Events / Detecting Local and Global Changes in Terrorism Incidence and the Effects of the COVID-19 Pandemic

Detecting Local and Global Changes in Terrorism Incidence and the Effects of the COVID-19 Pandemic

3rd December 2020
11:00 - 12:00



Over the past fifty years, terrorism has become highly globalised. Where before there was a collection of highly disparate groups with local or national grievances, the presence of terror groups with a more ubiquitous presence is now an unfortunate assumption of necessity for today’s policymakers. One quantitative means of experiencing this story is through the Global Terrorism Database (GTD). The GTD is an open-source collation of terrorist events which have occurred worldwide since 1970. A natural question which arises from this very rich source of information concerns the presence of changepoints: namely, are there specific points or periods of time in the recent past in which the probability of a terrorist attack has increased, either in a specific global region or worldwide?

This requirement to distinguish between local and global changes motivates the use of SUBSET, a new method for detecting changes in high-dimensional datasets. I’ll talk about a few of the properties of SUBSET (in particular, its ability to detect and distinguish between localised and global changes); discuss the results of its application to the GTD; and conclude with a second application involving an analysis of the effects of the COVID-19 in various European countries.

About the speaker

DrSam Tickle joined the Heilbronn Institute for Mathematical Research in December 2019 as a Data Science Research Fellow. His principal interests include time series analysis, high-dimensional inference and sequential algorithm development, particularly as these relate to changepoint detection. Much of his previous work has involved the identification of changes and other features of importance in settings as diverse as the stock market, telecoms and terrorism incidence. Other interests include machine learning, neural networks – particularly in their application to Natural Language Processing – and applied probability. One recent application of the latter involved the assessment of risk in the retail environment during the COVID-19 pandemic.

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About the seminar series

This is the first in this years series of seminars. The Jean Golding Institute has teamed up with the Heilbronn Institute for Mathematical Research to showcase the latest research in Data Science – methodology with roots in Mathematics and Computer Science with important applied implications.

Our seminar series features a range of internationally regarded high-profile speakers on topics that will be relevant to a broad audience.