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

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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.”
A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships (2011, background): “Harris et al (2010; 2011), as well, report that methods that use spatial subsampling in general, and GWR in particular, perform well for interpolation.”
Spatial interpolation methods applied in the environmental sciences: A review (2014, methodology): “The application of this method to spatial interpolation would be worthwhile (Wheeler and Páez, 2010); but its applications to environmental sciences are still rare (Harris et al., 2011) and should be cautiously exercised particularly when sample size is small (Páez et al., 2011).”
Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data (2014, methodology): “This predictive work could extend that conducted by Harris et al. (2010, 2011b).”