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.

中文翻译：

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一种改进的广义套索算法，用于检测计数数据的局部空间簇

为计数数据检测局部空间簇是空间流行病学中的重要任务。在一些文献中已经提出了两种广泛的方法，即移动窗口法和疾病作图法，以寻找聚类。但是，现有方法采用了一些任意选择的调整参数，并且局部聚类结果对这些选择很敏感。在本文中，我们提出了一种惩罚似然法，以克服现有的用于计数数据的局部空间聚类方法的局限性。我们从泊松回归模型开始以适应任何类型的协变量，然后将聚类问题公式化为惩罚似然估计问题，以找到二维空间中截距的变化点。通过修改现有的最小绝对收缩和选择算子算法，可将开发新算法的成本降至最低。显示了有关修改的计算细节，并用汉城结核病数据说明了所提出的方法。