Good Example Of Critical Thinking On Data Visualization

In his presentation of child mortality rate, world income distribution and Internet users (TED, 2006), Rosling re-examines how data on critical areas in question are misrepresented and emphasizes new data visualization strategies as essential to mine data meaningfully. By (re)visualizing data, Rosling sheds new light into interesting data and hence reimagining data as not only ends but means to further information and knowledge visualization (Chen et al., 2009). Two chart types are used by Rosling, i.e. bubble and gap charts, in order to represent child mortality rate and world income distribution, respectively. This paper aims, accordingly, to explore Rosling's visualization of data as well as potential weaknesses in his presentation.
Essentially, Rosling re-configures existing data by international sources such as UN in order to re-imagine data and re-appraise how data could be interpreted. For example, in his bubble chart for child mortality rate, Rosling stretches data representation such as to incorporate data from different periods in different world regions and not in indistinct amalgamations. Although apparently at extreme points of data representation as regards child mortality rate, Vietnam (being "Third World") and U.S. (Being "Western World") converge by 2003 to show similar smaller family and longer life pattern.
Similarly, Rosling re-configures data for world income distribution in a combination of gap and bubble charts. This, according to Rosling, casts "developing countries" concept in extremely doubtful lights. For example, Rosling's combined charts show African countries – such as Sierra Leone, Ghana and Mauritius – express differentials in GDP and should not be viewed as just similar.
One potential, major weakness of Rosling's visualizations is his dismissal of co-factors which might impact data visualization. For example, in his attempt to debunk a gap between Western and Third World countries as regards child mortality rate, he incorporates data for family size and longer life but seems to drop altogether macro-data cutting across different areas of country in question which might influence how data are not only visualized but initially created. This might include co-factors such as internationalization of education and globalizing world economy.
As well, Rosling's visualization of income distribution gap lacks representation of data on international influences over local economic climates. True, not all African countries experience similar patterns of income distribution. The shown differences do not seem, however, to be accounted for and whether such differences will grow, subside or stabilize.

References

Chen, M., Ebert, D., Hagen, H., Laramee, R. S., van Liere, R., Ma, K.-L.,Silver, D. (2009). Data, Information, and Knowledge in Visualization. Computer Graphics and Applications, 29, 12 – 19. IEEE. doi: 10.1109/MCG.2009.6
TED. (2006, February). Hans Rosling: The best stats you've ever seen [Video file]. Retrieved from http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen#t-666699