Abstract
This study presents «trajectory_analyzer», a Python-based system designed for the forensic analysis and visualization of geolocation data extracted from mobile devices. With the increasing volume of spatial-temporal data collected from sources such as GPS, Wi-Fi, and image metadata, forensic professionals face growing challenges in structuring and interpreting mobility patterns. Existing solutions often lack flexibility, require supervised models, or depend on proprietary infrastructure. Our approach applies an unsupervised DBSCAN-based trajectory clustering method, temporal ordering, and a real-time web map interface to reveal behavioral insights without the need for manual labeling or cloud services. Compared to prior research, the system improves spatial accuracy, source transparency, and visual clarity. Experimental results show that the proposed clustering method identifies movement clusters and transitions with high precision and responsiveness, while maintaining full offline operability. However, this improvement comes at the expense of more local storage because of embedded map tiles. Overall, this work provides a practical, understandable, and independent foundation for investigators dealing with unstructured multi-source geolocation data.