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.

中文翻译：

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用于城市恐怖主义结果基准风险或脆弱性评估的自逻辑模型。

我们开发了一种定量方法来描述 132 个美国城市中心（“城市”）对恐怖事件的脆弱性，将基于地点的脆弱性指数应用于恐怖事件和相关人员伤亡的数据库。采用中心自逻辑回归模型将城市脆弱性与恐怖分子后果联系起来，并调整地理空间数据的自相关性。然后，风险分析“基准”技术被纳入建模框架，其中确定了城市对恐怖主义脆弱性的高低水平。这种新的风险基准方法的转化适应，包括其解释地理空间自相关的能力，被认为在这种社会地理环境中运行得相当灵活。