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Activity Number:
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529
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #309665 |
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Title:
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Error Models in Geographic Information Systems Vector Data Using Bayesian Methods
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Author(s):
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Kimberly Love*+ and Eric P. Smith and Stephen Prisley and Keying Ye
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Companies:
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Virginia Polytechnic Institute and State University and Virginia Polytechnic Institute and State University and Virginia Polytechnic Institute and State University and University of Texas at San Antonio
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Address:
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Department of Statistics, 1307 University City Blvd, Blacksburg, VA, 24060,
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Keywords:
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error ; GIS ; vector data ; Bayesian
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Abstract:
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Existing models for vector data error in the field of GIS center on a bivariate normal model for points, and this normal model extends to line segments and polygons. In this talk we propose 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. We will explore this addition and provide some examples.
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