I try to be up-to-date on data section in digital newspapers, because of data driven journalism is one of my interests, being an exciting field which mixes diferent disciplines. Recently La Vanguardia newspaper has started a data analysis journalism section called VangData, which looks quite promising.

Today they have published a chart to show the average height on OECD countries, here a snapshot:
vangdata_20150410

Although the chart fulfills its purpose which is showing a rank of the countries based on its population’s height, here one of the principles in effective data visualization, the Data-Ink ratio, has definitely a room for improvement.

The Data-Ink ratio is a concept introduced by Edward Tufte, who has contributed significantly to designing effective data presentations. Tufte refers to this concept as the non-erasable core of a graphic, the non-redundant ink arranged in response to varioation in the numbers represented. This leads to Tufte’s goal: “Above all else show the data”.

As you can see in the chart, the amount of ink used to display such a small amount of data is not proportional at all. This leads also to the amount of space used to show the ranking for both genders, forcing the user to scroll to see one gender or another.

So in an attempt to reduce the Data-Ink ratio and add some improvements I made a quick interactive chart here:
http://www.xavigimenez.net/oecd-height-population/
vangdata_redesign

As you will notice, almost all the ink used here is data-ink. You can also notice that now you can compare
a) together both genders and its respective differences and
b) if values are more or less dispersed in a gender or another.

I also find useful to have the median of the OECD countries as a threshold instead of being part of the values in the vertical axis (although this threshold is visually 100% effective for the sorted gender). Another possible change would be to swap the axis, arranging the height scale vertically, so it’s more ‘natural’ to encode height’s values in the vertical axis.

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Data Visualization · Interactive · HCI · Open Data · Data Science · Digital Humanities


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