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
MetadataShow full item record
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.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
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