Discriminant Models of Uncertainty in Nominal Fields

Despite developments in error modeling in discrete objects and continuous fields, there exist substantial and largely unsolved conceptual problems in the domain of nominal fields. This article explores a novel strategy for uncertainty characterization in spatial categorical information. The proposed strategy is based on discriminant space, which is defined with essential properties or driving processes underlying spatial class occurrences, leading to discriminant models of uncertainty in area classes. This strategy reinforces consistency in categorical mapping by imposing class-specific mean structures that can be regressed against discriminant variables, and facilitates scale-dependent error modeling that can effectively emulate the variation found between observers in terms of classes, boundary positions, numbers of polygons, and boundary network topology. Based on simulated data, comparisons with stochastic simulation based on indicator kriging confirmed the replicability of the discriminant models, which work by determining the mean area classes based on discriminant variables and projecting spatially correlated residuals in discriminant space to uncertainty in area classes. Address for correspondence: Jingxiong Zhang, School of Remote Sensing Information Engineering and Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 LuoYu Road, Wuhan 430079, China. E-mail: [email protected] 8 M Goodchild, J Zhang and P Kyriakidis © 2009 Blackwell Publishing Ltd Transactions in GIS , 2009, 13(1)