Predicting non-state terrorism worldwide

Several thousand people die every year worldwide because of terrorist attacks perpetrated by non-state actors. In this context, reliable and accurate short-term predictions of non-state terrorism at the local level are key for policy makersto target preventative measures. Using only publicly available data, we show that predictive models that includestructural and procedural predictors can accurately predict the occurrence of non-state terrorism locally and a weekahead in regions affected by a relatively high prevalence of terrorism. In these regions, theoretically informed modelssystematically outperform models using predictors built on past terrorist events only. We further identify andinterpret the local effects of major global and regional terrorism drivers. Our study demonstrates the potential oftheoretically informed models to predict and explain complex forms of political violence at policy-relevant scales