Big Data and Regional Science: Opportunities, Challenges, and Directions for Future Research
Laurie A. Schintler and
No 2018/02, Working Papers in Regional Science from WU Vienna University of Economics and Business
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.
Keywords: Spatial Big Data; data analysis pipeline; methodological and technical challenges; cross-cutting challenges; regional science (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big and nep-geo
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