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A Bayesian approach to modelling subnational spatial dynamics of worldwide non‐state terrorism, 2010–2016
Author(s) -
Python André,
Illian Janine B.,
JonesTodd Charlotte M.,
Blangiardo Marta
Publication year - 2019
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12384
Subject(s) - terrorism , context (archaeology) , population , bayesian probability , state (computer science) , islam , democracy , geography , development economics , political science , demography , computer science , sociology , economics , law , archaeology , algorithm , politics , artificial intelligence
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.