Liu Jingyu, Piegorsch Walter W, Schissler A Grant, McCaster Rachel R, Cutter Susan L
Interdisciplinary Program in Statistics & Data Science, University of Arizona, Tucson, AZ, USA.
BIO5 Institute, University of Arizona, Tucson, AZ, USA.
J Appl Stat. 2021 Apr 1;49(9):2349-2369. doi: 10.1080/02664763.2021.1904385. eCollection 2022.
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.
当样本单位之间存在潜在自相关时，我们在当代风险评估的“基准风险”范式内开发并研究了一种用于进行统计风险分析的定量跨学科策略。我们使用该方法来探索美国本土48个州3108个县对自然灾害的脆弱性信息，将基于地点的恢复力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来关联地方县级对危险结果的脆弱性。通过一种新颖的非空间邻域结构对恢复力信息中嵌入的自相关进行调整。然后将统计风险基准技术纳入建模框架，在该框架中识别对灾害的高脆弱性和低脆弱性水平。
