AI and Machine Learning-Enabled Cognitive Digital Twin for Crime Hotspot Detection and Analysis

A cognitive digital twin using AI and machine learning is proposed in this study. System of crime hot spot identification and investigations in cities. The suggested system forms up a simulated copy of city areas through blending historical crime observations, the environment and real. Introduction of time sensors to model, simulate and predict criminal activity. In this cognitive brother, machine learning algorithms such as time-series forecasting, classification and clustering techniques are used to determine the spatial and temporal trends of crime, which will make the high-risk regions predictable. The AI motor cognitive layer improves reasoning and flexibility in the digital space and enables the twin to constantly learn with new information and simulate different policing policies, including optimized patrol pathfinding and allocation of resources. The prediction of data by transforming both the real-time and non-real-time data into predictive knowledge. System facilitates decision-making proactively and effective law enforcement planning. The results demonstrate that the AI-ML-powered Cognitive Digital Twin (CDT) has a strong enhancement in the accuracy of crime hotspots prediction and situational consciousness, building scalable, adaptive, and data-driven system of smart, preventive policing and urban safety enhancement.