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
History
Author affiliation
College of Life Sciences, Population Health SciencesVersion
- VoR (Version of Record)
Published in
Science AdvancesVolume
7Issue
31Pagination
eabg4778Publisher
American Association for the Advancement of Science (AAAS)issn
2375-2548eissn
2375-2548Acceptance date
2021-06-15Copyright date
2021Available date
2024-07-31Publisher DOI
Language
enPublisher version
Deposited by
Dr Tim LucasDeposit date
2024-07-25Rights Retention Statement
- No