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Data Science Applied to Sustainability Analysis

Data Science Applied to Sustainability Analysis
A Book

by Jennifer Dunn,Prasanna Balaprakash

  • Publisher : Elsevier
  • Release : 2021-05-28
  • Pages : 310
  • ISBN : 0128179775
  • Language : En, Es, Fr & De
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Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery Includes considerations sustainability analysts must evaluate when applying big data Features case studies illustrating the application of data science in sustainability analyses

Big Data Science and Analytics for Smart Sustainable Urbanism

Big Data Science and Analytics for Smart Sustainable Urbanism
Unprecedented Paradigmatic Shifts and Practical Advancements

by Simon Elias Bibri

  • Publisher : Springer
  • Release : 2019-05-30
  • Pages : 337
  • ISBN : 3030173127
  • Language : En, Es, Fr & De
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We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data–intensive science and its application, particularly in relation to sustainability.

Research Handbook on Big Data Law

Research Handbook on Big Data Law
A Book

by Roland Vogl

  • Publisher : Edward Elgar Publishing
  • Release : 2021-05-28
  • Pages : 544
  • ISBN : 1788972821
  • Language : En, Es, Fr & De
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This state-of-the-art Research Handbook provides an overview of research into, and the scope of current thinking in, the field of big data analytics and the law. It contains a wealth of information to survey the issues surrounding big data analytics in legal settings, as well as legal issues concerning the application of big data techniques in different domains.

Computational Intelligent Data Analysis for Sustainable Development

Computational Intelligent Data Analysis for Sustainable Development
A Book

by Ting Yu,Nitesh Chawla,Simeon Simoff

  • Publisher : CRC Press
  • Release : 2016-04-19
  • Pages : 440
  • ISBN : 1439895953
  • Language : En, Es, Fr & De
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Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present

Data Analysis and Statistics for Geography, Environmental Science, and Engineering

Data Analysis and Statistics for Geography, Environmental Science, and Engineering
A Book

by Miguel F. Acevedo

  • Publisher : CRC Press
  • Release : 2012-12-07
  • Pages : 557
  • ISBN : 143988501X
  • Language : En, Es, Fr & De
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Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental Science, and Engineering guides readers in learning quantitative methodology, including how to implement data analysis methods using open-source software. Given the importance of interdisciplinary work in sustainability, the book brings together principles of statistics and probability, multivariate analysis, and spatial analysis methods applicable across a variety of science and engineering disciplines. Learn How to Use a Variety of Data Analysis and Statistics Methods Based on the author’s many years of teaching graduate and undergraduate students, this textbook emphasizes hands-on learning. Organized into two parts, it allows greater flexibility using the material in various countries and types of curricula. The first part covers probability, random variables and inferential statistics, applications of regression, time series analysis, and analysis of spatial point patterns. The second part uses matrix algebra to address multidimensional problems. After a review of matrices, it delves into multiple regression, dependent random processes and autoregressive time series, spatial analysis using geostatistics and spatial regression, discriminant analysis, and a variety of multivariate analyses based on eigenvector methods. Build from Fundamental Concepts to Effective Problem Solving Each chapter starts with conceptual and theoretical material to give a firm foundation in how the methods work. Examples and exercises illustrate the applications and demonstrate how to go from concepts to problem solving. Hands-on computer sessions allow students to grasp the practical implications and learn by doing. Throughout, the computer examples and exercises use seeg and RcmdrPlugin.seeg, open-source R packages developed by the author, which help students acquire the skills to implement and conduct analysis and to analyze the results. This self-contained book offers a unified presentation of data analysis methods for more effective problem solving. With clear, easy-to-follow explanations, the book helps students to develop a solid understanding of basic statistical analysis and prepares them for learning the more advanced and specialized methods they will need in their work.

