Liu Jingyu, Piegorsch Walter W, Schissler A Grant, Cutter Susan L
Interdisciplinary Program in Statistics, University of Arizona, Tucson, AZ, USA.
Interdisciplinary Program in Statistics and Department of Mathematics, University of Arizona, Tucson, AZ, USA.
J R Stat Soc Ser A Stat Soc. 2018 Jun;181(3):803-823. doi: 10.1111/rssa.12323. Epub 2017 Oct 10.
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.
我们开发了一种定量方法，通过将基于地点的脆弱性指数应用于恐怖事件及相关人员伤亡数据库，来描述美国132个城市中心（“城市”）面对恐怖事件时的脆弱性。我们采用中心自逻辑回归模型来关联城市脆弱性与恐怖事件后果，并对地理空间数据中的自相关性进行调整。然后，将风险分析“基准”技术纳入建模框架，以此识别城市面对恐怖主义时的高脆弱性和低脆弱性水平。这种风险基准方法的新的转化性应用，包括其考虑地理空间自相关性的能力，在这种社会地理环境中表现得相当灵活。
