Moving window kriging with geographically weighted variograms

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Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor (2011, background): “However for basic constructions, this approach is rarely recommended as the prediction variances are unlikely to vary in accordance with the variability in the local data used to provide the predictions (with respect to kriging see, Journel 1986; Switzer 1993; Heuvelink and Pebesma 2002; Harris et ”
Second-order non-stationary modeling approaches for univariate geostatistical data (2017, methodology): “Harris et al. (2010) replace the moving window estimator of the local stationary spatial dependence structure by a weighted estimator.”
GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models (2013, methodology): “Conceptual links between GW regression and kriging are discussed in Harris, Brunsdon, and Fotheringham (2011b) and various hybrid predictors can be found in Lloyd (2010b); Harris et al. (2010b); Harris and Juggins (2011), where GW regression can provide a useful trend component of a kriging model.”