7 papers with code • 0 benchmarks • 1 datasets
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We additionally introduce a novel Frobenius norm-based contrastive learning objective to improve latent representational generalization. Empirically, we validate MAPSED on two publicly accessible urban crime datasets for spatiotemporal sparse event prediction, where MAPSED outperforms both classical and state-of-the-art deep learning models.
Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions.
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e. g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes.
Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.
Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence.