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Geographically Varying Coefficient Regression: GWR-Exit and GAM-On? (Short Paper)

Authors Alexis Comber , Paul Harris , Daisuke Murakami , Narumasa Tsutsumida , Chris Brunsdon

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Author Details

Alexis Comber
  • University of Leeds, UK
Paul Harris
  • Rothamsted Research, Harpenden, UK
Daisuke Murakami
  • Institute of Statistical Mathematics, Tokyo, Japan
Narumasa Tsutsumida
  • Saitama University, Japan
Chris Brunsdon
  • Maynooth University, Ireland

Funding

This work was supported by ROIS-DS-JOINT (005RP2021), https://ds.rois.ac.jp/en/.

Cite As Get BibTex

Alexis Comber, Paul Harris, Daisuke Murakami, Narumasa Tsutsumida, and Chris Brunsdon. Geographically Varying Coefficient Regression: GWR-Exit and GAM-On? (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 13:1-13:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.COSIT.2022.13

Abstract

This paper describes initial work exploring two spatially varying coefficient models: multi-scale GWR and GAM Gaussian Process spline parameterised by observation location. Both approaches accommodate process spatial heterogeneity and both generate outputs that can be mapped indicating the nature of the process heterogeneity. However the nature of the process heterogeneity they each describe are very different. This suggests that the underlying semantics of such models need to be considered in order to refine the specificity of the questions that are asked of data: simply seeking to understand process spatial heterogeneity may be too semantically coarse.

Subject Classification

ACM Subject Classification
  • Information systems
  • Theory of computation
Keywords
  • Geographically weighted regression
  • Spatial Analysis
  • Process Spatial Heterogeneity
  • Model Semantics

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References

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