Towards Better Illegal Chemical Facility Detection with Hazardous Chemicals Transportation Trajectories

Unregistered illegal facilities that do not qualify for chemical production pose substantial threats to human lives and the environment. For human safety and environmental protection, the government
needs to figure out the illegal facilities and shut them down. A new, convenient, and affordable approach to detect such facilities is to analyze the trajectories of hazardous chemicals transportation (HCT) trucks. The existing study leverages a machine learning model to predict how likely a place is illegal. However, such a model lacks interpretability and cannot provide actionable justifications required for decision-making. In this study, we collaborate with HCT experts
and propose an interactive visual analytics approach to explore the suspicious stay points, analyze abnormal HCT truck behaviors, and figure out unregistered illegal chemical facilities. First, experts receive an initial result from the detection model for reference. Then, they are supported to check the detailed information of the suspicious places with three coordinated views. We apply a visualization that tightly encodes the geo-referred movement activities along the timeline to present the HCT truck behaviors, which can help experts finally verify their conclusions. We demonstrate the effectiveness of the system with two case studies on real-world data. We also received experts’ positive feedback from an expert interview.