Abstract:
This report summarizes preliminary work on a framework for imprecise reasoning about spatial
information, in particular spatial information in geographic information systems. It is based on
papers previously published by Jonathan Histed, Ute Lörch, David Poon, and the author.
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. They do not
allow for any form of reasoning with imprecision.
In this report, we describe 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 whose membership grades are computed by
applying the concept of proximity.
In addition, we will show how images like geographic maps or satellite images can be analyzed
by computing the distances between given reference colors and the colors that occur in the
image, and how the results of this analysis can be used in the fuzzy spatial reasoner.