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Big Data Science and Analytics for Smart Sustainable Urbanism

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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
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Title
Big Data Science and Analytics for Smart Sustainable Urbanism
Published by
Springer International Publishing, July 2019
DOI 10.1007/978-3-030-17312-8
ISBNs
978-3-03-017311-1, 978-3-03-017312-8
Authors

Bibri, Simon Elias

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X Demographics

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

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%