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Regional Policy Analysis in the Era of Spatial Big Data

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Development Studies in Regional Science

Part of the book series: New Frontiers in Regional Science: Asian Perspectives ((NFRSASIPER,volume 42))

Abstract

New and expanding sources of spatial big data hold tremendous potential for regional policy analysis. Such data enable us to analyze regional policies in ways not possible with traditional sources of data, such as administrative records. At the same time, the use of spatial big data is fraught with issues and challenges that must be addressed. In this paper, we discuss both the opportunities and challenges of using spatial big data for regional policy analysis. We also explore analytical issues tied to the use of regional policy analysis methods in the era of big data, as well as the state of art in applying such methods to spatial big data. Our discussion focusses on three types of methods: (1) statistical and regression modeling, (2) traditional nonparametric modeling, and (3) deep neural learning.

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Author information

Authors and Affiliations

  1. Schar School of Policy and Government, George Mason University, Arlington, VA, USA

    Laurie A. Schintler

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  1. Laurie A. Schintler
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Correspondence to Laurie A. Schintler .

Editor information

Editors and Affiliations

  1. Knowlton School of Architecture, City and Regional Planning, The Ohio State University, Columbus, OH, USA

    Zhenhua Chen

  2. Department of Urban Studies, Levin College of Urban Affairs, Cleveland State University, Cleveland, OH, USA

    William M. Bowen

  3. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dale Whittington

  4. University of Manchester, Manchester, UK

    Dale Whittington

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Schintler, L.A. (2020). Regional Policy Analysis in the Era of Spatial Big Data. In: Chen, Z., Bowen, W.M., Whittington, D. (eds) Development Studies in Regional Science. New Frontiers in Regional Science: Asian Perspectives, vol 42. Springer, Singapore. https://doi.org/10.1007/978-981-15-1435-7_7

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  • DOI: https://doi.org/10.1007/978-981-15-1435-7_7

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