ESA Annual Meetings Online Program

Co-clustering spatial data using a generalized linear mixed model with application to integrated pest management

Sunday, November 11, 2012: 1:54 PM
Summit (Holiday Inn Knoxville Downtown)
Zhanpan Zhang , Applied Statistics Lab, GE Global Research, Niskayuna, NY
Daniel R. Jeske , Statistics, University of California, Riverside, CA
Xinping Cui , Statistics, University of California, Riverside, CA
Mark S. Hoddle , Entomology, University of California, Riverside, Riverside, CA
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.