Advanced Information Networking and Applications

Advanced Information Networking and Applications
Proceedings of the 35th International Conference on Advanced Information Networking and Applications (AINA-2021), Volume 2

by Leonard Barolli

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030750752
  • Language : En, Es, Fr & De
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Recent Developments in Data Science and Intelligent Analysis of Information

Recent Developments in Data Science and Intelligent Analysis of Information
Proceedings of the XVIII International Conference on Data Science and Intelligent Analysis of Information, June 4–7, 2018, Kyiv, Ukraine

by Oleg Chertov,Tymofiy Mylovanov,Yuriy Kondratenko,Janusz Kacprzyk,Vladik Kreinovich,Vadim Stefanuk

  • Publisher : Springer
  • Release : 2018-08-04
  • Pages : 384
  • ISBN : 3319978853
  • Language : En, Es, Fr & De
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This book constitutes the proceedings of the XVIII International Conference on Data Science and Intelligent Analysis of Information (ICDSIAI'2018), held in Kiev, Ukraine on June 4-7, 2018. The conference series, which dates back to 2001 when it was known as the Workshop on Intelligent Analysis of Information, was renamed in 2008 to reflect the broadening of its scope and the composition of its organizers and participants. ICDSIAI'2018 brought together a large number of participants from numerous countries in Europe, Asia and the USA. The papers presented addressed novel theoretical developments in methods, algorithms and implementations for the broadly perceived areas of big data mining and intelligent analysis of data and information, representation and processing of uncertainty and fuzziness, including contributions on a range of applications in the fields of decision-making and decision support, economics, education, ecology, law, and various areas of technology. The book is dedicated to the memory of the conference founder, the late Professor Tetiana Taran, an outstanding scientist in the field of artificial intelligence whose research record, vision and personality have greatly contributed to the development of Ukrainian artificial intelligence and computer science.

Sustainable Development and Social Responsibility—Volume 1

Sustainable Development and Social Responsibility—Volume 1
Proceedings of the 2nd American University in the Emirates International Research Conference, AUEIRC'18 – Dubai, UAE 2018

by Miroslav Mateev,Jennifer Nightingale

  • Publisher : Springer Nature
  • Release : 2020-02-13
  • Pages : 323
  • ISBN : 3030329224
  • Language : En, Es, Fr & De
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The book presents high-quality research papers presented at the 2nd American University in the Emirates International research conference, AUEIRC'18, organized by the American University in the Emirates, Dubai, held on November 13th-15th, 2018. The book is broadly divided into four sections: Sustainability and Smart Technology, Sustainability and Social Responsibility, Sustainability, Human Security and Legislation, Sustainability and Education. The topics covered under these sections are sustainable smart technology such as developing green curriculum for information technology, use ultrasonic velocity to predict quality of wheat, improve security features for visa system, factors affecting the cost of production of electricity and desalination plants, impact of smart traffic sensing in smart cities, smart healthcare system, simulation of Grey wolf optimization algorithm in painting digital forensics. The topics covered for sustainability and creative industries such as sustainable concrete production, multimedia applications in digital transformation art, integrating biomimicry principles in sustainable architecture. Sustainability, human security and legislation covered topics of urban performance and sustainable environment, Eco-certification as response on climate change, the criminal offence of tax evasion in law: case study, skills engineering in sustainable counter defense against Cyber extremism, the international law and challenges of trans-boundary water resources governance, the legal status of nuclear energy: case study, sustainable energy development and nuclear energy legislation in UAE, corruption specific safety challenge, environmental management and sustainability, sustainable farming models for desert agro-ecosystems, future directions of climate change, earth and built environment towards new concept of sustainability, institution building from emotional intelligence perspective, virtue ethics, technology and sustainability, the role of humor in a sustainable education, HEIs practices and strategic decisions toward planning for sustainable education programs, TQM in higher education for sustainable future. The papers in this book present high-quality original research work, findings and practical development experiences.

Data Science and Social Research

Data Science and Social Research
Epistemology, Methods, Technology and Applications

by N. Carlo Lauro,Enrica Amaturo,Maria Gabriella Grassia,Biagio Aragona,Marina Marino

  • Publisher : Springer
  • Release : 2017-11-17
  • Pages : 300
  • ISBN : 3319554778
  • Language : En, Es, Fr & De
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This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.

