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@article{Python:2019:10.1111/rssa.12384,
author = {Python, A and Illian, J and Joness-Todd, C and Blangiardo, MAG},
doi = {10.1111/rssa.12384},
journal = {Journal of the Royal Statistical Society: Series A},
pages = {323--344},
title = {A bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010 - 2015},
url = {http://dx.doi.org/10.1111/rssa.12384},
volume = {182},
year = {2019}
}

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TY  - JOUR
AB  - Terrorism persists as a worldwide threat, as exemplified by the ongoinglethal attacks perpetrated by ISIS in Iraq, Syria, Al Qaeda in Yemen, and Boko Haramin Nigeria. In response, states deploy various counterterrorism policies, the costsof which could be reduced through efficient preventive measures. Statistical modelsable to account for complex spatio-temporal dependencies have not yet been applied,despite their potential for providing guidance to explain and prevent terrorism. In aneffort to address this shortcoming, we employ hierarchical models in a Bayesian context,where the spatial random field is represented by a stochastic partial differentialequation. Our main findings suggest that lethal terrorist attacks tend to generate moredeaths in ethnically polarised areas and in locations within democratic countries. Furthermore,the number of lethal attacks increases close to large cities and in locationswith higher levels of population density and human activity.
AU  - Python,A
AU  - Illian,J
AU  - Joness-Todd,C
AU  - Blangiardo,MAG
DO  - 10.1111/rssa.12384
EP  - 344
PY  - 2019///
SN  - 0964-1998
SP  - 323
TI  - A bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010 - 2015
T2  - Journal of the Royal Statistical Society: Series A
UR  - http://dx.doi.org/10.1111/rssa.12384
UR  - http://hdl.handle.net/10044/1/59742
VL  - 182
ER  -

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