API reference


esda.adbscan.ADBSCAN(eps, min_samples[, …])

A-DBSCAN, as introduced in [ab_gl_vm2020joue].

Gamma Statistic

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

Gamma index for spatial autocorrelation

Geary Statistics

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

Global Geary C Autocorrelation statistic

esda.Geary_Local([connectivity, labels, …])

Local Geary - Univariate

esda.Geary_Local_MV([connectivity, permutations])

Local Geary - Multivariate

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

Join Count Local Statistics

esda.Join_Counts_Local([connectivity, …])

Univariate Local Join Count Statistic

esda.Join_Counts_Local_BV([connectivity, …])

Univariate Local Join Count Statistic

esda.Join_Counts_Local_MV([connectivity, …])

Multivariate Local Join Count Statistic

LOSH Statistics

esda.LOSH([connectivity, inference])

Local spatial heteroscedasticity (LOSH)

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.path_silhouette(data, labels, W[, D, …])

Compute a path silhouette for all observations [WKR19][Rou87].

Spatial Pearson Statistics

esda.Spatial_Pearson([connectivity, …])

Global 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.