2026-08-16T23:02:06Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18954 2024-03-06T11:03:03Z ddc:004 open_access

Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data (Short Paper) Peng, Zhan Inoue, Ryo Compositional data analysis Spatial heterogeneity Moran eigenvectors Spatial analysis of data with compositional structure has gained increasing attention in recent years. However, the spatial heterogeneity of compositional data has not been widely discussed. This study developed a Moran eigenvectors-based spatial heterogeneity analysis framework to investigate the spatially varying relationships between the compositional dependent variable and real-value covariates. The proposed method was applied to municipal-level household income data in Tokyo, Japan in 2018. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Zhan Peng and Ryo Inoue 2023 Is Part Of LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2023.59 urn:nbn:de:0030-drops-189540 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.59 eng https://creativecommons.org/licenses/by/4.0/legalcode