5 Must-See TED Talks on Data Visualization!

Data visualization is crucial in understanding data and identifying hidden connections that matter. Below are 5 TED talks on data visualization you don’t want to miss!

1. Hans Rosling: The best stats you’ve ever seen

Han Rosling, cofounder of the Gapminder Foundation, developed the Trendalyzer software that converts international statistics – such as life expectancy and child mortality rate – into innovative, interactive graphics. The statistics guru is a strong advocate for public access to data and the development of tools that make it accessible and usable for all.  In this classic talk, Rosling highlights the importance of data in debunking myths about the gap between developed countries and the so-called “developing world.” Even though the talk was filmed 10 years ago, it still carries very important and relevant messages.

Watch more of Rosling’s TED talks here.

2. David McCandless: The beauty of data visualization

In this visually captivating talk, data journalist David McCandless suggests that data visualization is a quick solution to our current problem of information overload. Visualizations allow us to see the hidden patterns, identify connections that matter, and tell stories with data. To McCandless, “even when the information is terrible, the visual can be quite beautiful”; this is a controversial claim, however, since the main goal of data visualization should be to communicate information effectively through graphical means.

3. Dave Troy: Social maps that reveal a city’s intersections – and separations

A serial entrepreneur and data-viz fan, Dave Troy takes a people-focused approach to data visualization. Troy has been mapping tweets among city dwellers, revealing what connects communities and what separates them – above and beyond demographic factors such as race or ethnicity. He compares a city to a “giant high school cafeteria” and suggests that we see “how everybody arranged themselves in a seating chart”, arguing that “maybe it’s time to shake up the seating chart a little bit” to reshape our cities.

4. Eric Berlow & Sean Gourley: Mapping ideas worth spreading

An ecologist and a physicist, Eric Berlow and Sean Gourley, collaborate in this presentation to create stunning 3D visualizations demonstrating the interconnectedness of ideas. Taking 4,000 TEDx talks from 147 countries representing 50 languages, they explore their “meme-omes” – the mathematical structures that underlie the ideas behind these talks – and discover similarities between seemingly unconnected topics. Berlow and Gourley also broke down complex themes into multiple more specific ones, seeing what topics resonated with viewers and what kind of audience looked at what topic. To Gourley, mapping ideas in this way will help us “to see what’s being said, to see what’s not being said, and to be a little bit more human and, hopefully, a little smarter.”

5. Manuel Lima: A visual history of human knowledge

Founder of VisualComplexity.com Manuel Lima, described by Wired Magazine as “the man who turns data into art,” explains the visual metaphor shift from the tree to the network as “a new lens to understand the world around us.” Lima argues that the tree – an important tool to map everything from genealogy to systems of law to Darwin’s “Tree of Life” – is being replaced by a new metaphor – the network. Rigid structures are evolving into interdependent systems, and networks emerge to embody the nonlinearity, decentralization, interconnectedness, and multiplicity of ideas and knowledge. The shift in visual metaphor also represents a new way of thinking – one that is critical for us to solve many complex problems we are facing.

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The Common Mistakes Made in Creating a Data Visualization

Oftentimes the best way to learn about how to do something right is by learning what not to do, especially for how to make good data visualizations. WTF Visualizations is a website that compiles poorly crafted data visualizations from across the web and media. Below is a sampling of some of the visualizations featured that illustrate some of the most common data visualization mistakes:

  • Absence of Proper Scaling

Including proper scaling is essential in accurately representing your data. In the example below, the differentiation between values is misrepresented due to the absence of a clear scaling measure. The 52% measure does not appear to be as large as it should be in comparison to the other bars, and the 13% figure appears to be much larger than 3% when compared to the two 10% figures.

datavizmistake1

  • Too Much Information

While the inclination is to include as much information in visualizations as possible, oftentimes including too much information detracts from the clarity and concision that is essential to good data visualization. The example below perfectly illustrates how including a myriad of different categories can muddle your visualization, as well as the importance of clear axis labels and descriptive titles.

Ensure that the data that you do decide to visualize is comprehensible to your audience: recode categories when there are too many; don’t include measures that illustrate the same phenomenon; don’t include 10 different variables when 3 will do. If need be, include more than one visualization to highlight different sub categories or variables.

datavizmistake2

  • Bad Math

Always double check your math before sharing your visualization to the public. You may run the risk of misrepresenting your data, as well as appearing as though you are not capable of simple arithmetic. The example below illustrates this point perfectly: while the creator uses a pie chart, the sections do not add up to 100%, but rather, 128%. The sections of the pie chart also do not accurately reflect the values they supposedly represent: The “51% Today” section, for instance, should be taking up a little more than half of the pie chart.

datavizmistake3

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What Makes a Good Data Visualization?

Being able to represent data in a clear, concise, and engaging way is an essential skill. While an effective poster is key, data visualizations are a tool that enhance the communication of narratives underlying the data. David McCandless, a world-renown data visualization maker and creator of Information is Beautiful, constructed a Venn diagram that depicts the essential elements of a successful data visualization.

DataViz

 

The irony of this data visualization, which aims at serving as an aesthetically-pleasing vehicle for what comprises a good visualization, is that there are several elements that don’t make it a successful one. Firstly, the information he wants to communicate is not immediately obvious. Figuring out how each of the circle categories and their intersections relate to the associated examples (e.g. information x goal = plot) takes time and is distracting. In addition, some of the examples he gives aren’t very descriptive. What does he mean by “pure data viz” in the visual form x information intersection? What about “proof of concept” at the intersection of goal and story? There isn’t enough context available to make sense of these examples and categories. While the visualization is accessible to colorblind audiences (a very important element to a good data visualization), the point that McCandless wants to communicate is lost due to its lack of description and over-complicated use of the Venn diagram model.

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