A Hybrid Geometric-Qualitative Spatial Reasoning System and Its Application in GIS

  • Giorgio De Felice
  • Paolo Fogliaroni
  • Jan Oliver Wallgrün
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6899)


We propose a hybrid geometric-qualitative spatial reasoning system that is able to simultaneously deal with input information that is partially given geometrically and partially qualitatively using spatial relations of different qualitative spatial calculi. The reasoning system combines a geometric reasoning component based on computational geometry methods with a qualitative reasoning component employing relation algebraic reasoning techniques. An egg-yolk representation approach is used to maintain information about objects with underdetermined geometry and also allows for vague objects in the input. In an experimental evaluation we apply the reasoning system to infer geometric information for a set of only qualitatively described objects. The experiments demonstrate that the hybrid reasoning approach produces better results than geometric and qualitative reasoning individually.


Reference Object Reasoning System Simple Polygon Constraint Network Volunteer Geographic Information 
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  1. 1.
    Clementini, E., Di Felice, P.: Spatial operators. SIGMOD Record 29, 31–38 (2000)Google Scholar
  2. 2.
    Cohn, A.G., Hazarika, S.M.: Qualitative spatial representation and reasoning: An overview. Fundamenta Informaticae 46(1-2), 1–29 (2001)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Cohn, A.G., Renz, J.: Qualitative spatial reasoning. In: van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Handbook of Knowledge Representation. Elsevier, Amsterdam (2007)Google Scholar
  4. 4.
    Cohn, A., Gotts, N.: The ‘egg-yolk’ representation of regions with indeterminate boundaries. In: Geographical Objects with Undetermined Boundaries, pp. 171–187. Francis Taylor (1996)Google Scholar
  5. 5.
    Egenhofer, M.J.: A formal definition of binary topological relationships. In: Litwin, W., Schek, H.J. (eds.) FODO 1989. LNCS, vol. 367, pp. 457–472. Springer, Heidelberg (1989)Google Scholar
  6. 6.
    Egenhofer, M.J.: Query processing in spatial-query-by-sketch. Journal of Visual Languages and Computing 8(4), 403–424 (1997)CrossRefGoogle Scholar
  7. 7.
    Fogliaroni, P., Wallgrün, J.O., Clementini, E., Tarquini, F., Wolter, D.: A qualitative approach to localization and navigation based on visibility information. In: Hornsby, K.S., Claramunt, C., Denis, M., Ligozat, G. (eds.) COSIT 2009. LNCS, vol. 5756, pp. 312–329. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Freksa, C., Moratz, R., Barkowsky, T.: Schematic maps for robot navigation. In: Habel, C., Brauer, W., Freksa, C., Wender, K.F. (eds.) Spatial Cognition 2000. LNCS (LNAI), vol. 1849, pp. 100–114. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Gantner, Z., Westphal, M., Wölfl, S.: GQR – A fast reasoner for binary qualitative constraint calculi. In: Proceedings of the AAAI 2008 Workshop on Spatial and Temporal Reasoning (2008)Google Scholar
  10. 10.
    Goodchild, M.F.: Citizens as sensors: The world of volunteered geography. GeoJournal 69(4), 211–221 (2007)CrossRefGoogle Scholar
  11. 11.
    Goyal, R., Egenhofer, M.: Consistent queries over cardinal directions across different levels of detail. In: Proceedings of the 11th International Workshop on Database and Expert System Applications, pp. 876–880 (2000)Google Scholar
  12. 12.
    Herring, J.: The OpenGIS abstract specification, Topic 1: Feature geometry (ISO 19107 Spatial schema), version 5. In: OGC Document, pp. 01–101 (2001)Google Scholar
  13. 13.
    Hugentobler, M.: Quantum GIS. In: Encyclopedia of GIS, pp. 935–939. Morgan Kaufmann Publishers Inc., San Francisco (2008)CrossRefGoogle Scholar
  14. 14.
    Liu, W., Li, S., Renz, J.: Combining RCC-8 with qualitative direction calculi: Algorithms and complexity. In: Proceedings of the 21st International Joint Conference on Artifical Intelligence, pp. 854–859. Morgan Kaufmann Publishers Inc., San Francisco (2009)Google Scholar
  15. 15.
    Mackworth, A.K.: Consistency in networks of relations. Artificial Intelligence 8(1), 99–118 (1977)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Randell, D.A., Cui, Z., Cohn, A.: A spatial logic based on regions and connection. In: Principles of Knowledge Representation and Reasoning: Proceedings of the Third International Conference, pp. 165–176. Morgan Kaufmann, San Francisco (1992)Google Scholar
  17. 17.
    Renz, J., Nebel, B.: Qualitative spatial reasoning using constraint calculi. In: Aiello, M., Pratt-Hartmann, I.E., van Benthem, J.F. (eds.) Handbook of Spatial Logics, pp. 161–215. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Rigaux, P., Scholl, M.O., Voisard, A.: Spatial databases with application to GIS. Morgan Kaufmann, San Francisco (2002)Google Scholar
  19. 19.
    Sharma, J.: Integrated spatial reasoning in geographic information systems: Combining topology and direction. Ph.D. thesis, University of Maine (1996)Google Scholar
  20. 20.
    Skiadopoulos, S., Koubarakis, M.: Composing cardinal direction relations. Artificial Intelligence 152(2), 143–171 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Tarquini, F., De Felice, G., Fogliaroni, P., Clementini, E.: A qualitative model for visibility relations. In: Hertzberg, J., Beetz, M., Englert, R. (eds.) KI 2007. LNCS (LNAI), vol. 4667, pp. 510–513. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Wallgrün, J.O., Frommberger, L., Wolter, D., Dylla, F., Freksa, C.: Qualitative spatial representation and reasoning in the sparQ-toolbox. In: Barkowsky, T., Knauff, M., Ligozat, G., Montello, D. (eds.) Spatial Cognition 2007. LNCS (LNAI), vol. 4387, pp. 39–58. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Winter, S.: Bridging vector and raster representation in GIS. In: GIS 1998: Proceedings of the 6th ACM International Symposium on Advances in Geographic Information Systems, pp. 57–62. ACM, New York (1998)Google Scholar
  24. 24.
    Wölfl, S., Westphal, M.: On combinations of binary qualitative constraint calculi. In: Boutilier, C. (ed.) Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 967–973 (2009)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Giorgio De Felice
    • 1
  • Paolo Fogliaroni
    • 1
  • Jan Oliver Wallgrün
    • 1
  1. 1.Department of Mathematics and InformaticsUniversität BremenBremenGermany

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