A Generic Statistical Approach for Modelling Error of Geometric Features in GIS

This paper describes a newly developed statistical approach for modelling positional error of geometric features in GIS. The generic statistical models for N-dimensional features are ® rstly derived. The models for oneand twodimensional features are then developed as the speci® c cases of the generic models. In each dimension, the GIS features are classi® ed as points, line segments and line features. Because of the errors, features stored in GIS may not correspond with their actual location in the real world. The true location of a GIS feature is only known within a certain area around the represented location in GIS. This newly developed approach can be used to provide a statistical description of such areas. For one-, twoand N-dimensional GIS features, they are de® ned as con® dence intervals, con® dence regions and con® dence spaces respectively. The areas are related to the positional errors of the composite points of the features and to the prede® ned con® dence level. The models are derived based on the assumptions that the errors of the composite points are independent and follow multi-dimensional normal distributions.