ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Crime amount prediction based on 2D convolution and long short-term memory neural network

  • Dong, Qifen (Department of Computer and Information Security, Zhejiang Police College) ;
  • Ye, Ruihui (Department of Intelligent Security, Zhejiang College of Security Technology) ;
  • Li, Guojun (Department of Basic Courses, Zhejiang Police College)
  • Received : 2021.10.27
  • Accepted : 2022.02.20
  • Published : 2022.04.10
Download PDF
⟨ Previous Next ⟩

Abstract

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

Keywords

Acknowledgement

This study was funded by Natural Science Foundation of Zhejiang Province in China under Grant LQ18G010001.

References

  1. C. Couch and A. Dennemann, Urban regeneration and sustainable development in britain, Cities 17 (2000), no. 2, 137-147. https://doi.org/10.1016/S0264-2751(00)00008-1
  2. L. E. Cohen and M. Felson, Social change and crime rate trends: a routine activity approach, Am. Sociol. Rev. 44 (1979), no. 4, 588-608. https://doi.org/10.2307/2094589
  3. D. B. Cornish and R. V. Clarke, The Reasoning Criminal: Rational Choice Perspectives on Offending, Transaction Publishers, New Jersey, 2014.
  4. X. Y. Zhao and J. Tang, Modeling temporal-spatial correlations for crime prediction, (Proceedings of the ACM Conference on Information and Knowledge Management, Singapore), 2017, pp. 497-506.
  5. H. J. Wang, D. Kifer, C. Graif, and Z. H. Li, Crime rate inference with big data, (Proceedings of the International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA), 2016, pp. 635-644.
  6. H. J. Wang, H. X. Yao, D. Kifer, C. Graif, and Z. H. Li, Nonstationary model for crime rate inference using modern urban data, IEEE Trans. Big Data 5 (2019), no. 2, 180-194. https://doi.org/10.1109/tbdata.2017.2786405
  7. X. Y. Zhao and J. L. Tang, Crime in urban areas: a data mining perspective, ACM SIGKDD Explor. Newslett. 20 (2018), no. 1, 1-12. https://doi.org/10.1145/3229329.3229331
  8. W. Gorr, A. Olligschlaeger, and Y. Thompson, Short-term forecasting of crime, Int. J. Forecast. 19 (2003), no. 4, 579-594. https://doi.org/10.1016/S0169-2070(03)00092-X
  9. P. Chen, H. Yuan, and X. Shu, Forecasting crime using the ARIMA model, (Proceedings of the International Conference on Fuzzy Systems and Knowledge Discovery, Jinan China), 2008, pp. 627-630.
  10. E. Cesario, C. Catlett, and D. Talia, Forecasting crimes using autoregressive models, (Proceedings of the International Conference on Dependable, Autonomic and Secure Computing, Pervasive Intelligence and Computing, Big Data Intelligence and Computing and Cyber Science and Technology Congress, Auckland, New Zealand), 2016, pp. 795-802.
  11. J. H. Yan and M. M. Hou, Predicting time series of theft crimes based on LSTM network, Data Anal. Knowl. Disc. 4 (2020), no. 11, 84-91.
  12. L. Anselin, J. Cohen, D. Cook, W. Gorr, and G. Tita, Spatial analyses of crime, Criminal Justice: Measurement and Analysis of Crime and Justice (USA) (D Duffe), US Department of Justice, Office of Justice Programs, National Institute of Justice, 2000, pp. 213-262.
  13. C. Graif and R. J. Sampson, Spatial heterogeneity in the effects of immigration and diversity on neighborhood homicide rates, Homicide Studies 13 (2009), no. 3, 242-260. https://doi.org/10.1177/1088767909336728
  14. C. Kadar, J. Iria, and I. Pletikosa, Exploring Foursquare-derived features for crime prediction in New York City, (Proceedings of Urban Computing Workshop of the ACM International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA), 2016.
  15. H. Wang and Z. Li, Region representation learning via mobility flow, (Proceedings of the 26th ACM International Conference on Information and Knowledge Management, Singapore), 2017, pp. 237-246.
  16. C. Kadar, B. R. Roses, and I. Pletikosa, Measuring ambient population from location-based social networks to describe urban crime, (Proceedings of the 9th International Conference on Social Informatics, Oxford, United Kingdom), 2017, pp. 521-535.
  17. C. Kadar and I. Pletikosa, Mining large-scale human mobility data for long-term crime prediction, EPJ Data Sci. 7 (2018), no. 26, 1-27. https://doi.org/10.1140/epjds/s13688-017-0128-2
  18. G. O. Mohler, M. B. Short, P. J. Brantingham, F. P. Schoenberg, and G. E. Tita, Self-exciting point process modeling of crime, J. Am. Stat. Assoc. 106 (2011), no. 493, 100-108. https://doi.org/10.1198/jasa.2011.ap09546
  19. M. L. Lin, J. Gao, H. Z. Huang, and D. Y. Yuan, A model of crime intelligence prediction based on a hybrid model of spatiotemporal sequence, J. Intell. 37 (2018), no. 9, 27-31.
  20. C. Catlett, E. Cesario, D. Talia, and A. Vinci, Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments, Pervasive Mob. Comput. 53 (2019), 62-74. https://doi.org/10.1016/j.pmcj.2019.01.003
  21. B. Yang, L. Liu, M. X. Lan, Z. L. Wang, H. L. Zhou, and H. J. Yu, A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery, Int. J. Geogr. Inf. Sci. 34 (2020), no. 9, 1740-1764. https://doi.org/10.1080/13658816.2020.1737701
  22. K. Leong and A. Sung, A review of spatio-temporal pattern analysis approaches on crime analysis, Int. e-J. Crim. Sci. 9 (2015), 1-33.
  23. City of chicago data portal. Available from: https://data.cityofchicago.org
  24. S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput. 9 (1997), no. 8, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  25. X. Li, L. Peng, X. J. Yao, S. L. Cui, Y. Hu, C. Z. You, and T. H. Chi, Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation, Environ. Pollut. 231 (2017), 997-1004. https://doi.org/10.1016/j.envpol.2017.08.114
  26. S. Lin, G. Wang, and S. Zhang, Time series prediction based on support vector regression, Inf. Technol. J. 5 (2006), no. 2, 533-537.