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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.

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