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.

中文翻译：

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校园设计的数据驱动规划支持系统

本文旨在通过比较两种设计方法，预测和优化，开发一个由数据分析驱动的校园级规划支持系统。校园被定义为建筑和基础设施的小型复杂城市系统。解决了三个问题：（1）什么产生了校园设计？制定设计决策采用哪些原则？(2) 我们如何基于多标准性能和多目标优化设计选项？(3) 我们如何管理复杂系统设计的过程，从场景制作、性能评估、设计优化到设计生成？从上述过程中可以得出哪些属性来为校园设计决策提供信息？在上述问题的驱动下，在校园场地设计中采用了预期和优化的设计方法。通过审查这些流程，提出了一个数据驱动的校园规划支持系统，以通过可视化平台管理复杂的决策并传达设计决策。这项研究将有助于探索数据分析如何驱动城市设计，以提高能源效率和系统弹性。