A Route Map for Successful Applications of Geographically Weighted Regression

A - Papers appearing in refereed journals

Comber, A., Brunsdon, C., Charlton, M., Dong, G., Harris, R., Lu, B., Lu, Y., Murakami, D., Nakaya, T., Wang, Y. and Harris, P. 2021. A Route Map for Successful Applications of Geographically Weighted Regression. Geographical Analysis . https://doi.org/10.1111/gean.12316

AuthorsComber, A., Brunsdon, C., Charlton, M., Dong, G., Harris, R., Lu, B., Lu, Y., Murakami, D., Nakaya, T., Wang, Y. and Harris, P.
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 single global one. Standard GWR assumes that 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 decide 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 3 primary steps that should always be undertaken: (1) a basic linear regression, (2) a MS-GWR,
and (3) investigations of the results of these in order to decide whether to use a GWR approach, and if so for determining the appropriate GWR variant. The paper also highlights 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.

KeywordsSpatially varying coefficient model; Non-stationarity; Spatial heterogeneity; Autocorrelation; Regression
Year of Publication2021
JournalGeographical Analysis
Digital Object Identifier (DOI)https://doi.org/10.1111/gean.12316
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Natural Environment Research Council
National Natural Science Foundation of China
National Natural Science Foundation of China
Funder project or codeThe North Wyke Farm Platform- National Capability [2017-22]
S2N - Soil to Nutrition - Work package 2 (WP2) - Adaptive management systems for improved efficiency and nutritional quality
S2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
Publisher's version
Accepted author manuscript
Output statusPublished
Publication dates
OnlineDec 2021
Publication process dates
Accepted29 Nov 2021
PublisherWiley
ISSN1538-4632

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