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Abstract

Cite as

Zhan Peng and Ryo Inoue. Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 59:1-59:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

@InProceedings{peng_et_al:LIPIcs.GIScience.2023.59,
  author =	{Peng, Zhan and Inoue, Ryo},
  title =	{{Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{59:1--59:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.59},
  URN =		{urn:nbn:de:0030-drops-189540},
  doi =		{10.4230/LIPIcs.GIScience.2023.59},
  annote =	{Keywords: Compositional data analysis, Spatial heterogeneity, Moran eigenvectors}
}