A Bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010–2016

Terrorism persists as a worldwide threat, as exemplified by the on-going lethal attacks perpetrated by Islamic State in Iraq and Syria, Al Qaeda in Yemen and Boko Haram in Nigeria. In response, states deploy various counterterrorism policies, the costs of which could be reduced through efficient pre...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society. Series A, Statistics in society Statistics in society, 2019-01, Vol.182 (1), p.323-344
Main Authors: Python, André, Illian, Janine B., Jones-Todd, Charlotte M., Blangiardo, Marta
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian hierarchical models
Counterterrorism
Gaussian Markov random field
Islam
Partial differential equations
Population (statistical)
Population density
Prevention programs
Space–time models
Statistical models
Stochastic partial differential equation
Stochastic processes
Terrorism
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Summary:Terrorism persists as a worldwide threat, as exemplified by the on-going lethal attacks perpetrated by Islamic State in Iraq and Syria, Al Qaeda in Yemen and Boko Haram in Nigeria. In response, states deploy various counterterrorism policies, the costs of which could be reduced through efficient preventive measures. Statistical models that can account for complex spatiotemporal dependences have not yet been applied, despite their potential for providing guidance to explain and prevent terrorism. To address this shortcoming, we employ hierarchical models in a Bayesian context, where the spatial random field is represented by a stochastic partial differential equation. Our main findings suggest that lethal terrorist attacks tend to generate more deaths in ethnically polarized areas and in locations within democratic countries. Furthermore, the number of lethal attacks increases close to large cities and in locations with higher levels of population density and human activity.
ISSN:0964-1998
1467-985X
DOI:10.1111/rssa.12384