一种多技术方法用于改进摘要极图
A Multi-Technique Approach for Improving Summary Polar Diagrams
AI总结：
本文提出结合概览+细节、聚合、交互过滤、笛卡尔链接和小众图的技术，提升摘要极图的清晰度和实用性，并通过用户研究和专家评估验证其有效性。
摘要：
尽管极坐标系统不如笛卡尔坐标系统普遍熟悉，但其在某些任务中仍不可或缺。摘要极图，如泰勒图和互信息图，用于发现关系、可视化数据相似性和量化对应关系。尽管这些图是揭示数据关系的重要工具，但其极性可能阻碍直观性，并导致过绘制问题。我们提出了一种混合方法，结合概览+细节、聚合、交互过滤、笛卡尔链接和小众图，以提高摘要极图的清晰度、完整性和功能性。我们进行了用户研究来评估这种方法的有效性，注意到参与者响应时间相当。此外，三位具有不同可视化经验的领域专家审查了应用摘要极图到气候、数据科学（新领域）和机器学习的实现方案，从而在用户研究之前完善了该方法。研究结果强调了我们的方法在提高理解、可访问性和实用性方面的多功能性。
英文摘要：
While the polar system may lack the universal familiarity of its Cartesian counterpart, it remains indispensable for certain tasks. Summary polar diagrams, such as Taylor and mutual information diagrams, address tasks like discovering relationships, visualizing data similarity, and quantifying correspondence. Although these diagrams are invaluable tools for uncovering data relationships, their polar nature can hinder intuitiveness and lead to issues like overplotting. We present a hybrid approach that combines overview+detail, aggregation, interactive filtering, Cartesian linking, and small multiples to enhance the clarity, comprehensiveness, and functionality of summary polar diagrams. We performed a user study to assess this approach's effectiveness, noting comparable response times among participants. Additionally, three domain experts with varying visualization experience reviewed an implemented solution applying summary polar diagrams to climate, data science (novel), and machine learning, refining the approach prior to the user study. The findings underscore the versatility of our approach in enhancing comprehension, accessibility, and utility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
