Earth observation (EO) technologies, such as very high-resolution optical satellite data available from Maxar, can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public. We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects. We applied our methodology to the COVID-19 pandemic experience in Los Angeles County, California as a case study. We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context. We found that despite limitations, EO data can increase sectoral and temporal resolution, which leads to significant differences from other data sources in terms of direct and total impact results. The findings from this analytical approach have important implications for economic consequence modeling of disasters, as well as providing useful information to policymakers and emergency managers, whose goal is to reduce disaster costs and to improve economic resilience.

地球观测 (EO) 技术，例如 Maxar 提供的超高分辨率光学卫星数据，可以通过捕获企业和公众的细粒度和实时行为响应来增强灾害的经济后果模型。我们研究了这种独特的经济后果建模方法，以确定是否可以使用对 EO 数据的众包解释来阐明关键的经济行为反应，这些反应可用于供应链影响和弹性效应的可计算一般均衡模型。我们将我们的方法应用于加利福尼亚州洛杉矶县的 COVID-19 大流行经历作为案例研究。我们还提出了一种动态调整方法，以通过经济建模背景下的长期灾难来解释 EO 的变化特征。我们发现，尽管存在局限性，EO 数据可以提高部门和时间分辨率，这导致在直接和总体影响结果方面与其他数据源存在显着差异。这种分析方法的结果对灾害的经济后果建模具有重要意义，并为政策制定者和应急管理人员提供有用的信息，其目标是降低灾害成本并提高经济复原力。