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

HCC：小：重新审视图形感知：开发和验证数据可视化设计指南

• 批准号: 1017745
• 负责人: Jeffrey Heer
• 金额: $ 25万
• 依托单位: Stanford University
• 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 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017745&HistoricalAwards=false
• 关键词: HCC; Small; Graphical; Perception; Revisited

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.

设计良好的可视化利用人类的视觉系统来帮助人们理解大型数据集。然而，产生有效的可视化效果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位置、大小、形状和颜色等视觉变量。在这个过程中，他们在平衡知觉和认知权衡的同时，做出了数百个细微差别的判断。作为回应，心理学、地图学、统计学和计算机科学的研究人员调查了不同视觉变量对图形感知的影响：观看者解释视觉编码从而解码图形信息的能力。尽管在制定基于实验室实验的设计指南方面取得了很大进展，但对可视化设计空间的全面评估和由此产生的指南的现实世界验证仍然是空白。研究正在推动我们对图形感知的理解，并制定新的可视化设计指南。这项研究涉及新的实验，以解决图形感知中尚未解决的问题，包括使用众包技术的大规模网络研究，以及使用敏感测量(即眼球跟踪)的对照实验室研究。研究人员正在将这些研究的结果应用于：(A)为有效的可视化设计制定指南，(B)在自动化设计程序中实例化这些指南，以及(C)通过案例研究部署来验证指南和所产生的工具。