Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions. This paper introduces a new architecture with the graph convolutional networks (GCN) and multivariate Gaussian distributions to perform high-resolution forecasting that applies to any spatiotemporal data. We tackle the sparsity problem in high resolution by leveraging the flexible structure of GCNs and providing a subdivision algorithm. We build our model with Graph Convolutional Gated Recurrent Units (Graph-ConvGRU) to learn spatial, temporal, and categorical relations. In each node of the graph, we learn a multivariate probability distribution from the extracted features of GCNs. We perform experiments on real-life and synthetic datasets, and our model obtains the best validation and the best test score among the baseline models with significant improvements. We show that our model is not only generative but also precise.

翻译：犯罪预测问题的现有方法没有成功表达细节,因为它们将概率值分配给大区域,本文件采用了新的结构,以图变动网络和多变量高斯分布方式进行适用于任何波状时空数据的高分辨率预报。我们利用GCN的灵活结构和提供子配置算法,以高分辨率解决宽度问题。我们用图变动常数单元(Graph-ConvGRU)构建模型,以学习空间、时间和绝对关系。在图表的每个节点,我们从GCN的提取特征中学习了多变量概率分布。我们在实际生活和合成数据集上进行了实验,我们的模型在基线模型中获得了最佳的验证和最佳测试分数,并取得了显著的改进。我们证明我们的模型不仅具有基因特征,而且十分精确。