  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.