Data Science Landscape

Data Science Landscape
Towards Research Standards and Protocols

by Usha Mujoo Munshi,Neeta Verma

  • Publisher : Springer
  • Release : 2018-03-01
  • Pages : 339
  • ISBN : 9811075158
  • Language : En, Es, Fr & De
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The edited volume deals with different contours of data science with special reference to data management for the research innovation landscape. The data is becoming pervasive in all spheres of human, economic and development activity. In this context, it is important to take stock of what is being done in the data management area and begin to prioritize, consider and formulate adoption of a formal data management system including citation protocols for use by research communities in different disciplines and also address various technical research issues. The volume, thus, focuses on some of these issues drawing typical examples from various domains. The idea of this work germinated from the two day workshop on “Big and Open Data – Evolving Data Science Standards and Citation Attribution Practices”, an international workshop, led by the ICSU-CODATA and attended by over 300 domain experts. The Workshop focused on two priority areas (i) Big and Open Data: Prioritizing, Addressing and Establishing Standards and Good Practices and (ii) Big and Open Data: Data Attribution and Citation Practices. This important international event was part of a worldwide initiative led by ICSU, and the CODATA-Data Citation Task Group. In all, there are 21 chapters (with 21st Chapter addressing four different core aspects) written by eminent researchers in the field which deal with key issues of S&T, institutional, financial, sustainability, legal, IPR, data protocols, community norms and others, that need attention related to data management practices and protocols, coordinate area activities, and promote common practices and standards of the research community globally. In addition to the aspects touched above, the national / international perspectives of data and its various contours have also been portrayed through case studies in this volume.

Strategic Engineering for Cloud Computing and Big Data Analytics

Strategic Engineering for Cloud Computing and Big Data Analytics
A Book

by Amin Hosseinian-Far,Muthu Ramachandran,Dilshad Sarwar

  • Publisher : Springer
  • Release : 2017-03-15
  • Pages : 226
  • ISBN : 3319524917
  • Language : En, Es, Fr & De
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This book demonstrates the use of a wide range of strategic engineering concepts, theories and applied case studies to improve the safety, security and sustainability of complex and large-scale engineering and computer systems. It first details the concepts of system design, life cycle, impact assessment and security to show how these ideas can be brought to bear on the modeling, analysis and design of information systems with a focused view on cloud-computing systems and big data analytics. This informative book is a valuable resource for graduate students, researchers and industry-based practitioners working in engineering, information and business systems as well as strategy.

Environmental Assessment on Energy and Sustainability by Data Envelopment Analysis

Environmental Assessment on Energy and Sustainability by Data Envelopment Analysis
A Book

by Toshiyuki Sueyoshi,Mika Goto

  • Publisher : John Wiley & Sons
  • Release : 2018-01-29
  • Pages : 720
  • ISBN : 111897929X
  • Language : En, Es, Fr & De
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Introduces a bold, new model for energy industry pollution prevention and sustainable growth Balancing industrial pollution prevention with economic growth is one of the knottiest problems faced by industry today. This book introduces a novel approach to using data envelopment analysis (DEA) as a powerful tool for achieving that balance in the energy industries—the world’s largest producers of greenhouse gases. It describes a rigorous framework that integrates elements of the social sciences, corporate strategy, regional economics, energy economics, and environmental policy, and delivers a methodology and a set of strategies for promoting green innovation while solving key managerial challenges to greenhouse gas reduction and business growth. In writing this book the authors have drawn upon their pioneering work and considerable experience in the field to develop an unconventional, holistic approach to using DEA to assess key aspects of sustainability development. The book is divided into two sections, the first of which lays out a conventional framework of DEA as the basis for new research directions. In the second section, the authors delve into conceptual and methodological extensions of conventional DEA for solving problems of environmental assessment in all contemporary energy industry sectors. Introduces a powerful new approach to using DEA to achieve pollution prevention, sustainability, and business growth Covers the fundamentals of DEA, including theory, statistical models, and practical issues of conventional applications of DEA Explores new statistical modeling strategies and explores their economic and business implications Examines applications of DEA to environmental analysis across the complete range of energy industries, including coal, petroleum, shale gas, nuclear energy, renewables, and more Summarizes important studies and nearly 800 peer reviewed articles on energy, the environment, and sustainability Environmental Assessment on Energy and Sustainability by Data Envelopment Analysis is must-reading for researchers, academics, graduate students, and practitioners in the energy industries, as well as government officials and policymakers tasked with regulating the environmental impacts of industrial pollution.

