The GWR route map: a guide to the informed application of Geographically Weighted Regression

TL;DR

This paper provides a comprehensive guide for selecting and applying different variants of Geographically Weighted Regression (GWR) in spatial data analysis, emphasizing decision steps and considerations for model choice.

Contribution

It introduces a route map for choosing among GWR variants, including standard, mixed, and multiscale GWR, with practical guidance and case study illustrations.

Findings

Guidance on when to use GWR versus global models

Comparison of GWR variants in different spatial contexts

Practical steps for diagnosing model suitability

Abstract

Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a global one. Standard GWR assumes that the relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to inform the choice of whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS-GWR). The route map comprises primary steps: a basic linear regression, a MS-GWR, and investigations of the results of these. The paper provides guidance for deciding whether to use a GWR approach, and if so for determining the appropriate GWR variant. It describes the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided, and further considerations are described in an extensive Appendix.