A Multi-Technique Approach for Improving Summary Polar Diagrams

Abstract

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.