Analyzing the Role of Citizen Science in Modern Research

Analyzing the Role of Citizen Science in Modern Research
A Book

by Ceccaroni, Luigi,Piera, Jaume

  • Publisher : IGI Global
  • Release : 2016-10-25
  • Pages : 355
  • ISBN : 1522509631
  • Language : En, Es, Fr & De
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As the need for sustainable development practices around the world continues to grow, it has become imperative for citizens to become actively engaged in the global transition. By evaluating data collected from various global programs, researchers are able to identify strategies and challenges in implementing civic engagement initiatives. Analyzing the Role of Citizen Science in Modern Research focuses on analyzing data on current initiatives and best practices in citizen engagement and education programs across various disciplines. Highlighting emergent research and application techniques within citizen science initiatives, this publication appeals to academicians, researchers, policy makers, government officials, technology developers, advanced-level students and program developers interested in launching or improving citizen science programs across the globe.

Open Data and Energy Analytics

Open Data and Energy Analytics
A Book

by Benedetto Nastasi,Massimiliano Manfren,Michel Noussan

  • Publisher : MDPI
  • Release : 2020-06-25
  • Pages : 218
  • ISBN : 3039362186
  • Language : En, Es, Fr & De
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Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.

Applied Data Science

Applied Data Science
Lessons Learned for the Data-Driven Business

by Martin Braschler,Thilo Stadelmann,Kurt Stockinger

  • Publisher : Springer
  • Release : 2019-06-13
  • Pages : 465
  • ISBN : 3030118215
  • Language : En, Es, Fr & De
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This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

Sustainability Analysis

Sustainability Analysis
An Interdisciplinary Approach

by S. Shmelev,I. Shmeleva

  • Publisher : Springer
  • Release : 2012-01-27
  • Pages : 335
  • ISBN : 0230362435
  • Language : En, Es, Fr & De
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Sustainability Analysis provides a detailed exploration of current environmental thinking from a variety of perspectives, including institutional and psychological angles. Primarily focusing on macroeconomic policies and green national accounting, this book provides a strong basis for further study in sustainable development.

Industry 4.0 – Shaping The Future of The Digital World

Industry 4.0 – Shaping The Future of The Digital World
Proceedings of the 2nd International Conference on Sustainable Smart Manufacturing (S2M 2019), 9–11 April 2019, Manchester, UK

by Paulo Jorge da Silva Bartolo,Fernando Moreira da Silva,Shaden Jaradat,Helena Bartolo

  • Publisher : CRC Press
  • Release : 2020-10-06
  • Pages : 356
  • ISBN : 1000289281
  • Language : En, Es, Fr & De
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The City of Manchester, once the birthplace of the 1st Industrial Revolution, is today a pioneering hub of the 4th Industrial Revolution (Industry 4.0), offering Industry 4.0 solutions in advanced materials, engineering, healthcare and social sciences. Indeed, the creation of some of the city’s greatest academic institutions was a direct outcome of the industrial revolution, so it was something of a homecoming that the Sustainable Smart Manufacturing (S2M) Conference was hosted by The University of Manchester in 2019. The conference was jointly organised by The University of Manchester, The University of Lisbon and The Polytechnic of Leiria – the latter two bringing in a wealth of expertise in how Industry 4.0 manifests itself in the context of sustainably evolving, deeply-rooted cities. S2M-2019 instigated the development of 61 papers selected for publication in this book on areas of Smart Manufacturing, Additive Manufacturing and Virtual Prototyping, Materials for Healthcare Applications and Circular Economy, Design Education, and Urban Spaces.

