Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Choi, C.-y.: Urban Growth Modelling by Cellular Automata. M.A. dissertation. Graduate School Gyeongsang National University (2001)
Jeong, J.-J., Lee, C.-M., Kim, Y.-I.: Developments of Cellular Automata Model for the Urban Growth. Korean Planner Association 37(1) (2002)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)
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)
Jang, N.-S., Hong, S.-W., Jang, J.-H.: DataMining. Daechung media (2002)
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)
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)
Zhang, X., Wang, Y.: Spatial Dynamic Modeling for Urban Development. Photogrammetric Engineering & Remote Sensing 67(9), 1049–1057 (2001)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, J., Kang, Y., Hong, S., Park, S. (2006). Extraction of Spatial Rules Using a Decision Tree Method: A Case Study in Urban Growth Modeling. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_25
Download citation
DOI: https://doi.org/10.1007/11892960_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
Online ISBN: 978-3-540-46536-2
eBook Packages: Computer ScienceComputer Science (R0)