Crime Prediction
11 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Crime Prediction
Most implemented papers
Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspots
This paper focuses on finding spatial and temporal criminal hotspots.
San Francisco Crime Classification
San Francisco Crime Classification is an online competition administered by Kaggle Inc.
AIST: An Interpretable Attention-based Deep Learning Model for Crime Prediction
Accuracy and interpretability are two essential properties for a crime prediction model.
Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction
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.
Crime Prediction with Graph Neural Networks and Multivariate Normal Distributions
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.
Multi-Graph Fusion Networks for Urban Region Embedding
Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.
Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction
Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence.
Explainable Spatio-Temporal Graph Neural Networks
Furthermore, we propose a structure distillation approach based on the Graph Information Bottleneck (GIB) principle with an explainable objective, which is instantiated by the STG encoder and decoder.
Spatio-Temporal Meta Contrastive Learning
Although recent STGNN models with contrastive learning aim to address these challenges, most of them use pre-defined augmentation strategies that heavily depend on manual design and cannot be customized for different Spatio-Temporal Graph (STG) scenarios.