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

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  • Jingyu Liu
  • Walter W. Piegorsch
  • A. Grant Schissler
  • Susan L. Cutter

Abstract

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.

Suggested Citation

  • Jingyu Liu & Walter W. Piegorsch & A. Grant Schissler & Susan L. Cutter, 2018. "Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 803-823, June.
    • Handle: RePEc:bla:jorssa:v:181:y:2018:i:3:p:803-823
    DOI: 10.1111/rssa.12323
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    References listed on IDEAS

    as
    1. Kenny S. Crump, 1995. "Calculation of Benchmark Doses from Continuous Data," Risk Analysis, John Wiley & Sons, vol. 15(1), pages 79-89, February.
    2. Walter W. Piegorsch & Susan L. Cutter & Frank Hardisty, 2007. "Benchmark Analysis for Quantifying Urban Vulnerability to Terrorist Incidents," Risk Analysis, John Wiley & Sons, vol. 27(6), pages 1411-1425, December.
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    Cited by:

    1. Signe M. Jensen & Felix M. Kluxen & Christian Ritz, 2019. "A Review of Recent Advances in Benchmark Dose Methodology," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2295-2315, October.

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