2026-07-27T21:43:44Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:16906 2024-03-06T10:58:19Z ddc:004 open_access

A Comparison of Geographically Weighted Principal Components Analysis Methodologies (Short Paper) Tsutsumida, Narumasa Murakami, Daisuke Yoshida, Takahiro Nakaya, Tomoki Lu, Binbin Harris, Paul Comber, Alexis Spatial heterogeneity Geographically weighted Sparsity PCA Principal components analysis (PCA) is a useful analytical tool to represent key characteristics of multivariate data, but does not account for spatial effects when applied in geographical situations. A geographically weighted PCA (GWPCA) caters to this issue, specifically in terms of capturing spatial heterogeneity. However, in certain situations, a GWPCA provides outputs that vary discontinuously spatially, which are difficult to interpret and are not associated with the output from a conventional (global) PCA any more. This study underlines a GW non-negative PCA, a geographically weighted version of non-negative PCA, to overcome this issue by constraining loading values non-negatively. Case study results with a complex multivariate spatial dataset demonstrate such benefits, where GW non-negative PCA allows improved interpretations than that found with conventional GWPCA. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Narumasa Tsutsumida and Daisuke Murakami and Takahiro Yoshida and Tomoki Nakaya and Binbin Lu and Paul Harris and Alexis Comber 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.2022.21 urn:nbn:de:0030-drops-169062 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.21 eng https://creativecommons.org/licenses/by/4.0/legalcode