ERROR PROPAGATION IN CARTOGRAPHIC MODELING USING BOOLEAN LOGIC AND CONTINUOUS CLASSIFICATION

When data on environmental attributes such as those of soil or groundw ater are manipulated by logical cartographic modelling, the results ar e usually assumed to be exact. However, in reality the results will be in error because the values of input attributes cannot be determined exactly. This paper analyses how errors in such values propagate throu gh Boolean and continuous modelling, involving the intersection of sev eral maps. The error analysis is carried out using Monte Carlo methods on data interpolated by block kriging to a regular grid which yields predictions and prediction error standard deviations of attribute valu es for each pixel. The theory is illustrated by a case study concernin g the selection of areas of medium textured, non-saline soil at an exp erimental farm in Alberta, Canada. The results suggest that boolean me thods of sieve mapping are much more prone to error propagation than t he more robust continuous equivalents. More study of the effects of er rors and of the choice of attribute classes and of class parameters on error propagation is recommended.