首页|Towards better illegal chemical facility detection with hazardous chemicals transportation trajectories

Towards better illegal chemical facility detection with hazardous chemicals transportation trajectories

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  • NETL
  • NSTL
  • Springer Nature
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.

Stay pointSpatialtemporal analysisHazardous chemicals transportationVisual analysis

Junxiu Tang、Huimin Ren、Zikun Deng、Di Weng、Tan Tang、Lingyun Yu、Jie Bao、Yu Zheng、Yingcai Wu

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State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, Zhejiang, China

Li Auto Inc, Beijing, Beijing 101318, China

School of Software Engineering, South China University of Technology, Guangzhou 510641, Guangdong, China

School of Software Technology, Zhejiang University, Ningbo 315103, Zhejiang, China

School of Art and Archeology, Zhejiang University, Hangzhou 315103, Zhejiang, China

Department of Computing, Xi'an Jiaotong-Liverpool University, Suzhou 215123, Jiangsu, China

JD iCity, JD Technology, Beijing, Beijing 100176, China||JD Intelligent Cities Research, Beijing, Beijing 100176, China

JD Intelligent Cities Research, Beijing, Beijing 100176, China

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2025

10.1007/s12650-025-01055-8

Journal of visualization

Journal of visualization

ISSN：1343-8875
年,卷(期)：2025.28(3)
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