Citation

Liu J, Piegorsch WW, Schissler AG, Cutter SL. J. R .Stat. Soc. Ser. A Stat. Soc. 2018; 181(3): 803-823.

Affiliation

Hazards & Vulnerability Research Institute and Department of Geography University of South Carolina, Columbia, SC, USA.

Copyright

(Copyright © 2018, Royal Statistical Society (Great Britain), Publisher John Wiley and Sons)

DOI

10.1111/rssa.12323

PMID

29904240

PMCID

PMC5994772

Abstract

We develop a quantitative methodology to characterize vulnerability among 132 U.S. urban centers ('cities') to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centered autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for autocorrelation in the geospatial data. Risk-analytic 'benchmark' techniques are then incorporated into the modeling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new, translational adaptation of the risk-benchmark approach, including its ability to account for geospatial autocorrelation, is seen to operate quite flexibly in this socio-geographic setting.

Language: en

Keywords

Benchmark dose; Centered autologistic model; Geospatial analysis; Maximum pseudo-likelihood; Quantitative risk analysis; Spatial autocorrelation