Covid-19 data visualization III

Unfortunately the theme of visualization of Covid-19-Data continues with the ongoing pandemic. Most of us never could imagine that this virus within his almost 1 year of existence could have infected more than 90 million people worldwide and caused the loss of more than 2 million lifes on earth.

If we see the numbers in charts we rarely can imagine the impact of the disease on the different levels like continental, regional and per country.

What can we see is that certain charts changed their layouts by time. For example the German overview chart received a new item on “vacine” due to the fact that there are now several vacines available and the number of infections should decrease within a certain space of time.

When looking at the curves of regional and country based overviews we can observe that it is almost impossible to compare their magnitudes as they apply different scales of measure.

An important step in getting insights is comparing data. Comparing could be done by putting charts of different countries or regions into one overall chart. When we do this, as can be seen, correct scaling becomes crucial.

Without scaling we can follow the curves and get an idea of the trends over time, but fails to compare one with each other. But we should do this, because without comparing we won’t formulate important questions like ”why are incidences in Asia compared to the Americas and Europe are so low?” and start investigating the reasons for those facts.

 

the curves tell a trend - but not a comparable magnitude

unscaled multiple charts - good for an overview, but not comparable

unscaled multiple charts - good for an overview, but not comparable

by introducing scaling lines we get an idea of the true relations

in order to avoid the impact of exceptions, we can give them a special signature... 

and present the data in a more legible way presenting a more realistic view....