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Examining Deep Learning Architectures for Crime Classification and Prediction

Author

Listed:
  • Panagiotis Stalidis

    (Center of Research and Technologies Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Theodoros Semertzidis

    (Center of Research and Technologies Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Petros Daras

    (Center of Research and Technologies Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

Abstract

In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction.

Suggested Citation

  • Panagiotis Stalidis & Theodoros Semertzidis & Petros Daras, 2021. "Examining Deep Learning Architectures for Crime Classification and Prediction," Forecasting, MDPI, vol. 3(4), pages 1-22, October.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:4:p:46-762:d:654392
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    References listed on IDEAS

    as
    1. Gorr, Wilpen & Olligschlaeger, Andreas & Thompson, Yvonne, 2003. "Short-term forecasting of crime," International Journal of Forecasting, Elsevier, vol. 19(4), pages 579-594.
    2. Mohler, G. O. & Short, M. B. & Brantingham, P. J. & Schoenberg, F. P. & Tita, G. E., 2011. "Self-Exciting Point Process Modeling of Crime," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 100-108.
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