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

Authors Zhan Peng , Ryo Inoue



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Author Details

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)
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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  3. Daniel A Griffith. Spatial-filtering-based contributions to a critique of geographically weighted regression (GWR). Environment and Planning A: Economy and Space, 40(11):2751-2769, November 2008. URL: https://doi.org/10.1068/a38218.
  4. Joanna Morais, Christine Thomas-Agnan, and Michel Simioni. Interpretation of explanatory variables impacts in compositional regression models. Austrian Journal of Statistics, 47(5):1-25, September 2018. URL: https://doi.org/10.17713/ajs.v47i5.718.
  5. Vera Pawlowsky-Glahn and Juan José Egozcue. Spatial analysis of compositional data: A historical review. Journal of Geochemical Exploration, 164:28-32, May 2016. URL: https://doi.org/10.1016/j.gexplo.2015.12.010.
  6. Vera Pawlowsky‐Glahn, Juan José Egozcue, and Raimon Tolosana‐Delgado. Modelling and Analysis of Compositional Data. John Wiley & Sons, 2015. URL: https://doi.org/10.1002/9781119003144.
  7. Hajime Seya, Daisuke Murakami, Morito Tsutsumi, and Yoshiki Yamagata. Application of LASSO to the eigenvector selection problem in eigenvector-based spatial filtering. Geographical Analysis, 47(3):284-299, 2015. URL: https://doi.org/10.1111/gean.12054.
  8. Takahiro Yoshida, Daisuke Murakami, Hajime Seya, Narumasa Tsutsumida, and Tomoki Nakaya. Geographically weighted regression for compositional data: An application to the U.S. household income compositions. GIScience 2021 Short Paper Proceedings. 11th International Conference on Geographic Information Science. September 27-30, 2021. Poznań:Poland (Online), 2021. URL: https://doi.org/10.25436/E2G599.
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