Two lines dropping
The wall of blinking lights

Showing off the world in charts

Un_lifexpectStefan S. who works for the UN data project and is a regular contributor to this blog, points us to a new report they have issued that contain a host of charts. The report is an update on what has happened to our Earth since 1992 (The Earth Summit). Link to the PDF file here.


This life expectancy chart (shown on left) uses a Bumps-type chart, and is very nicely done, clean and informative. 


Un_agedistThis age distribution chart shown on the right is unusual. It's a case of the data defeating the chart type. The magnitude of the 5-year changes is just not large enough as a percentage of the total to register. On a different data set, I can see this chart type being more effective.


Now, this criss-cross chart (bottom left) reminds me of Friedman's foolish attempt some time ago. It has various issues, like dual axes, excessive labels and inattentive titles (not indicating that the base population was only of developing countries).


  Instead, I attempted an area chart, using population size as the primary metric. Perhaps a more direct way to illustrate this point is to plot the growth rate of the slum population versus that of the total population.


This map is excellent, showing the spatial distribution of the countries with above-average and below-average GDP per capita. It would be even better if smaller geographic units can be used so that the distribution within each country can also be seen.



I'd like to salute all the people around the world who work at statistical agencies and who collect and make sense of all of this data, without which any of these charts would not have been possible.


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I like that area chart a lot, it perfectly illustrates a point: the number of people in slums is constant, while those above slum status are increasing.

I like the map too - I had no idea Botswana had higher-than-average per-capita GDP. I think the starkness of above-below is a bit misleading, though - some of these countries are very close to the global mean like Brazil or South Africa, some are very very far away like Haiti or Ethiopia or Qatar. for a quickly whipped-up example (based on IMF data), which doesn't quite do it but you see the difference between South America and Africa.


Ah, this is better: - the rich outliers were killing differentiation further down.


Concerning the map, and the two classes (below and above average): We had a map with a "normal" classification before (four or five classes), but then decided we'd need something "easier" for the general public and quick readers. The thing with the below/above is that one will always have a good bunch of countries below the average, even if many of them experienced high increases over recent years. So, this map is only telling half of the truth, if one will...


A picture speaks a thousand words!

Jörgen Abrahamsson

About the life expectancy chart. The angles are too steep should average around 45. Just widen the chart by one third or so. Also the global value should be faded and not highlited being the least specific(and containing the other values)
About the age distribution chart. Stacked bar charts are almost always bad. And when comparing distribution you need to use relative figures % of population not absolute millions of people.
That is just plain wrong.

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