Crime Hotspot Prediction Using Deep Graph Convolutional Networks

2025-06-16

AI Summary

Crime hotspot prediction faces significant challenges due to complex spatial dependencies, which conventional methods struggle to capture—particularly the geographic interactions among regions. To address this, we propose a graph neural network–based spatiotemporal forecasting framework: urban areas are partitioned into geographic grids and represented as a graph; for the first time, a multi-layer Graph Convolutional Network (GCN) is systematically incorporated to explicitly encode both spatial proximity and crime diffusion dynamics. The framework integrates domain-informed spatial feature engineering with an interpretable heatmap generation module. Evaluated on the Chicago crime dataset, our model achieves 88% accuracy in crime type classification—substantially outperforming baseline methods including Kernel Density Estimation (KDE) and Support Vector Machines (SVM). Moreover, it produces high-resolution, interpretable risk heatmaps, offering decision-support tools that balance predictive accuracy and operational utility for urban public safety governance.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• Predicting crime hotspots with complex spatial dependencies
• Overcoming limitations of classical algorithms in capturing spatial relationships
• Using GCNs to model geographic interactions for crime prediction

Innovation

• Graph Convolutional Networks model spatial dependencies
• Nodes represent geographic grid cells
• Multi-layer GCN classifies crime types