ARCHIVES

. M. Puur, M. Camacho Doyle, N. Guldåker, and M. Gerell, “Spatial and Temporal Patterns of Risk: A Risk Terrain Modeling Approach in
Stockholm, Sweden,” European Journal on Criminal Policy and Research, 2025.
2. N. Shahmoradi, A. A. Alesheikh, A. Jafari, and A. Lotfata, “Hybrid ST-ResNet and LSTM approach for precise crime hotspot prediction,”
Scientific Reports, vol. 15, p. 40754, Nov. 2025, doi: 10.1038/s41598-025-24559-7
3. S. Wang, Y. Zhang, X. Piao, X. Lin, Y. Hu, and B. Yin, “MRAGNN: Refining urban spatio-temporal prediction of crime occurrence with
multi-type crime correlation learning,” Expert Systems with Applications, vol. 244, p. 125940, Dec. 2024, doi:
10.1016/j.eswa.2024.125940.
4. A. Albors Zumel, M. Tizzoni, and G. M. Campedelli, “Deep learning for crime forecasting: The role of mobility at fine-grained
spatiotemporal scales,” Journal of Quantitative Criminology, 2025.
5. E. Gül Ilgün and M. Dener, “Exploratory data analysis, time series analysis, crime type prediction, and trend forecasting in crime data
using machine learning, deep learning, and statistical methods,” Neural Computing and Applications, Mar. 2025, doi: 10.1007/s00521-
025-11094-9.
6. K. Sriram, A. Sinha, and S. Choudhary, “Predictive hotspot mapping for data-driven crime prediction,” arXiv preprint arXiv:2602.23750,
Feb. 2026.
7. T. Zubair, S. K. Fatima, N. Ahmed, and A. Khan, “Crime hotspot prediction using deep graph convolutional networks,” arXiv preprint
arXiv: 2506.13116, Jun. 2025.
8. Y. Huang, B. Yang, X. Ren, Y. Lu, M. Lan, and X. Gong, “Spatio-temporal cokriging crime predictions using social media data: A multi-
type case study in San Jose, California,” Human-centric Intelligent Systems, 2025.
9. I. Raji and D. B. Sholademi, “Predictive policing: The role of AI in crime prevention,” International Journal of Computer Applications
Technology and Research, vol. 13, no. 10, pp. 66–78, 2024, doi: 10.7753/IJCATR1310.1006.
10. J. Jin, Y. Hong, G. Xu, J. Zhang, J. Tang, and H. Wang, “An event-centric framework for predicting crime hotspots with flexible time
intervals,” arXiv preprint arXiv:2411.01134, Nov. 2024.
11. X. Liang, L. Zhou, S. Wang, X. Zhao, J. Xue, Q. Ding, and Y. Pan, “Spatiotemporal crime prediction and fairness-constrained spatial
optimization with deep reinforcement learning for patrol region design,” International Journal of Applied Earth Observation and
Geoinformation, vol. 145, p. 104973, Dec. 2025, doi: 10.1016/j.jag.2025.104973.
12. T. Wang, P. Chen, and M. Shan, “Crime spatiotemporal prediction through urban region representation by using building footprints,” Big
Data and Cognitive Computing, vol. 9, no. 12, p. 301, Nov. 2025, doi: 10.3390/bdcc9120301.
13. M. Li, W. Du, S. Wen, Q. Li, T. Zhang, and W. Zhong, “Crime forecasting: A spatio-temporal analysis with deep learning models,” arXiv
preprint arXiv:2502.07465, Feb. 2025.
14. E. Cesario, P. Lindia, and A. Vinci, “Multi-density crime predictor: An approach to forecast criminal activities in multi-density crime
hotspots,” Journal of Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-024-00935-4.
15. C. Jing, X. Lv, Y. Wang, M. Qin, S. Jin, S. Wu, and G. Xu, “A deep multi-scale neural networks for crime hotspot mapping prediction,”
Computers, Environment and Urban Systems, vol. 109, p. 102089, Apr. 2024, doi: 10.1016/j.compenvurbsys.2024.102089.
16. M. Shan, C. Ye, P. Chen, and S. Peng, “Ada-GCNLSTM: An adaptive urban crime spatiotemporal prediction model,” Journal of Safety
Science and Resilience, vol. 6, no. 2, pp. 226–236, Jun. 2025, doi: 10.1016/j.jnlssr.2025.03.004.
17. A. K, S. N. V, and P. B. S, “Crime prediction using ensemble approach,” IRE Journals, vol. 8, no. 5, Nov. 2024.
18. A. Roy, N. Neetu, and A. R. Singh, “The role of artificial intelligence in predictive policing and its legal implications,” International Journal
of Law and Policy, 2024.
19. A. Alexander and B. M. Singh, “Crime rate detection and prediction using AI,” International Journal of Engineering Development and
Research, vol. 13, no. 3, pp. 107–111, Jul. 2025.
20. K. Madeswaran, J. Siva, and K. S. Kawsalya, “Smart crime forecasting using machine learning techniques,” Research Journal of Education
and Technology, vol. 9, no. 3, Mar. 2026.
21. Y. Guo, “Multimodal spatio-temporal fusion: A generalizable GCN-LSTM with attention framework for urban application,” Information
Fusion, vol. 131, p. 104164, Jul. 2026, doi: 10.1016/j.inffus.2026.104164.
22. T. C. Hakyemez and B. Badur, “Enhancing deep learning-based crime hotspot predictions with theory-based environmental risk scores,”
Journal of Quantitative Criminology, 2026, doi: 10.1007/s10940-025-09688-6.
23. Z. Wang, X. Ma, H. Yang, W. Lü, P. Sun, and S. C. Guntuku,
“Uncertainty-aware crime prediction with spatial temporal multivariate graph neural networks,” arXiv preprint arXiv:2408.04193, Aug.
2024. [Online]. Available: https://doi.org/10.48550/arXiv.2408.04193.
24. M. Lnenicka, P. Hervert, and O. Horak, “Understanding big data and data protection measures in smart city strategies: An anal ysis of 28
cities,” Urban Governance, vol. 4, no. 4, pp. 255–273, Nov. 2024, doi: 10.1016/j.ugj.2024.08.003.
25. Y. Sahu and V. Kumar, “Spatial clustering and machine learning for crime prediction: A case study on women safety in Bhopal,” in
Proceedings of the Asian Conference on Computer Vision (ACCV) Workshops, 2024, pp. 648–663, doi: 10.1007/978-981-96-2644-1_21.