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The Datatext: A multilevel-discursive theory for improved public health data visualizations

Lookup NU author(s): Dr Murray Dick



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


In the run up to the COVID-19 lockdown in March 2020, Prime Minister Boris Johnson challenged the British public to ‘squash the sombrero’, and so save thousands of lives in the event of the pandemic overburdening an already stretched National Health Service. There was a jarring sense of incongruity between this tabloid metaphor, and the minimalist line-graph to which the prime minister was referring (shown on live television and circulated widely online). Infographics have, it is argued, the potential to save lives. But how they may be harnessed most effectively towards achieving optimal public health outcomes, particularly in times of crisis, is another matter. Conventional best practice in infographic design, epistemically informed by a range of notions, including logical monosemy, naive empiricism and ‘objective’ minimalism, may be well-suited to the communication of data amongst scientists and certain literate publics. But matters of public health play out in a public sphere where (some) citizens actively create and circulate knowledge, and where the biocommunicability of health messages is key. Here a different epistemic approach, and different assumptions about design, are required. When conceiving of the infographic (or data visualization) in public health communications as a multilevel discourse containing visual arguments mutually re-enforced by combinations of words, numbers and images, in the form of a datatext (after WJT Mitchell), it may be possible to design more effective data visualizations for public health. In this paper I set out a theoretical approach to infographic design drawing upon image schema theory, (from cognitive metaphor theory), as well as conventional standards of best practice concerning accuracy. I conclude with a series of recommendations for designing effective datatexts for optimal biocommunicability.

Publication metadata

Author(s): Dick, M

Publication type: Article

Publication status: Published

Journal: Javnost: The Public

Year: 2022

Volume: 29

Issue: 2

Pages: 130-146

Online publication date: 03/05/2022

Acceptance date: 15/02/2020

Date deposited: 26/06/2023

ISSN (print): 1318-3222

ISSN (electronic): 1854-8377

Publisher: Taylor & Francis


DOI: 10.1080/13183222.2022.2042785


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