At the new documentation page you will find the documentation for the indicators compiled by Gapminder, including population. There you can also download an Excel-file that includes not only all the data, but also information on sources and estimation methods for each observation.

To the extent possible we have also used this information to give each observation one of these “data quality ratings”:

1. Very Poor data quality - the population figure is based on guesstimates or rough extrapolations
2. Poor data quality
3. Fair data quality
4. Good data quality
5. Very Good data quality - the population figure is based on a rigourous census or equivalent

We have turned this rating into an indicator on its own, called “Data quality – population”. You find this indicator under “For advanced users” -> “Data quality”. However, this work has only just begun so most observations are still unrated.

You can use this indicator for the color of the bubbles, like in this example. The observations for which the population figure is highly uncertain will be red and the observations for which the population figure is more certain will be blue.

However, since most observations are still unrated most bubbles are still not colored in the example graph above. As we add more quality ratings in the future this information will become more useful.

We have updated the indicator “population, total” so that it covers all countries and territories from 1800 to 2008. Population is by default used for the size of the bubbles in Gapminder World.

With “all countries and territories” we mean all the 192 UN-member plus 61 other entities (e.g. semi-autonomous territories, former countries and disputed territories). This gives a total of 253 countries and territories.

This work rests heavily on the work of Angus Maddison and is, to our knowledge, the most complete data set for population, containing over 20.000 observations. To the extent possible, we have also included meta-data for each observation with information on sources and estimation methods. Where possible, we have also included a quality rating of the observations, of which more of in the following blog-post.

Note: some of the observations, especially the earlier ones, are based on very rough estimates or extrapolations. Please check the data quality rating of the observations, described in the next blog-post.

At request we added a new indicator for consumption of sugar and sweeteners per day and person, i.e. it measures how many grams sugar and sweeteners does each person consume in one day.

In this graph we plotted sugar consumption against income per person.

It is of course tempting to plot this indicator against “bad teeth per child”.

In this graph we have done precisely that. However, the link between the two indicators seems to be relatively weak.

NOTE: the figures for 2004 is based on a very rough extrapolation (i.e. we use the data for 2003) and this was only done to allow a comparison with “bad teeth per child”.

We have updated the map in gapminder world. We removed all “former countries” and “ad-hoc” areas, but included all small countries and territies. We included even the smallest countries and territories, such as Pitcairn (where the mutineers of bounty settled) and Canada’s somewhat less known neighbour St.Pierre-et-Miquelon.

You find the map under the tab “map” in Gapminder World. You can also follow this link which takes you directly there, although it should be noted that the direct link is slower than normal since additional map-related data has to be loaded.

We have added a menu to the right of the Gapminder World to improve the usability. This new feature allows you to directly from the graph access information about the graph, find relevant links, watch tutorials, participate in discussions about the data, get tips on how to use the graph, and much more.

Go to the graph to see the new feature.

We have just started to experiment with this feature so for now we only have included a few points, but more will be added with time. Comments and suggestions are warmly welcome.