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In this study, we link and compare the geographically\udweighted regression (GWR) model with the\udkriging with an external drift (KED) model of geostatistics.\udThis includes empirical work where models are performance\udtested with respect to prediction and prediction\uduncertainty accuracy. In basic forms, GWR and KED\ud(specified with local neighbourhoods) both cater for nonstationary\udcorrelations (i.e. the process is heteroskedastic\udwith respect to relationships between the variable of\udinterest and its covariates) and as such, can predict more\udaccurately than models that do not. Furthermore, on specification\udof an additional heteroskedastic term to the same\udmodels (now with respect to a process variance), locallyaccurate\udmeasures of prediction uncertainty can result.\udThese heteroskedastic extensions of GWR and KED can be\udpreferred to basic constructions, whose measures of prediction\uduncertainty are only ever likely to be globallyaccurate.\udWe evaluate both basic and heteroskedastic\udGWR and KED models using a case study data set, where\uddata relationships are known to vary across space. Here\udGWR performs well with respect to the more involved\udKED model and as such, GWR is considered a viable\udalternative to the more established model in this particular\udcomparison. Our study adds to a growing body of empirical\udevidence that GWR can be a worthy predictor; complementing\udits more usual guise as an exploratory technique for investigating relationships in multivariate spatial data\udsets

Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor