Categorical mapping and error modeling based on the discriminant space

Zhang Jingxiong, Michael Goodchild, Phaedon Kyriakidis, Rao Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Despite developments in error analysis for discrete objects and interval/ratio fields, there exist conceptual problems with the case of nominal fields. This paper seeks to consolidate a conceptual framework based on the discriminant space for categorical mapping and error modeling. The discriminant space is defined upon the essential properties and processes underlying occurrences of spatial classes, and lends itself to geostatistical analysis and modeling. The discriminant space furnishes consistency in categorical mapping by imposing class-conditional mean structures that are associated with discriminant or "environmental" variables in various statistical models, and facilitates physically interpretable and scale-dependent error modeling. Further research will focus on models and methods based on multi-dimensional discriminant space and at multiple scales.

Original languageEnglish (US)
Title of host publicationGeoinformatics 2006
Subtitle of host publicationGeospatial Information Science
StatePublished - 2006
Externally publishedYes
EventGeoinformatics 2006: Geospatial Information Science - Wuhan, China
Duration: Oct 28 2006Oct 29 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherGeoinformatics 2006: Geospatial Information Science


  • Area-class maps
  • Discriminant space
  • Error models
  • Generalized linear models
  • Geostatistics
  • Stochastic simulation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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