The best way to see a ranking is by using a bar chart. It’s the simplest visualization format and ANYONE can easily understand the information.

By placing multiple bar charts next to each other as a snapshot of a time series is an effective way to tell a story. You can see how the ranking changes rapidly every few weeks. Having the reference line to point to the stock price line chart below is also effective to give a specific timeline to associate two different charts.

Timing Patterns

Next, let’s look at confirmed cases from a different angle. In this distribution plot, each dot represents a reported date. January is green, February is yellow, and March is orange. Only a few countries display green dots, and China is the only country with the highest concentration of visible green dots since COVID-19 originated from there. Yellow dots are visible for a few more countries including China, Italy, Iran, South Korea, and Japan. The majority of countries mainly have orange dots. These clusters of colored dots indicate how and when COVID-19 cases have increased. This is a unique and effective way to show the timing of data flow over time.

Spread and Density

Instead of using color for each month, I have colored squares based on the number of cases reported each day. Clearly, China showed an epidemic earlier than any other country and numbers there have since decreased when other countries started showing spread by a higher volume (darker-color squares). Also, while Italy has dark squares spread out,  squares for the US are more concentrated towards the end of the graph (right) and have much darker intense colors. Unlike the above distribution plot visualization, a heatmap gives equal weight in a time series so that you can see a pattern of spread as well as its density.

Another way to show the same information is to sort the chart by measure, in this scenario, the number of cases. Regardless of when the cases started, you see the ranking by states for total cases.

Geography Spread Stages

Placing several maps side by side gives a time dimension to the overall picture. This shows each stage of the virus spread in the US. A map is a very effective way of displaying data without necessary needing additional explanations.

Summary

From the same dataset, you can tell several different stories. Data visualization is all about what to communicate and how to best communicate it. You’ve seen fancy wow-factor visualizations before. But if it doesn’t tell a story, it’s just a pretty picture to look at. You want readers to understand the information within 5 seconds, or at least that has always been my objective when creating a data visualization.

COVID-19 is affecting all of us and it’s very difficult to comprehend that only a couple of weeks ago, we had a “normal” life. We’re trying to adapt to this new norm and not knowing when we can go back to our old norm is daunting, but let’s start with what we can do today. That is washing our hands and self-quarantine and we will get through this together. Help flatten the curve and stay safe! 

Shima Auzins

Infographics: Apeksha Pathak
BI (Qlik/Tableau): Duc Ngo

Sources: www.finance.yahoo.com, www.kaggle.com