Important reminders for social scientists when creating visualizations

Adapted from a talk for the American Evaluation Association’s Data Visualization and Reporting Technical Interest Group

I remember the first COVID-19 chart that came across my news feed that prompted questions I couldn’t answer. At the time, the charts were double-digit case counts from Singapore with active, recovered, and fatal cases displayed on individual graphs — a simpler time than what we see on the tracker charts of cases and deaths from COVID-19, crowded with lines from dozens of countries and still trending up in December.

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From Our World In Data, downloaded 15 December 2020 at 8:32PM EST

A month later, I still couldn’t get a clear definition for what qualified as a “recovered” case, even asking colleagues working at the CDC. The Morbidity and Mortality Weekly Reports (MMWRs) had started to contain early learning about the novel coronavirus and the growing case counts in the US. I felt alarmed when I saw that Tableau had launched a ‘ready to use’ workbook for anyone to jump start their analysis of the case data and wrote 10 Considerations Before You Create Another Chart about COVID-19, which still has relevant lessons months later. …

Exploring knowledge, attitudes, and practices from 67 countries

Co-authored with Marla Shaivitz, Director, Digital Strategy, Johns Hopkins Center for Communication Programs

Counting coronavirus cases and related statistics can tell us where infections are rising. But what if we could use data about COVID-19 prevention behaviors to keep those infections from happening in the first place?

Researchers can analyze data on individual behaviors like mask wearing, handwashing, physical distancing, perceived community norms, and where people get their information, to help policymakers and health communicators more effectively target messages related to perceptions, knowledge, and critical prevention actions. …

The journey from a test swab to a record in a database

This week, health informatics became a hot topic in the US as the responsibility for collecting COVID-19 case data shifted from the CDC to the US Department of Health and Human Services.

I have worked on health information system strengthening projects in other countries. In the past, I’ve been met with blank stares when talking about the importance of robust, interoperable information systems with friends (it’s not exactly cocktail party banter), but perhaps the focus on COVID-19 data can kick off these conversations around the range of processes and systems used to collect health information.

Despite the wide demand for daily case-count updates shared across hundreds of global, country, and local dashboards, the results you see today in some of the best-case scenarios are cases that were infected (and potentially infectious to others) days or even more than a week prior. When analyzing COVID-19 case data, we can better make sense of the noise in daily case counts or how new cases lag behind tests administered by understanding how the data is collected. …

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illustration by Jason Forrest

Data visualization designers can be anti-racist by raising awareness of inequities and finding ways to #VizResponsibly

For the last three months visualizations of COVID-19 case data — with all of its uncertainty and flaws — occupied the 24-hour news cycle and dominated home pages of every major media outlet.

We saw a rush to visualize COVID-19 case data by a wide range of analysts and designers. We talked about visualizing data responsibly. But most of the news stories I’ve seen over the past week are photos of the protests, not visualizations of the systemic injustices our Black community in America has faced. …

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Heatmap by the Center for Systems Science and Engineering (CSSE) at John Hopkins University

Viz Responsibly is a new video series hosted by the Data Visualization Society, featuring interviews with people collecting, managing, analyzing, visualizing, and using data with a social impact. If you have a recommendation for a great story to share through this series, please send your recommendation to or send a note on slack to Amanda Makulec.

The COVID-19 models and associated visualizations have been held up by some policymakers as our roadmap to re-opening. But do decision-makers understand the nuances of those models?

Transparency around limitations and methods vary widely, though, and we now have amateur data scientists around the world trying to make sense of this information. The available models have also shown just how challenging it can be to make accurate forecasts based on fundamentally uncertain and incomplete data, with predictions varying widely depending on the methods used. …

Data humanism, pizza, and baby photos through the lens of a data viz designer

I wrote a draft of this article in early March. The world has changed a lot over the past two months, but one thing I know for certain: I need more data visualization that makes me smile and brings me joy right now. So here I am, in the midst of more writing and speaking about responsible data visualization, refocusing on something with a bit more whimsy.

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Every month, most new parents I know take a photo of their little one on a blanket or with a stuffed animal to visualize growth over time. There’s a whole cottage industry of companies that make blankets with 0–12 month marks printed on the fabric and a detached marker that you move to indicate in which month the photo is taken. …

Expanding our mental model of flattening the curve to make sense of the uncertain road ahead

The “flatten the curve graphic” and message are mainstream news here in the United States. Stephen Colbert issued his opening monologue from his bathtub while his studio was closed, and brought along the most famous chart of 2020.

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Stephen Colbert explains flattening the curve from his bathtub monologue. (Full video)

Harry Stevens’s animated explainer in the Washington Post is the most-viewed page of all time on the paper’s website. Perhaps this helps to make the case for greater investment in data visualization expertise in newsrooms and beyond: charts and maps make information accessible and interesting.

The infographic version of “flatten the curve” has been reinterpreted a number of ways (including with cats and baby Yoda). Most iterations are conceptual graphics though, not rooted in the actual case counts of COVID-19. Opinion articles have proliferated, speculating on what the future holds for the United States: Can we turn a corner? Will the economic fall out be worse than the the human toll of the disease?

Lessons from a life in public health

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Having quality data is paramount to how we make decisions to combat COVID-19. The decisions made now at an institutional level have life-and-death consequences, as do the choices we make around individual actions like social distancing.

In the midst of today’s pandemic, I wish we still had Dr. Bill Bicknell, one of my professors from the Boston University School of Public Health, who passed in 2012. He worked in public health policy, program implementation, and clinical medicine around the world, and served as Massachusetts Commissioner of Public Health. …

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To sum it up — #vizresponsibly; which may mean not publishing your visualizations in the public domain at all

Teams are making ready-to-use COVID-19 datasets easily accessible for the wider data visualization and analysis community. Johns Hopkins posts frequently updated data on their github page, and Tableau has created a COVID-19 Resource Hub with the same data reshaped for use in Tableau.

These public assets are immensely helpful for public health professionals and authorities responding to the epidemic. They make data from multiple sources easy to use, which can enable quick development of visualizations of local case numbers and impact.

At the same time, the stakes are high around how we communicate about this epidemic to the wider public. Visualizations are powerful for communicating information, but can also mislead, misinform, and — in the worst cases — incite panic. We are in the middle of complete information overload, with hourly case updates and endless streams of information. …

When my son was eight weeks old, Medela, Lansinoh, and Maymom were playing mind games with me. I was breastfeeding and pumping, and every time I’d move milk from a tube to bag or a bottle, the volume measurement would change. Sometimes dramatically — 1.5 oz is very different from 2 oz when you’re feeding a tiny human.

I talked with some other new moms who had the same frustration (well documented in our WhatsApp group). So, four months later when I saw a post on NextDoor from a mom who had tried more than 20 different bottles and was giving them away, I did what a new mom should not do when she’s recently returned to work: I decided to collect some data. …


Amanda Makulec

Data viz designer and enthusiast for using data for social good and public health. MPH. Operations Director @datavizsociety and Data Viz Lead @excellaco.

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