Abstract

Since the information used in a Geographic Information System has a certain degree of uncertainly, in general classical mathematics models should not be applied to solve geographical problems computationally. Therefore, probabilistic or fuzzy-related methods should be considered, in order to model the behaviour of real problems that have to be solved by or with a Geographic Information System.

In this paper, a review of the application of Dempster–Shafer Theory of Evidence—also called “belief functions”—in relation to Geographic Information System is given. The review will focus on classification as a way of fusing information in a Geographic Information System. Information fusion, for classification, represents the first step in the abstraction of information and a means of data mining, and both the advantages and limitations of the technique of the Theory of Evidence in comparison to other techniques are analysed.

Keywords: Uncertainty in GIS; Theory of evidence; Dempster–Shafer Theory; Decision making within GIS; Fusion of information

Article Outline

1. Introduction

2. GIS as a geospatial database

3. Classification under uncertainty: fusion of information in a GIS

3.1. Uncertainty in GIS

3.2. Problem definition

3.3. Classification

3.4. The fusion method

4. TE: theory for information fusion

5. Application of TE to GIS

5.1. Natural hazard mitigation

5.2. Natural resource inventory

5.3. Mineral exploration

5.4. Cartography

5.5. Geology

5.6. Business and service planning

6. Conclusions

Acknowledgements

References