API reference

Gamma Statistic

esda.Gamma(y, w[, operation, standardize, …])

Gamma index for spatial autocorrelation

Geary Statistic

esda.Geary(y, w[, transformation, permutations])

Global Geary C Autocorrelation statistic

Getis-Ord Statistics

esda.G(y, w[, permutations])	Global G Autocorrelation Statistic
esda.G_Local(y, w[, transform, …])	Generalized Local G Autocorrelation

Join Count Statistics

esda.Join_Counts(y, w[, permutations])

Binary Join Counts

Modifiable Areal Unit Tests

esda.Smaup(n, k, rho)

S-maup: Statistical Test to Measure the Sensitivity to the Modifiable Areal Unit Problem

Moran Statistics

esda.Moran(y, w[, transformation, …])	Moran’s I Global Autocorrelation Statistic
esda.Moran_BV(x, y, w[, transformation, …])	Bivariate Moran’s I
esda.Moran_BV_matrix(variables, w[, …])	Bivariate Moran Matrix
esda.Moran_Local(y, w[, transformation, …])	Local Moran Statistics
esda.Moran_Local_BV(x, y, w[, …])	Bivariate Local Moran Statistics
esda.Moran_Rate(e, b, w[, adjusted, …])	Adjusted Moran’s I Global Autocorrelation Statistic for Rate Variables [AR99]
esda.Moran_Local_Rate(e, b, w[, adjusted, …])	Adjusted Local Moran Statistics for Rate Variables [AR99].

Silhouette Statistics

esda.boundary_silhouette(data, labels, W[, …])	Compute the observation-level boundary silhouette score [WKR19].
esda.nearest_label(data, labels[, metric, …])	Find the nearest label in attribute space.
esda.path_silhouette(data, labels, W[, D, …])	Compute a path silhouette for all observations [WKR19][Rou87].
esda.silhouette_alist(data, labels, alist[, …])	Compute the silhouette for each edge in an adjacency graph.

Spatial Pearson Statistics

esda.Spatial_Pearson([connectivity, …])	Global Spatial Pearson Statistic
esda.Local_Spatial_Pearson([connectivity, …])	Local Spatial Pearson Statistic

Utility Functions

esda.fdr(pvalues[, alpha])

Calculate the p-value cut-off to control for the false discovery rate (FDR) for multiple testing.