{"title":"从仪表盘动物园到人口普查:使用 Tableau Public 的案例研究。","authors":"Arjun Srinivasan, Joanna Purich, Michael Correll, Leilani Battle, Vidya Setlur, Anamaria Crisan","doi":"10.1109/TVCG.2024.3490259","DOIUrl":null,"url":null,"abstract":"<p><p>Dashboards remain ubiquitous tools for analyzing data and disseminating the findings. Understanding the range of dashboard designs, from simple to complex, can support development of authoring tools that enable end-users to meet their analysis and communication goals. Yet, there has been little work that provides a quantifiable, systematic, and descriptive overview of dashboard design patterns. Instead, existing approaches only consider a handful of designs, which limits the breadth of patterns that can be surfaced. More quantifiable approaches, inspired by machine learning (ML), are presently limited to single visualizations or capture narrow features of dashboard designs. To address this gap, we present an approach for modeling the content and composition of dashboards using a graph representation. The graph decomposes dashboard designs into nodes featuring content \"blocks'; and uses edges to model \"relationships\", such as layout proximity and interaction, between nodes. To demonstrate the utility of this approach, and its extension over prior work, we apply this representation to derive a census of 25,620 dashboards from Tableau Public, providing a descriptive overview of the core building blocks of dashboards in the wild and summarizing prevalent dashboard design patterns. We discuss concrete applications of both a graph representation for dashboard designs and the resulting census to guide the development of dashboard authoring tools, making dashboards accessible, and for leveraging AI/ML techniques. Our findings underscore the importance of meeting users where they are by broadly cataloging dashboard designs, both common and exotic.</p>","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From Dashboard Zoo to Census: A Case Study With Tableau Public.\",\"authors\":\"Arjun Srinivasan, Joanna Purich, Michael Correll, Leilani Battle, Vidya Setlur, Anamaria Crisan\",\"doi\":\"10.1109/TVCG.2024.3490259\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Dashboards remain ubiquitous tools for analyzing data and disseminating the findings. Understanding the range of dashboard designs, from simple to complex, can support development of authoring tools that enable end-users to meet their analysis and communication goals. Yet, there has been little work that provides a quantifiable, systematic, and descriptive overview of dashboard design patterns. Instead, existing approaches only consider a handful of designs, which limits the breadth of patterns that can be surfaced. More quantifiable approaches, inspired by machine learning (ML), are presently limited to single visualizations or capture narrow features of dashboard designs. To address this gap, we present an approach for modeling the content and composition of dashboards using a graph representation. The graph decomposes dashboard designs into nodes featuring content \\\"blocks'; and uses edges to model \\\"relationships\\\", such as layout proximity and interaction, between nodes. To demonstrate the utility of this approach, and its extension over prior work, we apply this representation to derive a census of 25,620 dashboards from Tableau Public, providing a descriptive overview of the core building blocks of dashboards in the wild and summarizing prevalent dashboard design patterns. We discuss concrete applications of both a graph representation for dashboard designs and the resulting census to guide the development of dashboard authoring tools, making dashboards accessible, and for leveraging AI/ML techniques. Our findings underscore the importance of meeting users where they are by broadly cataloging dashboard designs, both common and exotic.</p>\",\"PeriodicalId\":94035,\"journal\":{\"name\":\"IEEE transactions on visualization and computer graphics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on visualization and computer graphics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TVCG.2024.3490259\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on visualization and computer graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TVCG.2024.3490259","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
仪表盘仍然是分析数据和传播结果的常用工具。了解从简单到复杂的各种仪表盘设计,有助于开发能帮助最终用户实现分析和交流目标的制作工具。然而,对仪表盘设计模式进行量化、系统化和描述性概述的工作还很少。相反,现有的方法只考虑了少数几种设计,这就限制了可浮现的模式的广度。受机器学习(ML)启发的更多量化方法目前仅限于单一的可视化或捕捉仪表盘设计的狭隘特征。为了弥补这一不足,我们提出了一种使用图表示仪表盘内容和组成的建模方法。该图将仪表盘设计分解为以内容 "块 "为特征的节点,并使用边来模拟节点之间的 "关系",如布局接近性和交互性。为了证明这种方法的实用性及其对先前工作的扩展,我们应用这种表示法对 Tableau Public 中的 25,620 个仪表盘进行了普查,提供了对主流仪表盘核心构件的描述性概述,并总结了流行的仪表盘设计模式。我们讨论了仪表盘设计图表表示法的具体应用以及由此产生的普查,以指导仪表盘制作工具的开发、仪表盘的可访问性以及人工智能/ML 技术的利用。我们的发现强调了通过对常见和奇特的仪表盘设计进行广泛编目来满足用户需求的重要性。
From Dashboard Zoo to Census: A Case Study With Tableau Public.
Dashboards remain ubiquitous tools for analyzing data and disseminating the findings. Understanding the range of dashboard designs, from simple to complex, can support development of authoring tools that enable end-users to meet their analysis and communication goals. Yet, there has been little work that provides a quantifiable, systematic, and descriptive overview of dashboard design patterns. Instead, existing approaches only consider a handful of designs, which limits the breadth of patterns that can be surfaced. More quantifiable approaches, inspired by machine learning (ML), are presently limited to single visualizations or capture narrow features of dashboard designs. To address this gap, we present an approach for modeling the content and composition of dashboards using a graph representation. The graph decomposes dashboard designs into nodes featuring content "blocks'; and uses edges to model "relationships", such as layout proximity and interaction, between nodes. To demonstrate the utility of this approach, and its extension over prior work, we apply this representation to derive a census of 25,620 dashboards from Tableau Public, providing a descriptive overview of the core building blocks of dashboards in the wild and summarizing prevalent dashboard design patterns. We discuss concrete applications of both a graph representation for dashboard designs and the resulting census to guide the development of dashboard authoring tools, making dashboards accessible, and for leveraging AI/ML techniques. Our findings underscore the importance of meeting users where they are by broadly cataloging dashboard designs, both common and exotic.