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Deciphering small business community disaster support using machine learning

Published

Author(s)

Eleanor Pierel, Jennifer Helgeson, Kirstin Dow

Abstract

With the increase in severity and frequency of natural hazards due to climate change, developing a holistic understanding of community resilience factors is critically important to disaster response and community support. Our investigation of small business survey responses about COVID-19 impacts finds that they are conduits of national support to their local communities. Small businesses that have demonstrated high levels of pre-disaster local involvement are more likely to take an active role in community resilience during a disaster, regardless of their own financial security. In addition, businesses with natural hazard experience before or during COVID-19 provided help to more community groups than hazard inexperienced businesses. While community resilience models often characterize small businesses as passive actors using variables such as employment or financial security, this research suggests that small businesses take an active role in community resilience by providing critical local support. The pandemic presented an opportunity to consider small business' role in community resilience nationally, which was utilized here to identify the multi-dimensional factors that predict small business operators' community disaster support. This study improves upon previous research by studying the small business-community resilience interface at both regional (n = 184) and national (n = 6,121) scales. We predict small business' active involvement in community resilience using random forest machine learning, and find that adding social capital predictors greatly increases model performance (F1 score of 0.88, Matthews Correlation Coefficient of 0.67).
Citation
PLOS Climate

Citation

Pierel, E. , Helgeson, J. and Dow, K. (2023), Deciphering small business community disaster support using machine learning, PLOS Climate, [online], https://doi.org/10.1371/journal.pclm.0000155, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933305 (Accessed July 18, 2024)

Issues

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Created March 3, 2023