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A Study on Crime Prediction to Reduce Crime Rate Based on Artificial Intelligence

  • Received : 2021.02.21
  • Accepted : 2021.06.05
  • Published : 2021.06.30

Abstract

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

Keywords

References

  1. Joo, I. Y. (2012). A Case Study on Crime Prediction using Time Series Models, Korean security science review, 30, 139 - 169.
  2. Chung, Y. S., Kim, J. M., & Park, K. R. (2012). A study of improved ways of the predicted probability to criminal types, Journal of The Korea Society of Computer and Information, 17(4), 12-21.
  3. Tak H. S. (2015). Building Crime Prevention System Utilizing Big Data(II), Korean Institute of Criminology, 1, 703.
  4. Park, J. Y., Chae, M. S., & Jung, S. K. (2016). Classification Model of Types of Crime based on Random-Forest Algorithms and Monitoring Interface Design Factors for Real-time Crime Prediction, KIISE Transactions on Computing Practices, 22(9), 455-460. https://doi.org/10.5626/KTCP.2016.22.9.455
  5. Kang, M. S., Kang, H. J., Yoo, K. B., Ihm, C. H., & Choi, E. S. (2018). Getting started with Machine Learning using Azure Machine Learning studio. Seoul, Korea: Hanti media.
  6. Kim, K. P., & Song, S. W. (2018). A Study on Prediction of Business Status Based on Machine Learning, Korean Journal of Artificial Intelligence, 6(2), 23-27.
  7. Dataworld. (2016). City of Baltimore Crime Data. Retrieved October, 2020 from https://data.world/data-society/city-of-baltimore-crime-data
  8. Wikipedia. (2020). Retrieved October, 2020 from https://en.wikipedia.org/wiki/Multinomial_logistic_regression
  9. Baek, J. R. (2013). Development of accident prediction model for military aircraft by using logistic regression, Domestic Master's Thesis Graduate School, Yonsei University, Seoul.
  10. Park, J. H. (2014). Comparing performances of logistic regression and decision tree for classifying infection risk with patients in chemotherapy, Domestic Master's Thesis Graduate School, Kyunghee University, Seoul.
  11. Kim, M. S., & Kang, T. W. (2018). Proposal and Analysis of Various Link Architectures in Multilayer Neural Network, Journal of KIIT, 16 (4), 11-19. https://doi.org/10.14801/jkiit.2018.16.4.11
  12. Kim, O. H. (1999). Case analysis using neural network data analysis technique, Domestic Master's Thesis Graduate School, Ewha Womans University, Seoul.
  13. Park, J. M. (2018). A Study on the Prediction Model of Crime Frequency Using Big Data, Domestic doctoral dissertation Graduate School, Kongju National University, Chungcheongnam-do.
  14. Heo, S. Y., Kim, J. Y., & Moon, T. H. (2018). Predicting Crime Risky Area Using Machine Learning, Journal of the Korean Association of Geographic Information Studies, 21(4), 34-43.
  15. Yoon, H. S et al., (2014). Building Crime Prevention System Utilizing Big Data(I), Korean Institute of Criminology.
  16. Microsoft Azure Machine Learning Studio (classic) (azureml.net).
  17. You, S. H., & Kang, M. S. (2020). A Study on Methods to Prevent Pima Indians Diabetes using SVM. Korean Journal of Artificial Intelligence 8(2), 7-10. https://doi.org/10.24225/kjai.2020.8.1.7