DOI: 10.5593/SGEM2014/B21/S8.081


R. Duraciova
Wednesday 1 October 2014 by Libadmin2014

References: 14th International Multidisciplinary Scientific GeoConference SGEM 2014, www.sgem.org, SGEM2014 Conference Proceedings, ISBN 978-619-7105-10-0 / ISSN 1314-2704, June 19-25, 2014, Book 2, Vol. 1, 627-634 pp

Spatial data is very often uncertain. The quality of spatial data is also very variable. The functions and operations which are performed with the data in information systems and object-relational database systems are mostly implemented by the use of crisp rules and criteria based only on Boolean logic. This classical approach often leads to a loss of information resulting from the uncertainty in the input data or decision criteria. Fuzzy sets and fuzzy logic provide a description of uncertainty and allow working with it. Fuzzy sets are usually used in geographical information system (GIS) in raster spatial data analysis only. Vector data analysis by fuzzy sets in GIS environment or spatial database systems could be useful too, but there are only limited capabilities in common GIS tools to use it. This paper deals with implementation of the principles of fuzzy set theory and fuzzy logic into spatial database systems and vector data analysis in GIS. In this paper, we created fuzzy queries by the use of Structured Query Language (SQL) and spatial data types in accordance with ISO 19125-1 and ISO 19125-2 standards. We also used selected fuzzy aggregation operators (e.g., the minimum t-norm, the product t-norm and the Łukasiewicz t-norm) in spatial queries. We implemented the resulting fuzzy spatial queries in the PostgreSQL database system with the PostGIS extension. The result of fuzzy spatial queries is for example a selection of the spatial objects with information about their degree of compliance with the decision criteria (degree of membership in fuzzy set). Data obtain as a fuzzy selection can be further analysed in a GIS software environment. The implementation of the principles of fuzzy sets to spatial database systems and GIS brings an opportunity for instance for the efficient processing of uncertain data or fuzzy criteria in multi-criteria decision making. Spatial data uncertainty modeling is also quite current topic in relation to the integration of heterogeneous geospatial data sources.

Keywords: spatial database, uncertainty modeling, fuzzy sets, GIS, SQL

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