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Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I

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  • Sang-Il Lee

    (Department of Geography, The Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, OH 43210-1361, USA (e-mail: slee@geography.ohio-state.edu))

Abstract

. This research is concerned with developing a bivariate spatial association measure or spatial correlation coefficient, which is intended to capture spatial association among observations in terms of their point-to-point relationships across two spatial patterns. The need for parameterization of the bivariate spatial dependence is precipitated by the realization that aspatial bivariate association measures, such as Pearson's correlation coefficient, do not recognize spatial distributional aspects of data sets. This study devises an L statistic by integrating Pearson's r as an aspatial bivariate association measure and Moran's I as a univariate spatial association measure. The concept of a spatial smoothing scalar (SSS) plays a pivotal role in this task.

Suggested Citation

  • Sang-Il Lee, 2001. "Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I," Journal of Geographical Systems, Springer, vol. 3(4), pages 369-385, December.
    • Handle: RePEc:kap:jgeosy:v:3:y:2001:i:4:d:10.1007_s101090100064
    DOI: 10.1007/s101090100064
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    Cited by:

    1. Shrestha, Shikhar & Bauer, Cici X.C. & Hendricks, Brian & Stopka, Thomas J., 2022. "Spatial epidemiology: An empirical framework for syndemics research," Social Science & Medicine, Elsevier, vol. 295(C).
    2. Bell, Adrian V. & Rader, Russell B. & Peck, Steven L. & Sih, Andrew, 2009. "The positive effects of negative interactions: Can avoidance of competitors or predators increase resource sampling by prey?," Theoretical Population Biology, Elsevier, vol. 76(1), pages 52-58.
    3. Hu, Lirong & He, Shenjing & Han, Zixuan & Xiao, He & Su, Shiliang & Weng, Min & Cai, Zhongliang, 2019. "Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies," Land Use Policy, Elsevier, vol. 82(C), pages 657-673.

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