Mary Kate Beard-Tisdale, Professor of Spatial Information Science and EngineeringMODELING BOUNDARIES OF INnUENCE AMONG POSITIONAL UNCERTAINTY FIELDS

Within a CIS environment, the proper use of information requires the identification of the uncertainty associated with it. As such, there has been a substantial amount of research dedicated to describing and quantifying spatial data uncertainty. Recent advances in sensor technology and image analysis techniques are making image-derived geospatial data increasingly popular. Along with development in sensor and image analysis technologies have come departures from conventional point-by-point measurements. Current advancements support the transition from traditional point measures to novel techniques that allow the extraction of complex objects as single entities (e.g., road outlines, buildings). As the methods of data extraction advance, so too must the methods of estimating the uncertainty associated with the data. Not only will object uncertainties be modeled, but the connections between these uncertainties will also be estimated. The current methods for determining spatial accuracy for lines and areas typically involve defining a zone of uncertainty around the measured line, within which the "actual " line exists with some probability. Yet within the research community, the proper shape of this 'uncertaintyband ' is a topic with much dissent. Less contemplated is the manner in which such areas of uncertainty interact and influence one another. The development of positional error models, fro