Month: December 2020

By Lindsay Lamb

As we near the end of what was a year like no other, I wanted to reflect on what I have learned and shared with you all while writing this blog.

Front and center for me are all of the innovative, telling, and nuanced data visualizations that have come from modeling and documenting Covid-19 cases. Initially, my go-to source was the John Hopkins Coivd-19 tracker. This interactive website was critical when the virus was beginning to spread worldwide and across the US. It painted a realistic picture and helped us mentally prepare for what was to come. We could drill down to countries, states, and counties with alarming precision. As the pandemic progressed, I found myself turning more and more to the visualizations presented in the New York Times. To this day, a visualization is included front and center and it can be filtered based on all sorts of criteria. Additionally, there are usually several other visualizations showing trends based on different populations, trends intersecting with state policies, and more. I have really enjoyed digging into these visualizations (okay, I guess enjoyed is the wrong word as I am constantly looking up Travis County (where I live), Dona Ana County where my folks live, and Orange County in California where my brother and his family live). In a weird way, I find comfort in looking at the trends in the data. It helps me mentally prepare for what is to come and to take comfort in knowing that we are all in this together. I also think they are really thought-provoking and have made folks who are not usually comfortable with data become more so.

I have also enjoyed the infographics the CDC have created. Some are pretty alarming, such as this one:

To be fair, I have not spent a lot of time reviewing the CDC’s infographics prior to the pandemic, so maybe they have always produced high quality work. Regardless, I have enjoyed their infographics and have gotten a lot of great ideas from them.

On a more personal level, one lesson I learned this year, okay re-learned, is to let your data tell the story. It is okay to have complicated data, let it tell its own story. I wrote a post about this issue earlier in the year when Andrea and I took data from a line graph and changed it into a lollipop graph. I also wrote about it in reworking some Covid data displays, which can get confusing. I think we all remember my extreme Covid makeover post 🙂 As a rule of thumb, if data cannot be explained on their own, consider breaking it up, making an infographic, or creating a one-pager.

Finally, as I recently discussed, simplify your presentations/reports/data visualizations and then simplify again. PowerPoint is a great tool to use in creating reports, and obviously presentations, and sometimes can get too busy with all the bells and whistles. As I wrote back in March, simpler is better.

This year was a challenge, to say the least. I spent a lot of time looking inward and forcing myself to work even when the world around me felt like it was falling apart. Having a blog was a good outlet for me and allowed me to share my experiences, some personal and some less so, in a way that also taught me important lessons. My hope is that at least one of you out there learned something or enjoyed my musings this year. I honestly believe that you are never too old/young/experienced/unexperienced/exhausted/stressed to learn something new.

So, my friends, go forth and visualize your data and remember my biggest lesson of this year: have grace and patience.

By Lindsay Lamb

During the times of Covid-19, we have seen a boom for data visualizations. In my opinion, this is great for everyone (one of the few benefits of this awful pandemic)! Sure, there have been some misses (well, maybe some were more than misses) but people who never talked about or heard the term data visualization are now talking about data. Data visualizations are making it into mainstream media – TV news channels, newspapers, school district dashboards… everywhere.

We have written blogs about Covid-related data visualizations in the past, and as the end of the year is approaching — and with it, hopes of a vaccine — I thought it would be a nice time to share how folks are depicting vaccine timelines.

I came across this one on the Orange County Register that depicts how long it took other vaccines to get approved and stop the spread of a particular virus.

When I first saw it, I thought the syringe was a cannon. (Perhaps that says something about my current mental state.) I like the idea of using an icon (you know how I love icons!), but perhaps the shot icon is just too big. I do like the overall concept of the visualization — how you can see the timeline of when the disease emerged and when the vaccine was created. Simplifying could make this graphic so much more clear.

Here is a simpler version from A Learning a Day that averages the number of years it has taken to produce a vaccine and compares it to the rapid timeline for the Covid-19 vaccine. You can easily see that most vaccines take about 5 years to develop with several years devoted to the trial process. I like this graphic much better, but I also think it would be good to also include number of years across the top so you could see that the Covid-19 vaccine will be distributed in about a year and a half compared to 5 years. That information can be inferred, but it takes some previous knowledge.

Here is a similar graph from the New York Times:

The grey-ed out blocks indicate the normal timeline for producing a vaccine, which is also helpful in showcasing how truncated the timeline is for the Covid-19 vaccine. I also like this visualization because it shows the projected year when each component of the vaccine timeline is typically completed, which really hammers home how quickly and unprecedented the Covid-19 vaccination timeline has been.

As you can see, there are several ways to depict timeline data. When making a timeline visualization, the important rule is to choose what works best for the data. Let the data tell their story and build a visualization around that story. You might start with an idea in mind, but after messing around with your visualization realize you need to change plans. That is totally ok! Unsure what to do? Ask a colleague, friend, or family member (or all three)! Ideally, all of these folks should be able to read your visualization and understand it without having you explain it to them.

I hope you have found this era of data visualizations as inspiring as I have. Now get out there and share some data stories!