用图卷积网络预测犯罪热点，比传统方法更准且结果可解释。
Crime Hotspot Prediction Using Deep Graph Convolutional Networks
- 将城市网格建模为图，用GCN捕捉犯罪事件的空间依赖关系。
- 在芝加哥数据集上实现78%的犯罪类型分类准确率。
- 生成可解释的犯罪热点热力图，适合警务规划与城市安全研究。
犯罪热点预测对保障城市安全和高效执法至关重要，但因犯罪活动内在的复杂空间依赖性而面临挑战。传统方法如KDE和SVM常将犯罪事件视为独立事件，忽略地理关联，难以捕捉空间模式。为此，我们提出一种基于图卷积网络（GCN）的新框架，将犯罪数据表示为图结构：节点为离散地理网格单元，边表示邻近关系。基于芝加哥犯罪数据集的多层GCN模型被训练用于犯罪类型分类和高风险区域预测。实验表明，该方法显著优于传统方法，在犯罪类型分类上达到78%的准确率。此外，模型能生成可解释的犯罪热点热力图，验证了图学习在预测警务与空间犯罪学中的有效性。
原文摘要 · Abstract (English)
Crime hotspot prediction is critical for ensuring urban safety and effective law enforcement, it remains challenging due to complex spatial dependencies that are inherent in criminal activities. The traditional approaches 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 all of spatial dependencies by representing crime data as a graph. In this graph, nodes represent discrete geographic grid cells and edges capture proximity relationships. The spatial features from Chicago Crime Dataset are used in this system, a multi-layer GCN model is trained to classify crime types and predict high-risk zones. Our approach significantly outperforms traditional approaches, achieving 78% classification accuracy. Moreover, the model generates interpretable heat maps of crime hotspots, demonstrating the usefulness of graph-based learning for predictive policing and spatial criminology.
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