Steepest Descent Method for Representing Spatially Correlated Uncertainty in GIS

dc.contributor.author	Joon Heo
dc.date.accessioned	2017-05-08T21:01:39Z
dc.date.available	2017-05-08T21:01:39Z
dc.date.copyright	November 2003
dc.date.issued	2003
dc.identifier.other	%28asce%290733-9453%282003%29129%3A4%28151%29.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl/handle/yetl/35888
dc.description.abstract	All spatial data in a geographic information system (GIS) intrinsically contain uncertainty. Simulations could be used for many GIS applications in order to estimate confidence ranges of certain analyses and project worst-case scenarios. For those applications, generation of Gaussian random fields is essential to simulate the uncertainty effects, because errors in spatial data are assumed dependent upon the Gaussian distribution. Gaussian fields with no spatial dependency could be assumed because of their simple concept and easy computation, but the reality is that spatial errors have a spatially correlated nature. For this reason, the intensive matrix computation for generating spatially autocorrelated Gaussian random fields requires the solution of a large, sparse linear system:
dc.publisher	American Society of Civil Engineers
dc.title	Steepest Descent Method for Representing Spatially Correlated Uncertainty in GIS
dc.type	Journal Paper
dc.journal.volume	129
dc.journal.issue	4
dc.journal.title	Journal of Surveying Engineering
dc.identifier.doi	10.1061/(ASCE)0733-9453(2003)129:4(151)
dc.tree	Journal of Surveying Engineering:;2003:;Volume ( 129 ):;issue: 004
dc.contenttype	Fulltext