A Novel Spatial-Aware Deep Learning Approach for Exploring the Environmental Context of Terrorist Attacks and Armed Conflicts

Abstract

The quantitative assessment of terrorist attacks and armed conflicts (TAACs) is a crucial component of global public safety research and is vital for societal stability and national security. This study addresses the spatial dependency of such events, i.e., the relationship between the outbreak of an event and its environment. Based on geographic big data and artificial intelligence (AI), we propose a spatial feature utilization pattern that takes into account the impact of the event environment, and established a deep learning (DL) framework of features within the joint event location and space neighborhood to improve the precision of the quantitative assessment. The results demonstrate that in scenarios under a combination of 14 social, natural, and geographic driving factors, models that incorporate spatial features outperform those that only use location features during both the training and testing phases. Furthermore, models that consider both location and spatial features outperform models using only a single feature across various evaluation metrics. Global attribution analysis further confirms the spatial dependency of events, manifested in the mutual influence on the likelihood of events occurring among adjacent cities and the correlation with various environmental factors, particularly elements related to human activities and living environments. We find that both prosperous urban centers and underdeveloped rural areas are hotspots for TAACs, and that such events more likely to occur in harsh climatic patterns characterized by high temperatures and low precipitation. This enhances our understanding and preparedness for managing and preventing such events.