Error Models in Geographic Information Systems Vector Data Using Bayesian Methods
Love, Kimberly R.
Smith, Eric P.
Prisley, Stephen P.
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Geographic Information Systems, or GIS, has been an evolving science since its introduction. Recently, many users have become concerned with the incorporation of error analysis into GIS map products. In particular, there is concern over the error in the location of features in vector data, which relies heavily on geographic x—; y— coordinates. Current work in the field is based on bivariate normal distributions for these points, and their extension to line and polygon features. We propose here to incorporate Bayesian methodology into this existing model, which presents multiple advantages over existing methods. Bayesian methods allow for the incorporation of expert and historical knowledge and reduce the number of observations required to perform an accurate analysis. This is essential to the field of GIS where multiple observations are rare and outside knowledge is often very informative. Bayesian methods also provide results that are more easily understood by the average GIS user. We explore this addition and provide several examples based on our calculations. We conclude by discussing the advantages of Bayesian analysis for GIS vector data, and discuss our ongoing work, which is being conducted under a research grant from the National Geospatial Intelligence Agency.