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서울 데이터 기반 필지별 용도전환 발생 예측

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

  • 윤성범 (서울기술연구원 스마트도시연구실) ;
  • 문성철 (서울기술연구원 스마트도시연구실) ;
  • 박순용 (서울기술연구원 스마트도시연구실) ;
  • 김태현 (서울기술연구원 스마트도시연구실)
  • 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)
  • 투고 : 2019.12.27
  • 심사 : 2020.02.13
  • 발행 : 2020.03.30

초록

지속적인 서울시의 발전과 쇠퇴에 따라 서울시는 정책 차원에서 도시재생을 진행하기 위해 지역별 용도전환 등의 정책을 진행하고 있지만, 이는 다양한 결과를 야기한다. 본 연구는 이런 용도전환이 발생하는 원인을 도출하고자 다양한 공공데이터를 활용하여 서울지역에서 지난 2011~2015년에 발생한 용도전환에 대한 예측 모델을 구축하고 용도전환을 야기하는 요인을 도출하고자 한다. 이를 구현하기 위해 서울시 및 국가 공공기관에서 취득한 서울시 필지에 대한 다양한 데이터를 의사결정 나무 기반 머신러닝 기법인 Random Forest에 적용하고 높은 정확도를 가지는 예측 모델을 구축하였으며, 용도전환을 야기하는 중요 요인들을 도출하였다. 해당 연구의 결과는 나아가 서울시의 당면 과제인 젠트리피케이션이 발생하는 요인연구와 예측 연구에 활용될 수 있을 것으로 판단되며, 공공의 정책 의사결정을 지원할 것으로 판단된다.

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.

키워드

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