6 Months Later: A Retrospective

March 18, 2020 – e-learning begins for our school district in Ohio. We adapt a routine, the crisis still fresh and new to us. We avoid others, keeping distance both literally and figuratively.

Elsewhere, throughout the day, 188 unique workbooks are published to Tableau Public about the coronavirus.

March 31, 2020 – it’s a Tuesday and we practice taekwondo from home. It’s odd, but the routine helps.

A record-breaking 235 new visualizations are posted to Public about the coronavirus. Per my data, this remains the peak to this day.

March seems like a lifetime ago. Last September may as well be a myth – it will soon be a year since Kelly died. Time feels both long and short, the days fresh out of Groundhog Day, the rhythms a blur with limited change in scene.

6 months ago, I discussed the ethics of visualizing during the pandemic. Now, 6 months into this crisis, it’s moved into the background. Except, it hasn’t. Over 200,000 people have died just in the US. I lost one person near and dear to me almost a year ago and the effects linger. Others have lost more and the ramifications will carry through generations.

We have yet to revisit March as a profession.

Along with the pandemic, we had an infodemic – analysts hungry for answers took to case data in the midst of the storm. Surely, with enough data, we could solve this. Throughout March, April, and May, dashboards across all software platforms streamed endlessly through Twitter. You could get access to case data in any number of platforms, with permutation after permutation of chart type.

We didn’t find our answers, though.

March 8, 2020 – It’s Sunday, a day before Italy goes into a national quarantine. Soon, the internet will fill with images from the country – the sheer loss of life, videos of people singing from their balconies, and other vignettes of lockdown life. In Ohio, it still seems surreal.

In Seattle, cases are growing. Cities and counties start to make incremental moves towards lockdown.

Worldwide, only a few countries have any restrictions in place. Visualizations regarding Covid-19 start to increase.

March 9, various locations start locking down. Visualizations multiply as well. And many of us take to Twitter to begin discussing both the pandemic and infodemic. Progressively, over time, the interest shifts towards mobility trends, economic impact, and the impacts of racism and sexism on health across a number of countries. A few visualizations start capturing personal aspects of the virus.

The Anatomy of an Infodemic

On Netflix, The Social Dilemma is one of the top shows, discussing what’s wrong with social media. It breaks down what happened and why we get hooked.

In that same vein of thought, here’s why I suspect our infodemic took off…

Novel diseases take time to crack. They, like us, are living and ever-changing. One key question is how much do they change over time? As researchers track this down, they work with what knowledge they have and the knowledge that’s evolving. For most of us, that communication process still translates to high ambiguity when we want hard, concrete answers the most. As Kate Starbird notes: that’s when we hit the rumor mill.

Yet, the modern rumor engine is more powerful than ever. Supported by the internet, there is almost no physical place sheltered from it. Here’s where things get sticky: we often think of data as cold, hard facts. When we use it for sense-making, we come off as a higher authority because our charts are clean, and the data comes from somewhere with an amazing logo. It all seems extremely reputable.

Except, data literacy is an ethics issue. It’s not just how we, as analysts, interpret the chart, but how those consuming it without us there assess and understand the message.

Data literacy, however, doesn’t start at the chart. It begins with how the data came to be in the first place. For example, what gets counted as a case? So often, we passed these numbers forward: it was someone else’s job – the news, the user, anyone – to clarify it. Without controls and guidance, our sense-making made it easy to spread rumors at a time when clear information was critical.

But, it wasn’t just failing at the inputs. In seeking comfort from the number, we failed to recognize the flip side of what that number meant. It was only this group. It only hits you hard if…

The numbers allow removal. We can look at the ebbs and tides of our bars, lines, and area charts and so easily forget these were people. Until, we can’t. When it hits home and we see a face, a name, within one of those dates.

I spent nearly 6 months in Canada last year because saying goodbye virtually simply wasn’t acceptable. So many people have not had that opportunity, and many more will say goodbye from afar as the pandemic continues. Loved ones. Parents. Children. Family. Friends. Generations gone, without a last touch or embrace.

Numerous others face various life-altering consequences. Polio was unpredictable when it spread. We have absolutely no idea what to expect from COVID-19 long term.

We think our job is to visualize the numbers and forget the humanity. We perpetuate harm by allowing numbness to be our modus operandi.

We think our job is to visualize the numbers and forget the humanity. We perpetuate harm by allowing numbness to be our modus operandi. Our numbness spreads, inuring others. Today, in the US, we have over 200,000 deaths.

The Conversation We’re Not Having

6 months ago, a vast number of us sought information. We published vizzes out on the internet quickly, hoping to fill a void. Since that time, the data sources have shifted, measures have changed, and calls to action have morphed, yet only 12% have data that’s been updated in the last 7 days.

Beyond recent updates, many were created and abandoned, updating if the Google sheet did, but breaking or stalling out when no new data came. Throughout March and April, measures shifted and changed, often requiring edits. By May, Tableau changed their chosen methodology, requiring the users to change data sources entirely and remap fields.

The chart below captures where the 856 vizzes created before March 9 landed. These are still accessible today, many ceasing updates shortly after they were created. The ones that are still updated often have numerous revisions and have been manually edited recently.

When we step into the role of sense-making in public, what are our duties? So often, documentaries like the one on Netflix focuses on ethics by remorse. Yet those who sounded the klaxon early, like Ruha Benjamin, Safiya Noble, and countless more went unheard both before and in that documentary.

Nothing will change unless we have real discussions about how our sense-making affected others. Proactive actions are always better than reactive ones, but course corrections also make change.

It’s been 6 months since many of us discovered the pandemic and contributed to an infodemic. It’s time we commit to an ethical review of what happened in March by having open discussions about what went wrong and how we can course correct.

Postscript: I pulled the data from Public using “Covid” and “coronavirus” to frame this discussion. Not all visualizations are case data. I have attempted to clean out case data (defined as “clear cut case visualizations”) by searching sheets, descriptions, and titles for key words that indicate case visualizations. A number used words like “tracker” that were not conclusive enough to be used as search terms, so my clear-cut number is low. I also excluded certain terms when odd things fell in (the most notable being “cheese,” in case you wondered).