DETECTION OF SPATIAL CLUSTERS WITH HIGH-RISK REGIONS BY USING RESTRICTED HIERARCHICAL STRUCTURE

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  • Takemura Yusuke
    Graduate School of Environmental and Life Science, Okayama University
  • Ishioka Fumio
    Graduate School of Environmental and Life Science, Okayama University
  • Kurihara Koji
    Graduate School of Environmental and Life Science, Okayama University

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Other Title
  • Echelon scan法による高リスクな空間クラスター検出法の提案
  • Echelon scanホウ ニ ヨル コウリスク ナ クウカン クラスター ケンシュツホウ ノ テイアン

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Abstract

In the statistical analysis of spatial data, spatial events such as the “mortality rate of the disease observed in each municipality” and “measured value of harmful substances at each measurement point” may be concentrated in a specific area. This point, indicates that “a cluster exists.” Herein, we propose a new method for detecting a spatial cluster. To date, many methods have been proposed; however, problems exist wherein only the clusters of limited shape or clusters having low-risk regions can be detected. Thus, we focus on the hierarchical spatial data structure and try to solve the problems of existing methods by extracting the upper hierarchy. Moreover, the proposed method can be applied to large-scale spatial data because it significantly reduces calculation costs. To verify the cluster-detection accuracy and effectiveness of the proposed method, we compare it with existing methods using numerical experiments.

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