Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco

Abstract

There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.

VisuaLint: Sketchy In Situ Annotations of Chart Construction Errors

“…Despite this potential for harm or misuse, guidance on avoiding visualization “pitfalls” [BE15] is often absent in visualization tools. When best practices or design issues are codified, they are often presented to the user implicitly: for instance, through “smart defaults” in languages like Vega‐Lite [SMWH16], or as constraints in recommendation systems like Draco [MWN*19]. While helpful, these implicit approaches can fail when users deviate from expected use cases.…”

“…Although the research community has increasingly been devoting attention to the issue of visualization errors, a canonical list of such errors does not yet exist. As a result, we began our design process by compiling a list of data visualization best practices and associated errors, starting with a review of the literature on perceptual studies, visualization systems [MWN*19, SMWH16, MK18], and widely‐read, non‐academic material such as Tufte's The Visual Display of Quantitative Information [Tuf01]. To ensure our list of errors also reflected real‐world practice, we additionally collated discussions of visualization best practices found in online forums (e.g., VisGuides [DAREA*18], and Reddit's r/DataIsUgly ) as well as examples drawn from current affairs and the media (e.g., congressional hearings as in Fig.…”

“…Identifying chart construction errors is not always straightforward. Dozens, if not hundreds, of rules are necessary to express best practices in visualization design [MK18, MWN*19, Mee17] — an infeasible level of knowledge to expect of casual authors and readers, exacerbated by the ever‐evolving nature of visualization best practices [Kos16]. Even for experts that possess a high level of data and design literacy, catching these mistakes is non‐trivial and requires critically reading charts with careful attention — an activity made all the more challenging due to the sense of authority and certainty visualizations convey [KHAA16, Kos08].…”

“…There is a growing research interest in addressing these issues, but existing work has primarily done so via systems‐building. For instance, novel systems that automatically detect errors in a visualization [MK18], codify best practices and recommend alternative designs [MWN*19], or learn design principles from corpora of examples [SML*18] have been proposed or developed. While valuable, this focus approaches the problem space from the perspective of visualization authors and does not grapple with how visualization readers reason about chart construction errors that they might experience in the wild.…”

v‐plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions

“…While such tools target the broad spectrum of data‐driven visualization designs, none are specifically focused on authoring visualizations for the comparative analysis of data distributions for descriptive statistics. Additionally, these approaches do not encode knowledge on choosing appropriate designs to provide useful constraints on the outputs of the systems, which has proven to be effective for non‐visualization‐experts [MWN∗19].…”