智能系统学报2025，Vol.20Issue(6)：1339-1354,16.DOI:10.11992/tis.202502022
融合时空交互特征与犯罪时空关联性的多类型犯罪预测模型
Multitype crime prediction model integrating spatiotemporal interaction features and spatiotemporal correlation of crimes
摘要
Abstract
To address the high deployment costs of single-crime prediction models in policing practice,a multitype crime prediction model integrating spatiotemporal interaction features and spatiotemporal correlation of crimes(MCPM)was constructed.The model's fundamental functionality encompasses two primary components:spatiotemporal interac-tion feature extraction and multitype joint learning.The spatiotemporal interaction feature extraction component is de-signed to capture the key characteristics of environmental features data related to different types of crime,while the mul-titype joint learning component integrates the spatiotemporal correlations among different types of crime,facilitating the joint optimization of spatiotemporal prediction for multiple crime types.A series of experiments have been conducted on data concerning robbery and burglary crime from Chicago and New York.The following conclusions were reached:The MCPM model demonstrates superior performance in terms of prediction accuracy,with a minimum prediction root mean square error of 0.365 for robbery and 0.288 for burglar and mean absolute error reaches a minimum of 0.277 and 0.226,respectively,indicating a significant margin of improvement over baseline models,with a maximum difference of 31.1%and 36.6%,respectively.Ablation experiments reveal that environmental features data variables contribute the most to the model's predictions,followed by spatiotemporal correlations between different types of crime.The MCPM model effectively captures the differentiated impact of environmental features data on various crime types,enhancing model performance through the integration of spatiotemporal correlations among crimes.关键词
犯罪时空预测/域适应技术/犯罪类型关联/图卷积神经网络/时空数据挖掘/深度学习/注意力机制/数据融合Key words
crime spatiotemporal prediction/domain adaptation techniques/crime type correlation/graph convolutional neural network/spatiotemporal data mining/deep learning/attention mechanisms/data fusion分类
信息技术与安全科学引用本文复制引用
LI Zehui,SUI Jinguang,CHEN Peng,SHAN Miaoxuan,CHEN Jiaqi..融合时空交互特征与犯罪时空关联性的多类型犯罪预测模型[J].智能系统学报,2025,20(6):1339-1354,16.基金项目
中国人民公安大学基本科研业务费项目(2024JKF04) （2024JKF04）
高等学校学科创新引智基地项目(B20087). （B20087）
