融合图自编码器与GRU的城市盗窃犯罪时空分布预测模型

Spatio-temporal Distribution Prediction Model of Urban Theft by Fusing Graph Autoencoder and GRU

犯罪预测是进行犯罪预防的前提,高效准确的犯罪预测对于提高城市管理效率、保障公共安全都具有重要的意义。当前,关于犯罪预测的已有研究大多采用单一的机器学习方法或深度学习模型,忽略了犯罪的时空依赖关系,往往难以获得准确的预测结果。本文提出一个基于深度学习技术的犯罪时空预测模型—GAERNN:(1)利用GAE模型捕获犯罪案件的空间分布特征;(2)将带有空间依赖关系的特征经序列化处理后作为GRU模型的输入,进一步提取犯罪序列的时间特征;(3)经全连接层处理获得犯罪时空预测结果,并选取MLP、GCN等基准模型进行对比实验,结合RMSE、MSE等多个指标对模型预测结果进行评估。实验结果表明:对于各模型预测结果可视化分析,GAERNN模型预测的可视化结果与实际数据分布最相符合;在各模型误差分析方面,相比预测性能较差的MLP,GAERNN模型各月份的RMSE分别降低了1.02、3.58、1.29以及0.45;在子模块有效性评估方面,相比其变体模型GAE-LSTM,GAERNN模型在各月份的MAPE分别降低了2.15%、10.07%、1.92%以及2.54%,说明GAERNN模型能显著提高盗窃犯罪时空预测精度,可用于城市盗窃犯罪的积极预防和有效治理。

Crime prediction is a prerequisite for crime prevention.Forecasting crime efficiently and accurately is of great significance for improving urban management and public safety.At present,it is difficult for most of the existing studies to obtain accurate crime prediction,because they usually utilize a single machine learning or deep learning model,ignoring the spatiotemporal dependence of the crime.In this paper,we propose a spatiotemporal prediction model GAERNN based on deep learning techniques.Firstly,the GAE model is used to capture the spatial distribution characteristics of crime cases.Secondly,the features with spatial dependencies are input into the GRU model after serialization to further extract the temporal features of crime sequences.Then,the spatial and temporal prediction results of theft crime are obtained by the fully connected layer processing.Finally,we select MLP and GCN to carry out contrastive experiments by using several indicators,such as RMSE and MSE,to verify the performance of our model.The results show that our model is significantly superior to other benchmark models in spatiotemporal prediction,and it can be used to prevent and control theft crime effectively.