Construction of Crime Predicting Model Based on Macro Data of Society

doi:10.3969/j.issn.1671-1122.2022.10.016

Abstract

Abstract:

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 2035 may 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.

Key words: crime predicting, six sides elastic equilibrium model, determining coefficient of crime rates, countermeasure

CLC Number:

TP309

XU Boyang, WANG Dawei, TANG He, JIN Yifeng. Construction of Crime Predicting Model Based on Macro Data of Society[J]. Netinfo Security, 2022, 22(10): 114-120.

Figures/Tables 5

References 17

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	a	b	c	d	e	f	g	h	i	j	k
a.犯罪率	1
b.城乡收入不均	0.051	1
c.全国基尼系数	0.344	0.551*	1
d.教育程度	0.707**	-0.224	0.543*	1
e.城镇失业率	0.152	0.132	0.580**	0.417	1
f.城市化率	0.608**	-0.500*	0.341	0.941**	0.403	1
g.离婚率	0.337	-0.772**	-0.086	0.590**	0.235	0.804**	1
h.人口流动性	0.784**	-0.227	0.462*	0.975**	0.327	0.907**	0.566**	1
i.人均GDP	0.500*	-0.631**	0.207	0.883**	0.364	0.983**	0.858**	0.848**	1
j.性别结构	-0.600**	0.579**	-0.161	-0.846**	-0.38	-0.948**	-0.828**	-0.850**	-0.947**	1
k.人口总数	0.592**	-0.466*	0.383	0.953**	0.423	0.997**	0.783**	0.910**	0.979**	-0.928**	1

被解释变量	解释变量	解释变量滞后期数	F 值	显著性	被解释变量	解释变量	解释变量滞后期数	F 值	显著性
城市化率	人均GDP	1	3.519	0.078	性别结构	人口总数	2	3.008	0.082
教育程度	人均GDP	4	4.811	0.028	人口流动性	城市化率	1	4.090	0.059
教育程度	性别结构	3	6.437	0.009	人口流动性	教育程度	1	7.202	0.016
人均GDP	城市化率	1	13.412	0.002	人口流动性	教育程度	2	2.754	0.098
人均GDP	城市化率	2	5.629	0.016	人口流动性	人均GDP	1	4.349	0.052
人均GDP	教育程度	1	10.484	0.005	人口流动性	人均GDP	2	3.840	0.047
人均GDP	教育程度	2	4.656	0.028	人口流动性	人口总数	1	5.086	0.038
人均GDP	人口流动性	1	6.356	0.022	人口流动性	人口总数	2	5.100	0.022
人均GDP	人口流动性	2	2.727	0.100	人口流动性	人口总数	3	14.217	0.000
人均GDP	人口总数	1	16.729	0.001	人口总数	教育程度	1	4.099	0.059
人均GDP	人口总数	2	9.457	0.003	人口总数	教育程度	3	6.821	0.007
性别结构	教育程度	1	3.241	0.090	人口总数	教育程度	4	8.613	0.005
性别结构	教育程度	2	4.837	0.025	人口总数	人均GDP	2	2.871	0.090
性别结构	教育程度	3	3.158	0.068	人口总数	人均GDP	3	8.510	0.003
性别结构	人口流动性	1	5.970	0.026	人口总数	人均GDP	4	2.848	0.097
性别结构	人口流动性	2	7.532	0.006	人口总数	人均GDP	1	6.823	0.018
性别结构	人口流动性	3	3.661	0.048

年份	教育程度	城市化	人口流动性	人均GDP	性别结构	人口总数	犯罪率判定系数
2021	0.921	1.326	0.858	0.912	0.961	1.170	1.544
2022	1.137	1.128	1.171	0.995	1.099	1.143	1.177
2023	1.274	1.132	1.282	0.920	1.142	0.983	1.393
2024	1.062	0.961	1.045	1.335	1.296	1.091	1.390
2025	1.548	1.122	0.905	1.297	1.278	1.688	1.865
2026	0.889	1.106	0.789	0.930	1.074	1.022	1.401
2027	1.404	0.984	1.222	1.582	1.607	1.185	1.633
2028	1.528	1.633	1.028	1.046	1.414	1.233	1.589
2029	0.679	0.726	0.921	0.904	0.915	0.962	1.416
2030	1.252	1.175	1.163	0.965	1.262	0.875	1.442
2031	0.979	0.907	1.294	0.987	0.746	1.201	1.733
2032	1.580	1.243	1.585	0.965	1.374	1.157	1.644
2033	0.888	1.557	1.130	0.942	1.227	1.220	1.754
2034	1.329	1.507	1.394	1.024	1.459	1.448	1.472
2035	1.000	1.185	1.690	1.014	1.666	1.114	1.690