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Comparison of Crisp, Fuzzy and Possibilistic Threshold in Spatial Queries

  • Jan Caha
  • Alena Vondráková
  • Jiří Dvorský
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)

Abstract

Decision making is one of the most important application areas of geoinformatics. Such support is mainly oriented on the identification of locations that fulfil certain criterion. The contribution presents the suitability of various approaches of spatial query using different types of Fuzzy thresolds. Presented methods are based on the classical logic (Crisp queries), Fuzzy logic (Fuzzy queries) and Possibility theory (Possibilistic Queries). All presented approaches are applied in the case study. Use these findings may contribute to the better understanding of the nature of the methods used and can help to obtain more accurate results, which have a determining influence on subsequent decision-making process.

Keywords

decision making Possibility theory spatial query 

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References

  1. 1.
    Bosc, P., Kraft, D., Petry, F.: Fuzzy sets in database and information systems: Status and opportunities. Fuzzy Sets and Systems 156(3), 418–426 (2005)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Burrough, P., McDonnell, R.: Principles of geographical information systems. Oxford University Press, Oxford (1998)Google Scholar
  3. 3.
    Devillers, R., Stein, A., Bédard, Y., Chrisman, N., Fisher, P., Shi, W.: Thirty Years of Research on Spatial Data Quality: Achievements, Failures, and Opportunities. Transactions in GIS 14(4), 387–400 (2010)CrossRefGoogle Scholar
  4. 4.
    Dubois, D., Prade, H.: Ranking Fuzzy Numbers in the Setting of Possibility Theory. Information Sciences 30(3), 183–224 (1983)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Dubois, D., Prade, H.: Possibility Theory: An approach to Computerized Processing of Uncertainty. Plenum Press, New York (1986)Google Scholar
  6. 6.
    Dubois, D.: The role of fuzzy sets in decision sciences: Old techniques and new directions. Fuzzy Sets and Systems 184(1), 3–28 (2011)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Dunn, M., Hickey, R.: The effect of slope algorithms on slope estimates within a GIS. Cartography 27(1), 9–15 (1998)CrossRefGoogle Scholar
  8. 8.
    Fisher, P.: Sorites paradox and vague geographies. Fuzzy Sets and Systems 113(1), 7–18 (2000)CrossRefGoogle Scholar
  9. 9.
    Fonte, C.C., Lodwick, W.A.: Modelling the Fuzzy Spatial Extent of Geographical Entities. In: Petry, F., Robinson, V.B., Cobb, M.A. (eds.) Fuzzy Modeling with Spatial Information for Geographic Problems, pp. 120–142. Springer, Berlin (2005)Google Scholar
  10. 10.
    Hanss, M.: Applied fuzzy arithmetic: an introduction with engineering applications. Springer, Berlin (2005)Google Scholar
  11. 11.
    Horn, B.: Hill shading and the reflectance map. Proceedings of the IEEE 69(1), 14–47 (1981)CrossRefGoogle Scholar
  12. 12.
    Hwang, S., Thill, J.-C.: Modeling Localities with Fuzzy Sets and GIS. In: Petry, F., Robinson, V.B., Cobb, M.A. (eds.) Fuzzy Modeling with Spatial Information for Geographic Problems, pp. 120–142. Springer, Berlin (2005)Google Scholar
  13. 13.
    Janoška, Z., Dvorský, J.: P systems: State of the art with respect to representation of geographical space. In: CEUR Workshop Proceedings - 12th Annual Workshop on Databases, Texts, Specifications and Objects, DATESO 2012, pp. 13–24 (2012)Google Scholar
  14. 14.
    Longley, P., Goodchild, M., Maguire, D., Rhind, D.: Geographical information systems and science, 2nd edn. Wiley, Chichester (2005)Google Scholar
  15. 15.
    Witlox, F., Derudder, B.: Spatial Decision-Making Using Fuzzy Decision Tables: Theory, Application and Limitations. In: Petry, F., Robinson, V.B., Cobb, M.A. (eds.) Fuzzy Modeling with Spatial Information for Geographic Problems, pp. 120–142. Springer, Berlin (2005)Google Scholar
  16. 16.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8(3), 338–353 (1965)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1, 3–28 (1978)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Zadeh, L.A.: Is there a need for fuzzy logic? Information Sciences 178(13), 2751–2779 (2008)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Geoinformatics, Faculty of SciencePalacký University OlomoucOlomoucCzech Republic

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