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Crime Prediction Using Data Mining and Machine Learning

  • Shaobing Wu
  • Changmei WangEmail author
  • Haoshun Cao
  • Xueming Jia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

In order to predict the crime in YD county, data mining and machine learning are used in this paper. The aim of the study is to show the pattern and rate of crime in YD county based on the data collected and to show the relationships that exist among the various crime types and crime Variable. Analyzing this data set can provide insight on crime activities within YD county. By introducing formula and methods of Bayesian network, random tree and neural network in machine learning and big data, to analyze the crime rules from the collected data. According to the statistics released by the YD county From 2012-09-01 to 2015-07-21, The crime of smuggling, selling, transporting and manufacturing drugs, Theft, Intentional injury, Illegal business crime, Illegal possession of drugs, Rape, Crime of fraud, Gang fighting, manslaughter, Robbery made the top ten list of crime types with high number of crimes. The crime rate of drugs was the highest, reaching 46.86%, farmers are the majority, accounting for 97.07%, people under the age of 35 are the subject of crime. Males accounted for 90.17% of crimes committed, while females accounted for 9.83%. For ethnic groups, the top five were han, yi, wa, dai and lang, accounting for 68.43%, 23.43%, 1.88%, 1.67% and 1.25% respectively. By adopting random forest, Bayesian networks, and neural network methods, we obtained the decision rules for criminal variables. By comparison, the classification effect of Random Trees is better than that of Neural Networks and Bayesian Networks. Through the data collection of the three algorithms, the validity and accuracy of the random tree algorithm in predicting crime data are observed. The performance of the Bayesian network algorithm is relatively poor, probably due to the existence of certain random factors in various crimes and related features (the correlation between the three algorithms is low).

Keywords

Crime prediction Data mining Machine learning 

Notes

Acknowledgements

This study is supported by scientific research projects of National Social Science Foundation (13CFX038). The authors would like to express their gratitude to the Office of the national social science foundation.

References

  1. 1.
    McClendon, L., Meghanathan, N.: Using machine learning algorithms to analyze crime data. Mach. Learn. Appl. Int. J. 2(1), 1–2 (2015)CrossRefGoogle Scholar
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
    Ronsivalle, G.B.: Neural and bayesian networks to fight crime: the NBNC meta-model of risk analysis. In: Artificial Neural Networks-Application, pp. 29–42 (2011)Google Scholar
  8. 8.
    Ward, J.T., Ray, J.V., Fox, K.A.: Developed a computer model exploring differences in self-control across sex, race, age, education, and language: considering a bifactor MIMIC model. J. Crim. Justice 56, 29–42 (2018)CrossRefGoogle Scholar
  9. 9.
    Nirkhi, S.M., Dharaskar, R.V., Thakre, V.M.: Data mining: a prospective approach for digital forensics. Int. J. Data Min. Knowl. Manag. Process 2(6), 41–48 (2012)CrossRefGoogle Scholar
  10. 10.
    Ngai, E.W.T., Xiu, L., Chau, D.C.K.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 2592–2602 (2008)CrossRefGoogle Scholar
  11. 11.
    McCarthy, J.: Arthur samuel: pioneer in machine learning. AI Mag. 11(3), 10–11 (1990)Google Scholar
  12. 12.
    Aldous, D.: The continuum random tree I. Ann. Probab. 19(1), 1–28 (1991)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Aldous, D.: The continuum random tree III. Ann. Probab. 21(1), 248–289 (1993)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Haas, B., Miermont, G.: The genealogy of self-similar fragmentations with negative index as a continuum random tree. Electron. J. Probab. 9, 57–97 (2004)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Le Gall, J.F.: Spatial Branching Processes, Random Snakes and Partial Differential Equations. Birkhauser, Boston (1999)CrossRefGoogle Scholar
  16. 16.
    Heckerman, D.: A Bayesian approach to learning causal networks. Technical report MSR-TR-95-04, pp. 1–23 (1995)Google Scholar
  17. 17.
    Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, New York (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shaobing Wu
    • 1
  • Changmei Wang
    • 2
    Email author
  • Haoshun Cao
    • 1
  • Xueming Jia
    • 1
  1. 1.Institute of Information SecurityYunnan Police CollegeKunmingChina
  2. 2.Solar Energy InstituteYunnan Normal UniversityKunmingChina

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