An Algorithm for identification of terror events and hotspots using K-means and discriminant analysis approach

Ndambuki, John Kelvin
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Strathmore University
This study aimed at developing an algorithm for the identification of terror events and hotspots using K-means clustering and discriminant analysis, with pre-terror recruitment, planning and preparatory activities as the determinant factors. Rules and logic for quantifying the risk state of a pre-terror event based on the values of the constituent determinants of that pre-terror event, for example recruitment as determined by factors such as age of recruits, location and terror organization involved, were developed. Kmeans clustering was used to come up with two clusters based on the risk value combination of the pre-terror event activities. The two clusters represented the outcome of having a terrorist event happening and a terrorist event not happening, and were duly labelled. Discriminant analysis was used on the now labelled clustered dataset to come up with two identification functions, one for terror event happening and another for a terror event not happening. Unseen possible values for pre-terror event activities were fed into the developed algorithm and an identification of whether a terror event would take place and possibly where was accomplished. The purpose of this identification as per the aim of the study was to offer insights to the organizations dealing with counterterrorism activities. The general public would benefit from this effort once a possible terror attack was prevented before it actually took place. The main research question that guided this study was how accurately a possible terrorist attack incident could be identified before it happened. This study used data from the Global Terrorism Database (GTD) retrieved from the National Consortium for the Study of Terrorism And Responses of Terrorism (START) to come up with geospatial datasets as part of a geodatabase with spatial and temporal information on areas that have been attacked before and the risk values of their constituent pre-terror events.
A Thesis Submitted to the School of Computing and Engineering Sciences in Partial Fulfilment for the Requirement of the Degree of Master of Science in Information Technology of Strathmore University
Terrorism, Hotspot, Identification, Spatial-temporal, Mapping