Advertisement

An Association Rule Discovery System Applied to Geographic Data

  • Laura C. RodmanEmail author
  • John Jackson
  • Ross K. Meentemeyer
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

An association rule discovery system has been developed for geographic data. Association rules are applicable to the interpretation of remote sensing images, in which rules derived from another data set can provide ancillary data to guide land cover mapping. The software system developed, called Aspect, works with standard geographic data formats and extends the association rule formulation to handle spatial relationships. Multiple strategies provide guidance for selecting the relevant variables to include in the rules. Association rule results are presented that are derived from environmental conditions, anthropogenic features, land cover, and vegetation.

Keywords

Geographic Information System Association Rule Road Density Support Threshold Secondary Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was sponsored by the U. S. Army Topographic Engineering Center, Vicksburg Consolidated Contracting Office, under Contract No. W9132V-04-C-0025. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the US Army Corps of Engineers.

Robert Huizar III, of the University of North Carolina at Charlotte, assisted with the Sonoma County data sets. His contributions are gratefully acknowledged.

References

  1. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI (1996) Fast Discovery of Association Rules. In: Fayyad UM et al. (eds) Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, CA, pp. 307–328Google Scholar
  2. Agrawal R, Srikant R (1994) Fast Algorithms for Mining Association Rules. In: Proc. 1994 Int. Conf. Very Large Data Bases. Santiago, Chile, pp. 487–499Google Scholar
  3. Daly C, Taylor GH, Gibson WP, Parzybok TW, Johnson GL, Pasteris, P (2001) High-Quality Spatial Climate Data Sets for the United States and Beyond. Trans. Am. Soc. Agric. Eng. 43:1957–1962Google Scholar
  4. Han J, Kamber M (2001) Data Mining: Concepts and Techniques. Academic Press, San Diego, p 261Google Scholar
  5. Hipp J, Guntzer U, Nakhaeizadeh G (2000) Algorithms for Association Rule Mining: A General Survey and Comparison. In: SIGKDD Explorations. 2:58–64CrossRefGoogle Scholar
  6. King RB (2002) Land Cover Mapping Principles: a Return to Interpretation Fundamentals. Int. J. Remote Sens. 23:3525–3545CrossRefGoogle Scholar
  7. Koperski K, Han J (1995) Discovery of Spatial Association Rules in Geographic Information Databases. In: Proc. 4th Int. Symp. on Large Spatial Databases, Maine, pp 47–66Google Scholar
  8. Rodman LC, Jackson J (2007) Spatial Association Rule Discovery with Rule Classification and Variable Sensitivity. NEAR TR 624, Nielsen Engineering & Research, Mountain View, CAGoogle Scholar
  9. Scheffer T (2001) Finding Association Rules that Trade Support Optimally Against Confidence. In: Lecture Notes in Computer Science, Springer, 2168:424–435CrossRefGoogle Scholar
  10. USDA Forest Service RSL (2003) CALVEG Vegetation Mapping Program, 1920 20th Street, Sacramento, CA 95814Google Scholar
  11. USDA SSURGO, Natural Resources Conservation Service, Soil Survey Geographic (SSURGO) Database for Sonoma County, CAGoogle Scholar
  12. WHR Classification, CA Department of Fish and Game, Biogeographic Data BranchGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Laura C. Rodman
    • 1
    Email author
  • John Jackson
    • 1
  • Ross K. Meentemeyer
    • 2
  1. 1.Nielsen Engineering and Research, Inc.Mountain ViewUSA
  2. 2.Department of Geography and Earth SciencesUniversity of North CarolinaCharlotteUSA

Personalised recommendations