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When Red Means Good, Bad, or Canada: Exploring People’s Reasoning for Choosing Color Palettes 当红色意味着好，坏，或加拿大:探索人们选择调色板的原因
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623314
Jarryullah Ahmad, Elaine Huynh, Fanny Chevalier
{"title":"When Red Means Good, Bad, or Canada: Exploring People’s Reasoning for Choosing Color Palettes","authors":"Jarryullah Ahmad, Elaine Huynh, Fanny Chevalier","doi":"10.1109/VIS49827.2021.9623314","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623314","url":null,"abstract":"Color palette selection is an essential aspect of visualization design, influencing data interpretation and evoking emotions in the viewer. Rules of thumb grounded in perceptual science and visual arts generally form the basis of recommendation tools to support color assignment, but palette design is more nuanced than optimizing for perceptual tasks. In this work, we investigate how the general public reconciles the varied facets of color design in visualization. Does their decision-making align with established rules of thumb? What factors do they take into consideration? Through a crowd-sourced study with 63 participants, we find that the majority of palette choices are perceptually motivated, but other factors such as semantic associations and bias also play a role. We identify some flaws in participant reasoning, highlight clashes in opinions, and present some implications for future work in this space.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134599031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
TimeElide: Visual Analysis of Non-Contiguous Time Series Slices TimeElide:非连续时间序列切片的可视化分析
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623320
Michael Oppermann, Luce Liu, T. Munzner
{"title":"TimeElide: Visual Analysis of Non-Contiguous Time Series Slices","authors":"Michael Oppermann, Luce Liu, T. Munzner","doi":"10.1109/VIS49827.2021.9623320","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623320","url":null,"abstract":"We introduce the design and implementation of TimeElide, a visual analysis tool for the novel data abstraction of non-contiguous time series slices, namely time intervals that contain a sequence of time-value pairs but are not adjacent to each other. This abstraction is relevant for analysis tasks where time periods of interest are known in advance or inferred from the data, rather than discovered through open-ended visual exploration. We present a visual encoding design space as an underpinning of TimeElide, and the new sparkbox technique for visualizing fine and coarse grained temporal structures within one view. Datasets from different domains and with varying characteristics guided the development and their analysis provides preliminary evidence of TimeElide’s utility. We provide open-source code and demo at https://github.com/UBC-InfoVis/time-elide and supplemental materials at https://osf.io/yqvmf/.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132981532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Visually Connecting Historical Figures Through Event Knowledge Graphs 通过事件知识图直观地连接历史人物
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-09-20 DOI: 10.1109/VIS49827.2021.9623313
Shahid Latif, S. Agarwal, Simon Gottschalk, Carina Chrosch, Felix Feit, Johannes Jahn, Tobias Braun, Yanick Christian Tchenko, Elena Demidova, Fabian Beck
{"title":"Visually Connecting Historical Figures Through Event Knowledge Graphs","authors":"Shahid Latif, S. Agarwal, Simon Gottschalk, Carina Chrosch, Felix Feit, Johannes Jahn, Tobias Braun, Yanick Christian Tchenko, Elena Demidova, Fabian Beck","doi":"10.1109/VIS49827.2021.9623313","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623313","url":null,"abstract":"Knowledge graphs store information about historical figures and their relationships indirectly through shared events. We developed a visualization system, VisKonnect, for analyzing the intertwined lives of historical figures based on the events they participated in. A user’s query is parsed for identifying named entities, and related data is retrieved from an event knowledge graph. While a short textual answer to the query is generated using the GPT-3 language model, various linked visualizations provide context, display additional information related to the query, and allow exploration.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116396957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Automatic Y-axis Rescaling in Dynamic Visualizations 动态可视化中的自动y轴重新缩放
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-09-20 DOI: 10.1109/VIS49827.2021.9623319
J. Fisher, Remco Chang, Eugene Wu
