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

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

doi:10.1111/rssa.12323

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: Contribution to journal › Article › peer-review

10 Scopus citations

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.

Original language	English (US)
Pages (from-to)	803-823
Number of pages	21
Journal	Journal of the Royal Statistical Society. Series A: Statistics in Society
Volume	181
Issue number	3
DOIs	https://doi.org/10.1111/rssa.12323
State	Published - Jun 2018

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

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10.1111/rssa.12323

Cite this

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title = "Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes",

abstract = "We develop a quantitative methodology to characterize vulnerability among 132 US urban centres ({\textquoteleft}cities{\textquoteright}) 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 {\textquoteleft}benchmark{\textquoteright} 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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AB - 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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Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes

Abstract

Keywords

ASJC Scopus subject areas

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