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http://dx.doi.org/10.5909/JBE.2020.25.2.176

Data-driven Analysis for Future Land-use Change Prediction : Case Study on Seoul  

Yun, Sung Bum (Seoul Institute of Technology, Department of Smart City Research)
Mun, Sungchul (Seoul Institute of Technology, Department of Smart City Research)
Park, Soon Yong (Seoul Institute of Technology, Department of Smart City Research)
Kim, Taehyun (Seoul Institute of Technology, Department of Smart City Research)
Publication Information
Journal of Broadcast Engineering / v.25, no.2, 2020 , pp. 176-184 More about this Journal
Abstract
Due to constant development and decline on Seoul areas the Seoul government is pushing various policies to regenerate declined Seoul areas. Theses various policies lead to land-use changes around numerous Seoul districts. This study aims to create prediction model which can foresee future land-use changes and while doing so, tried to derive various influential factors which leads to land-use changes. To do so, various open-data from national departments and Seoul government have been collected and implemented into random forest algorithm. The results showed promising accuracy and derived multiple influential factors which causes land-use changes around Seoul districts. The result of this study could further be implemented in policy makings for the public sectors, or could also be used as basis for studying gentrification problems happening in Seoul Area.
Keywords
Land use policy; Prediction; Classification; Random Forest; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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