{"title":"Automatic Y-axis Rescaling in Dynamic Visualizations","authors":"J. Fisher, Remco Chang, Eugene Wu","doi":"10.1109/VIS49827.2021.9623319","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623319","url":null,"abstract":"Animated and interactive data visualizations dynamically change the data rendered in a visualization (e.g., bar chart). As the data changes, the y-axis may need to be rescaled as the domain of the data changes. Each axis rescaling potentially improves the readability of the current chart, but may also disorient the user. In contrast to static visualizations, where there is considerable literature to help choose the appropriate y-axis scale, there is a lack of guidance about how and when rescaling should be used in dynamic visualizations. Existing visualization systems and libraries adapt a fixed global y-axis, or rescale every time the data changes. Yet, professional visualizations, such as in data journalism, do not adopt either strategy. They instead carefully and manually choose when to rescale based on the analysis task and data. To this end, we conduct a series of Mechanical Turk experiments to study the potential of dynamic axis rescaling and the factors that affect its effectiveness. We find that the appropriate rescaling policy is both task- and data-dependent, and we do not find one clear policy choice for all situations.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122708404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets 视觉因子在分类规则集理解中的探索与验证
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-09-19 DOI: 10.1109/VIS49827.2021.9623303
Jun Yuan, O. Nov, E. Bertini
{"title":"An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets","authors":"Jun Yuan, O. Nov, E. Bertini","doi":"10.1109/VIS49827.2021.9623303","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623303","url":null,"abstract":"Rule sets are often used in Machine Learning (ML) as a way to communicate the model logic in settings where transparency and intelligibility are necessary. Rule sets are typically presented as a text-based list of logical statements (rules). Surprisingly, to date there has been limited work on exploring visual alternatives for presenting rules. In this paper, we explore the idea of designing alternative representations of rules, focusing on a number of visual factors we believe have a positive impact on rule readability and understanding. We then presents a user study exploring their impact. The results show that some design factors have a strong impact on how efficiently readers can process the rules while having minimal impact on accuracy. This work can help practitioners employ more effective solutions when using rules as a communication strategy to understand ML models.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122295724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Segmentation Driven Peeling for Visual Analysis of Electronic Transitions 电子跃迁视觉分析的分割驱动剥离
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-09-18 DOI: 10.1109/VIS49827.2021.9623300
Mohit K. Sharma, Talha Bin Masood, S. S. Thygesen, M. Linares, I. Hotz, V. Natarajan
{"title":"Segmentation Driven Peeling for Visual Analysis of Electronic Transitions","authors":"Mohit K. Sharma, Talha Bin Masood, S. S. Thygesen, M. Linares, I. Hotz, V. Natarajan","doi":"10.1109/VIS49827.2021.9623300","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623300","url":null,"abstract":"Electronic transitions in molecules due to absorption or emission of light is a complex quantum mechanical process. Their study plays an important role in the design of novel materials. A common yet challenging task in the study is to determine the nature of those electronic transitions, i.e. which subgroups of the molecule are involved in the transition by donating or accepting electrons, followed by an investigation of the variation in the donor-acceptor behavior for different transitions or conformations of the molecules. In this paper, we present a novel approach towards the study of electronic transitions based on the visual analysis of a bivariate field, namely the electron density in the hole and particle Natural Transition Orbital (NTO). The visual analysis focuses on the continuous scatter plots (CSPs) of the bivariate field linked to their spatial domain. The method supports selections in the CSP visualized as fiber surfaces in the spatial domain, the grouping of atoms, and segmentation of the density fields to peel the CSP. This peeling operator is central to the visual analysis process and helps identify donors and acceptors. We study different molecular systems, identifying local excitation and charge transfer excitations to demonstrate the utility of the method.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116759679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Understanding the Effects of Visualizing Missing Values on Visual Data Exploration 理解可视化缺失值对可视化数据探索的影响
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-09-17 DOI: 10.1109/VIS49827.2021.9623328
Hayeong Song, Yu Fu, B. Saket, J. Stasko
