Semantics and Geography Aware Hierarchical Learning for Sequential Crime Prediction

Sequential Crime Prediction (SCP) aims to analyze future criminal intents within historical event transitions and predict next crime event. A problem lies in the correlations among different event features (e.g., time, locations, and categories), posing challenges to capture a comprehensive criminal intent. Most existing methods are hard to fully exploit event descriptions and locations in raw crime records to model such correlations. To this end, this letter proposes a Semantics and Geography aware hierarchical learning framework (SaGCrime). First, we employ BERT to encode semantic representations from descriptions and a proposed geography encoder to learn geographical representations from exact GPS-based locations, respectively. Then, these representations are fed into a stacked Transformer encoder to learn multi-modal interactive intent representation of next crime event. Experiments on real-world crime datasets show that our SaGCrime achieves relatively 4.70% and 3.64% improvements in terms of NDCG@5, compared with state-of-the-art methods.