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A New Methodology of Spatial Cross-Correlation Analysis

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  • Yanguang Chen

Abstract

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran’s index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson’s correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China’s urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes.

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  • Yanguang Chen, 2015. "A New Methodology of Spatial Cross-Correlation Analysis," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-20, May.
    • Handle: RePEc:plo:pone00:0126158
    DOI: 10.1371/journal.pone.0126158
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    References listed on IDEAS

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    Cited by:

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    3. Gabriel S. Lee & Stefanie Braun, 2021. "Agglomeration Spillover Effects in German Land and House Prices at the City and County Levels," Working Papers 207, Bavarian Graduate Program in Economics (BGPE).
    4. Oksana Yaskal & Ihor Yaskal & Mariana Kolosinska & Svitlana Boyda, 2021. "The Informal Employment – Factors and Public Policies for Its Limitation," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 56-73.
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    6. Paolo Maranzano & Matteo Maria Pelagatti, 2022. "Spatio-temporal Event Studies for Air Quality Assessment under Cross-sectional Dependence," Papers 2210.17529, arXiv.org.
    7. Yanguang Chen, 2016. "Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-19, January.
    8. Marè, M.; & Porcelli, F.; & Vidoli, F.;, 2024. "Does private supply drive personal health choices? A spatial approach of health tax detractions at municipal level," Health, Econometrics and Data Group (HEDG) Working Papers 24/03, HEDG, c/o Department of Economics, University of York.
    9. Amir Hassan Zadeh & Hamed M. Zolbanin & Ramesh Sharda & Dursun Delen, 2019. "Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis," Information Systems Frontiers, Springer, vol. 21(4), pages 743-760, August.

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