Visual Human Development Index

Alex Simoes, MIT Media Lab student working with Professor Cesar Hidalgo, developed a graphical representation of the Human Development Index (HDI). The so-called HDI trees are based on data published in the United Nations 2010 edition of the Human Development Report. The interactive version on their website allows for comparisons between two countries, or between two years of one country.

Human Development Index – HDI Tree Representation

From Hidalgo’s website:

The HDI Tree aggregates data in the Human Development Index graphically instead of numerically. A long standing criticism of the Human Development Index is that, because it averages indicators of Income, Health and Education, it is possible for countries to obtain the same score with different combinations of indicators. This creates the possibility of substituting Education for Health, Health for Income or Income for Education.

The HDI tree deals with the numerical aggregation problem by using a graphical representation in which the total value of a country’s HDI is presented together with that of its components and subcomponents. This way it is possible to see immediately the contribution of each dimension to the value of a country’s HDI.

Moreover, the HDI tree represents an alternative way of branding the idea of Human Development and communicating its message graphically to a wide audience. For more on the HDI tree, see the original report or this summary document.

Inevitably, there are times when one wishes to collapse multiple dimensions or factors into one numerical score. However, one loses the details underlying the score. Such tree-like visual representations of aggregate information can be used for compound measurements used in business, such as the Balanced Scorecard.

Note: Hidalgo’s gallery features many more interesting projects, such as Disease Network Data visualizing disease associations or the Product Space visualizing economic capabilities of countries based on their trading activities.

Addendum: I did some more research on this and found a great summary on the HDI tree posted under the title “Visualizing Human Development” at Visualizing.org. One particularly interesting chart is a summary of 35 African nations, showing their respective HDI tree for both 1970 and 2005.

From the original summary paper “A Visual HDI” by C. Hidalgo:

The Development Tree also facilitates searching and comparing features over large volumes of data. For example, consider Figure [above], a chart in which the HDI trees of 35 African Nations are shown for both 1975 and 2005. This figure shows information on 420 numerical values (35 countries x 2 years x 6 values). In this chart, however, there are several observations that are easy to spot despite the large amount of information being presented. For instance, it is relatively easy to find out what are the countries in the set with higher levels of development. Algeria, Botswana, Libya, Mauritius, Morocco, South Africa and Tunisia in this case. Moreover, their increases are also rather conspicuous. Also, the lopsidedness of some nations also becomes conspicuous, as it can be seen in the examples of Botswana, South Africa and Swaziland, regarding the life dimension, and that of Libya in 1970, regarding high Income, or of Congo DRC in 2005, regarding low income.

Again, I can easily picture applications of this visual representation of an aggregate score in a typical business environment. Consider an internal ranking of employees based on an aggregation of several orthogonal dimensions such as skill, teamwork, communication, innovation and business savvy. You could look at a dozen of these employees and their respective visual aggregate tree scores to spot trends, outliers, and relative strengths. Another example is the Balanced Scorecard approach mentioned above. Suppose you are aggregating measures about Finance, Schedule, Quality, Innovation, and People into the score of an Engineering organization. Then you could picture the tree for aggregate performance of this business unit over time (quarters or years) to spot trends.