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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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    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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