Cargando…

Learning from lines: Critical COVID data visualizations and the quarantine quotidian

In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. In this intervention, we describe the various functions served by these projects. First, they offer t...

Descripción completa

Detalles Bibliográficos
Autores principales: Bowe, Emily, Simmons, Erin, Mattern, Shannon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387849/
https://www.ncbi.nlm.nih.gov/pubmed/34191994
http://dx.doi.org/10.1177/2053951720939236
_version_ 1783564207771353088
author Bowe, Emily
Simmons, Erin
Mattern, Shannon
author_facet Bowe, Emily
Simmons, Erin
Mattern, Shannon
author_sort Bowe, Emily
collection PubMed
description In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. In this intervention, we describe the various functions served by these projects. First, they offer tutorials and tools for both dataviz practitioners and their publics to encourage critical thinking about how COVID-19 data is sourced and modeled—and to consider which subjects are not interpellated in those data sets, and why not. Second, they demonstrate how the pandemic’s spatial logics inscribe themselves in our immediate material landscapes. And third, they remind us of our capacity to personalize and participate in the creation of meaningful COVID visualizations—many of which represent other scales and dimensions of the pandemic, especially the quarantine quotidian. Together, the official maps and counter-plots acknowledge that the pandemic plays out differently across different scales: COVID-19 is about global supply chains and infection counts and TV ratings for presidential press conferences, but it is also about local dynamics and neighborhood mutual aid networks and personal geographies of mitigation and care.
format Online
Article
Text
id pubmed-7387849
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-73878492020-07-29 Learning from lines: Critical COVID data visualizations and the quarantine quotidian Bowe, Emily Simmons, Erin Mattern, Shannon Big Data Soc Commentary In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. In this intervention, we describe the various functions served by these projects. First, they offer tutorials and tools for both dataviz practitioners and their publics to encourage critical thinking about how COVID-19 data is sourced and modeled—and to consider which subjects are not interpellated in those data sets, and why not. Second, they demonstrate how the pandemic’s spatial logics inscribe themselves in our immediate material landscapes. And third, they remind us of our capacity to personalize and participate in the creation of meaningful COVID visualizations—many of which represent other scales and dimensions of the pandemic, especially the quarantine quotidian. Together, the official maps and counter-plots acknowledge that the pandemic plays out differently across different scales: COVID-19 is about global supply chains and infection counts and TV ratings for presidential press conferences, but it is also about local dynamics and neighborhood mutual aid networks and personal geographies of mitigation and care. SAGE Publications 2020-07-27 /pmc/articles/PMC7387849/ /pubmed/34191994 http://dx.doi.org/10.1177/2053951720939236 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons NonCommercial-NoDerivs CC BY-NC-ND: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Commentary
Bowe, Emily
Simmons, Erin
Mattern, Shannon
Learning from lines: Critical COVID data visualizations and the quarantine quotidian
title Learning from lines: Critical COVID data visualizations and the quarantine quotidian
title_full Learning from lines: Critical COVID data visualizations and the quarantine quotidian
title_fullStr Learning from lines: Critical COVID data visualizations and the quarantine quotidian
title_full_unstemmed Learning from lines: Critical COVID data visualizations and the quarantine quotidian
title_short Learning from lines: Critical COVID data visualizations and the quarantine quotidian
title_sort learning from lines: critical covid data visualizations and the quarantine quotidian
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387849/
https://www.ncbi.nlm.nih.gov/pubmed/34191994
http://dx.doi.org/10.1177/2053951720939236
work_keys_str_mv AT boweemily learningfromlinescriticalcoviddatavisualizationsandthequarantinequotidian
AT simmonserin learningfromlinescriticalcoviddatavisualizationsandthequarantinequotidian
AT matternshannon learningfromlinescriticalcoviddatavisualizationsandthequarantinequotidian