We develop a quantitative methodology to characterize vulnerability among 132 US urban centres (‘cities’) to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centred autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for auto-correlation in the geospatial data. Risk analytic ‘benchmark’ techniques are then incorporated in the modelling 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 auto-correlation, is seen to operate quite flexibly in this sociogeographic setting.
Liu, Jingyu, Walter W. Piegorsch, A. Grant Schissler, and Susan L. Cutter. 2017. "Autologistic Models for Benchmark Risk or Vulnerability Assessment of Urban Terrorism Outcomes." Series A: Statistics in Society (October). http://onlinelibrary.wiley.com/doi/10.1111/rssa.12323/abstract