This book introduces the latest thinking on the use of Big Data in the context of urban systems, including research and insights on human behavior, urban dynamics, resource use, sustainability and spatial disparities, where it promises improved planning, management and governance in the urban sectors (e.g., transportation, energy, smart cities, crime, housing, urban and regional economies, public health, public engagement, urban governance and political systems), as well as Big Data’s utility in decision-making, and development of indicators to monitor economic and social activity, and for urban sustainability, transparency, livability, social inclusion, place-making, accessibility and resilience.

Recent technological advancements and other related factors and trends are contributing to the production of an astoundingly large and rapidly accelerating collection of data, or ‘Big Data’. This data now allows us to examine urban and regional phenomena in ways that were previously not possible. Despite the tremendous potential of big data for regional science, its use and application in this context is fraught with issues and challenges. This book brings together leading contributors to present an interdisciplinary, agenda-setting and action-oriented platform for research and practice in the urban and regional community. This book provides a comprehensive, multidisciplinary and cutting-edge perspective on big data for regional science. Chapters contain a collection of research notes contributed by experts from all over the world with a wide array of disciplinary backgrounds. The content is organized along four themes: sources of big data; integration, processing and management of big data; analytics for big data; and, higher level policy and programmatic considerations. As well as concisely and comprehensively synthesising work done to date, the book also considers future challenges and prospects for the use of big data in regional science. Big Data for Regional Science provides a seminal contribution to the field of regional science and will appeal to a broad audience, including those at all levels of academia, industry, and government.

A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization’s Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.

This book explores the rise of technology-centered urban planning and the diffusion of these practices around the world. Seven axes of urban planning have been selected to highlight how data and technology currently work and how these systems can be improved going forward. Each aspect is explored in its own chapter that combines narrative description, illustrations, and case studies to show how technology currently shapes our cities and how this may impact the urban environments. Topics include infrastructure, mobility, energy use and distribution, work, public health, and knowledge transfer among others. The book also demonstrates how these aspects are tied to and affect the four pillars of sustainability: environment, society, economy, and culture.

Big Data and Mobility as a Service explores MaaS platforms that can be adaptable to the ever-evolving mobility environment. It looks at multi-mode urban crowd data to assess urban mobility characteristics, their shared transportation potential, and their performance conditions and constraints. The book analyzes the roles of multimodality, travel behavior, urban mobility dynamics and participation. Combined with insights on using big data to analyze market and policy decisions, this book is an essential tool for urban transportation management researchers and practitioners. - Summarizes current fundamental MaaS technologies - Shows how to utilize anonymous big data for transportation analysis and problem-solving - Illustrates, with data-enabled shared transportation service examples from different countries, the similarities and differences within a global urban mobility framework

Artificial intelligence and the use of computational methods to extract information from data are providing adequate tools to monitor and predict water pollutants and water quality issues faster and more accurately. Smart sensors and machine learning models help detect and monitor dispersion and leakage of pollutants before they reach groundwater. With contributions from experts in academia and industries, who give a unified treatment of AI methods and their applications in water science, this book help governments, industries, and homeowners not only address water pollution problems more quickly and efficiently, but also gain better insight into the implementation of more effective remedial measures. FEATURES Provides cutting-edge AI applications in water sector. Highlights the environmental models used by experts in different countries. Discusses various types of models using AI and its tools for achieving sustainable development in water and groundwater. Includes case studies and recent research directions for environmental issues in water sector. Addresses future aspects and innovation in AI field related to watersustainability. This book will appeal to scientists, researchers, and undergraduate and graduate students majoring in environmental or computer science and industry professionals in water science and engineering, environmental management, and governmental sectors. It showcases artificial intelligence applications in detecting environmental issues, with an emphasis on the mitigation and conservation of water and underground resources.

This book presents papers based on the presentations and discussions at the international workshop on Big Data Smart Transportation Analytics held July 16 and 17, 2016 at Tongji University in Shanghai and chaired by Professors Ukkusuri and Yang. The book is intended to explore a multidisciplinary perspective to big data science in urban transportation, motivated by three critical observations: The rapid advances in the observability of assets, platforms for matching supply and demand, thereby allowing sharing networks previously unimaginable. The nearly universal agreement that data from multiple sources, such as cell phones, social media, taxis and transit systems can allow an understanding of infrastructure systems that is critically important to both quality of life and successful economic competition at the global, national, regional, and local levels. There is presently a lack of unifying principles and methodologies that approach big data urban systems. The workshop brought together varied perspectives from engineering, computational scientists, state and central government, social scientists, physicists, and network science experts to develop a unifying set of research challenges and methodologies that are likely to impact infrastructure systems with a particular focus on transportation issues. The book deals with the emerging topic of data science for cities, a central topic in the last five years that is expected to become critical in academia, industry, and the government in the future. There is currently limited literature for researchers to know the opportunities and state of the art in this emerging area, so this book fills a gap by synthesizing the state of the art from various scholars and help identify new research directions for further study.

How the marriage of Industry 4.0 and the Circular Economy can radically transform waste management—and our world Do we really have to make a choice between a wasteless and nonproductive world or a wasteful and ultimately self-destructive one? Futurist and world-renowned waste management scientist Antonis Mavropoulos and sustainable business developer and digital strategist Anders Nilsen respond with a ringing and optimistic “No!” They explore the Earth-changing potential of a happy (and wasteless) marriage between Industry 4.0 and a Circular Economy that could—with properly reshaped waste management practices—deliver transformative environmental, health, and societal benefits. This book is about the possibility of a brand-new world and the challenges to achieve it. The fourth industrial revolution has given us innovations including robotics, artificial intelligence, 3D-printing, and biotech. By using these technologies to advance the Circular Economy—where industry produces more durable materials and runs on its own byproducts—the waste management industry will become a central element of a more sustainable world and can ensure its own, but well beyond business as usual, future. Mavropoulos and Nilsen look at how this can be achieved—a wasteless world will require more waste management—and examine obstacles and opportunities such as demographics, urbanization, global warming, and the environmental strain caused by the rise of the global middle class. · Explore the new prevention, reduction, and elimination methods transforming waste management · Comprehend and capitalize on the business implications for the sector · Understand the theory via practical examples and case studies · Appreciate the social benefits of the new approach Waste-management has always been vital for the protection of health and the environment. Now it can become a crucial role model in showing how Industry 4.0 and the Circular Economy can converge to ensure flourishing, sustainable—and much brighter—future.

The two-volume set, LNCS 13325 and 13326, are conference proceedings that constitutes the refereed proceedings of the 10th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2022, held as part of the 24th International Conference, HCI International 2022, which took place during June-July 2022. The conference was held virtually due to the COVID-19 pandemic. The 58 papers of DAPI 2022 are organized in topical sections named for each volume: Part I: User Experience and Interaction Design for Smart Ecosystems; Smart Cities, Smart Islands, and Intelligent Urban Living; Smart Artifacts in Smart Environments; and Opportunities and Challenges for the Near Future Smart Environments Part II: Smart Living in Pervasive IoT Ecosystems; Distributed, Ambient, and Pervasive Education and Learning; Distributed, Ambient, and Pervasive Well-being and Healthcare; and Smart Creativity and Art.

This book provides theories, experiences, reflections and future directions for social scientists who wish to engage with policy-oriented research in cities and regions. The ‘policy learning’ perspective is comprehensively discussed, focusing on actors promoting ‘policy knowledge’ and interaction among different stakeholders. The book also aims to provide practical insights for policy-makers and practitioners interested in research-based approaches to cities and regions.