The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models

In this study, we present a collection of local models, termed geographically weighted (GW) models, that can be found within the GWmodel R package. A GW model suits when spatial data are poorly described by the global form, and for some the localised fit provides a better description. The approach uses a moving weighting technique, where a collection of local models are estimated at target . Commonly, model parameters or outputs are mapped so that the nature of heterogeneity can be explored and assessed. In particular, we present case using: (i) GW summary statistics and a GW principal components analysis; (ii) GW regression fits and diagnostics; (iii) associated Monte Carlo significance for non-stationarity; (iv) a GW discriminant analysis; and (v) enhanced kernel selection procedures. General Election data sets from the Republic of and US are used for demonstration. This study is designed to complement a GWmodel study, which focuses on basic and robust GW models.