Terrorist attacks change dynamically in social and geographic spaces. In this paper, terrorist attacks in the Middle East are analyzed using methods of network science, statistical methods, geographic information science, and artificial neural networks designed from a socio-spatial perspective. Based on the Global Terrorism Database (GTD), firstly the distribution and trends of terrorist attacks are detected. Then approaches for building diffusion network and identifying diffusion patterns of transnational and transyearly attacks are developed. Finally a Back Propagation Neural Network (BPNN) model is built for predicting future attacks. Results lead to a greater understanding of socio-spatial dependencies and diffusion regularities of terrorist attacks. The findings have significant implications for multinational security and the need to coordinate transnationally.
['Ze Li', 'Duoyong Sun', 'Hsinchun Chen', 'Shin Ying Huang']
Identifying the socio-spatial dynamics of terrorist attacks in the Middle East