You are looking at 1 - 1 of 1 items

  • Author or Editor: Ying Long x
  • Chapters/Articles x
Clear All Modify Search
You do not have access to this content

Tianyu Su, Shihui Li, Jing Li, Hungyu Chou and Ying Long

With the rise of city science and data science, big data such as records of bike-sharing, mobile phone signalling, public transportation records and open data from various sources, jointly promote the formation of a new data environment, which provides a stable underpinning for the emergence of innovative planning and design methodologies. Also, historical areas in the existing built environment require renewal and redevelopment to adjust to the spatial requirements of the twenty-first century. Given this situation, this chapter delivers a new quantitative methodology for urban planning and design, termed data augmented design (DAD), and tests its application in an urban redevelopment design project. The main steps and two primary methods of DAD for urban redevelopment design – existing condition analysis and spatial parameter extraction – are introduced. The chapter applies these methods in the urban redevelopment design for the Panyu-Xinhua Area in Shanghai, China. Their effectiveness is evaluated from the perspective of planners, officers and citizens. I addition to the academic and practical contributions, potential applications, potential bias and future research using DAD methods are also discussed.