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Identifying local spatial association in flow data

Author

Listed:
  • Svante Berglund

    (Department of Infrastructure and Planning, Royal Institute of Technology, SE-100 44 Stockholm, Sweden (e-mail: svante@infra.kth.se))

  • Anders Karlström

    (Department of Infrastructure and Planning, Royal Institute of Technology, SE-100 44 Stockholm, Sweden (e-mail: svante@infra.kth.se))

Abstract

. In this paper we develop a spatial association statistic for flow data by generalizing the statistic of Getis-Ord, G i (and G i *). This local measure of spatial association, G ij, is associated with each origin-destination pair. We define spatial weight matrices with different metrics in flow space. These spatial weight matrices focus on different aspects of local spatial association. We also define measures which control for generation or attraction nonstationarity. The measures are implemented to examine the spatial association of residuals from two different models. Using the permutation approach, significance bounds are computed for each statistic. In contrast to the G i statistic, the normal approximation is often appropriate, but the statistics are still correlated. Small sample properties are also briefly discussed.

Suggested Citation

  • Svante Berglund & Anders Karlström, 1999. "Identifying local spatial association in flow data," Journal of Geographical Systems, Springer, vol. 1(3), pages 219-236, October.
    • Handle: RePEc:kap:jgeosy:v:1:y:1999:i:3:d:10.1007_s101090050013
    DOI: 10.1007/s101090050013
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    Citations

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    Cited by:

    1. Matthias Eckardt & Jorge Mateu, 2021. "Second-order and local characteristics of network intensity functions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 318-340, June.
    2. F. Bavaud & M. Kordi & C. Kaiser, 2018. "Flow autocorrelation: a dyadic approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 95-111, July.
    3. Mengjie Zhou & Mengjie Yang & Tinghua Ai & Jiannan Cai & Zhe Chen, 2024. "Rethinking the null hypothesis in significant colocation pattern mining of spatial flows," Journal of Geographical Systems, Springer, vol. 26(3), pages 375-405, July.
    4. Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.

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