Editorials
Vol. 19 No. 1 (2024)
https://doi.org/10.4081/gh.2024.1271

Mastering geographically weighted regression: key considerations for building a robust model

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Published: 29 February 2024
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Geographically weighted regression, spatial regression, Spatial epidemiology

Authors

Behzad Kiani
B.kiani@uq.edu.au
https://orcid.org/0000-0002-8816-328X
UQ Centre for Clinical Research, The University of Queensland, Brisbane, Australia.
Benn Sartorius
UQ Centre for Clinical Research, The University of Queensland, Brisbane, Australia; Centre for Tropical Medicine and Global Health, Faculty of Medicine, University of Oxford, Oxford, UK; Department of Health Metric Sciences, Faculty of Medicine, University of Washington, Seattle, United States.
Colleen L. Lau
https://orcid.org/0000-0001-8288-4169
UQ Centre for Clinical Research, The University of Queensland, Brisbane, Australia.
Robert Bergquist
Ingerod, Brastad, Sweden.

Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...]

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Citations

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Mastering geographically weighted regression: key considerations for building a robust model. (2024). Geospatial Health, 19(1). https://doi.org/10.4081/gh.2024.1271
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