Spatial Clustering to Search for Hot Spots

The cluster analysis has been widely applied to many fields. In this dissertation, Hot spot detection, as an important application of the spatial clustering, is thoroughly introduced and the current methodologies used in hot spot detection are presented and compared. In addition, we introduce a model based scan method to identify hot spots on spatial lattice arrays. Four features introduced by the proposed methodology are: (1) A Generalized Linear Mixed Model (GLMM) that provides a realistic model for correlated count data; (2) A border comparison that is used to determine the significance of a candidate hot spot at each stage of sequential searches; (3) A confirmation step better separates the homogeneous and heterogeneous hot spots; (4) An iterative process that finds secondary hot spots by conditioning on previously found hot spots. A heuristic search algorithm (MBSM-H) is proposed that reduces the high computational demands associated with a global search algorithm (MBSM-G). Both algorithms are illustrated through simulated examples and an application to Integrated Pest Management where an orchard is assessed for potential pest problems. Comparisons between the model based scan method and alternative methodologies are presented in this dissertation as well