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Point cluster analysis using weighted random labeling

Author

Listed:
  • Yukio Sadahiro

    (The University of Tokyo)

  • Ikuho Yamada

    (The University of Tokyo)

Abstract

This paper proposes a new method of point cluster analysis. There are at least three important points that we need to consider in the evaluation of point clusters. The first is spatial inhomogeneity, i.e., the inhomogeneity of locations where points can be located. The second is aspatial inhomogeneity, which indicates the inhomogeneity of point characteristics. The third is an explicit representation of the geographic scale of analysis. This paper proposes a method that considers these points in a statistical framework. We develop two measures of point clusters: local and global. The former permits us to discuss the spatial variation in point clusters, while the latter indicates the global tendency of point clusters. To test the method’s validity, this paper applies it to the analysis of hypothetical and real datasets. The results supported the soundness of the proposed method.

Suggested Citation

  • Yukio Sadahiro & Ikuho Yamada, 2025. "Point cluster analysis using weighted random labeling," Journal of Geographical Systems, Springer, vol. 27(1), pages 63-83, January.
    • Handle: RePEc:kap:jgeosy:v:27:y:2025:i:1:d:10.1007_s10109-024-00447-y
    DOI: 10.1007/s10109-024-00447-y
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    References listed on IDEAS

    as
    1. Kulldorff, Martin, 2006. "Tests of Spatial Randomness Adjusted for an Inhomogeneity: A General Framework," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1289-1305, September.
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    4. Boulesteix, Anne-Laure & Strobl, Carolin, 2007. "Maximally selected Chi-squared statistics and non-monotonic associations: An exact approach based on two cutpoints," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6295-6306, August.
    5. Taylor M. Oshan & Levi J. Wolf & Mehak Sachdeva & Sarah Bardin & A. Stewart Fotheringham, 2022. "A scoping review on the multiplicity of scale in spatial analysis," Journal of Geographical Systems, Springer, vol. 24(3), pages 293-324, July.
    6. Kulldorff, M. & Athas, W.F. & Feuer, E.J. & Miller, B.A. & Key, C.R., 1998. "Evaluating cluster alarms: A space-time scan statistic and brain cancer in Los Alamos, New Mexico," American Journal of Public Health, American Public Health Association, vol. 88(9), pages 1377-1380.
    7. Oshan, Taylor M. & Wolf, Levi John & Sachdeva, Mehak & Bardin, Sarah & Fotheringham, Alexander Stewart, 2022. "A Scoping Review on the Multiplicity of Scale in Spatial Analysis," OSF Preprints ahwe3, Center for Open Science.
    8. Peter J. Diggle & Barry S. Rowlingson, 1994. "A Conditional Approach to Point Process Modelling of Elevated Risk," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 433-440, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Point clusters; Spatial inhomogeneity; Aspatial inhomogeneity; Weighted random labeling; Geographical scale of analysis;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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