Big Data for Regional Science

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.

Laurie A. Schintler is a computational social scientist with interests and research activity in the following areas related to Big Data analytics: geocomputation (socio-spatio modelling), transportation, regional science, scientometrics/bibliometrics and network modeling and analysis. She also has expertise on the policy-side of Big Data - specifically, issues related to the digital divide, job automation, workforce education and training and emerging technologies.Zhenhua Chen is an assistant professor in City and Regional Planning at the Knowlton School of Architecture at The Ohio State University. His research interest includes regional science, big data analytics, risk and resilience, infrastructure planning and policy.

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