Discriminant models of uncertainty in nominal fields

Michael Goodchild, Jingxiong Zhang, Phaedon Kyriakidis

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)7-23
Number of pages17
JournalTransactions in GIS
Volume13
Issue number1
DOIs
StatePublished - 2009
Externally publishedYes

Keywords

  • Area classes
  • Discriminant space
  • Replicability
  • Stochastic simulation
  • Uncertainty

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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