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Activity Number: 70
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #313400
Title: Spatial Clustering Methods to Search for Hot Spots
Author(s): Fei He*+
Companies:
Keywords: Clustering ; Spatial ; GLMM
Abstract:

This paper introduces a new model based clustering methodology that utilizes Kulldorff's scan statistics for count data on a spatial grid. Three features introduced by our proposed methodology are: (1) A Generalized Linear Mixed Model (GLMM) that captures correlation among the data; (2) A border comparison that is used to determine the significance of a candidate cluster at each stage of a sequential search; (3) An iterative process that finds secondary clusters by conditioning on previously found clusters. In addition, a heuristic scan algorithm is proposed that reduces the high computational demands associated with a global scan algorithm. Performance analysis of the two scan algorithms is conducted through simulated examples and an application to Integrated Pest Management where we assess an orchard of fruit-bearing trees for potential pest problems. The procedure used to compare the scan algorithms to existing methods are established and presented in the paper as well.


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