Zhenhua Chen Publishes Big Data for Regional Science: An Interview

Assistant Professor of City and Regional Planning Zhenhua Chen has published Big Data for Regional Science, along with co-editor, Laurie A. Schintler. The book is part of the Routledge Advances in Regional Economics, Science and Policy series.

Big Data for Regional Science provides a comprehensive, multidisciplinary and cutting-edge perspective on big data for regional science. 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.

The Knowlton School sat down with Professor Chen on the occasion of the book’s publication.

Within the diverse field of regional science, can you provide a few examples of big data?

Data from social media sites, crowdsourcing and citizen sensing platforms and related “apps”, such as Twitter data, GPS data, etc.

What are some of the concerns or complicating factors in our current ability to process, analyze and manage big data?

Big data, in general, is typically described in terms of four dimensions - volume, velocity, variety and veracity - colloquially referred to as the 4 V’s. That is, the data tends to be relatively large (i.e., terabytes, petabytes, and beyond), fast-moving (i.e., real-time or near-real-time), heterogeneous (i.e., in disparate and incompatible formats, structured and un-structured) and noisy (i.e., errors, outliers). This means it requires advanced analytical platform to collect and process this kind of data. It also raises new challenges in terms of data analytics for researchers given that the data is quite heterogeneous and contains a large volume of noises.

Can you comment on the multidisciplinary perspectives this book incorporates to survey and present the topic of big data for regional science?

A multidisciplinary perspective is important for big data analytics as it requires knowledge not only in designing and deploying new technological and mathematical tools and solutions, but also skills to connect, involve and manage stakeholders and a better alignment of planned and more informed policies, projects, programs, facilities and services across a wide range of communities, fields and areas.

Although the book has an appeal for a broad audience, including areas of academia, industry and government, in what ways may this book further the research and practice in urban and regional planning?

These new and expanding sources of spatial data provide tremendous opportunities for urban planning communities. For instance, big data can help in designing livable, healthy and sustainable communities and moreover in creating spaces that are personalized to the needs and preferences of those who use and reside in them. It can also enable planners to better understand and potentially tackle large-scale wicked problems like climate change and involuntary migration.

Big Data for Regional Science is available at Amazon and Routledge.

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