Predict the Next Attack Location via An Attention-based Fused-SpatialTemporal LSTM

Abstract

With the frequent occurrence of unconventional global emergencies, the public security field has received more and more attention. As an unconventional emergency, terrorist attacks have aroused global attention. So, how should we extract useful information from a large number of terrorist attacks and find the law of the attack, so that we can effectively prevent or take early measures to reduce losses? To this end, we are based on the Global Terrorism Database (GTD), and aim to predict the next province or state a terrorist organization may attack at a specific time point by mining the terrorist organizations’ historical records and other types of information availabl, such as incident information and so on. Then, Based on these incident information and spatiotemporal information, we propose a neural network called ATtention-based Fused-SpatialTemporal LSTM (ATFST-LSTM) to predict the next location which may be attacked. We test the efficiency of our models on GTD, experiments show that our models has achieved better results.