@article{ref1,
title="Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes",
journal="Journal of the Royal Statistical Society, Series A (Statistics in Society)",
year="2018",
author="Liu, Jingyu and Piegorsch, Walter W. and Schissler, A. Grant and Cutter, Susan L.",
volume="181",
number="3",
pages="803-823",
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.<p /> <p>Language: en</p>",
language="en",
issn="0964-1998",
doi="10.1111/rssa.12323",
url="http://dx.doi.org/10.1111/rssa.12323"
}