What I do not like about the viz:
- There are too many labels. The y-axis already shows the scale for the line so there is no need for data labels for each year. Labels at the start and at the end of the line would be sufficient.
- The title does not call out a finding. The audience is expected to interpret the viz itself.
- The colors in viz do not distinguish between forecast and actual figures.
- The viz looks dull. There is really nothing which would catch the attention of the audience. No picture, no interesting color choices, no highlighter, no use of preattentive attributes.
The key message I want to convey:
The viz is supposed to give an overview over all producing wine countries in the dataset. So I decided to not call out single findings already in the title, but let the audience draw the information from the viz which they need. This can be done by selecting a country in the highlight box or by hovering over the respective line of the Sankey chart.
However, I did decide to call out the biggest wine producers (TOP 3) in a separate table on the lower left.
The design choices I made:
- I decided to create a Sankey chart. This chart type works well with data flows. I had the image in my head to show the flow of data/wine out of a wine bottle. By using this imagery the audience is exposed immediately to the context of the data – The world of wine
- The lines are double coded by color and size. Higher production output of wine in mhl (million hectoliters) is illustrated by a darker red / thicker lines.
- Clicking on a line will update the production output in mhl on the wine cork on the upper left for the selected country.