Terrorism persists as a worldwide threat, as exemplified by the ongoing lethal attacks perpetrated by Islamic State in Iraq and 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. Predictive models that can account for complex spatiotemporal dependencies have not yet been investigated, despite their potential for providing guidance to prevent terrorism.
In this talk, Dr Andre Python will first briefly review recent literature discussing prediction of terrorist events, including few important papers in armed conflict. He will then introduce the preliminary results of a study which uses a machine-learning algorithm to predict the locations of terrorist events a week ahead at fine spatial scale in Iraq, Iran, Afghanistan, and Pakistan. He will conclude the talk by discussing the opportunities and challenges in predicting terrorist events.
Dr Andre Python is a Post-Doctoral Research Scientist in Geospatial Epidemiology in the Malaria Atlas Project at the Big Data Institute, University of Oxford. In 2017, Andre completed a PhD in Statistics (University of St Andrews, UK), applying a Bayesian spatio-temporal geostatistical model to capture fine-scale patterns of non-state terrorism across the world.
His current research interests are in extending geostatistical models to address policy-relevant issues raised by the spread of infectious diseases and other contagious phenomena including terrorism. In line with the objectives and philosophy of the Royal Statistical Society, Andre believes that statistics and data have a key role in society. His academic research work aims to provide evidence-based results to help decision makers use statistics effectively in the public interest.
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