Combining incompatible spatial data

• 成果类型: Review
• 署名作者: Gotway, CA; Young, LJ
• 署名单位: Centers for Disease Control & Prevention - USA; University of Nebraska System; University of Nebraska Lincoln
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214502760047140
• 发表日期: 2002
• 页码: 632-648
• 关键词: smooth pycnophylactic interpolation; areal unit problem; Poisson regression; fractal geometry; weighting values; data aggregation; models; scale; pattern; HEALTH

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.