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Geographically Weighted Regression (GWR) is increasingly used in spatialanalyses of social and environmental data. It allows spatial heterogeneities inprocesses and relationships to be investigated through a series of localregression models rather than a global one. Standard GWR assumes that therelationships between the response and predictor variables operate at the samespatial scale, which is frequently not the case. To address this, several GWRvariants have been proposed. This paper describes a route map to inform thechoice of whether to use a GWR model or not, and if so which of three corevariants 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, andinvestigations of the results of these. The paper provides guidance fordeciding whether to use a GWR approach, and if so for determining theappropriate GWR variant. It describes the importance of investigating a numberof secondary issues at global and local scales including collinearity, theinfluence of outliers, and dependent error terms. Code and data for the casestudy used to illustrate the route map are provided, and further considerationsare described in an extensive Appendix.

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