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Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes
Jingyu Liu1, Walter W Piegorsch2, A Grant Schissler3
1Interdisciplinary Program in Statistics, University of Arizona, Tucson, AZ, USA.
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.
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