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

arXiv: https://arxiv.org/abs/1312.2753 · HTML: https://mlsanity.com/paper/1312.2753/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2013.

## Built on by (most cited first, 1 of 1)

- [Geographically Weighted Surrogate Models for Rapid Small-Area Chronic Disease Estimation](https://mlsanity.com/paper/2607.28655/) (2026)

## Abstract (arXiv)

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.