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Spatio-temporal clustering analysis using generalized lasso with an application to reveal the spread of Covid-19 cases in Japan

Author

Listed:
  • Septian Rahardiantoro

    (Okayama University
    IPB University)

  • Wataru Sakamoto

    (Okayama University)

Abstract

This study addressed the issue of determining multiple potential clusters with regularization approaches for the purpose of spatio-temporal clustering. The generalized lasso framework has flexibility to incorporate adjacencies between objects in the penalty matrix and to detect multiple clusters. A generalized lasso model with two $${L}_{1}$$ L 1 penalties is proposed, which can be separated into two generalized lasso models: trend filtering of temporal effect and fused lasso of spatial effect for each time point. To select the tuning parameters, the approximate leave-one-out cross-validation (ALOCV) and generalized cross-validation (GCV) are considered. A simulation study is conducted to evaluate the proposed method compared to other approaches in different problems and structures of multiple clusters. The generalized lasso with ALOCV and GCV provided smaller MSE in estimating the temporal and spatial effect compared to unpenalized method, ridge, lasso, and generalized ridge. In temporal effects detection, the generalized lasso with ALOCV and GCV provided relatively smaller and more stable MSE than other methods, for different structure of true risk values. In spatial effects detection, the generalized lasso with ALOCV provided higher index of edges detection accuracy. The simulation also suggested using a common tuning parameter over all time points in spatial clustering. Finally, the proposed method was applied to the weekly Covid-19 data in Japan form March 21, 2020, to September 11, 2021, along with the interpretation of dynamic behavior of multiple clusters.

Suggested Citation

  • Septian Rahardiantoro & Wataru Sakamoto, 2024. "Spatio-temporal clustering analysis using generalized lasso with an application to reveal the spread of Covid-19 cases in Japan," Computational Statistics, Springer, vol. 39(3), pages 1513-1537, May.
  • Handle: RePEc:spr:compst:v:39:y:2024:i:3:d:10.1007_s00180-023-01331-x
    DOI: 10.1007/s00180-023-01331-x
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    References listed on IDEAS

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    1. Hosik Choi & Eunjung Song & Seung-sik Hwang & Woojoo Lee, 2018. "A modified generalized lasso algorithm to detect local spatial clusters for count data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 537-563, October.
    2. Robert Tibshirani & Michael Saunders & Saharon Rosset & Ji Zhu & Keith Knight, 2005. "Sparsity and smoothness via the fused lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 91-108, February.
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