J 2022

Visualizing COVID-19 : an analytical model to understand and compose continuously evolving data visualization projects

HIMMA-KADAKAS, Marju, Pille PRUULMANN-VENGERFELDT and Signe IVASK

Basic information

Original name

Visualizing COVID-19 : an analytical model to understand and compose continuously evolving data visualization projects

Authors

HIMMA-KADAKAS, Marju, Pille PRUULMANN-VENGERFELDT and Signe IVASK

Edition

Mediální studia, Praha, FSV, UK, 2022, 1801-9978

Other information

Language

English

Type of outcome

Article in a journal

Country of publisher

Czech Republic

Confidentiality degree

is not subject to a state or trade secret

References:

URL

Organization

Fakulta sociálních studií – Repository – Repository

EID Scopus

2-s2.0-85138646785

Keywords in English

data visualization; data journalism; data activism; data literacy; digital journalism; Covid-19

Links

EF18_053/0016952, research and development project.
Changed: 7/3/2023 04:15, RNDr. Daniel Jakubík

Abstract

V originále

The increased demand for information during the Covid-19 pandemic inspired projects to describe the pandemic’s progress via data visualization. Critically analyzing the published data visualization projects (DVPs) contributes to establishing a framework that supports both understanding and composing DVPs that evolve over time. Drawing upon constructed grounded theory, we develop an analytical model for creating DVPs in a journalistic or public communication context. For our analysis, we selected Covid-19 public service media DVPs in the United Kingdom, Norway, Sweden and Estonia as well as DVPs created by global and local data activists. The analysis of these examples provides an understanding of (1) the implied agency standing of the authors of the visualizations, (2) the kinds of editorial layer (data, visual representation, annotation or interactivity) that inform the creation process and (3) what newsrooms and data visualizers can learn from this practice to create understandable, meaningful and engaging DVPs of (critical) events that evolve over an extended period. Our model supports data visualization practitioners in making informed choices when creating data stories.
Displayed: 19/6/2025 14:41