Point Event Cluster Detection via the Bayesian Generalized Fused Lasso

Ryo Masuda, Ryo Inoue

研究成果: Article査読

2 被引用数 (Scopus)


Spatial cluster detection is one of the focus areas of spatial analysis, whose objective is the identification of clusters from spatial distributions of point events aggregated in districts with small areas. Choi et al. (2018) formulated cluster detection as a parameter estimation problem to leverage the parameter selection capability of the sparse modeling method called the generalized fused lasso. Although this work is superior to conventional methods for detecting multiple clusters, its estimation results are limited to point estimates. This study therefore extended the above work as a Bayesian cluster detection method to describe the probabilistic variations of clustering results. The proposed method combines multiple sparsity-inducing priors and encourages sparse solutions induced by the generalized fused lasso. Evaluations were performed with simulated and real-world distributions of point events to demonstrate that the proposed method provides new information on the quantified reliabilities of clustering results at the district level while achieving comparable detection performances to that of the previous work.

ジャーナルISPRS International Journal of Geo-Information
出版ステータスPublished - 2022 3月

ASJC Scopus subject areas

  • 地理、計画および開発
  • 地球科学におけるコンピュータ
  • 地球惑星科学(その他)


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