Combining Incompatible Spatial Data

@article{Gotway2002_uncertainty, 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.}, author = {Gotway, C. A. and Young, L. J. }, citeulike-article-id = {1714360}, journal = {Journal of the American Statistical Association}, keywords = {error, error-propagation, gis, gps, remote-sensing, scale, spatial, uncertainty}, month = {June}, number = {458}, pages = {632--648}, posted-at = {2007-10-01 09:18:15}, priority = {2}, title = {Combining Incompatible Spatial Data}, url = {http://www.ingentaconnect.com/content/asa/jasa/2002/00000097/00000458/art00023}, volume = {97}, year = {2002} }

TY - JOUR ID - Gotway2002_uncertainty L3 - citeulike-article-id:1714360 TI - Combining Incompatible Spatial Data JF - Journal of the American Statistical Association VL - 97 IS - 458 SP - 632 EP - 648 N2 - 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. KW - error KW - error-propagation KW - gis KW - gps KW - remote-sensing KW - scale KW - spatial KW - uncertainty AU - Gotway, CA AU - Young, LJ PY - 2002/06// UR - http://www.ingentaconnect.com/content/asa/jasa/2002/00000097/00000458/art00023 ER -