Learning a Scalable Neural Surrogate for Event-Level Statistically Grounded Hotspot Detection in Spatial Networks

The growing availability of large-scale spatial event data has made hotspot detection increasingly challenging in spatial data analytics, with applications in public health, public safety, and urban analytics. In these domains, statistically grounded methods are widely used but computationally expensive when applied to millions of events on road networks. In this work, we propose a graph neural network framework that learns to approximate a computationally expensive statistical hotspot-detection method on road networks. Specifically, we use a network local K-function-based method to generate supervision, including event-level hotspot labels and associated hotspot radii, and train a graph neural network to jointly predict hotspot membership and radius directly from graph-structured spatial data. We construct heterogeneous graphs of events and road intersections, allowing the model to use both event features and road-network structure. By replacing repeated statistical evaluation at inference time, the learned surrogate accelerates event-level hotspot detection while maintaining good agreement with the statistical detector. We evaluate the surrogate on real-world road networks. On the Los Angeles test graph containing 8 million events, it achieves an F1 score of 0.897 and AP of 0.979. It processes the graph in 0.427 seconds, achieving an approximately 3,400-fold speedup over AH-IBT, the current state-of-the-art method. Radius predictions on the same graph have a mean absolute error of 22.1 meters. The model also transfers zero-shot to unseen cities.