Real Time Big Data Analytics for Predicting Terrorist Incidents

In recent decades, terrorist groups expanded their reach and their attacks are more frequent and more lethal. In this research, we developed a set of methodologies and a software system to address various aspects of terrorism. We developed a real time terrorist incidents data collection system in [14] to gather terrorist incidents data from reliable sources. Using the incidents data, we developed a risk model to calculate the terrorism risk level of different locations. Then, we proposed a set of rules along with our risk model to predict future terrorist incidents. Finally, we developed a novel risk projection model to project the terrorism risk levels into the near future. The results show emerging patterns of terrorist attacks. Our prediction method provides high precision values of up to 96.30%, and high recall values of up to 100%. Furthermore, our risk projection model provides accurate risk values. Our methodologies can assist terrorism analysts to improve counter-terrorism measures and potentially prevent future attacks.