Measuring Vulnerability and Resilience in the Time of Change Using Small Area Estimation

Bethany DeSalvo
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 From the beginning of the pandemic, the negative effects of COVID-19 have been strongly related to certain individual and household characteristics. With access to granular microdata from the Census Bureau, the CRE maps the risk assessment of local populations down to the neighborhood level and allows national and community leaders to more efficiently respond to emergencies.
 Federal statistical agencies are uniquely positioned to provide the most accurate and timely measures for an individually focused community resilience indicator. We use detailed demographic and economic data about individuals to build these estimates. Having the richest data sources, the federal government can produce estimates with the most granularity, highest statistical quality, and broadest coverage, while still protecting privacy. We do this through modeling multiple sources of data using small area estimation techniques.
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引用次数: 0

Abstract

The United States Census Bureau launched a new tool for national agencies and local communities, the Community Resilience Estimates (CRE). The CRE tracks social and economic vulnerability by measuring the capacity of individuals and households to cope with the external stresses of the impacts of a disaster. From the beginning of the pandemic, the negative effects of COVID-19 have been strongly related to certain individual and household characteristics. With access to granular microdata from the Census Bureau, the CRE maps the risk assessment of local populations down to the neighborhood level and allows national and community leaders to more efficiently respond to emergencies. Federal statistical agencies are uniquely positioned to provide the most accurate and timely measures for an individually focused community resilience indicator. We use detailed demographic and economic data about individuals to build these estimates. Having the richest data sources, the federal government can produce estimates with the most granularity, highest statistical quality, and broadest coverage, while still protecting privacy. We do this through modeling multiple sources of data using small area estimation techniques. This work will soon be built out to include exposure data (wind, flood, heat, fire, etc) so that federal agencies and researchers can perform experimental studies for better evidenced based policy making and evaluation.
用小面积估算法测量变化时期的脆弱性和恢复力
美国人口普查局为国家机构和地方社区推出了一个新工具——社区恢复力估算(CRE)。CRE通过衡量个人和家庭应对灾害影响的外部压力的能力来跟踪社会和经济脆弱性。从大流行开始,COVID-19的负面影响就与某些个人和家庭特征密切相关。通过获取人口普查局提供的细颗粒微观数据,CRE将当地人口的风险评估映射到社区一级,并使国家和社区领导人能够更有效地应对紧急情况。联邦统计机构具有独特的优势,可以为以个人为重点的社区恢复力指标提供最准确和及时的衡量标准。我们使用有关个人的详细人口统计和经济数据来建立这些估计。由于拥有最丰富的数据源,联邦政府可以提供粒度最高、统计质量最高、覆盖范围最广的估计,同时还能保护隐私。我们通过使用小面积估计技术对多个数据源建模来做到这一点。 这项工作将很快扩大到包括暴露数据(风、洪水、高温、火灾等),以便联邦机构和研究人员可以进行实验研究,以更好地以证据为基础制定政策和评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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