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Abstract:
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Map positional error refers to the difference between a feature's coordinate pair on a map and the corresponding true, unknown coordinate pair. In a geographic information system (GIS), this error is propagated through all operations that are functions of position, so that lengths, areas, etc., are uncertain. Often, a map's metadata provides a nominal statement on the positional error of a map, and such information has frequently been used to study the propagation of error through such operations. This talk will present a Bayesian statistical model for map positional error, incorporating positional error metadata as prior information, along with map coordinates, and, in particular, the information contained in the linearity of features. We demonstrate that using information in the linearity of features can greatly improve the precision of true location predictions.
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