| Summary: | 碩士 === 國立中山大學 === 應用數學系研究所 === 104 === Cluster detection is one of the most important topics in spatial statistics. With increasing
public health concerns about environmental risks, the development of statistical methods for analyzing spatial health events becomes immediate. In this thesis, we first introduce two cluster detection approaches named the Kulldorff’s scan statistics and hierarchical agglomerative clustering algorithm. In addition, we illustrate spatial autocorrelation which is an important factor but overlooked in Kulldorff’s scan statistics. Moreover,
Bayesian hierarchical structure, which is a modern method to fit spatial data with spatial autocorrelation, is illustrated by using Poisson log-linear conditional-autoregressive (CAR)
model. By comparing the above methods, we summarize their advantages and drawbacks
on cluster detection for spatial data. Finally, we analyze the dengue fever outbreak over south Taiwan in 2015 as an empirical study.
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