Climatic variability and shifting weather patterns, resulting in extreme weather events and natural disasters, pose risks to small businesses in the United States. This is particularly true in coastal regions of the southeast United States where extreme events such as hurricanes, flooding, and thunderstorms are projected to increase in frequency and intensity. Yet, the vast majority of small business owners do not have a disaster plan in place and an estimated 40% to 60% of small businesses that have experienced a natural disaster never reopen. This teaching case explores the impact of climatic trends and weather on one location of an outdoor tourism industry business in the coastal community of Virginia Beach, Virginia. The case draws from observed weather and sales data for the local small business. Students will draw from descriptive statistics, statistical analysis, and graphs to explore (a) long-term climatic trends for the business; (b) relationships between small business sales and local weather; and (c) strengths, weaknesses, opportunities, and threats relative to weather conditions and climate change. Instructors can give the body of this document to students. They can also make use of the supplemental teaching notes to assist them with teaching this case.
Quantifying the economic impact of disasters on businesses using human mobility data: a Bayesian causal inference approach
Abstract In recent years, extreme shocks, such as natural disasters, are increasing in both frequency and intensity, causing significant economic loss to many cities around the world. Quantifying the economic cost of local businesses after extreme shocks is important for post-disaster assessment and pre-disaster planning. Conventionally, surveys have been the primary source of data used to quantify damages inflicted on businesses by disasters. However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. In this work, we use location data collected from mobile phones to estimate and analyze the causal impact of hurricanes on business performance. To quantify the causal impact of the disaster, we use a Bayesian structural time series model to predict the counterfactual performances of affected businesses ( what if the disaster did not occur? ), which may use performances of other businesses outside the disaster areas as covariates. The method is tested to quantify the resilience of 635 businesses across 9 categories in Puerto Rico after Hurricane Maria. Furthermore, more »
- Award ID(s):
- 1638311
- Publication Date:
- NSF-PAR ID:
- 10204516
- Journal Name:
- EPJ Data Science
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2193-1127
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1140/epjds/s13688-020-00255-6
