Using Big Data to Measure the Impact of Economic Policy: Fracking in Texas

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Interdisciplinary Centers, Units and Projects::Center for Undergraduate Research and Fellowships (CURF)::Fall Research Expo
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Discipline
Applied Mathematics
Computer Sciences
Data Science
Economics
Subject
Big Data
Small-Scale Economies
Case Study
Fracking
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Copyright date
2025-10-06
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Author
Recce, Brandon
Zhang, Walter
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Abstract

Economic cycles describe national economies, but prosperity is inherently local. With availability of granular, large-scale datasets economic health can be aggregated at multiple scales. This is an analysis of an important highly-localized boom and bust cycle. The 2013-2014 fracking boom in Texas and Oklahoma produced localized prosperity and the bust produced localized economic pressure. Regional consumer purchases are evaluated throughout the cycle and compared to nearby unaffected regions using the Nielsen panel data. Purchases are classified using statistical analysis and machine learning, in order to develop a predictive model to show dense economic health maps of the United States.

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2025-09-15
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This project was supported with funding from a College Alumni Society Undergraduate Research Grant.
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