Impact of AI and Data Science in Response to Coronavirus Pandemic

Impact of AI and Data Science in Response to Coronavirus Pandemic
A Book

by Sushruta Mishra,Pradeep Kumar Mallick,Hrudaya Kumar Tripathy,Gyoo-Soo Chae,Bhabani Shankar Prasad Mishra

  • Publisher : Springer Nature
  • Release : 2021-07-22
  • Pages : 324
  • ISBN : 981162786X
  • Language : En, Es, Fr & De
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The book presents advanced AI based technologies in dealing with COVID-19 outbreak and provides an in-depth analysis of variety of COVID-19 datasets throughout globe. It discusses recent artificial intelligence based algorithms and models for data analysis of COVID-19 symptoms and its possible remedies. It provides a unique opportunity to present the work on state-of-the-art of modern artificial intelligence tools and technologies to track and forecast COVID-19 cases. It indicates insights and viewpoints from scholars regarding risk and resilience analytics for policy making and operations of large-scale systems on this epidemic. A snapshot of the latest architectures, frameworks in machine learning and data science are also highlighted to gather and aggregate data records related to COVID-19 and to diagnose the virus. It delivers significant research outcomes and inspiring new real-world applications with respect to feasible AI based solutions in COVID-19 outbreak. In addition, it discusses strong preventive measures to control such pandemic.

Advances in Data Science and Management

Advances in Data Science and Management
Proceedings of ICDSM 2019

by Samarjeet Borah,Valentina Emilia Balas,Zdzislaw Polkowski

  • Publisher : Springer Nature
  • Release : 2020-01-13
  • Pages : 563
  • ISBN : 9811509786
  • Language : En, Es, Fr & De
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This book includes high-quality papers presented at the International Conference on Data Science and Management (ICDSM 2019), organised by the Gandhi Institute for Education and Technology, Bhubaneswar, from 22 to 23 February 2019. It features research in which data science is used to facilitate the decision-making process in various application areas, and also covers a wide range of learning methods and their applications in a number of learning problems. The empirical studies, theoretical analyses and comparisons to psychological phenomena described contribute to the development of products to meet market demands.

Smart Sustainable Cities of the Future

Smart Sustainable Cities of the Future
The Untapped Potential of Big Data Analytics and Context–Aware Computing for Advancing Sustainability

by Simon Elias Bibri

  • Publisher : Springer
  • Release : 2018-02-24
  • Pages : 660
  • ISBN : 3319739816
  • Language : En, Es, Fr & De
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This book is intended to help explore the field of smart sustainable cities in its complexity, heterogeneity, and breadth, the many faces of a topical subject of major importance for the future that encompasses so much of modern urban life in an increasingly computerized and urbanized world. Indeed, sustainable urban development is currently at the center of debate in light of several ICT visions becoming achievable and deployable computing paradigms, and shaping the way cities will evolve in the future and thus tackle complex challenges. This book integrates computer science, data science, complexity science, sustainability science, system thinking, and urban planning and design. As such, it contains innovative computer–based and data–analytic research on smart sustainable cities as complex and dynamic systems. It provides applied theoretical contributions fostering a better understanding of such systems and the synergistic relationships between the underlying physical and informational landscapes. It offers contributions pertaining to the ongoing development of computer–based and data science technologies for the processing, analysis, management, modeling, and simulation of big and context data and the associated applicability to urban systems that will advance different aspects of sustainability. This book seeks to explicitly bring together the smart city and sustainable city endeavors, and to focus on big data analytics and context-aware computing specifically. In doing so, it amalgamates the design concepts and planning principles of sustainable urban forms with the novel applications of ICT of ubiquitous computing to primarily advance sustainability. Its strength lies in combining big data and context–aware technologies and their novel applications for the sheer purpose of harnessing and leveraging the disruptive and synergetic effects of ICT on forms of city planning that are required for future forms of sustainable development. This is because the effects of such technologies reinforce one another as to their efforts for transforming urban life in a sustainable way by integrating data–centric and context–aware solutions for enhancing urban systems and facilitating coordination among urban domains. This timely and comprehensive book is aimed at a wide audience across science, academia industry, and policymaking. It provides the necessary material to inform relevant research communities of the state–of–the–art research and the latest development in the area of smart sustainable urban development, as well as a valuable reference for planners, designers, strategists, and ICT experts who are working towards the development and implementation of smart sustainable cities based on big data analytics and context–aware computing.