{"title":"Understanding the Effects of Visualizing Missing Values on Visual Data Exploration","authors":"Hayeong Song, Yu Fu, B. Saket, J. Stasko","doi":"10.1109/VIS49827.2021.9623328","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623328","url":null,"abstract":"When performing data analysis, people often confront data sets containing missing values. We conducted an empirical study to understand the effect of visualizing those missing values on participants’ decision-making processes while performing a visual data exploration task. More specifically, our study participants purchased a hypothetical portfolio of stocks based on a data set where some stocks had missing values for attributes such as PE ratio, beta, and EPS. The experiment used scatterplots to communicate the stock data. For one group of participants, stocks with missing values simply were not shown, while the second group saw such stocks depicted with estimated values as points with error bars. We measured participants’ cognitive load involved in decision-making with data with missing values. Our results indicate that their decision-making workflow was different across two conditions.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122141019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Histogram binning revisited with a focus on human perception 以人类感知为重点重新审视了直方图分割
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-09-14 DOI: 10.1109/VIS49827.2021.9623301
Raphael Sahann, Torsten Möller, Johanna Schmidt
{"title":"Histogram binning revisited with a focus on human perception","authors":"Raphael Sahann, Torsten Möller, Johanna Schmidt","doi":"10.1109/VIS49827.2021.9623301","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623301","url":null,"abstract":"This paper presents a quantitative user study to evaluate how well users can visually perceive the underlying data distribution from a histogram representation. We used different sample and bin sizes and four different distributions (uniform, normal, bimodal, and gamma). The study results confirm that, in general, more bins correlate with fewer errors by the viewers. However, upon a certain number of bins, the error rate cannot be improved by adding more bins. By comparing our study results with the outcomes of existing mathematical models for histogram binning (e.g., Sturges’ formula, Scott’s normal reference rule, the Rice Rule, or Freedman–Diaconis’ choice), we can see that most of them overestimate the number of bins necessary to make the distribution visible to a human viewer.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122111083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation 建议:机器学习模型验证的聚合视觉反事实解释
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-09-12 DOI: 10.1109/VIS49827.2021.9623271
Oscar Gomez, Steffen Holter, Jun Yuan, E. Bertini
{"title":"AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation","authors":"Oscar Gomez, Steffen Holter, Jun Yuan, E. Bertini","doi":"10.1109/VIS49827.2021.9623271","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623271","url":null,"abstract":"Rapid improvements in the performance of machine learning models have pushed them to the forefront of data-driven decision-making. Meanwhile, the increased integration of these models into various application domains has further highlighted the need for greater interpretability and transparency. To identify problems such as bias, overfitting, and incorrect correlations, data scientists require tools that explain the mechanisms with which these model decisions are made. In this paper we introduce AdViCE, a visual analytics tool that aims to guide users in black-box model debugging and validation. The solution rests on two main visual user interface innovations: (1) an interactive visualization design that enables the comparison of decisions on user-defined data subsets; (2) an algorithm and visual design to compute and visualize counterfactual explanations - explanations that depict model outcomes when data features are perturbed from their original values. We provide a demonstration of the tool through a use case that showcases the capabilities and potential limitations of the proposed approach.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124401065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
VAINE: Visualization and AI for Natural Experiments VAINE:自然实验的可视化和人工智能
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-09-09 DOI: 10.1109/VIS49827.2021.9623285
G. Guo, M. Glenski, Z. Shaw, Emily Saldanha, A. Endert, Svitlana Volkova, Dustin L. Arendt
{"title":"VAINE: Visualization and AI for Natural Experiments","authors":"G. Guo, M. Glenski, Z. Shaw, Emily Saldanha, A. Endert, Svitlana Volkova, Dustin L. Arendt","doi":"10.1109/VIS49827.2021.9623285","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623285","url":null,"abstract":"Natural experiments are observational studies where the assignment of treatment conditions to different populations occurs by chance“in the wild”. Researchers from fields such as economics, healthcare, and the social sciences leverage natural experiments to conduct hypothesis testing and causal effect estimation for treatment and outcome variables that would otherwise be costly, infeasible, or unethical. In this paper, we introduce VAINE (Visualization and AI for Natural Experiments), a visual analytics tool for identifying and understanding natural experiments from observational data. We then demonstrate how VAINE can be used to validate causal relationships, estimate average treatment effects, and identify statistical phenomena such as Simpson’s paradox through two usage scenarios.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124376937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
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