Browsing by Author "Love, Kimberly R."

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    Error Models in Geographic Information Systems Vector Data Using Bayesian Methods
    Love, Kimberly R.; Ye, Keying; Smith, Eric P.; Prisley, Stephen P. (Virginia Tech, 2007)
    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.
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    Modeling Error in Geographic Information Systems
    Love, Kimberly R. (Virginia Tech, 2007-12-03)
    Geographic information systems (GISs) are a highly influential tool in today's society, and are used in a growing number of applications, including planning, engineering, land management,and environmental study. As the field of GISs continues to expand, it is very important to observe and account for the error that is unavoidable in computerized maps. Currently, both statistical and non-statistical models are available to do so, although there is very little implementation of these methods. In this dissertation, I have focused on improving the methods available for analyzing error in GIS vector data. In particular, I am incorporating Bayesian methodology into the currently popular G-band error model through the inclusion of a prior distribution on point locations. This has the advantage of working well with a small number of points, and being able to synthesize information from multiple sources. I have also calculated the boundary of the confidence region explicitly, which has not been done before, and this will aid in the eventual inclusion of these methods in GIS software. Finally, I have included a statistical point deletion algorithm, designed for use in situations where map precision has surpassed map accuracy. It is very similar to the Douglas-Peucker algorithm, and can be used in a general line simplification situation, but has the advantage that it works with the error information that is already known about a map rather than adding unknown error. These contributions will make it more realistic for GIS users to implement techniques for error analysis.