Testing the data quality of a GIS database by bootstrap methods and other nonparametric statistics

This paper describes a procedure, based on non-parametric statistics, for testing the data quality of a G.I.S. The procedure compares the contents of a database with a sample of control values captured on field and tests the hypothesis of no difference between the two samples, by the application of the Wilcoxon rank-sum test. The alternative hypothesis of the test is explicitly considered in order to evaluate the power function and the sensitivity of the test. Power formulas for the Wilcoxon test require some quantities to be estimated, and this is done by bootstrap techniques. The main advantage of the proposed procedure is the optimization of the number of control points required, since the sample size needed for the achievement of a certain sensitivity level is computed with the power function.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.