LIPIcs.GIScience.2023.59.pdf
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Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data (Short Paper)
Authors
Zhan Peng 
,
Ryo Inoue
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12th International Conference on Geographic Information Science (GIScience 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Geographic Information Science (GIScience) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-09-07
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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)
https://doi.org/10.4230/LIPIcs.GIScience.2023.59
https://doi.org/10.4230/LIPIcs.GIScience.2023.59
Abstract
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
Keywords
- Compositional data analysis
- Spatial heterogeneity
- Moran eigenvectors
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References
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