Computational Urban Design: An Exploration of New Urban Science

Classic urban design theories and research methods have been limited to qualitative approaches, such as subjective intuition and small-scale surveys. With the emergence of new urban science, adopting big data, virtual reality (VR), and wearable sensors, it is possible to achieve a precise analysis of people’s perceptions and behaviors in urban spaces. In this way, not only will new insights in urban studies be inspired but also high-quality, human-scale space design can be generated. This study constructs a systematic and computational urban design system that covers the three aspects of technical applications, data-informed, evidence-based, and algorithm-driven urban design. First, the data-informed urban design helps identify problems and formulate strategies. Multi-source city data, such as Point of Interest (PoIs)data and AutoNavi map path planning API, help to identify the needs, functions, and characteristics of the citizens. The evidence-based urban design with specific inquiries helps to select the design intervention point and control spatial elements. VR technology, wearable biosensors, and the visualizationSP method are combined to measure space utility. The final algorithm-driven part can use sDNA, UNA, and visualizer to summarize the types of urban spaces, perform morphological analysis and vitality evaluation, and further adjust the design. It can be seen that the application of this framework can make the entire design process more automatic, efficient, and robust under the existing constraints. In addition, it helps the perspective of urban design expand from the previous “top-down” perspective to the “bottom up” perspective, transforming from a two-dimensional to a three-dimensional, obtaining real-time feedback and optimizing design accordingly, emphasizing human perception and human-oriented scale.

https://doi.org/10.35483/ACSA.AM.110.23

Volume Editors
Robert Gonzalez, Milton Curry & Monica Ponce de Leon

ISBN
978-1-944214-40-1