Probabilistic Approach for Modeling and Presenting Error in Spatial Data
Publication: Journal of Surveying Engineering
Volume 135, Issue 3
Abstract
This paper presents a probabilistic approach to describe and visualize uncertainty and error in spatial data. The probabilistic approach assigns -dimensional probability zones to -dimensional measured feature locations. The size of each -dimensional zone depends on the uncertainty arising from imprecise measurements or derived inaccuracy values, and a user-selected probability threshold that the “true” feature location is to be found within this probabilistic space. The uncertainties relate to recorded measurement precision, accuracy of a linear network, and issues of scale and resolution. The confidence intervals are based on the distribution, where the probability that the measured point location is within the tabulated distance of the true point location can be tested. The error model computes the probability of the intersection of two features or data sources to determine whether they are compatible and if they should be used together. The error model allows the user to assess the potential quality implications of combining data from different sources and with different qualities. The error model is encapsulated in a software program that includes a graphic user interface that facilitates visualization of results. The ability to visualize the quality of spatial data at different significance levels of confidence provides a powerful tool for communicating the impacts of the quality of spatial data on applications of interest to users.
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Acknowledgments
The research reported in this paper was part of the findings under the NCHRP Special Project No. 20-47(1) sponsored by The Transportation Research Board. The writers acknowledge the guidance of the NCHRP project panel during the conduct of the research project. Material presented in this paper represents the views of the writers, who are responsible for the facts and data presented herein. The contents of this paper do not necessarily reflect the views of the Transportation Research Board.
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© 2009 ASCE.
History
Received: Nov 9, 2007
Accepted: Jan 22, 2009
Published online: Jul 15, 2009
Published in print: Aug 2009
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