A multi-layer GCN on a proximity graph of Chicago crime grid cells achieves 78% accuracy in crime type classification and hotspot prediction, outperforming KDE and SVM baselines.
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
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Crime Hotspot Prediction Using Deep Graph Convolutional Networks
A multi-layer GCN on a proximity graph of Chicago crime grid cells achieves 78% accuracy in crime type classification and hotspot prediction, outperforming KDE and SVM baselines.