Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data (Short Paper)

Authors Zhan Peng , Ryo Inoue

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Zhan Peng
  • Graduate School of Information Sciences, Tohoku University, Sendai, Japan
Ryo Inoue
  • Graduate School of Information Sciences, Tohoku University, Sendai, Japan

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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)


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.

Subject Classification

ACM Subject Classification
  • Applied computing → Mathematics and statistics
  • Compositional data analysis
  • Spatial heterogeneity
  • Moran eigenvectors


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