ABSTRACT

We develop and study a quantitative, interdisciplinary strategy for conducting statistical risk analyses within the ‘benchmark risk’ paradigm of contemporary risk assessment when potential autocorrelation exists among sample units. We use the methodology to explore information on vulnerability to natural hazards across 3108 counties in the conterminous 48 US states, applying a place-based resilience index to an existing knowledgebase of hazardous incidents and related human casualties. An extension of a centered autologistic regression model is applied to relate local, county-level vulnerability to hazardous outcomes. Adjustments for autocorrelation embedded in the resiliency information are applied via a novel, non-spatial neighborhood structure. Statistical risk-benchmarking techniques are then incorporated into the modeling framework, wherein levels of high and low vulnerability to hazards are identified.

摘要

当样本单元之间存在潜在的自相关时，我们开发和研究了一种定量的跨学科策略，用于在当代风险评估的“基准风险”范式中进行统计风险分析。我们使用该方法来探索美国 48 个州的 3108 个县对自然灾害的脆弱性信息，将基于地点的弹性指数应用于现有的危险事件和相关人员伤亡知识库。应用中心自逻辑回归模型的扩展，将地方、县级脆弱性与危险结果联系起来。嵌入在弹性信息中的自相关调整通过一种新颖的非空间邻域结构应用。然后将统计风险基准技术纳入建模框架，