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

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 provenance, processing, and modeling, and knowledge representation. The use and 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 pipeline. These relate to heterogeneity, uncertainty, scale, timeliness, privacy and human interaction. Using the Big Data 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.