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Geary’s c for Multivariate Spatial Data

Author

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  • Hiroshi Yamada

    (Graduate School of Humanities and Social Sciences, Hiroshima University, 1-2-1 Kagamiyama, Higashi-Hiroshima 739-8525, Japan)

Abstract

Geary’s c is a prominent measure of spatial autocorrelation in univariate spatial data. It uses a weighted sum of squared differences. This paper develops Geary’s c for multivariate spatial data. It can describe the similarity/discrepancy between vectors of observations at different vertices/spatial units by a weighted sum of the squared Euclidean norm of the vector differences. It is thus a natural extension of the univariate Geary’s c . This paper also develops a local version of it. We then establish their properties.

Suggested Citation

  • Hiroshi Yamada, 2024. "Geary’s c for Multivariate Spatial Data," Mathematics, MDPI, vol. 12(12), pages 1-12, June.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:12:p:1820-:d:1413086
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    References listed on IDEAS

    as
    1. Daisuke Murakami & Daniel Griffith, 2015. "Random effects specifications in eigenvector spatial filtering: a simulation study," Journal of Geographical Systems, Springer, vol. 17(4), pages 311-331, October.
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