Anthropographics in COVID-19 simulations

Big Data and Society 9 (1) (2022)
  Copy   BIBTEX

Abstract

Data visualization researchers and designers have explored a range of approaches to ensure that non-expert audiences understand and derive value from their work. Using anthropomorphized data graphics—or anthropographics—is one strategy that can help create a connection between data and audiences. Anthropographics have been defined as “visualizations that represent data about people in a way that is intended to promote prosocial feelings or prosocial behavior.” However, during the SARS-CoV-2 pandemic, anthropographics were used in data visualizations that had an expanded range of rhetorical goals beyond promoting prosocial feelings and behavior—for instance, informing people about the pandemic, persuading them to adopt certain behaviors, or memorializing those killed by the virus. In particular, anthropographics were used in visualized simulations to model possible futures for audiences, showing the spread and impact of the virus in various scenarios. These simulations used anthropomorphizing strategies in text as well as in graphics, along with interactive options that enabled audiences to explore personal connections with the data. As demonstrated through a close reading of several of these COVID-19 simulations, anthropographics can be viewed holistically as a design strategy that incorporates text and interactivity as well as graphical marks in representing data. Findings from this analysis suggest several additions to the design space for anthropographics.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 93,590

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

COVID-19, Graphic Medicine, and Thinking Beyond Data.Sathyaraj Venkatesan & Ishani Anwesha Joshi - 2022 - Perspectives in Biology and Medicine 65 (4):694-709.

Analytics

Added to PP
2022-07-02

Downloads
3 (#1,213,485)

6 months
3 (#1,723,834)

Historical graph of downloads
How can I increase my downloads?

Citations of this work

No citations found.

Add more citations

References found in this work

Data feminism.Catherine D'Ignazio - 2020 - Cambridge, Massachusetts: The MIT Press. Edited by Lauren F. Klein.

Add more references