Land-Use-Change Modeling Using Unbalanced Support-Vector Machines
Richard Tay () and
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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
Bo Wu: Spatial Information Research Center, Fuzhou University, 523 Gongye Road, Fuzhou, PR China
Environment and Planning B, 2009, vol. 36, issue 3, 398-416
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.
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:36:y:2009:i:3:p:398-416
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