Pick your metric from the list and observe Covid-19 cases throughout the world:
When you just picked a metric or hovered a country and saw a change in the map, did you just told yourself this nice? Did you wonder about the meaning of what you see? Did you ask yourself whether the numbers meant something? Did you consider it is actually about real people getting sick? Did you feel slightly uncomfortable with the previous question? How literate are you about data?
Data literacy covers the skills needed to extract knowledge from data.
We are living through the first world-wide pandemic with access to live data describing its evolution. Among the consequences, the news that reaches us has now two components. The first is an old friend: fear. Fear of being sick, fear of seeing friends and family infected and so on. It is the natural emotional response that most of us are likely to feel. The second is that you now know precisely how many people are hit, where and how: you are data-informed. If you do not understand what the numbers mean, it is likely going to increase your level of anxiety. Data literacy can help you with that, and you can get data fluent with the following two approaches.
The first is to boost your critical thinking when you read numbers or charts. Do you ask yourself questions when you see a number? Does it have a scale? What is the reference or the average measurement? Is there any chance that it is legit? It is a rich topic, and I haven’t found a better reference on the subject than Han Rosling’s book: Factfulness. The book is full of insights to help you think with numbers distilled without (too much) maths. For a few years now, universities have communicated more broadly about these topics. Calling bullsh*t from the University of Washington aims at teaching us constructive scepticism in a “data-driven world”.
The other is more about learning by doing. Nowadays, all it takes to build charts is to use online tools (e.g. infogram) or spreadsheet programs (Excel, Libre Office, etc.) or write down a few hundreds of code lines like for the map above. For instance, I used here a tool (D3.js) that I am relatively at ease with to generate a live map showing total Covid-19 cases counts per million population. It represents a couple of hours if you are used to doing this exercise to a few days if you know how to code just a little. The main point is not the technical part but to build it for someone, an audience. For example, creating the map above, here are some questions I asked myself:
- What do I want to show? (starters to viz with D3, data people are curious about, etc.)
- What do I want to learn? (even if I do it in the spirit of sharing, what would I learn along the way?)
- Which source has the best quality of data? Are they accessible?
- How to best present the information (line charts, maps, etc.)?
- Can I optimize the code so that I limit download times (prompted when I saw the size of some files)?
- Is there an unbiased way to show the map? Where should be the middle of the map?
- What measurement will best represent the number of ongoing cases? Should I include a scale?
- Is it normal that country X has a zero count? (answers included: nobody lives there, the map file has a mistake in the name of the country, etc.)
- Is it normal that country Y has the largest death toll per million? (answers included: cross check different sources, follow the news, etc. )
- If I use such colour scale can everybody read it? Is it friendly to colourblinds?
Going through this process is educational for everyone, asking questions like: Is it true? How was it proven? Can I trust the source? becomes natural and clearly separates emotional and rational responses.
Media and authorities have to not only navigate a pandemic but also to face challenges about communication. How to convey knowledge in our data era? I have seen, for example, how the French government has changed its communication with the pandemic. It is not only about explaining and handling the emotional toll but also about building a story based on data and facts to justify the ongoing course of action. The style is clearly different. If anything, the Covid-19 pandemic can make us all better data literates and could drive us to better informed decisions.
For the technical readers among you, below are shared all the code and data to reproduce the map.
The data comes from:
- The country shape files have been downloaded on Natural Earth Data, exported to geojson format with Qgis and finally converted to topojson. This gives us a rather small file size (< 500 Ko) which could be further optimized by filtering unused tags. You can download here the file: world.topojson
- Covid-19 cases count: disease.sh - Open Disease Data (aggregated from Worldometers)