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