Beyond Heuristics: Learning Visualization Design

TL;DR

This paper advocates for a data-driven approach to visualization design, emphasizing learning models from empirical data to replace traditional heuristics and improve adaptability and interpretability.

Contribution

It introduces a research agenda for deriving visualization design principles from data, proposing scalable data collection, interpretability, and adaptive modeling methods.

Findings

Proposes learning models from graphical perception data.

Highlights the need for scalable data collection methods.

Emphasizes interpretability and adaptability in models.

Abstract

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.