Maximun entropy inference for geographical information systems

This paper considers the problem of characterizing an inference process for reasoning under uncertainty in Geographic Information Systems (GIS). By focusing on a representative case study we outline the crucial aspects of the management of uncertainty in GIS. This enables us to argue, on methodological rather than practical grounds, in favour of the Maximum Entropy (ME) inference process. Speci¯cally, we show how this constitutes a theoretically well-founded solution to the problems that arise naturally in GIS facing imperfect information. We also put forward how, as a consequence of the encouraging developments on computational techniques for reasoning under maximum entropy, the latter must be considered as a most crucial approach to uncertainty management in various ¯elds of GIS science.