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Combining incompatible spatial data
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PRE
AI
DOI：10.1198/016214502760047140.png)
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Abstract
En 中文
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.
Keywords:
change of support
data assimilation
ecological inference
modifiable areal unit problem
multiscale processes
spatially-misaligned data
Journal
J
IF:
3
Papers:
5.1K
Citations:
4.8W
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