Co-clustering Spatial Data Using a Generalized Linear Mixed Model With Application to the Integrated Pest Management

  • Zhanpan Zhang
  • Daniel R. Jeske
  • Xinping Cui
  • Mark Hoddle
Article

Abstract

Co-clustering has been broadly applied to many domains such as bioinformatics and text mining. However, model-based spatial co-clustering has not been studied. In this paper, we develop a co-clustering method using a generalized linear mixed model for spatial data. To avoid the high computational demands associated with global optimization, we propose a heuristic optimization algorithm to search for a near optimal co-clustering. For an application pertinent to Integrated Pest Management, we combine the spatial co-clustering technique with a statistical inference method to make assessment of pest densities more accurate. We demonstrate the utility and power of our proposed pest assessment procedure through simulation studies and apply the procedure to studies of the persea mite (Oligonychus perseae), a pest of avocado trees, and the citricola scale (Coccus pseudomagnoliarum), a pest of citrus trees.

Key Words

GLMM Heuristic optimization Integrated pest management Spatial co-clustering 

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Copyright information

© International Biometric Society 2012

Authors and Affiliations

  • Zhanpan Zhang
    • 1
  • Daniel R. Jeske
    • 2
  • Xinping Cui
    • 2
  • Mark Hoddle
    • 3
  1. 1.One Research CircleGE Global ResearchNiskayunaUSA
  2. 2.Department of StatisticsUniversity of CaliforniaRiversideUSA
  3. 3.Department of EntomologyUniversity of CaliforniaRiversideUSA

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