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A tree-spatial scan statistic
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AI
DOI：10.1007/s10651-025-00670-w.png)
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We propose a tree-spatial scan statistic that combines Kulldorff’s circular scan method for detecting spatial clusters and the tree-based scan statistic algorithm for data mining. We feed the tree-based scan algorithm with spatial information of events, which are naturally arranged hierarchically. The tree-based scan statistic then examines all possible branches of the tree to identify the branch where the associated probability of cases is higher than expected under the hypothesis of event homogeneity. The algorithm was evaluated through simulations with hypothetical scenarios considering spatial and hierarchical structures, showing good performance in detecting these structures. The tree-spatial scan method was applied to infant mortality data for the Brazilian state of Rio de Janeiro in 2016. The proposed method identified a set of municipalities in Rio de Janeiro where a branch of diseases had a significantly higher number of deaths than expected under the homogeneity hypothesis.
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