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Data-centric Regenerative Built Environment: Big Data for Sustainable Regeneration [Hardback]

(University of Canberra, Australia), (University of Tehran, Iran)
  • Formāts: Hardback, 198 pages, height x width: 216x138 mm, weight: 640 g, 17 Tables, black and white; 72 Halftones, black and white; 72 Illustrations, black and white
  • Sērija : Routledge Research in Sustainable Planning and Development in Asia
  • Izdošanas datums: 18-Mar-2022
  • Izdevniecība: Routledge
  • ISBN-10: 0367689928
  • ISBN-13: 9780367689926
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  • Cena: 70,31 €
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Data-centric Regenerative Built Environment: Big Data for Sustainable RegenerationPalielināt
  • Formāts: Hardback, 198 pages, height x width: 216x138 mm, weight: 640 g, 17 Tables, black and white; 72 Halftones, black and white; 72 Illustrations, black and white
  • Sērija : Routledge Research in Sustainable Planning and Development in Asia
  • Izdošanas datums: 18-Mar-2022
  • Izdevniecība: Routledge
  • ISBN-10: 0367689928
  • ISBN-13: 9780367689926
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367689926.html
Keywords:
"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"--

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.

Recenzijas

"Sustainable built environment is one of, if not, the biggest challenges for humankind in our time. Big data offers a unique opportunity to collect and analyse information relating to our urban land use, transportation and traffic, environmental concerns and social issues. Potentially, with access to a mass of data on a scale not previously available, we can optimise the ways our cities function to deliver economic, social and environmental sustainability. In this innovative book, Dr Saeed Banihashemi and Sepideh Zarepour Sohi show us how this regenerative built environment might be achieved through big data technologies and applications."

Professor Sara Wilkinson, University of Technology Sydney

List of Figures
xi
List of Tables
xv
Preface xvi
List of Abbreviations
xviii
1 Classics of Data-Centric Regenerative Built Environment
1(19)
Introduction
1(1)
Urban Development and Regeneration
1(1)
Big Data Technology
2(3)
Tools and Sources of Big Data for Urban Regeneration
5(1)
Volunteered Data
5(1)
Location-Based Social Media Data
5(1)
Volunteered Geographic Information (VGI)
5(1)
Automatic
6(1)
GPS (Global Positioning System)
6(1)
SCD (Smart Card Data)
6(1)
MPD (Mobile Phone Data)
7(1)
Sensors and Objects (Fixed Sensors)
7(1)
Closed-Circuit Television (CCTV)
7(1)
Wireless Sensor Networks (WSNs)
8(1)
Monitored Data
8(1)
Administrative Data (Governmental Data)
8(1)
Private Sector Data
9(1)
Concepts and Classics of Data-Centric Regenerative Built Environment Framework
9(5)
References
14(6)
2 Big Data in Urban Land Use Regeneration
20(37)
Urban Land Use and Its Significance in Sustainable Urban Planning
20(1)
Current Situation of Spatial Patterns of Urban Land Use
21(1)
Data-Centric Land Use Patterns
22(1)
Taxi GPS Trajectories Data
22(3)
Geo-TaggedSocial Media
25(1)
Mobile Phone Data and Call Detail Records
26(2)
Summary
28(1)
Urban Land Use Regeneration of Beijing
28(1)
Background
28(2)
Big Data Sources
30(1)
Social Media Data
30(1)
Open Street Map Road Network
31(1)
Digital Elevation Model
31(1)
Methodology and Process of Data Analysis
31(1)
Data Analysis and Results
32(10)
Proposing the Areas
42(8)
Conclusion
50(1)
References
51(6)
3 Big Data in Urban Traffic and Transportation
57(35)
Traffic and Transportation Structure in Urban Planning
57(1)
Smart Traffic Control and Monitoring Through Big Data
58(1)
Background
58(1)
GPS Trajectories for Traffic Monitoring and Congestion Detection
58(2)
Social Media Data for Traffic Monitoring
60(2)
Summary
62(2)
Big Data Application in Transportation Behaviour and Patterns
64(1)
Background
64(1)
Smart Card Data for Transportation Behaviour
64(2)
Taxi GPS Trajectories for Transportation Behaviour
66(3)
Summary
69(2)
Data-Driven Traffic and Transportation Analysis of Beijing
71(1)
Background
71(1)
Big Data Sources
71(1)
Taxi GPS Trajectories
71(1)
Methodology and Process of Data Analysis
71(4)
Data Analysis and Results
75(1)
Holistic Analysis
75(1)
Space-Time Cube Analysis
75(1)
Hotspot and Cold Spot Analysis
76(2)
Design Solutions
78(4)
Conclusion
82(5)
References
87(5)
4 Big Data and Urban Environmental Sustainability
92(46)
Urban Environmental Issues and Impacts on Planning Approach
92(1)
Big Data for Air Pollution Control
93(1)
Background
93(1)
Fixed Stations
93(2)
Wireless Sensor Network (WSN)
95(1)
Geo-Tagged Social Media
96(2)
Summary
98(1)
Data-Driven Noise Management Principles
98(1)
Background
98(1)
Mobile Phone Application for Noise Monitoring
99(3)
WSNs for Noise Monitoring
102(1)
Summary
102(2)
Tehran's Urban Environmental Regeneration via Big Data Technologies
104(1)
Methodology and Process of Data Analysis
105(5)
Data Analysis and Results
110(8)
Region 10 for the Design Intervention
118(13)
Conclusion
131(1)
References
132(6)
5 Big Data and Urban Social Sustain ability
138(28)
Social Sustainability in Urban Planning and Design
138(1)
Walkability and Big Data
139(1)
Walkability and GPS Trajectories
140(1)
Urban Public Space and Big Data
141(1)
Urban Public Space and Social Media Data
141(4)
Urban Vibrancy and Big Data
145(1)
The Role of Social Media Data in Analyzing Urban Vibrancy
145(2)
Data-Centric Social Sustainability of Tehran
147(1)
Methodology and Process of Data Analysis
147(1)
Data Sources
147(1)
Data Processing and Analysis
147(3)
Data Analysis and Results
150(6)
Design Principles for Regeneration of Urban Public Space
156(7)
Conclusion
163(1)
References
164(2)
6 Data-Focused Visionary Leap for the Future Built Environment
166(7)
Data-Integrated Pathways Towards Regenerative Urban Environments
166(1)
Case Studies and Knowledge Factors
167(1)
Beijing's Urban Land Use Regeneration
168(1)
Beijing's Traffic and Transportation Regeneration
169(1)
Tehran's Urban Air Pollution Regeneration
169(1)
Tehran's Urban Public Space Regeneration
170(1)
Challenges, Implications and Visions
171(1)
Challenges
171(1)
Implications
171(1)
Visions
172(1)
Index 173
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.