Design Knowledge in Data Visualization: Mapping the Epistemic Landscape

Authors

Paul C Parsons (Purdue University), Colin M. Gray (Indiana University), Ali Baigelenov (Purdue University)

Presentation

Session

From design spaces to visual design

Time

Thursday, Nov 12, 13:36 – 13:48 (US/Eastern) · session 13:00 – 14:30

Location

Hall Essex north

Keywords

Design knowledge, design theory, visualization design

Abstract

Data visualization research has developed many influential forms of design knowledge, including perceptual principles, design guidelines, process models, and formalized representations of design constraints. These contributions have been effective at articulating explicit, portable, and codified forms of knowledge. Yet the broader landscape on which visualization design depends remains less clearly articulated, especially with respect to intermediate-level knowledge, precedents, tacit repertoires, and situated forms of knowing. In this paper, we draw on design theory to map this broader landscape of design knowledge in data visualization. Through this lens, we show how visualization research has built substantial strengths in some regions while leaving others comparatively underarticulated. We further argue that visualization design depends not only on knowledge artifacts such as theories, guidelines, and patterns, but also on knowledge-in-use---the situated interpretation, adaptation, and coordination of multiple forms of knowing in concrete design situations. This broader account has implications for how the field conceptualizes design expertise, evaluates and develops scholarly contributions, and approaches AI-assisted design. Rather than treating visualization design as either fully formalizable or wholly resistant to computational support, we argue for a differentiated view in which computational systems can support some forms of design knowing, while others remain inseparable from human judgment, contextual interpretation, and the ongoing reorganization of design work in practice.

For Practitioners

This paper will be most relevant to visualization designers and developers, data scientists and analysts who create visualizations, UX and interaction designers, visualization educators, and practitioners building visualization recommendation or AI-assisted design tools. It may also interest data journalists and domain specialists who regularly make consequential visualization decisions. Practitioners can use the framework to identify the different forms of knowledge involved in their work, including empirical principles, guidelines and methods, precedents, and tacit or situated judgment. This can help them select and interpret guidance more critically, build richer repertoires of examples, document design reasoning, and recognize when general rules must be adapted to a particular context. Tool builders can use the framework to determine which forms of design knowledge can be formalized computationally and where systems should instead support human interpretation and judgment.