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.