License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.COSIT.2022.21
URN: urn:nbn:de:0030-drops-169062
URL: https://drops.dagstuhl.de/opus/volltexte/2022/16906/
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Tsutsumida, Narumasa ; Murakami, Daisuke ; Yoshida, Takahiro ; Nakaya, Tomoki ; Lu, Binbin ; Harris, Paul ; Comber, Alexis

A Comparison of Geographically Weighted Principal Components Analysis Methodologies (Short Paper)

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LIPIcs-COSIT-2022-21.pdf (0.6 MB)


Abstract

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.

BibTeX - Entry

@InProceedings{tsutsumida_et_al:LIPIcs.COSIT.2022.21,
  author =	{Tsutsumida, Narumasa and Murakami, Daisuke and Yoshida, Takahiro and Nakaya, Tomoki and Lu, Binbin and Harris, Paul and Comber, Alexis},
  title =	{{A Comparison of Geographically Weighted Principal Components Analysis Methodologies}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{21:1--21:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/16906},
  URN =		{urn:nbn:de:0030-drops-169062},
  doi =		{10.4230/LIPIcs.COSIT.2022.21},
  annote =	{Keywords: Spatial heterogeneity, Geographically weighted, Sparsity, PCA}
}

Keywords: Spatial heterogeneity, Geographically weighted, Sparsity, PCA
Collection: 15th International Conference on Spatial Information Theory (COSIT 2022)
Issue Date: 2022
Date of publication: 22.08.2022


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