Crime Hotspot Prediction Using Deep Graph Convolutional Networks
This addresses the problem of improving urban safety and law enforcement efficiency through more accurate crime prediction, though it is incremental as it applies an existing graph-based method to a specific domain.
The paper tackled crime hotspot prediction by proposing a deep graph convolutional network framework to model spatial dependencies, achieving 88% classification accuracy on the Chicago Crime Dataset and outperforming traditional methods.
Crime hotspot prediction is critical for ensuring urban safety and effective law enforcement, yet it remains challenging due to the complex spatial dependencies inherent in criminal activity. The previous approaches tended to use classical algorithms such as the KDE and SVM to model data distributions and decision boundaries. The methods often fail to capture these spatial relationships, treating crime events as independent and ignoring geographical interactions. To address this, we propose a novel framework based on Graph Convolutional Networks (GCNs), which explicitly model spatial dependencies by representing crime data as a graph. In this graph, nodes represent discrete geographic grid cells and edges capture proximity relationships. Using the Chicago Crime Dataset, we engineer spatial features and train a multi-layer GCN model to classify crime types and predict high-risk zones. Our approach achieves 88% classification accuracy, significantly outperforming traditional methods. Additionally, the model generates interpretable heat maps of crime hotspots, demonstrating the practical utility of graph-based learning for predictive policing and spatial criminology.