Semantics and Geography Aware Hierarchical Learning for Sequential Crime Prediction
Article
Hu, Kaixi, Li, Lin, Tao, Xiaohui and Zhang, Jianwei. 2024. "Semantics and Geography Aware Hierarchical Learning for Sequential Crime Prediction." IEEE Signal Processing Letters. 31, pp. 1234-1238. https://doi.org/10.1109/LSP.2024.3393863
Article Title | Semantics and Geography Aware Hierarchical Learning for Sequential Crime Prediction |
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ERA Journal ID | 4438 |
Article Category | Article |
Authors | Hu, Kaixi, Li, Lin, Tao, Xiaohui and Zhang, Jianwei |
Journal Title | IEEE Signal Processing Letters |
Journal Citation | 31, pp. 1234-1238 |
Number of Pages | 5 |
Year | 2024 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 1070-9908 |
1558-2361 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/LSP.2024.3393863 |
Web Address (URL) | https://ieeexplore.ieee.org/document/10508389 |
Abstract | 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. |
Keywords | Event description; feature correlation; hierarchical learning; location; sequential crime prediction |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460208. Natural language processing |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Wuhan University of Technology, China |
University of Southern Queensland |
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