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This book examines the use of big data in regenerative urban environment and how data helps in functional planning and design solutions.
This book is one of the first endeavors to present the data-driven methods for regenerative built environments and integrate it with the novel design solutions. It looks at four specific areas in which data is used - urban land use, transportation and traffic, environmental concerns and social issues - and draws on the theoretical literature concerning regenerative built environments to explain how the power of big data can achieve the systematic integration of urban design solutions. It then applies an in-depth case study method on Asian metropolises including Beijing and Tehran to bring the developed innovation into a research-led practical context.
This book is a useful reference for anyone interested in driving sustainable regeneration of our urban environments through big data-centric design solutions.
Sommario
1. Classics of Data-centric Regenerative Built Environment 2. Big Data in Urban Land Use Regeneration 3. Big Data in Urban Traffic and Transportation 4. Big Data and Urban Environmental Sustainability 5. Big Data and Urban Social Sustainability 6. Data-focused Visionary Leap for the Future Built Environment
Info autore
Saeed Banihashemi is Assistant Professor of Built Environment discipline in the School of Design and Built Environment, Faculty of Arts and Design; University of Canberra (UC), Australia. He obtained his PhD from the Built Environment school of University of Technology Sydney (UTS).
Sepideh Zarepour Sohi has a mixed background of urban planning and design, graduated from the Faculty of Fine Arts, University of Tehran, Iran. She is the professional urban designer and planner.
Riassunto
This book examines the use of big data in regenerative urban environment and how data helps in functional planning and design solutions. With in-depth case studies, this book is a useful reference for anyone interested in driving sustainable regeneration of our urban environments through big data-centric design solutions.
