The GWmodel R package: Further Topics for Exploring Spatial Heterogeneity using Geographically Weighted Models

TL;DR

This paper introduces the GWmodel R package, which provides advanced geographically weighted models for analyzing spatial heterogeneity, including local regression, principal components, and significance testing, demonstrated through election data case studies.

Contribution

The paper presents new tools within the GWmodel R package for advanced local spatial modeling, diagnostics, and significance testing, enhancing analysis of spatial heterogeneity.

Findings

Effective local models for spatial data analysis

Significance tests for non-stationarity in spatial data

Enhanced bandwidth selection procedures

Abstract

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.