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  • © 2018

Big Data for Urban Sustainability

A Human-Centered Perspective

  • Demonstrates how big data empowered by cloud computing provides new possibilities for researchers tackling the grand challenges of sustainable urban development and transportation

  • Teaches readers how to launch new big data applications in urban sustainability, using successes in retail, telecoms, and banking to develop template patterns and design approaches

  • Provides the first framework for realizing the opportunities suggested by big data applied to urban sustainability, based on less expensive and heterogeneous infrastructures than those currently available

  • Transcends the theoretical by including a detailed case study derived from a real-world research project- the ISUNS transportation system in development in China

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xvi
  2. The Urgent Need for Advancing Urban Sustainability

    • Stephen Jia Wang, Patrick Moriarty
    Pages 1-21
  3. Urban Health and Well-Being Challenges

    • Stephen Jia Wang, Patrick Moriarty
    Pages 23-43
  4. The Potential for Big data for Urban Sustainability

    • Stephen Jia Wang, Patrick Moriarty
    Pages 45-63
  5. Barriers to the Implementation of Big Data

    • Stephen Jia Wang, Patrick Moriarty
    Pages 65-80
  6. Big Data for Sustainable Urban Transport

    • Stephen Jia Wang, Patrick Moriarty
    Pages 81-103
  7. Big Data for Urban Energy Reductions

    • Stephen Jia Wang, Patrick Moriarty
    Pages 105-118
  8. Big Data for Urban Health and Well-Being

    • Stephen Jia Wang, Patrick Moriarty
    Pages 119-140
  9. Big Data for a Future World

    • Stephen Jia Wang, Patrick Moriarty
    Pages 141-155
  10. Back Matter

    Pages 157-160

About this book

This book presents a practical framework for the application of big data, cloud, and pervasive and complex systems to sustainable solutions for urban environmental challenges.  It covers the technologies, potential, and possible and impact of big data on energy efficiency and the urban environment.

The book first introduces key aspects of big data, cloud services, pervasive computing, and mobile technologies from a pragmatic design perspective, including sample open source firmware. Cloud services, mobile and embedded platforms, interfaces, operating system design methods, networking, and middleware are all considered. The authors then explore in detail the framework, design principles, architecture and key components of developing energy systems to support sustainable urban environments. The included case study provides a pathway to improve the eco-efficiency of urban transport, demonstrating how to design an energy efficient next generation urban navigation system by leveraging vast cloud data sets on user-behavior. Ultimately, this resource maps big data’s pivotal intersection with rapid global urbanization along the path to a sustainable future.

Authors and Affiliations

  • School of Design, Royal College of Art, Kensington Gore, London, United Kingdom

    Stephen Jia Wang

  • Department of Design, Monash University-Caulfield Campus, Caulfield East, Australia

    Patrick Moriarty

About the authors

Dr. Stephen Jia Wang (DocFA M.Eng MA BA) works in the Department of Design at Monash University as a Senior Lecturer, Director of International Tangible Interaction Design Lab (ITIDLab), and Program Director of Interaction Design. He has an integrated background across the fields of industrial design, interaction design, computer engineering and architectural design. He earned his BA in Industrial Design from Tsinghua University in Beijing, his MA in Interaction Design from Tama Art University in Tokyo, his MA in Engineering from the Royal Melbourne Institute of Technology, and his PhD in Architectural Design from Tokyo University of the Arts. At Monash, he has created a Master's course in Interaction Design, in addition to lecturing and conducting ongoing research. He has also worked in industry as an Interactive Media Director and an Environmental Interaction Designer. He has been awarded grants from corporates and the government sector alike, from sources such as Microsoft Research Asia, and the Australia-Japan Foundation of the Department of Foreign Affairs and Trade. Dr. Wang is a full member of the Japanese Society for the Science of Design, and the Japanese Design Research Association, as well as a member of the Interaction Design Association. He sits on the scientific advisory board of the International Conference on Sustainable Development of Energy, Water and Environment Systems, and is a member of the Organizing Committee of the China International Tangible Interaction Design & Innovation Exhibition.

Bibliographic Information

  • Book Title: Big Data for Urban Sustainability

  • Book Subtitle: A Human-Centered Perspective

  • Authors: Stephen Jia Wang, Patrick Moriarty

  • DOI: https://doi.org/10.1007/978-3-319-73610-5

  • Publisher: Springer Cham

  • eBook Packages: Energy, Energy (R0)

  • Copyright Information: Springer International Publishing AG, part of Springer Nature 2018

  • Hardcover ISBN: 978-3-319-73608-2Published: 05 April 2018

  • Softcover ISBN: 978-3-030-08819-4Published: 26 January 2019

  • eBook ISBN: 978-3-319-73610-5Published: 22 March 2018

  • Edition Number: 1

  • Number of Pages: XVI, 160

  • Number of Illustrations: 5 b/w illustrations, 9 illustrations in colour

  • Topics: Renewable and Green Energy, Sustainable Development, Software Engineering, Transportation Technology and Traffic Engineering

Buy it now

Buying options

eBook USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access