HCC: Small: Collaborative Research: Graphical Perception Revisited: Developing and Validating Design Guidelines for Data Visualization

HCC：小型：协作研究：图形感知重温：开发和验证数据可视化设计指南

• 批准号: 1016920
• 负责人: Maneesh Agrawala
• 金额: $ 25万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016920&HistoricalAwards=false
• 关键词: HCC; Small; Collaborative; Research; Graphical; HCC; Small; Collaborative; Research; Graphical

Well-designed visualizations leverage the human visual system to help people understand large data sets. Yet, producing effective visualizations is a challenging design task. Designers must carefully choose how to map the data to visual variables such as position, size, shape and color. In this process they make hundreds of nuanced judgments while balancing perceptual and cognitive tradeoffs. In response, researchers in psychology, cartography, statistics, and computer science have investigated the effects of different visual variables on graphical perception: the ability of viewers to interpret visual encodings and thereby decode information in graphs. Despite great progress in developing design guidelines based on laboratory experiments, comprehensive evaluation of the visualization design space and real-world validation of the resulting guidelines have remained elusive.The research is advancing our understanding of graphical perception and formulate new guidelines for visualization design. The research involves new experiments to address unresolved issues in graphical perception, including large-scale web-based studies using crowdsourcing techniques and controlled laboratory studies using sensitive measurements, namely eye-tracking. The investigators are applying the results of these studies to (a) develop guidelines for effective visualization design, (b) instantiate these guidelines in automated design procedures, and (c) validate the guidelines and resulting tools through case study deployments.

精心设计的可视化利用人类视觉系统来帮助人们了解大型数据集。但是，产生有效的可视化是一项具有挑战性的设计任务。设计师必须仔细选择如何将数据映射到视觉变量，例如位置，大小，形状和颜色。在此过程中，他们做出了数百个细微的判断，同时平衡了知觉和认知权衡。作为回应，心理学，制图，统计和计算机科学领域的研究人员研究了不同视觉变量对图形感知的影响：观众解释视觉编码的能力，从而在图中解码信息。尽管基于实验室实验制定设计指南方面取得了长足的进步，但对可视化设计空间以及对所得指南的现实验证的全面评估仍然难以捉摸。这项研究正在促进我们对图形感知的理解，并为可视化设计制定新的指南。这项研究涉及新的实验，以解决图形感知中未解决的问题，包括使用众包技术和使用敏感测量的基于众包技术的大规模网络研究，即眼神跟踪。研究人员将这些研究的结果应用于（a）制定有效可视化设计指南，（b）在自动化设计程序中实例化这些准则，以及（c）通过案例研究部署验证指南和结果工具。