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Big Data Science and Analytics for Smart Sustainable Urbanism

Overview of attention for book
Cover of 'Big Data Science and Analytics for Smart Sustainable Urbanism'

Table of Contents

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    Book Overview
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    Chapter 1 The Evolving Data-Driven Approach to Smart Sustainable Urbanism for Tackling the Conundrums of Sustainability and Urbanization
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    Chapter 2 The Leading Smart Sustainable Paradigm of Urbanism and Big Data Computing: A Topical Literature Review
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    Chapter 3 The Theoretical and Disciplinary Underpinnings of Data–Driven Smart Sustainable Urbanism: An Interdisciplinary and Transdisciplinary Perspective
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    Chapter 4 Sustainable, Smart, and Data-Driven Approaches to Urbanism and their Integrative Aspects: A Qualitative Analysis of Long-Lasting Trends
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    Chapter 5 The Underlying Technological, Scientific, and Structural Dimensions of Data-Driven Smart Sustainable Cities and Their Socio-Political Shaping Factors and Issues
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    Chapter 6 Smart Sustainable Urbanism: Paradigmatic, Scientific, Scholarly, Epistemic, and Discursive Shifts in Light of Big Data Science and Analytics
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    Chapter 7 On the Sustainability and Unsustainability of Smart and Smarter Urbanism and Related Big Data Technology, Analytics, and Application
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    Chapter 8 Advancing Sustainable Urbanism Processes: The Key Practical and Analytical Applications of Big Data for Urban Systems and Domains
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    Chapter 9 The Unfolding and Soaring Data Deluge for Transforming Smart Sustainable Urbanism: Data-Driven Urban Studies and Analytics
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    Chapter 10 Novel Intelligence Functions for Data–driven Smart Sustainable Urbanism: Utilizing Complexity Sciences in Fashioning Powerful Forms of Simulations Models
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    Chapter 11 Toward the Integration of the Data-Driven City, the Eco-city and the Compact City: Constructing a Future Vision of the Smart Sustainable City
Overall attention for this book and its chapters
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Big Data Science and Analytics for Smart Sustainable Urbanism
Published by
Springer International Publishing, July 2019
DOI 10.1007/978-3-030-17312-8
978-3-03-017311-1, 978-3-03-017312-8

Bibri, Simon Elias

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 22%
Professor > Associate Professor 1 11%
Student > Doctoral Student 1 11%
Unknown 5 56%
Readers by discipline Count As %
Arts and Humanities 1 11%
Business, Management and Accounting 1 11%
Computer Science 1 11%
Unknown 6 67%