Beyond Heuristics: Learning Visualization Design
This addresses the need for more data-driven and scalable visualization design methods, but it is incremental as it outlines a vision rather than presenting new results.
The paper tackles the problem of manually curated visualization design guidelines by proposing a research agenda to learn design principles directly from data, aiming to build tools powered by learned models.
In this paper, we describe a research agenda for deriving design principles directly from data. We argue that it is time to go beyond manually curated and applied visualization design guidelines. We propose learning models of visualization design from data collected using graphical perception studies and build tools powered by the learned models. To achieve this vision, we need to 1) develop scalable methods for collecting training data, 2) collect different forms of training data, 3) advance interpretability of machine learning models, and 4) develop adaptive models that evolve as more data becomes available.