Skip to main content
SpringerLink
Log in

The Forecast of the Number of Police Cases Based on Time Series and Convolutional Neural Network Model

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

The number of police cases is related to various social factors, and the prediction of police situation has always been a research hotspot. This paper first analyzes the influential factors of the police situation analysis and carries on the correlation analysis. Then, by comparing five models include linear regression, time series model, back propagation neural network model optimized by simulated annealing algorithm, convolutional neural network, and time series combined with convolutional neural network, it is found that the experimental performance of time series combined with convolutional neural network model is the best. Therefore, this study found that the most suitable model to predict the number of police cases is the combination of MV and CNN.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

Similar content being viewed by others

REFERENCES

  1. Wang, Q., Jin, G., Zhao, X., Feng, Y., and Huang, J., CSAN: A neural network benchmark model for crime forecasting in spatio-temporal scale, Knowl.-Based Syst., 2020, vol. 189, p. 105120.  https://doi.org/10.1016/j.knosys.2019.105120

    Article  Google Scholar 

  2. Das, P., Das, A.K., Nayak, J., Pelusi, D., and Ding, W., Group incremental adaptive clustering based on neural network and rough set theory for crime report categorization, Neurocomputing, 2019, vol. 459, pp. 465–480.  https://doi.org/10.1016/j.neucom.2019.10.109

    Article  Google Scholar 

  3. Shukla, S., Jain, P.K., Babu, C.R., and Pamula, R., A multivariate regression model for identifying, analyzing and predicting crimes, Wireless Pers. Commun., 2020, vol. 113, pp. 2447–2461.  https://doi.org/10.1007/s11277-020-07335-w

    Article  Google Scholar 

  4. Ivaskevics, K. and Almond, L., Predicting the criminal record of Hungarian homicide offenders from crime scene behaviours, Psychol., Crime Law, 2020, vol. 26, no. 1, pp. 22–33.  https://doi.org/10.1080/1068316X.2019.1611831

    Article  Google Scholar 

  5. Wibowo, A.H. and Oesman, T.I., The comparative analysis on the accuracy of k-NN, Naive Bayes, and Decision Tree Algorithms in predicting crimes and criminal actions in Sleman Regency, J. Phys.: Conf. Ser., 2020, vol. 1450, p. 012076.  https://doi.org/10.1088/1742-6596/1450/1/012076

    Article  Google Scholar 

  6. Yoo, Y. and Wheeler, A.P., Using risk terrain modeling to predict homeless related crime in Los Angeles, California, Appl. Geogr., 2019, vol. 109, p. 102039.  https://doi.org/10.1016/j.apgeog.2019.102039

    Article  Google Scholar 

  7. Yuki, J.Q., Sakib, M.Q., Zamal, Z., Habibullah, K.M., and Das, A.K., Predicting crime using time and location data, Proc. 7th Int. Conf. on Computer and Communications Management, Bangkok, 2019, New York: Association for Computing Machinery, 2019, pp. 124–128.  https://doi.org/10.1145/3348445.3348483

  8. Li, Mao and Wei, Du, A method of crime rate forecast based on wavelet transform and neural network, Int. J. Embedded Syst., 2019, vol. 11, no. 6, pp. 731–737.  https://doi.org/10.1504/IJES.2019.103990

  9. Lops, Y., Choi, Y., Eslami, E., and Sayeed, A., Real-time 7-day forecast of pollen counts using a deep convolutional neural network, Neural Comput. Appl., 2020, vol. 32, pp. 11827–11836. https://doi.org/10.1007/s00521-019-04665-0

    Article  Google Scholar 

  10. Sayeed, A., Choi, Y., Eslami, E., Lops, Y., Roy, A., and Jung, J., Using a deep convolutional neural network to predict, Neural Networks, 2017, vol. 121, pp. 396–408. https://doi.org/10.1016/j.neunet.2019.09.033

    Article  Google Scholar 

  11. Shankar, V.V., Kumar, V., Devagade, U., Karanth, V., and Rohitaksha, K., Heart disease prediction using CNN algorithm, SN Comput. Sci., 2020, vol. 1, p. 170.  https://doi.org/10.1007/s42979-020-0097-6

    Article  Google Scholar 

  12. Qin, Y., Li, K., Liang, Z., Lee, B., Zhang, F., Gu, Y., Zhang, L., Wu, F., and Rodriguez, D., Hybrid forecasting model based on long short-term memory network and deep learning neural network for wind signal, Appl. Energy, 2019, vol. 236, pp. 262–272. https://doi.org/10.1016/j.apenergy.2018.11.063

    Article  Google Scholar 

  13. Ji, L., Zou, Y., He, K., and Zhu, B., Carbon futures price forecasting based with ARIMA-CNN-LSTM model, Procedia Comput. Sci., 2019, vol. 162, pp. 33–38. https://doi.org/10.1016/j.procs.2019.11.254

    Article  Google Scholar 

  14. Singh, P., Indian summer monsoon rainfall (ISMR) forecasting using time series data: A fuzzy-entropy-neuro based expert system, Geosci. Front., 2018, vol. 9, no. 4, pp. 1243–1257. https://doi.org/10.1016/j.gsf.2017.07.011

    Article  Google Scholar 

  15. Singh, P., High-order fuzzy-neuro-entropy integration based expert system for time series forecasting, Neural Comput. Appl., 2017, vol. 28, pp. 3851–3868. https://doi.org/10.1007/s00521-016-2261-4

    Article  Google Scholar 

  16. Singh, P., Rainfall and financial forecasting using fuzzy time series and neural networks based model, Int. J. Mach. Learn. Cybern., 2018, vol. 9, pp. 491–506. https://doi.org/10.1007/s13042-016-0548-5

    Article  Google Scholar 

  17. Singh, P. and Borah, B., High-order fuzzy-neuro expert system for daily temperature forecasting, Knowl.-Based Syst., 2013, vol. 46, pp. 12–21. https://doi.org/10.1016/j.knosys.2013.01.030

    Article  Google Scholar 

Download references

Funding

This study is partially supported by the Fundamental Research Funds for the Central Universities, project no. LGYB202102.

Author information

Authors and Affiliations

  1. School of Information Technology, Nanjing Forest Police College, 210023, Nanjing, China

    Qiu Mingyue, Wang Xinmeng, Wu Yubao & Luo Anran

Authors
  1. Qiu Mingyue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wang Xinmeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wu Yubao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luo Anran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiu Mingyue.

Ethics declarations

The authors declare that they have no conflicts of interest.

APPENDIX

APPENDIX

Table A1. Attributes that are used in the datasets
Full size table

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiu Mingyue, Xinmeng, W., Yubao, W. et al. The Forecast of the Number of Police Cases Based on Time Series and Convolutional Neural Network Model. Aut. Control Comp. Sci. 56, 230–238 (2022). https://doi.org/10.3103/S014641162203004X

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S014641162203004X

Keywords:

Search

Navigation