使用人员流动数据量化极端冲击对企业的经济影响：贝叶斯因果推理方法
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach
摘要：
近年来，自然灾害等极端冲击的频率和强度都在增加，给世界各地许多城市造成了重大经济损失。量化极端冲击后当地企业的经济成本对于灾后评估和灾前规划非常重要。传统上，调查一直是用来量化灾害对企业造成的损失的主要数据来源。然而，调查往往遭受高成本和长时间的实施，时空稀疏的观测，并在可扩展性的限制。最近，大规模的人类移动数据（例如，移动电话GPS）已被用于观察和分析人类移动模式在一个前所未有的时空粒度和规模。在这项工作中，我们使用从移动电话收集的位置数据来估计和分析飓风对企业业绩的因果影响。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（如果灾难没有发生呢？），其可以使用灾区以外的其他企业的业绩作为协变量。该方法被用来量化飓风玛丽亚过后波多黎各9个类别的635家企业的弹性。此外，层次贝叶斯模型被用来揭示业务特征，如位置和类别对企业的长期弹性的影响。该研究提出了一种新的，更有效的方法来量化企业的弹性，这可以帮助决策者在灾害准备和救济过程。
英文摘要：
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.
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