沉浸式虚拟环境中时间趋势可视化的散点图变量比较

Carlos Quijano-Chavez, L. Nedel, C. Freitas
{"title":"沉浸式虚拟环境中时间趋势可视化的散点图变量比较","authors":"Carlos Quijano-Chavez, L. Nedel, C. Freitas","doi":"10.1109/VR55154.2023.00082","DOIUrl":null,"url":null,"abstract":"Trends are changes in variables or attributes over time, often represented by line plots or scatterplot variants, with time being one of the axes. Interpreting tendencies and estimating trends require observing the lines or points behavior regarding increments, decrements, or both (reversals) in the value of the observed variable. Previous work assessed variants of scatterplots like Animation, Small Multiples, and Overlaid Trails for comparing the effectiveness of trends representation using large and small displays and found differences between them. In this work, we study how best to enable the analyst to explore and perform temporal trend tasks with these same techniques in immersive virtual environments. We designed and conducted a user study based on the approaches followed by previous works regarding visualization and interaction techniques, as well as tasks for comparisons in three-dimensional settings. Results show that Overlaid Trails are the fastest overall, followed by Animation and Small Multiples, while accuracy is task-dependent. We also report results from interaction measures and questionnaires.","PeriodicalId":346767,"journal":{"name":"2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparing Scatterplot Variants for Temporal Trends Visualization in Immersive Virtual Environments\",\"authors\":\"Carlos Quijano-Chavez, L. Nedel, C. Freitas\",\"doi\":\"10.1109/VR55154.2023.00082\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Trends are changes in variables or attributes over time, often represented by line plots or scatterplot variants, with time being one of the axes. Interpreting tendencies and estimating trends require observing the lines or points behavior regarding increments, decrements, or both (reversals) in the value of the observed variable. Previous work assessed variants of scatterplots like Animation, Small Multiples, and Overlaid Trails for comparing the effectiveness of trends representation using large and small displays and found differences between them. In this work, we study how best to enable the analyst to explore and perform temporal trend tasks with these same techniques in immersive virtual environments. We designed and conducted a user study based on the approaches followed by previous works regarding visualization and interaction techniques, as well as tasks for comparisons in three-dimensional settings. Results show that Overlaid Trails are the fastest overall, followed by Animation and Small Multiples, while accuracy is task-dependent. We also report results from interaction measures and questionnaires.\",\"PeriodicalId\":346767,\"journal\":{\"name\":\"2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VR55154.2023.00082\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VR55154.2023.00082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

趋势是变量或属性随时间的变化,通常用线形图或散点图变体表示,时间是轴之一。解释趋势和估计趋势需要观察与观察变量值的增量、减量或两者(反转)有关的线或点的行为。之前的工作评估了散点图的变体,如动画、小倍数和覆盖轨迹,以比较使用大型和小型显示器的趋势表示的有效性,并发现它们之间的差异。在这项工作中,我们研究了如何最好地使分析师能够在沉浸式虚拟环境中使用这些相同的技术探索和执行时间趋势任务。我们根据之前关于可视化和交互技术的工作所遵循的方法,以及在三维环境中进行比较的任务,设计并进行了一项用户研究。结果表明,覆盖轨迹是最快的,其次是动画和小倍数,而精度是任务相关的。我们还报告了互动测量和问卷调查的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing Scatterplot Variants for Temporal Trends Visualization in Immersive Virtual Environments
Trends are changes in variables or attributes over time, often represented by line plots or scatterplot variants, with time being one of the axes. Interpreting tendencies and estimating trends require observing the lines or points behavior regarding increments, decrements, or both (reversals) in the value of the observed variable. Previous work assessed variants of scatterplots like Animation, Small Multiples, and Overlaid Trails for comparing the effectiveness of trends representation using large and small displays and found differences between them. In this work, we study how best to enable the analyst to explore and perform temporal trend tasks with these same techniques in immersive virtual environments. We designed and conducted a user study based on the approaches followed by previous works regarding visualization and interaction techniques, as well as tasks for comparisons in three-dimensional settings. Results show that Overlaid Trails are the fastest overall, followed by Animation and Small Multiples, while accuracy is task-dependent. We also report results from interaction measures and questionnaires.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信