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This paper aims to apply geospatial analysis via k-means clustering on the global terrorism database supplied by the Study of Terrorism and Responses to Terrorism (START) consortium. By performing parameter sweeps for the algorithm we optimise implementation and identify patterns and gain insight into the nature of terrorist attacks in a case study, focusing specifically on the UK to analyse terrorist events in a localised environment. The aim of this paper is to show unsupervised machine learning (ML) methods to meet multiple targets set by the United Nations Sustainable Development Goals (UNSDG) 16.

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Unsupervised Geospatial Analysis of Terror in the UK

  • 会议论文

  • 发表日期：

    2026年

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  • Christopher Sinclair,
  • Saptarshi Das

摘要

This paper aims to apply geospatial analysis via k-means clustering on the global terrorism database supplied by the Study of Terrorism and Responses to Terrorism (START) consortium. By performing parameter sweeps for the algorithm we optimise implementation and identify patterns and gain insight into the nature of terrorist attacks in a case study, focusing specifically on the UK to analyse terrorist events in a localised environment. The aim of this paper is to show unsupervised machine learning (ML) methods to meet multiple targets set by the United Nations Sustainable Development Goals (UNSDG) 16.