Using the Linearity of Map Features in Statistical Modeling of Map Positional Error
Jarettt J. Barber, Ph.D.

Department of Statistics, University of Wyoming


Map positional error refers to the difference between a map feature's coordinate pair(s) on a map and the corresponding true, unknown coordinate pair(s). 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 frequently has 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 from multiple maps of the same area, and, in particular, will include the information contained in the linearity of features, e.g., a street network. We demonstrate that using information in the linearity of features can greatly improve the precision of true location predictions.

Keywords:  Bayesian; GIS; linear features; maps; positional error




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