Data Augmented Design

Embracing New Data for Sustainable Urban Planning and Design
1. Auflage 2021
ISBN: 978-3-030-49620-3
Verlag: Springer International Publishing

Buch, Englisch, 242 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 468 g

Reihe: Spatial Planning and Sustainable Development

This book offers an essential introduction to a new urban planning and design methodology called Data Augmented Design (DAD) and its evolution and progresses, highlighting data driven methods, urban planning and design applications and related theories. The authors draw on many kinds of data, including big, open, and conventional data, and discuss cutting-edge technologies that illustrate DAD as a future oriented design framework in terms of its focus on multi-data, multi-method, multi-stage and multi-scale sustainable urban planning. In four sections and ten chapters, the book presents case studies to address the core concepts of DAD, the first type of applications of DAD that emerged in redevelopment-oriented planning and design, the second type committed to the planning and design for urban expansion, and the future-oriented applications of DAD to advance sustainable technologies and the future structural form of the built environment. The book isgeared towards a broad readership, ranging from researchers and students of urban planning, urban design, urban geography, urban economics, and urban sociology, to practitioners in the areas of urban planning and design.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Enjia

Long, Ying

Fachgebiete

• Geowissenschaften Geographie | Raumplanung Regional- & Raumplanung Stadtplanung, Kommunale Planung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften

Inhaltsverzeichnis

Chapter 1. Cities in Transition. - Chapter 2. Data Augmented Design (DAD): Definitions, Dimensions, Performance, and Applications. - Chapter 3. Human-scale Urban Form and its Application in DAD. - Chapter 4. Data Adaptive Urban Design: A Case Study of Shanghai Hengfu Historical District. - Chapter 5. Multidimensional Data-based City Images: Cultural Reactivation of Waterfront Industrial Heritage Design in Shanghai. - Chapter 6. Fine-Scale Recognition-based Design Guidelines for Dealing with Shrinking Cities: A Case Study of Hegang. - Chapter 7. Quantifying Urban Form as a Case Study in Expansion-oriented Design: Design Practices in the Tongzhou Subcenter. - Chapter 8. Defining the Density of the Xiong’an New Area based on Global Experience. - Chapter 9. The Next Form of Human Settlement: A Design for Future Yilong City. - Chapter 10. The Future of the Smart Island: A Design for a Natural and Technological Experience District on Huangguan Island.

Über Autor(innen)

Ying Long, Ph.D. is now working in the School of Architecture, Tsinghua University, China as a research professor. His research focuses urban science, including applied urban modeling, urban big data analytics & visualization, quantitative urban studies, planning support systems, data augmented design and future cities. He has an education background in both environmental engineering and city planning. Before joining Tsinghua University, he has worked for Beijing Institute of City Planning as a senior planner for eleven years. Familiar with planning practices in China and versed in international literature, Dr. Long’s academic studies creatively integrate international methods and experiences with local planning practices. He has published almost two hundred papers and led over twenty research/planning projects. His funded projects range from international organizations like World Bank, World Health Organization, World Resource Institute and NRDC, and Wellcome Trust, internet companies like Alibaba, Baidu, Jingdong, Tencent, Didi, Mobike and Gudong, local governments like Beijing, Chengdu, Qingdao, Hefei, Zunyi, Rongcheng and Laizhou, to central governments like NDRC and MOHURD, and the NSFC. Dr. Long is also the founder of Beijing City Lab (BCL www.beijingcitylab.com), an open research network for quantitative urban studies. More information is available at http://www.beijingcitylab.com/longy.
 Enjia Zhang is a research fellow of Beijing City Lab and is in the second year of her PhD in Urban and Rural Planning at Tsinghua University. Her research focuses on data augmented design and quantitative urban studies, with emphasis on the application of data science in urban planning and design.