Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8671
Title: Discriminant Models of Uncertainty in Nominal Fields
Authors: Goodchild, Michael F. 
Jingxiong, Zhang 
Kyriakidis, Phaedon 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Nominal fields;Spatial categorical information;Stochasticsimulation;Kriging
Issue Date: Feb-2009
Source: Transactions in GIS, 2009, vol. 13, no. 1, pp. 7–23
Volume: 13
Issue: 1
Start page: 7
End page: 23
Journal: Transactions in GIS 
Abstract: 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.
URI: https://hdl.handle.net/20.500.14279/8671
ISSN: 14679671
DOI: 10.1111/j.1467-9671.2009.01141.x
Rights: © Wiley
Type: Article
Affiliation : University of California 
Wuhan University 
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