ABSTRACT
The functional model component of the Global Spatial Data Model (GSDM) embodies the geometry, reference frames, and coordinate systems critical to knowing where things are. The stochastic model component of the GSDM includes tools for working with both absolute accuracy and relative accuracy. A summary of spatial data accuracy includes mathematical concepts of calculus, probability and statistics, least squares, confidence intervals, hypothesis testing, error ellipses, and others. Practices for handling spatial data accuracy continue to evolve and include numerous advances over the years. There is a difference between the accuracy of a point and the accuracy of a derived quantity such as a distance or direction. Network accuracy and local accuracy are specifically applicable to derived quantities on a point-pair basis. Increasing the uncertainty of the control points degrades the network accuracy, but the local accuracy remains “tight” as determined by the quality of the original baseline and network observations.
