https://doi.org/10.4081/gh.2024.1271
Mastering geographically weighted regression: key considerations for building a robust model
Published: 29 February 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
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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How to Cite
Kiani, B., Sartorius, B., Lau, C. L., & Bergquist, R. (2024). Mastering geographically weighted regression: key considerations for building a robust model. Geospatial Health, 19(1). https://doi.org/10.4081/gh.2024.1271
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