Graph Representation Learning for Street-Level Crime Prediction

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Abstract
In contemporary research, the street network emerges as a prominent and recurring theme in crime prediction studies. Meanwhile, graph representation learning shows considerable success, which motivates us to apply the methodology to crime prediction research. In this article, a graph representation learning approach is utilized to derive topological structure embeddings within the street network. Subsequently, a heterogeneous information network that incorporates both the street network and urban facilities is constructed, and embeddings through link prediction tasks are obtained. Finally, the two types of high-order embeddings, along with other spatio-temporal features, are fed into a deep neural network for street-level crime prediction. The proposed framework is tested using data from Beijing, and the outcomes demonstrate that both types of embeddings have a positive impact on crime prediction, with the second embedding showing a more significant contribution. Comparative experiments indicate that the proposed deep neural network offers superior efficiency in crime prediction.
Funding Information
  • Fundamental Research Funds for the Central Universities (2023JKF01ZK12, 2023YFC3321604, 2022yjsky012)
  • National Science and Technology Support Program Project (2023JKF01ZK12, 2023YFC3321604, 2022yjsky012)
  • Research and Innovation Project of Graduate Students Supported by Top-notch Innovative Talents Training Funds of the People’s Public Security University of China (2023JKF01ZK12, 2023YFC3321604, 2022yjsky012)

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