A modified generalized lasso algorithm to detect local spatial clusters for count data

Hosik Choi, Eunjung Song, Seung sik Hwang, Woojoo Lee

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Detecting local spatial clusters for count data is an important task in spatial epidemiology. Two broad approaches—moving window and disease mapping methods—have been suggested in some of the literature to find clusters. However, the existing methods employ somewhat arbitrarily chosen tuning parameters, and the local clustering results are sensitive to the choices. In this paper, we propose a penalized likelihood method to overcome the limitations of existing local spatial clustering approaches for count data. We start with a Poisson regression model to accommodate any type of covariates, and formulate the clustering problem as a penalized likelihood estimation problem to find change points of intercepts in two-dimensional space. The cost of developing a new algorithm is minimized by modifying an existing least absolute shrinkage and selection operator algorithm. The computational details on the modifications are shown, and the proposed method is illustrated with Seoul tuberculosis data.

Original languageEnglish
Pages (from-to)537-563
Number of pages27
JournalAStA Advances in Statistical Analysis
Issue number4
StatePublished - 12 Oct 2018


  • Generalized LASSO
  • Penalized likelihood
  • Poisson regression
  • Spatial clustering


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