Abstract
People have the potential to be victims of elusive crimes such as stalking and indecent exposure anytime. To prevent the incidents, proposing an elusive crime prediction technique is a challenging work in Japan. This study assesses the efficiency of deep neural networks (DNNs) for grid-based elusive crime prediction using a private dataset obtained from Japanese municipalities that contains three crime categories (stalking, indecent exposure, and suspicious behavior) in five prefectures (Aichi, Fukuoka, Kanagawa, Osaka, and Tokyo) for 20 months (from July 2017 to February 2019). Through incremental training evaluation method that did not use future information of the testing 1-month data, the DNN-based technique using spatio-temporal and geographical information showed significant superior prediction performances (Mean ± SD%: 88.2 ± 3.0, 85.5 ± 4.5, and 85.8 ± 3.2 for stalking, indecent exposure, and suspicious behavior) to a random forest-based technique (81.9 ± 3.5, 83.3 ± 3.7, and 82.3 ± 2.1).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Watanabe, Y., Miyazaki, M.: Sex-related violence and the protection of women’s health in Japan. Med. Law 37, 353–362 (2018)
Police of Japan (2018). https://www.npa.go.jp/english/index.html
McClendon, L., Meghanathan, N.: Using machine learning algorithms to analysis crime data. Mach. Learn. Appl.: Int. J. 2, 1–12 (2015)
Marco, M., Gracia, E., López-Quílez, A.: Linking neighborhood characteristics and drug-related police interventions: a Bayesian spatial analysis. ISPRS Int. J. Geoinf. 6(3), 65 (2017)
Lin, Y.-L., Yen, M.-F., Yu, L.-C.: Grid-based crime prediction using geographical features. ISPRS Int. J. Geoinf. 7(8), 298 (2018)
Leong, K., Sung, A.: A review of spatio-temporal pattern analysis approaches on crime analysis. Int. E-J. Crim. Sci. 9, 1–33 (2015)
Weisburd, D., Telep, C.W.: Hot spots policing: what we know and what we need to know. J. Contemp. 30(2), 200–220 (2014)
Sypion-Dutkowska, N., Leitner, M.: Land use influencing the spatial distribution of urban crime: a case study of Szczecin, Poland. ISPRS Int. J. Geoinf. 6, 74 (2017)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM, Finland (2017)
Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: Advances in Neural Information Processing Systems, pp. 1790–1798 (2014)
Acknowledgments
This work was supported in part by the New Energy and Industrial Technology Development Organization (NEDO). The authors would like to thank Dr. Ying-Lung Lin for his helpful advice on the technical issues examined in this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kanoga, S., Kawai, N., Takaoka, K. (2021). Deep Neural Networks for Grid-Based Elusive Crime Prediction Using a Private Dataset Obtained from Japanese Municipalities. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-51328-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51327-6
Online ISBN: 978-3-030-51328-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)