Visual Analytics for Detecting Illegal Transport Activities
Yi Shan - Fudan University, Shanghai, China
Aolin Guo - Fudan University, Shanghai, China
Zekai Shao - Fudan University, Shanghai, China
Tian Qiu - Fudan University, Shanghai, China
Xueli Shu - Fudan University, Shanghai, China
Qianhui Li - Fudan University, Shanghai, China
Siming Chen - Fudan University, Shanghai, China
Room: Bayshore II
2024-10-13T12:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-13T12:30:00Z
Abstract
This paper presents a visual analytics system designed to address the IEEE VAST Challenge 2024 Mini-Challenge 2. The system can support the matching and anomaly detection of multi-source heterogeneous spatio-temporal data, thereby enabling the detection of illegal transport activities. The primary contribution of the system lies in its analysis-driven interaction design.