Decisions in spatial modeling processes are often based on uncertain knowledge. This is due to--among other reasons--inaccurate information, lack of proper data, or even ignorance. One way to cope with such uncertainties is using "belief functions." These originate in the probability theory. Well-known key items are Bayes' theorem and the Dempster-Shafer theory. Alternative outcomes of a model form a hierarchical structure of hypotheses and their combinations. Information required and available for the modeling process is pooled and aggregated to output a new probability value. The author shows a practical implementation of this process in ArcView GIS.