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