A Bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010-2016
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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.
Python , A , Illian , J B , Jones-Todd , C M & Blangiardo , M 2018 , ' A Bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010-2016 ' , Journal of the Royal Statistical Society: Series A (Statistics in Society) , vol. Early View . https://doi.org/10.1111/rssa.12384
Journal of the Royal Statistical Society: Series A (Statistics in Society)
© 2018 Royal Statistical Society. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1111/rssa.12384
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