Prediction and simulation in categorical fields: A transition probability combination approach

Guofeng Cao, Phaedon Kyriakidis, Michael Goodchild

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

4 Scopus citations

Abstract

The investigation of spatial patterns implied in categorical spatial data, such as land use and land cover (LULC) classes and socio-economic statistics data, is involved in many aspects of geographical information science, such as spatial uncertainty modeling and spatial data mining. The discrete nature of categorical fields limits the application of traditional analytical methods, such as kriging-type algorithms, widely used in Gaussian random fields. This paper presents a new probabilistic method for modeling the posterior probabilities of class occurrence at any location in space given known class labels at data locations within a neighborhood around that prediction location. In the proposed method, the conditional or posterior (multi-point) probabilities are approximated by weighted combinations of pre-posterior (two-point) transition probabilities (rather than indicator covariances or vari-ograms) while accounting for spatial interdependencies that most of current approaches often ignore. Using sequential indicator simulation based on the properties of a truncated multi-variate Gaussian field as reference, the advantages and disadvantages of this new proposed approach are analyzed and highlighted.

Original languageEnglish (US)
Title of host publication17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2009
Pages496-499
Number of pages4
DOIs
StatePublished - Dec 1 2009
Externally publishedYes
Event17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2009 - Seattle, WA, United States
Duration: Nov 4 2009Nov 6 2009

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2009
CountryUnited States
CitySeattle, WA
Period11/4/0911/6/09

Keywords

  • Categorical data
  • Conditional independence
  • Tau model

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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    Cao, G., Kyriakidis, P., & Goodchild, M. (2009). Prediction and simulation in categorical fields: A transition probability combination approach. In 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2009 (pp. 496-499). (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). https://doi.org/10.1145/1653771.1653853