatplotlib Figure instance
Moran scatterplot 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, Moran_BV, ... Moran_Local, Moran_Local_BV) >>> from splot.esda import moran_scatterplot
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 esda.moran Objects
>>> moran = Moran(y, w) >>> moran_bv = Moran_BV(y, x, w) >>> moran_loc = Moran_Local(y, w) >>> moran_loc_bv = Moran_Local_BV(y, x, w)
Plot
>>> fig, axs = plt.subplots(2, 2, figsize=(10,10), ... subplot_kw={'aspect': 'equal'}) >>> moran_scatterplot(moran, p=0.05, ax=axs[0,0]) >>> moran_scatterplot(moran_loc, p=0.05, ax=axs[1,0]) >>> moran_scatterplot(moran_bv, p=0.05, ax=axs[0,1]) >>> moran_scatterplot(moran_loc_bv, p=0.05, ax=axs[1,1]) >>> plt.show()