Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes.
AffiliationUniversity of Arizona
University of Arizona
University of Nevada
University of South Carolina
Centered autologistic model
Maximum pseudo-likelihood estimation
Quantitative risk analysis
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CitationLiu, J., Piegorsch, W. W., Grant Schissler, A., & Cutter, S. L. (2018). Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes. Journal of the Royal Statistical Society: Series A (Statistics in Society).
Rights© 2017 Royal Statistical Society
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AbstractWe 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.
Note12 month embargo; first published: 10 October 2017
VersionFinal accepted manuscript
SponsorsU.S. National Institutes of Health grant #ES027394