Predicting non-state terrorism worldwide

Python, Andre; Bender, Andreas; Nandi, Anita K.; Hancock, Penelope A.; Arambepola, Rohan; Brandsch, Jürgen und Lucas, Tim C. D. (2021): Predicting non-state terrorism worldwide. In: Science Advances, Bd. 7, Nr. 31, eabg4778

Abstract

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 makers to target preventative measures. Using only publicly available data, we show that predictive models that include structural and procedural predictors can accurately predict the occurrence of non-state terrorism locally and a week ahead in regions affected by a relatively high prevalence of terrorism. In these regions, theoretically informed models systematically outperform models using predictors built on past terrorist events only. We further identify and interpret the local effects of major global and regional terrorism drivers. Our study demonstrates the potential of theoretically informed models to predict and explain complex forms of political violence at policy-relevant scales.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Statistik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• ISSN: 2375-2548
• Sprache: Englisch