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Data-driven planning support system for a campus design

Author

Listed:
  • Perry Pei-Ju Yang
  • Soowon Chang
  • Nirvik Saha
  • Helen W Chen

Abstract

The paper aims to develop a campus-level planning support system that is driven by data analytics by comparing two design approaches, anticipation and optimization. A campus is defined as a small-scale complex urban system of buildings and infrastructure. Three questions are addressed: (1) What generates campus design? What principles are taken for making design decisions? (2) How do we optimize design options based on multi-criteria performance and multi-objectives? (3) How can we manage a process of complex systems design, from scenario making, performance evaluation, design optimization to design generation? What properties can be derived from the above processes to inform campus design decisions? Driven by the above questions, design approaches by anticipation and by optimization were employed in a campus site design. By reviewing those processes, a data-driven campus planning support system is proposed to manage complex decisions and communicate design decisions through a visualization platform. This research will contribute to exploring how urban design is driven by data analytics for promoting energy efficiency and system resilience.

Suggested Citation

  • Perry Pei-Ju Yang & Soowon Chang & Nirvik Saha & Helen W Chen, 2020. "Data-driven planning support system for a campus design," Environment and Planning B, , vol. 47(8), pages 1474-1489, October.
    • Handle: RePEc:sae:envirb:v:47:y:2020:i:8:p:1474-1489
    DOI: 10.1177/2399808320910164
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    References listed on IDEAS

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    1. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    2. Keirstead, James & Samsatli, Nouri & Shah, Nilay & Weber, Céline, 2012. "The impact of CHP (combined heat and power) planning restrictions on the efficiency of urban energy systems," Energy, Elsevier, vol. 41(1), pages 93-103.
    3. Best, Robert E. & Flager, Forest & Lepech, Michael D., 2015. "Modeling and optimization of building mix and energy supply technology for urban districts," Applied Energy, Elsevier, vol. 159(C), pages 161-177.
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    Cited by:

    1. Haozhi Pan & Stan Geertman & Brian Deal, 2020. "What does urban informatics add to planning support technology?," Environment and Planning B, , vol. 47(8), pages 1317-1325, October.
    2. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Elena Núñez Varela & Kristoffer Öhrling & Annika Moscati, 2022. "Analysis of the Challenges in the Swedish Urban Planning Process: A Case Study about Digitalization," Sustainability, MDPI, vol. 14(24), pages 1-14, December.

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