基于社会宏观数据的犯罪预测模型构建

Construction of Crime Predicting Model Based on Macro Data of Society

犯罪数量的波动会受到各类宏观社会因素的复杂影响,科学化地预测犯罪变化规律对社会治理对策的制定具有重要先导性价值。文章研究基于宏观犯罪学理论与社会风险评估的六边弹性均衡模型,并择取2001年~2021年中国统计年鉴中的社会结构变量与犯罪率数据,利用风险均衡数学模型公式与格兰杰因果关系检验方法,构建我国犯罪趋势预测的六边弹性均衡模型。实验结果显示,教育程度、城市化率、人口流动性、人均GDP、性别结构、人口总数6组变量对我国犯罪率变化具有显著影响;2025年、2027年、2031年、2032年、2033年与2035年的犯罪率判定系数可能会突破犯罪率稳定警戒线。研究结论可为社会治理与决策部门提供大数据预测模式应用、疫情防控常态化背景下社会结构转型,以及网络领域犯罪治理等情境的系统化对策提供建议。

The fluctuation of crime is affected by various macro social factors.Scientific predicting of the regular patterns of crime is undoubtedly of great guiding value for the organizations of social governing.Based on criminological theories and six sides elastic equilibrium model,the current study utilized social structure variables and crime rates’data in Chinese statistical yearbook from 2001 to 2021 and the major analysing method of Granger causality test to construct the six sides elastic equilibrium model for crime predicting in China.Results showed that education level,urbanization’s rate,population mobility,GDP per capita,gender structure and population’s number had significant effects on crime rates in China.The determining coefficient of crime rates in 2025,2027,2031,2032,2033 and 2035may break through the warning line of stable standardization.The conclusion aims to provide systematic suggestions for social governing departments on the application of prediction via big data,transformation of social structure under the background of normalization of pandemic prevention,and crime control in cyberspace.