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Journal of the American Statistical Association Volume 97, 2002 - Issue 458
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Primary Article

Combining Incompatible Spatial Data

Carol A Gotway Carol A. Gotway is Senior Mathematical Statistician, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA 30333. Linda J. Young is Professor, Department of Biometry, University of Nebraska, Lincoln, NE 68583. The authors gratefully acknowledge all of the authors whose work is represented and discussed in this article, who have made outstanding contributions to a persistent and perplexing problem. They especially thank Brad Carlin, Noel Cressie, Hsin-Cheng Huang, Chris Wikle, and Jeremy Aldworth, who were willing to share their work and ideas, some of which has not yet been published. Finally, they thank Noel Cressie for his review of this article during its infancy stages. His comments and suggestions, and his willingness to review a crude draft of a lengthy manuscript, are much appreciated.
&
Linda J Young Carol A. Gotway is Senior Mathematical Statistician, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA 30333. Linda J. Young is Professor, Department of Biometry, University of Nebraska, Lincoln, NE 68583. The authors gratefully acknowledge all of the authors whose work is represented and discussed in this article, who have made outstanding contributions to a persistent and perplexing problem. They especially thank Brad Carlin, Noel Cressie, Hsin-Cheng Huang, Chris Wikle, and Jeremy Aldworth, who were willing to share their work and ideas, some of which has not yet been published. Finally, they thank Noel Cressie for his review of this article during its infancy stages. His comments and suggestions, and his willingness to review a crude draft of a lengthy manuscript, are much appreciated.
Pages 632-648 | Published online: 31 Dec 2011
 
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Abstract

Global positioning systems (GPSs) and geographical information systems (GISs) have been widely used to collect and synthesize spatial data from a variety of sources. New advances in satellite imagery and remote sensing now permit scientists to access spatial data at several different resolutions. The Internet facilitates fast and easy data acquisition. In any one study, several different types of data may be collected at differing scales and resolutions, at different spatial locations, and in different dimensions. Many statistical issues are associated with combining such data for modeling and inference. This article gives an overview of these issues and the approaches for integrating such disparate data, drawing on work from geography, ecology, agriculture, geology, and statistics. Emphasis is on state-of-the-art statistical solutions to this complex and important problem.

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