社会宏观数据视角下严重暴力犯罪发案数关联因素及预测研究

Research on the Correlation Factors and Prediction Methods of the Number of Serious Violent Crimes from the Perspective of Social Macro Data

[目的/意义]严重暴力犯罪发案数是评价社会整体治安水平的重要指标,运用情报学方法,以社会宏观数据为基础,准确预测发案数量,对于科学配置安全防范资源,精准预防打击犯罪及提高社会治理能力具有重要意义。[方法/过程]基于社会宏观数据视角,运用犯罪学基本原理深刻剖析严重暴力犯罪的复杂影响因素,提出严重暴力犯罪发案数关联因素及预测方法的研究假设,选取对应的社会宏观数据作为变量,结合近20年8类严重暴力犯罪的历史发案数据进行测试建立自回归滞后模型,并预测严重暴力犯罪发案规律。[结果/结论]研究发现,GDP、结婚率、人口结构、安防投入、失业率等宏观社会指标与严重暴力犯罪发案数关联性明显,相比单一使用历史数据预测的准确度大幅提升,这为我国科学制定相关社会治理防范策略提供有力的技术支撑和方法指引。

[Purpose/Significance]The number of serious violent crimes is an important indicator to evaluate the overall level of public security,and the use of information science methods to accurately predict the number of cases based on social macro data is of great significance for scientifically allocating security and prevention resourc-es,accurately preventing and combating crimes and improving social governance capabilities.[Method/Process]Based on the perspective of social macro data,the basic principles of criminology were used to deeply analyze the complex influencing factors of serious violent crimes,and the research hypothesis of the correlation factors and prediction methods of the number of serious violent crimes was proposed,and the corresponding social macro data were selected as variables,then the autoregression lag model was established by combining the historical occur-rence data of eight types of serious violent crimes in the past 20 years,and the incidence pattern of serious violent crimes was predicted.[Result/Conclusion]The study finds that macro social indicators such as GDP,marriage rate,population structure,security input,and unemployment rate have a significant correlation with the number of serious violent crimes,and the accuracy of prediction is greatly improved compared with the single use of historical data,which provides strong technical support and methodological guidance for China's scientific formulation of rel-evant social governance prevention strategies.