Modelling map positional error to infer true feature location

Jarrett J. Barber, Alan E. Gelfand, John A. Silander

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

The authors consider the issue of map positional error, or the difference between location as represented in a spatial database (i.e., a map) and the corresponding unobservable true location. They propose a fully model-based approach that incorporates aspects of the map registration process commonly performed by users of geographic informations systems, including rubber-sheeting. They explain how estimates of positional error can be obtained, hence estimates of true location. They show that with multiple maps of varying accuracy along with ground truthing data, suitable model averaging offers a strategy for using all of the maps to learn about true location.

Original languageEnglish (US)
Pages (from-to)659-676
Number of pages18
JournalCanadian Journal of Statistics
Volume34
Issue number4
DOIs
StatePublished - Dec 2006

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Keywords

  • Bayesian inference
  • Bayesian model averaging
  • Berkson model
  • Bivariate spatial process
  • Coregionalization
  • Measurement error

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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