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丁伟杰, 梁荣华, 孙国道, 柴叶, 王勋, 郑滋椀. 犯罪数据可视化研究进展[J]. 计算机辅助设计与图形学学报, 2023, 35(7): 979-989. DOI: 10.3724/SP.J.1089.2023.19566
引用本文: 丁伟杰, 梁荣华, 孙国道, 柴叶, 王勋, 郑滋椀. 犯罪数据可视化研究进展[J]. 计算机辅助设计与图形学学报, 2023, 35(7): 979-989. DOI: 10.3724/SP.J.1089.2023.19566
Ding Weijie, Liang Ronghua, Sun Guodao, Chai Ye, Wang Xun, Zheng Ziwan. Research Progress of Crime Data Visualization[J]. Journal of Computer-Aided Design & Computer Graphics, 2023, 35(7): 979-989. DOI: 10.3724/SP.J.1089.2023.19566
Citation: Ding Weijie, Liang Ronghua, Sun Guodao, Chai Ye, Wang Xun, Zheng Ziwan. Research Progress of Crime Data Visualization[J]. Journal of Computer-Aided Design & Computer Graphics, 2023, 35(7): 979-989. DOI: 10.3724/SP.J.1089.2023.19566

犯罪数据可视化研究进展

Research Progress of Crime Data Visualization

  • 摘要: 犯罪数据分析与挖掘已成为大数据时代犯罪学研究及现代社会治理的基础手段,犯罪数据挖掘在揭示犯罪模式、分析犯罪原因、评估犯罪防控效果等方面具有重要作用.首先,以犯罪模式发现与预测、警力资源配置与优化、案件侦查分析与研判3种业务为导向,对犯罪数据可视分析任务进行归纳,系统地梳理了犯罪数据可视化领域的研究与应用现状;其次,针对犯罪时空数据、犯罪文本数据、犯罪网络关系数据等不同类型数据的可视化方式和技术手段,展开了系统性的分析;最后,总结和展望了犯罪数据可视化面临的挑战和发展方向.

     

    Abstract: Crime data analysis and mining have become the basic means of criminology research and modern social governance in the era of big data. Crime data plays an irreplaceable role in revealing the crime pattern, analyzing the crime causes and evaluating the effect of crime prevention. Firstly, guided by three business orientations including crime pattern discovery and prediction, police resource allocation and optimization, case investigation analysis and judgment, we summarize the crime data visual analysis tasks, systematically combed the research and application status of crime data visualization. Secondly, the paper also conducts systematic literature research on the visual analysis methods and technical means of different types of data, such as crime spatio-temporal data, crime text data and crime network data. Finally, the challenges of crime data visualization are summarized, as well as prospecting the development.

     

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