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.

In the era of big data, this book explores the new challenges of urban-rural planning and management from a practical perspective based on a multidisciplinary project. Researchers as contributors to this book have accomplished their projects by using big data and relevant data mining technologies for investigating the possibilities of big data, such as that obtained through cell phones, social network systems and smart cards instead of conventional survey data for urban planning support. This book showcases active researchers who share their experiences and ideas on human mobility, accessibility and recognition of places, connectivity of transportation and urban structure in order to provide effective analytic and forecasting tools for smart city planning and design solutions in China.

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.

To continue providing people with safe, comfortable, and affordable places to live, cities must incorporate techniques and technologies to bring them into the future. The integration of big data and interconnected technology, along with the increasing population, will lead to the necessary creation of smart cities. Big Data Analytics for Smart and Connected Cities is a pivotal reference source that provides vital research on the application of the integration of interconnected technologies and big data analytics into the creation of smart cities. While highlighting topics such as energy conservation, public transit planning, and performance measurement, this publication explores technology integration in urban environments as well as the methods of planning cities to implement these new technologies. This book is ideally designed for engineers, professionals, researchers, and technology developers seeking current research on technology implementation in urban settings.

Smart Cities: Big Data Prediction Methods and Applications is the first reference to provide a comprehensive overview of smart cities with the latest big data predicting techniques. This timely book discusses big data forecasting for smart cities. It introduces big data forecasting techniques for the key aspects (e.g., traffic, environment, building energy, green grid, etc.) of smart cities, and explores three key areas that can be improved using big data prediction: grid energy, road traffic networks and environmental health in smart cities. The big data prediction methods proposed in this book are highly significant in terms of the planning, construction, management, control and development of green and smart cities. Including numerous case studies to explain each method and model, this easy-to-understand book appeals to scientists, engineers, college students, postgraduates, teachers and managers from various fields of artificial intelligence, smart cities, smart grid, intelligent traffic systems, intelligent environments and big data computing.

This book presents the latest advances in computational intelligence and data analytics for sustainable future smart cities. It focuses on computational intelligence and data analytics to bring together the smart city and sustainable city endeavors. It also discusses new models, practical solutions and technological advances related to the development and the transformation of cities through machine intelligence and big data models and techniques. This book is helpful for students and researchers as well as practitioners.

An unflinching look at the aspiring city-builders of our smart, mobile, connected future. From Beijing to Boston, cities are deploying smart technology—sensors embedded in streets and subways, Wi-Fi broadcast airports and green spaces—to address the basic challenges faced by massive, interconnected metropolitan centers. In Smart Cities, Anthony M. Townsend documents this emerging futuristic landscape while considering the motivations, aspirations, and shortcomings of the key actors—entrepreneurs, mayors, philanthropists, and software developers—at work in shaping the new urban frontier.