Journal of the Korean Society of Environmental Restoration Technology (한국환경복원기술학회지)

The Korea Society of Environmental Restoration Technology (한국환경복원기술학회)
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Spatial Decision Support System for Development and Conservation of Unexecuted Urban Park using ACO - Ant Colony Optimization -

장기 미집행 도시계획시설 중 도시공원을 위한 보전/개발 공간의사결정 시스템 - 개미군집알고리즘(ACO)를 이용하여-

  • Yoon, Eun-Joo (Interdisciplinary Program in Landscape Architecture, Seoul National University) ;
  • Song, Eun-Jo (Dept. of Landscape Architecture and Rural System Engineering, Seoul National University) ;
  • Jeung, Yoon-Hee (Research Institute of Agriculture and Life Sciences, Seoul National University) ;
  • Kim, Eun-Young (Suwon Research Institute) ;
  • Lee, Dong-Kun (Dept. of Landscape Architecture and Rural System Engineering, Seoul National University)
  • 윤은주 (서울대학교 협동과정 조경학) ;
  • 송은조 (서울대학교 조경지역시스템공학부) ;
  • 정윤희 (서울대학교 농업생명과학연구원) ;
  • 김은영 (수원시정연구원) ;
  • 이동근 (서울대학교 조경지역시스템공학부)
  • Received : 2018.02.26
  • Accepted : 2018.03.21
  • Published : 2018.04.30
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Abstract

Long-term unexecuted urban parks will be released from urban planning facilities after 2020, this may result in development of those parks. However, little research have been focused on how to develop those parks considering conservation, development, spatial pattern, and so on. Therefore, in this study, we suggested an optimization planning model that minimizes the fragmentation while maximizing the conservation and development profit using ACO (Ant Colony Optimization). Our study area is Suwon Yeongheung Park, which is long-term unexecuted urban parks and have actual plan for private development in 2019. Using our optimization planning model, we obtained four alternatives(A, B, C, D), all of which showed continuous land use patterns and satisfied the objectives related to conservation and development. Each alternative are optimized based on different weight combinations of conservation, development, and fragmentation, and we can also generated other alternatives immediately by adjusting the weights. This is possible because the planning process in our model is very fast and quantitative. Therefore, we expected our optimization planning model can support "spatial decision making" of various issue and sites.

Keywords

References

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