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Land-Use-Change Modeling Using Unbalanced Support-Vector Machines

Author

Listed:
  • Bo Huang

    (Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong)

  • Chenglin Xie

    (North West Geomatics Ltd, 5438-11 Street NE, Calgary, AB T2E 7E9, Canada)

  • Richard Tay

    (Department of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

  • Bo Wu

    (Spatial Information Research Center, Fuzhou University, 523 Gongye Road, Fuzhou, PR China)

Abstract

Modeling land-use change is a prerequisite to understanding the complexity of land-use-change patterns. This paper presents a novel method to model urban land-use change using support-vector machines (SVMs), a new generation of machine learning algorithms used in classification and regression domains. An SVM modeling framework has been developed to analyze land-use change in relation to various factors such as population, distance to roads and facilities, and surrounding land use. As land-use data are generally unbalanced, in the sense that the unchanged data overwhelm the changed data, traditional methods are incapable of classifying relatively minor land-use changes with high accuracy. To circumvent this problem, an unbalanced SVM has been adopted by enhancing the standard SVMs. A case study of Calgary land-use change demonstrates that the unbalanced SVMs can achieve high and reliable performance for land-use-change modeling.

Suggested Citation

  • Bo Huang & Chenglin Xie & Richard Tay & Bo Wu, 2009. "Land-Use-Change Modeling Using Unbalanced Support-Vector Machines," Environment and Planning B, , vol. 36(3), pages 398-416, June.
    • Handle: RePEc:sae:envirb:v:36:y:2009:i:3:p:398-416
    DOI: 10.1068/b33047
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    References listed on IDEAS

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    Cited by:

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    2. Yi Lu & Shawn Laffan & Chris Pettit & Min Cao, 2020. "Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia," Environment and Planning B, , vol. 47(9), pages 1605-1621, November.
    3. Peng Zeng & Sihui Wu & Zongyao Sun & Yujia Zhu & Yuqi Chen & Zhi Qiao & Liangwa Cai, 2021. "Does Rural Production–Living–Ecological Spaces Have a Preference for Regional Endowments? A Case of Beijing-Tianjin-Hebei, China," Land, MDPI, vol. 10(11), pages 1-21, November.
    4. Hassan Mahmoudzadeh & Asghar Abedini & Farshid Aram, 2022. "Urban Growth Modeling and Land-Use/Land-Cover Change Analysis in a Metropolitan Area (Case Study: Tabriz)," Land, MDPI, vol. 11(12), pages 1-17, November.
    5. Fei Liu & Xinqi Zheng & Qing Huang, 2017. "Predictive Measurement of the Structure of Land Use in an Urban Agglomeration Space," Sustainability, MDPI, vol. 10(1), pages 1-13, December.
    6. Cong Cao & Suzana Dragićević & Songnian Li, 2019. "Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    7. Yang, Xin & Zheng, Xin-Qi & Lv, Li-Na, 2012. "A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata," Ecological Modelling, Elsevier, vol. 233(C), pages 11-19.

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