  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, hierarchical Bayesian models are used to
reveal the effect of business characteristics such as location and category on
the long-term resilience of businesses. The study presents a novel and more
efficient method to quantify business resilience, which could assist policy
makers in disaster preparation and relief processes.
