caa a22 4500 445331887 CHVBK 20180317142740.0 cr unu---uuuuu 170323e20110701xx s 000 0 eng 10.1007/s10707-010-0109-0 doi (NATIONALLICENCE)springer-10.1007/s10707-010-0109-0 Mining boundary effects in areally referenced spatial data using the Bayesian information criterion [Elektronische Daten] [Pei Li, Sudipto Banerjee, Alexander McBean] Statistical models for areal data are primarily used for smoothing maps revealing spatial trends. Subsequent interest often resides in the formal identification of ‘boundaries' on the map. Here boundaries refer to ‘difference boundaries', representing significant differences between adjacent regions. Recently, Lu and Carlin (Geogr Anal 37:265-285, 2005) discussed a Bayesian framework to carry out edge detection employing a spatial hierarchical model that is estimated using Markov chain Monte Carlo (MCMC) methods. Here we offer an alternative that avoids MCMC and is easier to implement. Our approach resembles a model comparison problem where the models correspond to different underlying edge configurations across which we wish to smooth (or not). We incorporate these edge configurations in spatially autoregressive models and demonstrate how the Bayesian Information Criteria (BIC) can be used to detect difference boundaries in the map. We illustrate our methods with a Minnesota Pneumonia and Influenza Hospitalization dataset to elicit boundaries detected from the different models. Springer Science+Business Media, LLC, 2010 Areal data nationallicence Bayesian information criteria nationallicence Boundaries nationallicence Conditionally autoregressive models nationallicence Simultaneous autoregressive models nationallicence Wombling nationallicence Li Pei Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, 55455-0392, Minneapolis, MN, USA aut Banerjee Sudipto Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, 55455-0392, Minneapolis, MN, USA aut McBean Alexander Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, 55455-0392, Minneapolis, MN, USA aut GeoInformatica Springer US; http://www.springer-ny.com 15/3(2011-07-01), 435-454 1384-6175 15:3<435 2011 15 10707 https://doi.org/10.1007/s10707-010-0109-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10707-010-0109-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Pei Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, 55455-0392, Minneapolis, MN, USA aut NATIONALLICENCE 700 1- Banerjee Sudipto Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, 55455-0392, Minneapolis, MN, USA aut NATIONALLICENCE 700 1- McBean Alexander Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, 55455-0392, Minneapolis, MN, USA aut NATIONALLICENCE 773 0- GeoInformatica Springer US; http://www.springer-ny.com 15/3(2011-07-01), 435-454 1384-6175 15:3<435 2011 15 10707 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer