Applying Earth Observation Technologies to Economic Consequence Modeling: A Case Study of COVID-19 in Los Angeles County, California

Fynnwin Prager; Marina T. Mendoza; Charles K. Huyck; Adam Rose; Paul Amyx; Gregory Yetman; Kristy F. Tiampo

doi:10.1007/s13753-024-00543-z

Volume 15 Issue 1

Feb. 2024

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Article Navigation > International Journal of Disaster Risk Science > 2024 > 15(1): 18-31

Fynnwin Prager, Marina T. Mendoza, Charles K. Huyck, Adam Rose, Paul Amyx, Gregory Yetman, Kristy F. Tiampo. Applying Earth Observation Technologies to Economic Consequence Modeling: A Case Study of COVID-19 in Los Angeles County, California[J]. International Journal of Disaster Risk Science, 2024, 15(1): 18-31. doi: 10.1007/s13753-024-00543-z

Citation:

Fynnwin Prager, Marina T. Mendoza, Charles K. Huyck, Adam Rose, Paul Amyx, Gregory Yetman, Kristy F. Tiampo. Applying Earth Observation Technologies to Economic Consequence Modeling: A Case Study of COVID-19 in Los Angeles County, California[J]. International Journal of Disaster Risk Science, 2024, 15(1): 18-31. doi: 10.1007/s13753-024-00543-z

Citation:

Fynnwin Prager, Marina T. Mendoza, Charles K. Huyck, Adam Rose, Paul Amyx, Gregory Yetman, Kristy F. Tiampo. Applying Earth Observation Technologies to Economic Consequence Modeling: A Case Study of COVID-19 in Los Angeles County, California[J]. International Journal of Disaster Risk Science, 2024, 15(1): 18-31. doi: 10.1007/s13753-024-00543-z

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Applying Earth Observation Technologies to Economic Consequence Modeling: A Case Study of COVID-19 in Los Angeles County, California

doi: 10.1007/s13753-024-00543-z

1. California State University, Dominguez Hills, Carson, CA, 90747, USA;
2. ImageCat, Inc., Long Beach, CA, 90802, USA;
3. University of Southern California, Los Angeles, CA, 90015, USA;
4. CIESIN – Center for International Earth Science Information Network, Columbia University, Palisades, NY, 10964, USA;
5. Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO, 80309, USA

Accepted Date: 2024-02-02

Available Online: 2024-03-14

Publish Date: 2024-02-26

Abstract

Abstract

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.
- Computable general equilibrium models,
- COVID-19,
- Disaster economic impacts,
- Earth observation,
- Economic consequence analysis,
- Los Angeles County

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References(40)

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