References
Adegboyega, B., & Alao, D. (2022). Urban spatial risk modeling of criminal activity in Ibadan using open-source data. GeoJournal, 87, 345–362. https://doi.org/10.1007/s10708- 020-10269-y Adewuyi, T. O., Eneji, P. A., Baduku, A. S., & Olofin, E. A. (2017). Spatio-temporal analysis of urban crime pattern and its implication for Abuja Municipal Area Council, Nigeria. Indonesian Journal of Geography. https://jurnal.ugm.ac.id/ijg/article/view/15341 Alghamdi, J., & Al-Dala’in, T. (2024). Towards spatio-temporal crime events prediction. Multimedia Tools and Applications, 83(7), 18721- 18737.https://link.springer.com/article/10.1007/s11042-023-16188-x Alraouf, A., & Al-Said, H. (2023). Smart surveillance and deep learning for crime prevention in public urban spaces. Smart Cities, 6(1), 93–110. https://doi.org/10.3390/smartcities6010006 Alves, M. C., & Bicharra G.P. (2018). Spatio-temporal variations in the urban rhythm: The traveling waves of crime. EPJ Data Science, 7, 17. https://doi.org/10.1140/epjds/s13688-018-0158-4 Baki, M. F., Aybar, U., & Gündüz, H. (2021). An intelligent spatio-temporal crime pattern detection system using SOM and fuzzy logic. Pattern Recognition Letters, 144, 28–35. https://doi.org/10.1016/j.patrec.2021.01.003 Catlett, C., Cesario, E., Talia, D., & Vinci, A. (2019). Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments. Pervasive and Mobile Computing, 53, 62–74. https://doi.org/10.1016/j.pmcj.2019.02.001 Cesario, E., Vinci, A., Talia, D., & Catlett, C. (2022). Multi-density urban hotspots detection in smart cities: A data-driven approach. Pervasive and Mobile Computing, 79, 101492. https://doi.org/10.1016/j.pmcj.2021.101492 Chen, J., & Xu, L. (2021). Evaluating predictive policing models in urban areas. Policing: A Journal of Policy and Practice, 44(5), 877-892. Chung, W. (2016). Crime analysis and visualization using social media. Information Systems Frontiers, 18(2), 235–246. https://doi.org/10.1007/s10796-016-9644-z Cozens, P. M., Neale, R. H., Whitaker, J., & Hillier, D. (2019). Crime prevention through environmental design : A review and modern perspectives. Journal of Environmental Psychology, 32(1), 89-103. Darge, R. (2022). Police Department Incident Reports: 2018-2021. https://www.kaggle.com/datasets/rushikeshdarge/police-department-incident-reports- 20182021 Islam, M. M., & Raza, M. Q. (2020). Forecasting Crime Using ARIMA Model. arXiv preprint arXiv:2003.08006. https://arxiv.org/abs/2003.08006 Liu, Q., Wu, S., & Wang, L. (2019). Predicting crime hotspots using recurrent neural networks. Information Sciences, 489, 30–40. Nwokaeze, E.C., Ikiriko, T.D., & Johnbull, S.W.(2022). Urban Security Challenges: A Review of Crime Prevention Strategies Adopted by Residents in Greater Port Harcourt City, Rivers State, Nigeria, International Journal of Research and Review, 9(3), 60- 74. DOI: https://doi.org/ 10.52403/ijrr.20220308 Onyeneke, C.C. & Karam, A. H. (2022). An Exploratory Study of Crime: Examining Lived Experiences of Crime through Socioeconomic, Demographic, and Physical Characteristics, UrbanSci. 2022, 6(3), 43; https://doi.org/10.3390/urbansci6030043 Piraján, F., Fajardo, A., Melgarejo, M. (2019). Towards a Deep Learning Approach for Urban Crime Forecasting. https://doi.org/10.1007/978-3-030-31019-6_16 Schubert, E., Sander, J., Ester, M., Kriegel, H.-P., & Xu, X. (2017). DBSCAN revisited: Why and how you should use DBSCAN. ACM Transactions on Database Systems, 42(3), 19. Han, X., Hu, X., Wu, H., Shen, B., & Wu, J. (2020). Risk prediction of theft crimes in urban communities: An integrated model of LSTM and ST-GCN. IEEE Access, 8, 217222– 217230. https://doi.org/10.1109/ACCESS.2020.3041924 Hirschfield, A., & Bowers, K. J. (2021). Socio-economic disparities and their impact on crime dynamics: A comprehensive review. Urban Studies, 58(6), 1005-1024. Hu, T., Zhu, X., Duan, L., & Guo, W. (2018). Urban crime prediction based on spatio-temporal Bayesian model. PLOS ONE, 13(10), e0206215. https://doi.org/10.1371/journal.pone.0206215 Huang, C., Wang, J., & Li, F. (2021). A spatiotemporal crime prediction model using CNN and Crime prediction. Neural Networks, 143, 232–244. https://doi.org/10.1016/j.neunet.2021.08.017 Huang, C., Wang, Y., & Zhang, Y. (2020). Deep learning for crime prediction: Spatial and temporal patterns. Applied Intelligence, 50(11), 3909–3923. Wang, P., & Zhang, L. (2020). A hybrid approach combining spatial clustering with time-series forecasting for crime prediction. Urban Computing and Informatics, 27(2), 122-140. Wang, B., Yin, L., & Bertozzi, A. L. (2017). Deep learning for spatio-temporal crime prediction. 2017 IEEE International Conference on Intelligence and Security Informatics , 361–366. https://doi.org/10.1109/ISI.2017.8004902 Xu, D., Zhang, J., & Wang, Z. (2022). Hybrid spatio-temporal deep learning for urban crime prediction. IEEE Transactions on Knowledge and Data Engineering. Yu, H., Chen, Z., & Wu, Y. (2017). Spatio-temporal recurrent convolutional networks for citywide crime prediction. Proceedings of AAAI Conference on Artificial Intelligence. Yu, H., Li, X., & Liu, Y. (2019). Crime prediction through LSTM and historical crime data. International Journal of Forecasting, 35(3), 1004–1015. https://doi.org/10.1016/j.ijforecast.2019.01.008 Yu, H., Liu, L., Yang, B., & Lan, M. (2020). Crime prediction with historical crime and movement data of potential offenders using a spatio-temporal cokriging method. ISPRS International Journal of Geo-Information, 9(12), 732. https://doi.org/10.3390/ijgi9120732 Zhang, J., Wang, T., & Lin, Y. (2019). Integrating temporal and spatial analysis for urban crime prediction. Crime Prevention and Community Safety, 21(1), 56-72. Zhang, J., Zheng, Y. & Qi, D. (2017). Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. https://arxiv.org/abs/1610.00081 Zhao, Z., Chen, W., & Cai, D. (2018). Crime prediction with convolutional neural networks. Proceedings of the International Conference on Data Mining .
