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Publication Detail

Title: Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes.

Authors: Liu, Jingyu; Piegorsch, Walter W; Schissler, A Grant; Cutter, Susan L

Published In J R Stat Soc Ser A Stat Soc, (2018 Jun)

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.

PubMed ID: 29904240 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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