Tagged: world

Interactive visualization of Global Gender Gap Index 2013 report

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This is a brief visualization of Global Gender Gap 2013 Index report by World Economic Forum. As the report authors say,

The Global Gender Gap Index examines the gap between men and women in four fundamental categories (subindexes): Economic Participation and Opportunity, Educational Attainment, Health and Survival and Political Empowerment. Table 1 displays all four of these subindexes and the 14 different indicators that compose them, along with the sources of data used for each.

I thought it would be nice to try to visualize the data and make it as interactive as I could, and learn d3.js in process. I actually tried to mobilize all the data in the report, which one can see in graphical form by clicking on countries on world map, or selecting the categories in the dropdown.

There are several categories:

  • economy,
  • education,
  • health, and
  • politics

In addition to that, I calculated the differences between 2013 and previous years. These maps are also accessible through dropdown menu, or simply by scrolling up and down.

Launch the viewer here, or click the image below.

Interactive visualization, Global gender gap 2013
Interactive visualization, Global gender gap 2013


Copied straight from the report:

Economic Participation and Opportunity

This subindex is captured through three concepts: the participation gap, the remuneration gap and the advancement gap. The participation gap is captured using the difference in labour force participation rates. The remuneration gap is captured through a hard data indicator (ratio of estimated female-to-male earned income) and a qualitative variable calculated through the World Economic Forum’s Executive Opinion Survey (wage equality for similar work). Finally, the gap between the advancement of women and men is captured through two hard data statistics (the ratio of women to men among legislators, senior officials and managers, and the ratio of women to men among technical and professional workers).

Educational Attainment

In this subindex, the gap between women’s and men’s current access to education is captured through ratios of women to men in primary-, secondary- and tertiary-level education. A longer-term view of the country’s ability to educate women and men in equal numbers is captured through the ratio of the female literacy rate to the male literacy rate.

Health and Survival

This subindex provides an overview of the differences between women’s and men’s health. To do this, we use two indicators. The first is the sex ratio at birth, which aims specifically to capture the phenomenon of “missing women” prevalent in many countries with a strong son preference. Second, we use the gap between women’s and men’s healthy life expectancy, calculated by the World Health Organization. This measure provides an estimate of the number of years that women and men can expect to live in good health by taking into account the years lost to violence, disease, malnutrition or other relevant factors.

Political Empowerment

This subindex measures the gap between men and women at the highest level of political decision-making, through the ratio of women to men in minister-level positions and the ratio of women to men in parliamentary positions. In addition, we include the ratio of women to men in terms of years in executive office (prime minister or president) for the last 50 years. A clear drawback in this category is the absence of any indicators capturing differences between the participation of women and men at local levels of government. Should such data become available at a global level in future years, they will be considered for inclusion in the Global Gender Gap Index.

Score changes

Out of the 110 countries that have been involved every year since 2006, 95 (86%) have improved their performance over the last four years, while 15 (14%) have shown widening gaps. Ten countries have closed the gap on both the Health and Survival and Educational Attainment subindexes. No country has closed the economic participation gap or the political empowerment gap. On the Economic Participation and Opportunity subindex, the highest-ranking country (Norway) has closed over 84% of its gender gap, while the lowest ranking country (Syria) has closed only 25% of its economic gender gap. There is similar variation in the Political Empowerment subindex. The highest-ranking country (Iceland) has closed almost 75% of its gender gap whereas the two lowest-ranking countries (Brunei Darussalam and Qatar) have closed none of the political empowerment gap according to this measure.

Grouping countries according to flag similarity

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This topic is apparently interesting enough that it warrants its own discussion on Quora. People there are relying on keen observational powers of human mind, but for this article, I tried to group the flags algorithmically.

I plotted the results on the map below. Countries with same colors have similar flags. The brighter the color, the bigger the group of countries with similar flags.

Launch the interactive viewer to explore the matter interactively.

Countries by flag similarity
Countries by flag similarity


Here are some flag groups. To see them all, click the image above.

flags_7flags_80 flags_124 flags_27

How I grouped the flags

I used a machine learning algorithm called k-means clustering. It’s really a rudimentary exercise, but the results are good enough to publish on this wee blog.

The algorithm accepts units to be grouped as vectors, so I had to vectorize the images first, that is to say, convert them in a long string of numbers. Each image was partitioned into a grid, then the average color  value for each cell was computed. The grid was 24 x 24 cells big. I found that enough for simple flags.  These color values were converted into HSB color space and experimentally weighted, then copied into a vector. These vectors were fed into the k-means algorithm with requested number of individual clusters set to 120 (there are 240 different flags). You can see results in the viewer.

Number of clusters was set experimentally, and the clustering is not perfect. For example, Canadian is grouped with some very unlikely lookalikes.

See also the other post with k-means clustering, K-means clustering with Processing.js


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