Extraction of Spatial Rules Using a Decision Tree Method: A Case Study in Urban Growth Modeling

  • Jungyeop Kim
  • Yunhee Kang
  • Sungeon Hong
  • Soohong Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


Data mining refers to the extraction of knowledge from large amounts of data using pattern recognition, statistical methods, and artificial intelligence. The decision tree method, a well-known data mining technique, is used to extract decision rules because using it, we can understand the grounds of classification or prediction. The decision tree method thus can be used to analyze spatial data. There is an enormous amount of spatial data in GIS (Geographic Information System), which has been studied in modeling with these data. Spatial modeling as applied to a decision tree method can be carried out more effectively. In this paper, we extracted spatial rules using the decision tree method, and then applied them to urban growth modeling based on Cellular Automata (CA). An evaluation comparing the model using the decision tree method (the proposed model) with the standard UGM showed that the proposed model is more accurate.


Cellular Automaton Cellular Automaton Information Gain Urban Growth Transition Rule 
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.


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  1. 1.
    Choi, C.-y.: Urban Growth Modelling by Cellular Automata. M.A. dissertation. Graduate School Gyeongsang National University (2001) Google Scholar
  2. 2.
    Jeong, J.-J., Lee, C.-M., Kim, Y.-I.: Developments of Cellular Automata Model for the Urban Growth. Korean Planner Association 37(1) (2002)Google Scholar
  3. 3.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  4. 4.
    Chen, J., Gong, P., He, C., Luo, W., Tamura, M., Sho, P.: Assessment of the Urban Development Plan of Beijing by Using a CA-Based Urban Growth Model. Photogrammetric Engineering & Remote Sensing 68(10), 1063–1071 (2002)Google Scholar
  5. 5.
    Jang, N.-S., Hong, S.-W., Jang, J.-H.: DataMining. Daechung media (2002)Google Scholar
  6. 6.
    Park, S.: Design and implementation of An Integrated CA-GIS System. The Journal of Geographic Information System Association of Korea 9(2), 185–206 (2001)Google Scholar
  7. 7.
    Park, S., Joo, Y.-G., Shin, Y.-H.: Design and Development of a Spatio-Temporal GIS Database for Urban Growth Modeling and Prediction. The Geographical Journal of Korea 36(4), 313–326 (2002)Google Scholar
  8. 8.
    Zhang, X., Wang, Y.: Spatial Dynamic Modeling for Urban Development. Photogrammetric Engineering & Remote Sensing 67(9), 1049–1057 (2001)Google Scholar
  9. 9.
    Kang, Y., Park, S.: A Study on the Urban Growth Forecasting for the Seoul Metropolitan Area. Journal of the Korean Geographical Society, 621–639 (2000)Google Scholar
  10. 10.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jungyeop Kim
    • 1
  • Yunhee Kang
    • 2
  • Sungeon Hong
    • 3
  • Soohong Park
    • 1
  1. 1.Dept. of Geoinformatic EngineeringInha UniversityIncheonKorea
  2. 2.Dept. of Computer Science & Information EngineeringInha UniversityIncheonKorea
  3. 3.Dept. of Welfare-Land InformationCheongju UniversityCheongjuKorea

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