Please, no more charts

Category
Education
Written By
Andrea Bertran López
During the current COVID-19 crisis, access to data is critical. Many webpages with tools, dashboards, and maps have emerged to show and track COVID-19 cases worldwide. The design of graphs is fundamental for clear communication and ultimately affects the spread of the virus. This means it is now essential, more than ever, that graphs are accurate, conclusive, and straightforward.

I want to point out some things I find essential as a regular user for data visualizations to have during this crisis.

1. Use of fonts

Please, make your headlines and legends clear. Use different fonts or even the same one but make it  bold to make sure we can rapidly notice what you are talking about.

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2. Put us in context

We’ve seen a thousand graphs already, we already know we are living a pandemic crisis. If you want to highlight something in particular, show us through colors (be aware of the ones you are using and their impact on our minds), subtitles, lines or by writing a short annotation of the interest of your graph.

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3. If your graph is complex, guide us through the information.

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4. Choose your chart wisely

Maybe you’ll avoid confusion if you show the data in another type of chart.

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5. The meaning of colors and their tones

Red is super aggressive, it can mean panic, stress or anger. Can you guys please at least change its tone to be less destructive? There is no need to alarm more.

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6. Avoid superfluous information

Symbols and images can create more confusion.

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7. Remind people of the good numbers

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8. Link your sources

Naming isn’t good enough.

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9. Think out of the box

What if you relate the info and the impact to other subjects? - The impact on business, events, people loosing their jobs, the influence on the internet as we are all moving to digital spheres, etc...

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Good examples

Media, journalists and dataviz experts, please reconsider over and over again what you want to show during moments of crisis.

A few,  good examples for (hopefully never again) next time.

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Sources

01_Johns Hopkins Coronavirus Resource Center - https://coronavirus.jhu.edu/map.html02_New York Times - https://www.nytimes.com/interactive/2020/03/15/business/economy/coronavirus-worker-risk.html?action=click03_Our World in Data - https://ourworldindata.org/coronavirus#all-charts-preview04_Reuters Graphics - - https://graphics.reuters.com/CHINA-HEALTH-SOUTHKOREA-CLUSTERS/0100B5G33SB/index.html05_SAS Coronavirus Report - https://tbub.sas.com/COVID19/06_Datawrapper - https://blog.datawrapper.de/coronaviruscharts/07_John Coene's Shiny projects - https://shiny.john-coene.com/coronavirus/08_Information is beautiful - https://informationisbeautiful.net/visualizations/covid-19-coronavirus-infographic-datapack/09_Qventus - - https://qventus.com/blog/predicting-the-effects-of-the-covid-pandemic-on-us-health-system-capacity/10_Covid19UK - https://covid19uk.live/11_BBC - https://www.bbc.com/news/health-5167474312_El Universal - https://interactivo.eluniversal.com.mx/2020/coronavirus-llega-a-mexico/13_Ventagium Data Consulting - https://app.powerbi.com/view?r=eyJrIjoiMTE1N2ExYzEtMThmNy00ZjdkLThiOGMtNzI5MjIwMjkwYjA5IiwidCI6IjFlYjcxYWY1LTBjNzAtNDU1Zi05MDk1LWU4NWU5N2M3MTM3MyIsImMiOjR914_University of Washington - https://hgis.uw.edu/virus/15_University of Virginia - https://nssac.bii.virginia.edu/covid-19/dashboard16_Nico Hahn - https://nicohahn.shinyapps.io/covid19/17_Data Science Plus - https://dash.datascienceplus.com/covid19/

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