Abstract Details
Activity Number:
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70
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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Abstract #313400
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Title:
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Spatial Clustering Methods to Search for Hot Spots
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Author(s):
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Fei He*+
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Companies:
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Keywords:
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Clustering ;
Spatial ;
GLMM
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Abstract:
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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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Authors who are presenting talks have a * after their name.
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