饼图邪恶吗?饼图和甜甜圈图与条形图相比的价值评估

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Information Visualization Pub Date : 2024-06-25 DOI:10.1177/14738716241259432
Andrew Hill
{"title":"饼图邪恶吗?饼图和甜甜圈图与条形图相比的价值评估","authors":"Andrew Hill","doi":"10.1177/14738716241259432","DOIUrl":null,"url":null,"abstract":"Many data visualization experts recommend the use of bar charts over pie charts because they consider comparing the area or angle of segments to be less accurate than comparing bars on a bar chart. However, many studies show that when the pie chart is used to estimate proportions (arguably its main function) it is as accurate as the bar chart. A major issue is that most previous studies have only looked at one method of extracting information from pie charts, for example either by comparing the segment to the circle (the part-whole relationship) or one segment to another (relative magnitude estimation). Therefore, in this study I test multiple metrics to provide a more holistic assessment of the pie and donut chart against the bar chart. I also measured cognitive load through pupillometry. In summary, bar charts were more precise than pie and donut charts for ranking elements, but all charts were equally accurate for extracting the part-whole relationship. There was little difference in cognitive load between chart types, although bar charts were consistently faster to use on average. Overall, the bar chart was more flexible, but where there were statistically significant differences between charts, the effect sizes were often small, and unlikely to prevent effective extraction of quantitative information. That is, as long as they were used appropriately, all chart types were arguably acceptable for displaying simple, categorical data.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Are pie charts evil? An assessment of the value of pie and donut charts compared to bar charts\",\"authors\":\"Andrew Hill\",\"doi\":\"10.1177/14738716241259432\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many data visualization experts recommend the use of bar charts over pie charts because they consider comparing the area or angle of segments to be less accurate than comparing bars on a bar chart. However, many studies show that when the pie chart is used to estimate proportions (arguably its main function) it is as accurate as the bar chart. A major issue is that most previous studies have only looked at one method of extracting information from pie charts, for example either by comparing the segment to the circle (the part-whole relationship) or one segment to another (relative magnitude estimation). Therefore, in this study I test multiple metrics to provide a more holistic assessment of the pie and donut chart against the bar chart. I also measured cognitive load through pupillometry. In summary, bar charts were more precise than pie and donut charts for ranking elements, but all charts were equally accurate for extracting the part-whole relationship. There was little difference in cognitive load between chart types, although bar charts were consistently faster to use on average. Overall, the bar chart was more flexible, but where there were statistically significant differences between charts, the effect sizes were often small, and unlikely to prevent effective extraction of quantitative information. That is, as long as they were used appropriately, all chart types were arguably acceptable for displaying simple, categorical data.\",\"PeriodicalId\":50360,\"journal\":{\"name\":\"Information Visualization\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Visualization\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1177/14738716241259432\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Visualization","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/14738716241259432","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

摘要

许多数据可视化专家建议使用柱形图而不是饼图,因为他们认为比较线段的面积或角度不如比较柱形图上的条形图准确。然而,许多研究表明,当饼状图用于估计比例时(可以说是其主要功能),其准确性不亚于条形图。一个主要的问题是,以前的大多数研究都只研究了从饼图中提取信息的一种方法,例如,要么是通过比较分段与圆圈(部分与整体的关系),要么是通过比较一个分段与另一个分段(相对大小估计)。因此,在本研究中,我测试了多个指标,以便对饼状图和甜甜圈图与条形图进行更全面的评估。我还通过瞳孔测量法测量了认知负荷。总之,条形图比饼状图和甜甜圈图在元素排序方面更精确,但所有图表在提取部分与整体的关系方面同样精确。虽然条形图的平均使用速度更快,但不同类型图表之间的认知负荷差别不大。总体而言,条形图更灵活,但如果图表之间存在统计学意义上的显著差异,其影响大小往往很小,不太可能妨碍有效提取定量信息。也就是说,只要使用得当,所有图表类型都可以用来显示简单的分类数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Are pie charts evil? An assessment of the value of pie and donut charts compared to bar charts
Many data visualization experts recommend the use of bar charts over pie charts because they consider comparing the area or angle of segments to be less accurate than comparing bars on a bar chart. However, many studies show that when the pie chart is used to estimate proportions (arguably its main function) it is as accurate as the bar chart. A major issue is that most previous studies have only looked at one method of extracting information from pie charts, for example either by comparing the segment to the circle (the part-whole relationship) or one segment to another (relative magnitude estimation). Therefore, in this study I test multiple metrics to provide a more holistic assessment of the pie and donut chart against the bar chart. I also measured cognitive load through pupillometry. In summary, bar charts were more precise than pie and donut charts for ranking elements, but all charts were equally accurate for extracting the part-whole relationship. There was little difference in cognitive load between chart types, although bar charts were consistently faster to use on average. Overall, the bar chart was more flexible, but where there were statistically significant differences between charts, the effect sizes were often small, and unlikely to prevent effective extraction of quantitative information. That is, as long as they were used appropriately, all chart types were arguably acceptable for displaying simple, categorical data.
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
Information Visualization
Information Visualization COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
5.40
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Information Visualization is essential reading for researchers and practitioners of information visualization and is of interest to computer scientists and data analysts working on related specialisms. This journal is an international, peer-reviewed journal publishing articles on fundamental research and applications of information visualization. The journal acts as a dedicated forum for the theories, methodologies, techniques and evaluations of information visualization and its applications. The journal is a core vehicle for developing a generic research agenda for the field by identifying and developing the unique and significant aspects of information visualization. Emphasis is placed on interdisciplinary material and on the close connection between theory and practice. This journal is a member of the Committee on Publication Ethics (COPE).
期刊最新文献
Multidimensional data visualization and synchronization for revealing hidden pandemic information Interactive visual formula composition of multidimensional data classifiers Exploring annotation taxonomy in grouped bar charts: A qualitative classroom study Designing complex network visualisations using the wayfinding map metaphor Graph & Network Visualization and Beyond
×
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