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

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

Research output: Research - peer-reviewArticle

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.

LanguageEnglish (US)
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
DOIs
StateAccepted/In press - 2017

Fingerprint

Terrorism
Vulnerability
Benchmark
Model
terrorism
vulnerability
Autocorrelation
Regression Model
Methodology
Modeling
Human
Framework
Regression model
Data base
Incidents
Benchmark approach
Casualties
city center
incident
regression

Keywords

  • Benchmark dose
  • Centred autologistic model
  • Geospatial analysis
  • Maximum pseudolikelihood
  • Quantitative risk analysis
  • Spatial auto-correlation

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Cite this

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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.",
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year = "2017",
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