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A Route Map for Successful Applications of Geographically Weighted Regression
Author(s) -
Comber Alexis,
Brunsdon Christopher,
Charlton Martin,
Dong Guanpeng,
Harris Richard,
Lu Binbin,
Lü Yihe,
Murakami Daisuke,
Nakaya Tomoki,
Wang Yunqiang,
Harris Paul
Publication year - 2023
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12316
Subject(s) - geographically weighted regression , collinearity , outlier , linear regression , regression , regression analysis , computer science , geography , cartography , statistics , mathematics , artificial intelligence , machine learning
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.