  To this day, 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 results confirm the contagious nature of the lethality of
terrorism and the number of lethal terrorist attacks in both space and time.
Moreover, the frequency of lethal attacks tends to be higher in richer areas,
close to large cities, and within democratic countries. In contrast, attacks
are more likely to be lethal far away from large cities, at higher altitudes,
in poorer areas, and in locations with higher ethnic diversity. We argue that,
on a local scale, the lethality of terrorism and the frequency of lethal
attacks are driven by antagonistic mechanisms.
