Examining data visualization pitfalls in scientific publications

作     者：Vinh T Nguyen Kwanghee Jung Vibhuti Gupta 

作者机构：Department of Information TechnologyTNU–University of Information and Communication TechnologyThai NguyenVietnam Department of Educational PsychologyLeadershipand CounselingTexas Tech UniversityLubbockTX 79409United States Department of Computer Science and Data ScienceMeharry Medical CollegeNashvilleTN 37208USA 

出 版 物：《Visual Computing for Industry,Biomedicine,and Art》 (工医艺的可视计算（英文）)

年 卷 期：2021年第4卷第1期

页      面：268-282页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Data visualization Graphical representations Misinformation Visual encodings Association rule mining Word cloud Cochran’s Q test McNemar’s test 

摘      要：Data visualization blends art and science to convey stories from data via graphical *** different problems,applications,requirements,and design goals,it is challenging to combine these two components at their full *** the art component involves creating visually appealing and easily interpreted graphics for users,the science component requires accurate representations of a large amount of input *** a lack of the science component,visualization cannot serve its role of creating correct representations of the actual data,thus leading to wrong perception,interpretation,and *** might be even worse if incorrect visual representations were intentionally produced to deceive the *** address common pitfalls in graphical representations,this paper focuses on identifying and understanding the root causes of misinformation in graphical *** reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color,shape,size,and spatial ***,a text mining technique was applied to extract practical insights from common visualization ***’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color,shape,size,and spatial *** findings showed that the pie chart is the most misused graphical representation,and size is the most critical *** was also observed that there were statistically significant differences in the proportion of errors among color,shape,size,and spatial orientation.