Autologistic Models for Benchmark Risk or Vulnerability Assessment of Urban Terrorism Outcomes
研究开发了一种量化方法，利用基于地点的脆弱性指数和自逻辑回归模型，评估美国132个城市中心对恐怖事件的脆弱性，并识别高风险和低风险区域。
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.