Visualization and visual analysis of vessel trajectory data: A survey

IF 3.8 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Visual Informatics Pub Date : 2021-12-01 DOI:10.1016/j.visinf.2021.10.002
Haiyan Liu , Xiaohui Chen , Yidi Wang , Bing Zhang , Yunpeng Chen , Ying Zhao , Fangfang Zhou
{"title":"Visualization and visual analysis of vessel trajectory data: A survey","authors":"Haiyan Liu ,&nbsp;Xiaohui Chen ,&nbsp;Yidi Wang ,&nbsp;Bing Zhang ,&nbsp;Yunpeng Chen ,&nbsp;Ying Zhao ,&nbsp;Fangfang Zhou","doi":"10.1016/j.visinf.2021.10.002","DOIUrl":null,"url":null,"abstract":"<div><p>Maritime transports play a critical role in international trade and commerce. Massive vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial–temporal patterns of vessel navigations. Analyzing and understanding these patterns are valuable for maritime traffic surveillance and management. As essential techniques in complex data analysis and understanding, visualization and visual analysis have been widely used in vessel trajectory data analysis. This paper presents a literature review on the visualization and visual analysis of vessel trajectory data. First, we introduce commonly used vessel trajectory data sets and summarize main operations in vessel trajectory data preprocessing. Then, we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in details. Finally, we expound on the prospects of the remaining challenges and directions for future research.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X21000401/pdfft?md5=c0cbcf1e197c069abdaada9d25a133d0&pid=1-s2.0-S2468502X21000401-main.pdf","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visual Informatics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468502X21000401","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 19

Abstract

Maritime transports play a critical role in international trade and commerce. Massive vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial–temporal patterns of vessel navigations. Analyzing and understanding these patterns are valuable for maritime traffic surveillance and management. As essential techniques in complex data analysis and understanding, visualization and visual analysis have been widely used in vessel trajectory data analysis. This paper presents a literature review on the visualization and visual analysis of vessel trajectory data. First, we introduce commonly used vessel trajectory data sets and summarize main operations in vessel trajectory data preprocessing. Then, we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in details. Finally, we expound on the prospects of the remaining challenges and directions for future research.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
船舶轨迹数据的可视化与可视化分析综述
海运在国际贸易和商业中起着至关重要的作用。世界各地航行的大量船舶不断产生船舶轨迹数据,这些数据包含丰富的船舶航行时空模式。分析和理解这些模式对海上交通监控和管理具有重要意义。可视化和可视化分析作为复杂数据分析和理解的关键技术,在船舶轨迹数据分析中得到了广泛的应用。本文对船舶航迹数据的可视化和可视化分析进行了综述。首先介绍了常用的船舶轨迹数据集,总结了船舶轨迹数据预处理的主要操作。然后,在现有方法的基础上,对船舶轨迹数据可视化和可视化分析进行了分类,并详细介绍了代表性的研究成果。最后,对研究中存在的挑战和未来的研究方向进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Visual Informatics
Visual Informatics Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.70
自引率
3.30%
发文量
33
审稿时长
79 days
期刊最新文献
RelicCARD: Enhancing cultural relics exploration through semantics-based augmented reality tangible interaction design JobViz: Skill-driven visual exploration of job advertisements Visual evaluation of graph representation learning based on the presentation of community structures DPKnob: A visual analysis approach to risk-aware formulation of differential privacy schemes for data query scenarios Visual exploration of multi-dimensional data via rule-based sample embedding
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1