Urban Computing (Information Systems)

An authoritative treatment of urban computing, offering an overview of the field, fundamental techniques, advanced models, and novel applications. Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. Using today's large-scale computing infrastructure and data gathered from sensing technologies, urban computing combines computer science with urban planning, transportation, environmental science, sociology, and other areas of urban studies, tackling specific problems with concrete methodologies in a data-centric computing framework. This authoritative treatment of urban computing offers an overview of the field, fundamental techniques, advanced models, and novel applications. Each chapter acts as a tutorial that introduces readers to an important aspect of urban computing, with references to relevant research. The book outlines key concepts, sources of data, and typical applications; describes four paradigms of urban sensing in sensor-centric and human-centric categories; introduces data management for spatial and spatio-temporal data, from basic indexing and retrieval algorithms to cloud computing platforms; and covers beginning and advanced topics in mining knowledge from urban big data, beginning with fundamental data mining algorithms and progressing to advanced machine learning techniques. Urban Computing provides students, researchers, and application developers with an essential handbook to an evolving interdisciplinary field. Although there are a few books on urban informatics, this is the first book dedicated to urban computing, with a broad spectrum of coverage and an authoritative overview. This book introduces a general framework, key research problems, methodologies and applications of urban computing from a computer science perspective. More specifically, this book focuses on data and computing, distinguishing urban computing from tradition urban science based on classical models and empirical assumptions. Rapid urbanization has led to the expansion of numerous large cities, not only modernizing many people's lives but also posing big challenges, such as air pollution, energy consumption and traffic congestion. Tackling these challenges seemed nearly impossible only a few years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructure have produced a variety of big data, such as human mobility, meteorology, traffic patterns and geographical data. The corresponding big data implies rich knowledge about a city and can help tackle these challenges when used correctly. In addition, the rise of computing technology, such as cloud computing and artificial intelligence (AI), has provided us with unprecedented data processing capabilities-- Provided by publisher