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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2020
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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 |
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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 |
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