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A Multi-Objective Optimization of Physical Activity Spaces

Author

Listed:
  • Fang Wei

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
    Center for Balance Architecture, Zhejiang University, Hangzhou 310058, China)

  • Wenwen Xu

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
    Urban and Rural Planning Research Center, Qujiang District Natural Resources and Planning Bureau, Quzhou 324022, China)

  • Chen Hua

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
    The Architectural Design & Research Institute of Zhejiang University Co., Ltd., Hangzhou 310028, China)

Abstract

Optimizing the location of physical activity spaces (PAS) to ensure health, equity and efficiency has long been an important issue in urban planning. Given the health benefits of urban green spaces (UGS), taking Gongshu District in Hangzhou as a case, we examine the issue of where such PAS should be located to optimize three objectives: (1) minimize the distance between PAS and UGS; (2) maximize the accessibility of PAS and (3) maximize the population that falls within the coverage range. This study develops a multi-objective optimization of physical activity spaces model (MOPAS) based upon multi-objective particle swarm optimization to yield a set of non-dominated Pareto optimum solutions that can be used to determine the most practical tradeoffs between the conflicting objectives. It compares the advantages and disadvantages of the Pareto solutions and evaluates the construction situation of locations and the implementation feasibility. Decision-makers can choose the best solution according to subjective preferences and objective conditions. The MOPSO holds great promise for improving the location optimization of PAS and the methods applied can be adapted to support multi-objective optimization of facilities in urban planning globally.

Suggested Citation

  • Fang Wei & Wenwen Xu & Chen Hua, 2022. "A Multi-Objective Optimization of Physical Activity Spaces," Land, MDPI, vol. 11(11), pages 1-17, November.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:11:p:1991-:d:965149
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    References listed on IDEAS

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    1. Junia N. de Brito & Zachary C. Pope & Nathan R. Mitchell & Ingrid E. Schneider & Jean M. Larson & Teresa H. Horton & Mark A. Pereira, 2019. "Changes in Psychological and Cognitive Outcomes after Green versus Suburban Walking: A Pilot Crossover Study," IJERPH, MDPI, vol. 16(16), pages 1-12, August.
    2. Charles S. Revelle & Gilbert Laporte, 1996. "The Plant Location Problem: New Models and Research Prospects," Operations Research, INFORMS, vol. 44(6), pages 864-874, December.
    3. Cho, Cheol-Joo, 1998. "An equity-efficiency trade-off model for the optimum location of medical care facilities," Socio-Economic Planning Sciences, Elsevier, vol. 32(2), pages 99-112, June.
    4. Richard C. Larson & Keith A. Stevenson, 1972. "On Insensitivities in Urban Redistricting and Facility Location," Operations Research, INFORMS, vol. 20(3), pages 595-612, June.
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