A bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010 - 2015

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Title: A bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010 - 2015
Authors: Python, A
Illian, J
Joness-Todd, C
Blangiardo, MAG
Item Type: Journal Article
Abstract: Terrorism persists as a worldwide threat, as exemplified by the ongoing lethal attacks perpetrated by ISIS in Iraq, 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 able to account for complex spatio-temporal dependencies have not yet been applied, despite their potential for providing guidance to explain and prevent terrorism. In an effort 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 polarised 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.
Issue Date: 31-Jan-2019
Date of Acceptance: 18-Apr-2018
URI: http://hdl.handle.net/10044/1/59742
DOI: https://doi.org/10.1111/rssa.12384
ISSN: 0964-1998
Publisher: Wiley
Start Page: 323
End Page: 344
Journal / Book Title: Journal of the Royal Statistical Society: Series A
Volume: 182
Issue: 1
Copyright Statement: © 2018 Owner. This is the accepted version of the following article: Python, A. , Illian, J. B., Jones‐Todd, C. M. and Blangiardo, M. (2019), A Bayesian approach to modelling subnational spatial dynamics of worldwide non‐state terrorism, 2010–2016. J. R. Stat. Soc. A, 182: 323-344. doi:10.1111/rssa.12384, which has been published in final form at https://doi.org/10.1111/rssa.12384.
Keywords: Social Sciences
Science & Technology
Physical Sciences
Social Sciences, Mathematical Methods
Statistics & Probability
Mathematical Methods In Social Sciences
Bayesian hierarchical models
Gaussian Markov random field
Space-time models
Stochastic partial differential equation
0104 Statistics
1403 Econometrics
Statistics & Probability
Publication Status: Published
Online Publication Date: 2018-05-28
Appears in Collections:Faculty of Medicine
Epidemiology, Public Health and Primary Care

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