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Big Data and Regional Science: Opportunities, Challenges, and Directions for Future Research

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  • Schintler, Laurie A.
  • Fischer, Manfred M.

Abstract

Recent technological, social, and economic trends and transformations are contributing to the production of what is usually referred to as Big Data. Big Data, which is typically defined by four dimensions -- Volume, Velocity, Veracity, and Variety -- changes the methods and tactics for using, analyzing, and interpreting data, requiring new approaches for data provenance, data processing, data analysis and modeling, and knowledge representation. The use and analysis of Big Data involves several distinct stages from "data acquisition and recording" over "information extraction" and "data integration" to "data modeling and analysis" and "interpretation", each of which introduces challenges that need to be addressed. There also are cross-cutting challenges, which are common challenges that underlie many, sometimes all, of the stages of the data analysis pipeline. These relate to "heterogeneity", "uncertainty", "scale", "timeliness", "privacy" and "human interaction". Using the Big Data analysis pipeline as a guiding framework, this paper examines the challenges arising in the use of Big Data in regional science. The paper concludes with some suggestions for future activities to realize the possibilities and potential for Big Data in regional science.

Suggested Citation

  • Schintler, Laurie A. & Fischer, Manfred M., 2018. "Big Data and Regional Science: Opportunities, Challenges, and Directions for Future Research," Working Papers in Regional Science 2018/02, WU Vienna University of Economics and Business.
    • Handle: RePEc:wiw:wus046:6122
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    References listed on IDEAS

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    1. Neil Reid & Ralph McLaughlin & Michael Moore, 2013. "The Ubiquity of Good Taste: A Spatial Analysis of the Craft Brewing Industry in the United States," ERSA conference papers ersa13p1026, European Regional Science Association.
    2. James Lesage & Manfred Fischer, 2008. "Spatial Growth Regressions: Model Specification, Estimation and Interpretation," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(3), pages 275-304.
    3. Manfred M. Fischer & Jinfeng Wang, 2011. "Spatial Data Analysis," SpringerBriefs in Regional Science, Springer, number 978-3-642-21720-3, July.
    4. Arthur Getis, 1999. "Some thoughts on the impact of large data sets on regional science," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 33(2), pages 145-150.
    5. Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2019. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 249-273, National Bureau of Economic Research, Inc.
    6. Jing Zhou & Yundong Tu & Yuxin Chen & Hansheng Wang, 2017. "Estimating Spatial Autocorrelation With Sampled Network Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 130-138, January.
    7. Charlie Karlsson & Martin Andersson & Therese Norman (ed.), 2015. "Handbook of Research Methods and Applications in Economic Geography," Books, Edward Elgar Publishing, number 14395.
    8. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    9. Zhenhua Chen & Laurie Schintler, 2015. "Sensitivity of location-sharing services data: evidence from American travel pattern," Transportation, Springer, vol. 42(4), pages 669-682, July.
    10. Smirnov, Oleg & Anselin, Luc, 2001. "Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach," Computational Statistics & Data Analysis, Elsevier, vol. 35(3), pages 301-319, January.
    11. Jurgen A. Doornik & David F. Hendry & Steve Cook, 2015. "Statistical model selection with “Big Data”," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1045216-104, December.
    12. Mei-Po Kwan, 2016. "Algorithmic Geographies: Big Data, Algorithmic Uncertainty, and the Production of Geographic Knowledge," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(2), pages 274-282, March.
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    Keywords

    Spatial Big Data; data analysis pipeline; methodological and technical challenges; cross-cutting challenges; regional science;
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