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The Analysis of Big Data on Cites and Regions - Some Computational and Statistical Challenges

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

Abstract

Big Data on cities and regions bring new opportunities and challenges to data analysts and city planners. On the one side, they hold great promise to combine increasingly detailed data for each citizen with critical infrastructures to plan, govern and manage cities and regions, improve their sustainability, optimize processes and maximize the provision of public and private services. On the other side, the massive sample size and high-dimensionality of Big Data and their geo-temporal character introduce unique computational and statistical challenges. This chapter provides overviews on the salient characteristics of Big Data and how these features impact on paradigm change of data management and analysis, and also on the computing environment.

Suggested Citation

  • Schintler, Laurie A. & Fischer, Manfred M., 2018. "The Analysis of Big Data on Cites and Regions - Some Computational and Statistical Challenges," Working Papers in Regional Science 2018/08, WU Vienna University of Economics and Business.
    • Handle: RePEc:wiw:wus046:6637
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    References listed on IDEAS

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    1. 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.
    2. Manfred M. Fischer & Jinfeng Wang, 2011. "Spatial Data Analysis," SpringerBriefs in Regional Science, Springer, number 978-3-642-21720-3.
    3. Charlie Karlsson & Martin Andersson & Therese Norman (ed.), 2015. "Handbook of Research Methods and Applications in Economic Geography," Books, Edward Elgar Publishing, number 14395.
    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. Manfred M. Fischer & Peter Nijkamp (ed.), 2014. "Handbook of Regional Science," Springer Books, Springer, edition 127, number 978-3-642-23430-9, December.
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    Keywords

    massive sample size; high-dimensional data; heterogeneity and incompleteness; data storage; scalability; parallel data processing; visualization; statistical methods;
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