Jasem, A., AL Rawi, A. (2025). Artificial Intelligence and Smart Security Strategies for Crime ‎Reduction in Fallujah City-Iraq. , 2025(3), 874-896. doi: 10.37653/juah.2025.158127.1338
Ayat Hazem Jasem; Abdulnaser Sabri Shaher AL Rawi. "Artificial Intelligence and Smart Security Strategies for Crime ‎Reduction in Fallujah City-Iraq". , 2025, 3, 2025, 874-896. doi: 10.37653/juah.2025.158127.1338
Jasem, A., AL Rawi, A. (2025). 'Artificial Intelligence and Smart Security Strategies for Crime ‎Reduction in Fallujah City-Iraq', , 2025(3), pp. 874-896. doi: 10.37653/juah.2025.158127.1338
Jasem, A., AL Rawi, A. Artificial Intelligence and Smart Security Strategies for Crime ‎Reduction in Fallujah City-Iraq. , 2025; 2025(3): 874-896. doi: 10.37653/juah.2025.158127.1338

Artificial Intelligence and Smart Security Strategies for Crime ‎Reduction in Fallujah City-Iraq

Journal of University of Anbar for Humanities
Volume 2025, Issue 3, September 2025, Pages 874-896    PDF (5.11 M)
Document Type: Research Paper
DOI: 10.37653/juah.2025.158127.1338
Authors
Ayat Hazem Jasem* 1; Abdulnaser Sabri Shaher AL Rawi2
1University of Anbar - College of Arts
2University of Anbar - College of Education for Humanities
Abstract
Aims: The current study investigates the role of smart policing strategies in strengthening public safety and reducing crime in urban areas through the application of advanced technologies and artificial intelligence (AI). Specifically, it examines how tools such as Geographic Information Systems (GIS) and intelligent image analysis contribute to crime detection, prevention, and the development of strategic plans that enhance community security. Methodology: A descriptive-analytical approach was adopted, drawing on data from police department reports, field observations, and interviews with security personnel. The study also reviewed international literature on smart policing practices in developed countries and assessed the integration of AI and GIS into crime prevention. Spatial analysis and data modeling techniques were employed to identify crime patterns, high-risk zones, and underlying drivers of criminal activity. Results: The findings revealed that traditional policing approaches are inadequate in addressing the growing complexity of urban crime. In contrast, smart policing technologies have significantly enhanced the effectiveness of police operations. The establishment of advanced databases has streamlined data collection, storage, and analysis, while smart surveillance systems and image analysis tools have enabled proactive monitoring and early identification of criminal activities. These innovations have led to improved crime prevention strategies, reduced crime rates, and heightened public confidence in safety measures. Conclusions: The study demonstrates that smart policing strategies, supported by AI and GIS technologies, play a pivotal role in addressing the limitations of traditional policing in urban contexts. By enabling proactive surveillance, data-driven decision-making, and predictive crime prevention, these tools contribute to reducing crime rates and enhancing public safety. However, the success of such strategies depends on proper training, robust data governance, and strict adherence to privacy protections. Strengthening these elements will not only improve the efficiency of police operations but also build greater trust between security institutions and the community, thereby fostering safer and more resilient urban environments. Recommendations: The study recommends expanding the adoption of AI and GIS technologies within police departments, providing specialized training for personnel, and incorporating predictive analytics into crime prevention frameworks. It also emphasizes the importance of establishing clear policies on data collection, privacy safeguards, and real-time monitoring to ensure the responsible and effective application of smart policing tools in urban security.
Keywords
Artificial Intelligence; Urban Crime; Security; Sensing; Modern ‎Technologies
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