Adjusting statistical benchmark risk analysis to account for non-spatial autocorrelation, with application to natural hazard risk assessment

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.

Keywords:

• Benchmark dose
• centered autologistic model
• maximum pseudo-likelihood
• quantitative risk assessment
• natural hazard vulnerability
• non-spatial autocorrelation

Acknowledgments

Thanks are due to Dr Stephan R. Sain for his seminal suggestions on developing non-spatial measures of autocorrelation, to Dr John Hughes for discussions on the centered autologistic model, and to an anonymous referee for quite helpful suggestions on how to improve the manuscript. This material represents a portion of the first author's PhD dissertation from the University of Arizona Graduate Interdisciplinary Program in Statistics.

Data availability

The full 3108-counties database was generated from the SHELDUS^TM knowledgebase (http://www.sheldus.org). Derived data employed in the calculations herein are available from the corresponding author [WWP] on request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research was supported in part by #ES027394 from the U.S. National Institutes of Health.