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.

近年来，自然灾害等极端冲击的频率和强度都在增加，给全球许多城市造成了巨大的经济损失。在遭受严重冲击之后，对本地企业的经济成本进行量化对于灾难后评估和灾难前规划非常重要。按照惯例，调查是用于量化灾难对企业造成的损害的主要数据来源。但是，调查通常会遭受高昂的成本和实施时间长，观测值时空稀疏以及可扩展性方面的限制。近来，大规模的人类移动性数据（例如移动电话GPS）已被用于以前所未有的时空粒度和规模观察和分析人类移动性模式。在这项工作中 我们使用从手机收集的位置数据来估计和分析飓风对业务绩效的因果影响。为了量化灾难的因果影响，我们使用贝叶斯结构时间序列模型来预测受影响企业的反事实表现（如果灾难没有发生怎么办？），可能会将灾区以外其他企业的业绩用作协变量。经过测试，该方法可以量化飓风玛丽亚之后波多黎各9个类别的635家企业的应变能力。此外，使用分级贝叶斯模型来揭示业务特征（如位置和类别）对业务长期弹性的影响。这项研究提出了一种新颖，更有效的方法来量化业务弹性，这可以帮助决策者进行灾难准备和救灾过程。