Fire! Firing Inductive Rules from Economic Geography for Fire Risk Detection

  • David Vaz
  • Vítor Santos Costa
  • Michel Ferreira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6489)

Abstract

Wildfires can importantly affect the ecology and economy of large regions of the world. Effective prevention techniques are fundamental to mitigate their consequences. The design of such preemptive methods requires a deep understanding of the factors that increase the risk of fire, particularly when we can intervene on these factors. This is the case for the maintenance of ecological balances in the landscape that minimize the occurrence of wildfires. We use an inductive logic programming approach over detailed spatial datasets: one describing the landscape mosaic and characterizing it in terms of its use; and another describing polygonal areas where wildfires took place over several years. Our inductive process operates over a logic term representation of vectorial geographic data and uses spatial predicates to explore the search space, leveraging the framework of Spatial-Yap, its multi-dimensional indexing and tabling extensions. We show that the coupling of a logic-based spatial database with an inductive logic programming engine provides an elegant and powerful approach to spatial data mining.

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References

  1. 1.
    Vaz, D., Ferreira, M., Lopes, R.: Spatial-yap: A logic-based geographic information system. In: Dahl, V., Niemelä, I. (eds.) ICLP 2007. LNCS, vol. 4670, pp. 195–208. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Open GIS Consortium, I.: OpenGIS Simple Features Specifications For SQL (1999), http://www.opengis.org/docs/99-049.pdf
  3. 3.
    Ceci, M., Appice, A., Loglisci, C., Caruso, C., Fumarola, F., Malerba, D.: Novelty detection from evolving complex data streams with time windows. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS, vol. 5722, pp. 563–572. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Ng, R.T., Han, J.: Efficient and effective clustering methods for spatial data mining. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB 1994, Proceedings of 20th International Conference on Very Large Data Bases, September 12-15, pp. 144–155. Morgan Kaufmann, Santiago de Chile (1994)Google Scholar
  5. 5.
    Malerba, D., Lanza, A., Appice, A.: 10. In: Geographic Knowledge Discovery and Data Mining, 2nd edn., pp. 258–291. CRC Press - Taylor and Francis (2009)Google Scholar
  6. 6.
    Malerba, D., Esposito, F., Lanza, A., Lisi, F.A., Appice, A.: Empowering a gis with inductive learning capabilities: the case of ingens. Computers, Environment and Urban Systems 27(3), 265–281 (2003)CrossRefGoogle Scholar
  7. 7.
    Malerba, D.: Learning recursive theories in the normal ilp setting. Fundam. Inf. 57(1), 39–77 (2003)MathSciNetMATHGoogle Scholar
  8. 8.
    Lisi, F.A., Malerba, D.: Inducing multi-level association rules from multiple relations. Mach. Learn. 55(2), 175–210 (2004)MATHCrossRefGoogle Scholar
  9. 9.
    Soares, T., Ferreira, M., Rocha, R.: The MYDDAS Programmer’s Manual. Technical Report DCC-2005-10, Department of Computer Science, University of Porto (2005)Google Scholar
  10. 10.
    Rocha, R., Silva, F., Santos Costa, V.: YapTab: A Tabling Engine Designed to Support Parallelism. In: Conference on Tabulation in Parsing and Deduction, pp. 77–87 (2000)Google Scholar
  11. 11.
    The GEOS Development Team: GEOS: Geometry Engine Open Source, http://geos.refractions.net/
  12. 12.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: Yormark, B. (ed.) SIGMOD 1984, Proceedings of Annual Meeting, Boston, Massachusetts, June 18-21, pp. 47–57. ACM Press, New York (1984)CrossRefGoogle Scholar
  13. 13.
    Vaz, D., Santos Costa, V., Ferreira, M.: User defined indexing. In: Hill, P.M., Warren, D.S. (eds.) ICLP 2009. LNCS, vol. 5649, pp. 372–386. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    The Postgis Development Team: Postgis adds support for geographic objects to the postgresql object-relational database, http://postgis.refractions.net/
  15. 15.
    Torres, J., GonĀ\(\oint\)alves, J., Torgo, L., Honrado, J.: Fire and landscape: A multi-scale assessment of a complex realation. In: Landscape Ecology International Conference (2010)Google Scholar
  16. 16.
    Stojanova, D., Panov, P., Kobler, A., Džeroski, S., Taškova, K.: Learning to predict forest fires with different data mining techniques. In: Proceedings of the 9th International Multiconference Information Society 2006 (IS 2006), Jožef Stefan Institute, pp. 255–258 (2006)Google Scholar
  17. 17.
    Srinivasan, A.: The Aleph Manual (2001)Google Scholar
  18. 18.
    Santos Costa, V.: The life of a logic programming system. In: de la Banda, M.G., Pontelli, E. (eds.) ICLP 2008. LNCS, vol. 5366, pp. 1–6. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • David Vaz
    • 1
  • Vítor Santos Costa
    • 2
  • Michel Ferreira
    • 3
  1. 1.LIACC and DCC/FCUPUniversity of PortoPortugal
  2. 2.CRACS-INESC Porto LA and DCC/FCUPUniversity of PortoPortugal
  3. 3.IT and DCC/FCUPUniversity of PortoPortugal

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