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

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Andre Python, Janine B. Illian, Charlotte M. Jones-Todd, Marta Blangiardo

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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.


Original languageEnglish
JournalJournal of the Royal Statistical Society: Series A (Statistics in Society)
VolumeEarly View
Early online date28 May 2018
Publication statusE-pub ahead of print - 28 May 2018

    Research areas

  • Bayesian hierarchical models, GMRF, Space-time models, SPDE, Terrorism

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  1. Journal of the Royal Statistical Society: Series A (Statistics in Society) (Journal)

    Michail Papathomas (Reviewer)

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