2026-07-22T11:38:05Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:16898 2024-03-06T10:58:18Z ddc:004 open_access

Geographically Varying Coefficient Regression: GWR-Exit and GAM-On? (Short Paper) Comber, Alexis Harris, Paul Murakami, Daisuke Tsutsumida, Narumasa Brunsdon, Chris Geographically weighted regression Spatial Analysis Process Spatial Heterogeneity Model Semantics 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Alexis Comber and Paul Harris and Daisuke Murakami and Narumasa Tsutsumida and Chris Brunsdon 2022 Is Part Of LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.COSIT.2022.13 urn:nbn:de:0030-drops-168986 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.13 eng https://creativecommons.org/licenses/by/4.0/legalcode