This paper describes a probabilistic approach to model positional uncertainty of linear features in a vector-based geographic information system (GIS). Positional uncertainty is one of the components of uncertainty inherent in any object description in GIS. With a number of assumptions, the positional error of an arbitrary point on a line segment is derived based on the distribution of errors at the end points of the segment. This defines the probability density and the confidence region of a line segment and a set of indicators for the error of a line segment. The union of the confidence regions of the line segments establishes the confidence region of a linear feature. The derived uncertainty model is computationally feasible and has a great promise for efficient implementation in several GIS applications.

This paper describes a probabilistic approach to model positional uncertainty of linear features in a vector-based geographic information system (GIS). Positional uncertainty is one of the components of uncertainty inherent in any object description in GIS. With a number of assumptions, the positional error of an arbitrary point on a line segment is derived based on the distribution of errors at the end points of the segment. This defines the probability density and the confidence region of a line segment and a set of indicators for the error of a line segment. The union of the confidence regions of the line segments establishes the confidence region of a linear feature. The derived uncertainty model is computationally feasible and has a great promise for efficient implementation in several GIS applications.