The Art of Transforming Data into Great Stories

What happened last week in the world of data visualization? This category provides you with remarkable visualizations – they can be remarkably beautiful, remarkably interactive or just remarkably interesting. Visualizations differ on so many levels, and so does their content. Let’s take a look at what week #9 brought us.

Who Pairs Up With Whom?

As the name implies, Bloomberg Business is an online magazine that is recognised for its stories around the world of business. But last week, surprisingly, they tried to shade light on a cliche of love. “This Chart Shows Who Marries CEOs, Doctors, Chefs and Janitors” displays in detail who is in a relationship with whom, based on job positions.

Do you know whom you doctor is married to? Or whom your hairdresser is in a relationship with? What about your friends and siblings? The visualization by Bloomberg Business draw attention on relationships between different groups of professions. The data is based on a survey covering 3.5 million US households, so almost everyone should be able to find an interesting angle in this visualization. Just give it a try and look for your own profession in the data.

A comprehensive legend explains how the clean looking chart works. Meanwhile, the search function is not very sophisticated (or is it just me and my computer?). I also could not figure out how the single positions are sorted within the visualization. Apart from this, it is simply both fun and funny to check if cliches about relationships are true or not. (Such as the one about male bosses marrying their female secretaries.)

Who marries whom?


Impact of China’s Economic Slowdown

Unsurprisingly, the drop of China’s economy was a great topic last week. A visualization by the Guardian displays how affected other countries are from China’s economic slowdown. Thus, this visualization under the headline “How China’s economic slowdown could weigh on the rest of the world” from August last year, is still – or again – of current relevance and interest.

What makes this visualization so special? For once, it deals with a currently hot topic; for another, the visualizations are different from the very common ones, such as line or  bar charts (although nothing is wrong with those). The Guardian team decided to use bubbles to display the economical (in)dependency of different countries from China. Like so often, the question of absolute numbers and their proportion in percentage needed to be taken into visual consideration.

In this case, this issue was addressed by the interplay of the volume of bubbles, their color and their position within the visualization. Since the chosen approach is not very common, it might take some time to fully comprehend the visualizations correctly. But it’s absolutely worth it! Commendaby, the Guardian stated the sources of the data used for the visualizations. Stated as in linking name and url of the statistic-platforms where the data was obtained from, like the world bank. We think that is a bit too generic. Why not disclosing from which statistics the data is exactly coming from? Such an approach would lead to even more transparency.

A drop of China's demand could drag these countries down...


Text Mining South Park

From business to entertainment, we’ve got it all in this week’s list. Neither economic or societal questions, nor political issues stay in focus. Instead, all attention is drawn to something created out of great trash TV. A single webpage was set-up by Data Scientist Kaylin Walker devoted to text mining in case of the infamous TV-series South Park.

The visualizations by Walker are based on a scientific approach; therefore, it is no wonder her webpage reads like an academic paper. However, the results of the text mining tasks are entertaining especially in visualized form. For instance, she shows that Kenny spoke the less throughout the last 15 seasons, but swore the most in comparison to the other characters. A word to the visualizations: the chosen graphs are nothing special but meet the standard requirements. Additionally, the underlying methodology and statistics, as well as the data-set and code are explained on the webpage. More overly, Walker enriched the unrepresented field of text data mining – also in this weekly format.



About the author : Eva Lopez (DW)