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

Behzad Kiani; Benn Sartorius; Colleen L. Lau; Robert Bergquist

doi:10.4081/gh.2024.1271

February 29, 2024Geospatial healthOpen Access

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

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Key Points

Spatial regression analysis requires rigorous evaluation before implementing localized techniques, ensuring localized models truly outperform standard global alternatives.
Methodological review identifies bandwidth calibration, weighting function choices, and kernel types as the primary structural parameters governing local spatial relationships.
Highlights the necessity of establishing clear conceptual rationales before applying geographically weighted regression alongside traditional non-spatial regression models.

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Implication

Methodological review demonstrates critical configurations for geographically weighted regression across spatial datasets, highlighting parameter selection for robust spatial modeling.

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Kiani et al. (2024) studied this question.

synapsesocial.com/papers/68e76cf5b6db6435876e2b76 https://doi.org/10.4081/gh.2024.1271

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

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

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