Combining incompatible spatial data

被引:472
|
作者
Gotway, CA [1 ]
Young, LJ
机构
[1] Ctr Dis Control & Prevent, Natl Ctr Environm Hlth, Atlanta, GA 30333 USA
[2] Univ Nebraska, Dept Biometry, Lincoln, NE 68583 USA
关键词
change of support; data assimilation; ecological inference; modifiable areal unit problem; multiscale processes; spatially-misaligned data;
D O I
10.1198/016214502760047140
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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.
引用
收藏
页码:632 / 648
页数:17
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