@inproceedings{58788aee9cea4649915a203303b5bd0b,
title = "Visual Analysis of Ship Trajectories Based on Kernel Density Estimation",
abstract = "Maritime data, such as Automatic Identification System (AIS)-based, are characterized as high volume and complex. Utilizing these data and discovering potential value is challenging for the maritime domain. This paper provides a framework for visual analysis of ship trajectory data. This framework first performs a wrangling process to get clean data. Then the density of ships over the map is estimated by the kernel density estimator (KDE). Furthermore, an interactive 3D model is carried out based on the estimated density for further analysis. We applied the framework to analyze the shipping traffic in the Qiongzhou Strait of China. The results prove that our framework can efficiently depict the ship's trajectory and regional ship operation and analyze the ship's behavior in a local area. We assert our work can fruitfully support further analysis and prediction of the ship's movement mode and detection of abnormal behavior.",
keywords = "AIS, human-data interaction, trajectory, visualization",
author = "Juhong Shi and Yushan Pan and Yang Xiang and Xinpeng Liu and Yihong Wang and Chengtao Ji",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th International Conference on Virtual Reality, ICVR 2023 ; Conference date: 12-05-2023 Through 14-05-2023",
year = "2023",
doi = "10.1109/ICVR57957.2023.10169721",
language = "English",
series = "2023 9th International Conference on Virtual Reality, ICVR 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "276--283",
booktitle = "2023 9th International Conference on Virtual Reality, ICVR 2023",
}