Geographic information systems have gained an increasing interest over the recent years. However, their abilities are restricted in that they usually reason about precise quantitative information only, which means that they fail whenever exact matches cannot be found.
This paper describes a way of incorporating imprecise qualitative spatial reasoning with quantitative reasoning in geographic information systems. In particular, we show how tessellation data models can be extended to allow for qualitative spatial reasoning. The idea is to associate qualitative information with fuzzy sets and to use the values of these fuzzy sets for spatial reasoning.
Geographic information systems, fuzzy reasoning, Allen's algorithm