Wealth Inequality in America

Data Storytelling in Video

I’m not a fan of video. I don’t spend time randomly surfing YouTube, and when given the choice between reading an article and watching a video, I’ll read. The reason is that videos often don’t work well for me: they’re too fast or too slow, they take a long time to get to the point, they don’t let me skip around and browse easily. I’d rather be in control than having the information pre-packaged for me. But two examples have surfaced in the last few days that show data visualization can tell a very effective story in well-designed, well-paced videos.

Inequality in America

This video called Inequality in America has made the rounds on social media in the last few days, and as of this writing has over three million views. This is a great example of how you walk people through a fairly complex set of data and explain things like quintiles quite clearly. Sure, it’s not high-dimensional or Big Data, but it’s complex enough that many people will struggle understanding it (comparing distributions is hard).

The video is paced well and has a nice dramatic structure: not all is revealed right away, so it builds the story up nicely and then makes its main point close to the end. This level of clarity would be needed for a lot more information, and it’s a shame that there aren’t many more examples like this.

The Economist: Diminuendo

The Economist just released a “video chart” about music sales. It is much simpler than the above one, but it does a fair bit of storytelling about a simple stacked bar chart. The trick here is that these charts can be easy to just skip over without really looking at what they’re telling you. By walking the viewer through the chart, you get a better sense of what’s in there and why they chose to make that chart.

(This video is not embedded because there doesn’t seem to be a way of turning off the rather obnoxious auto-play. Another reason I dislike videos ;)

Granted, this is really simple, and most people would be able to figure it out. But I’m guessing that The Economist is planning on also doing this with more complex charts and stories. Also, the comparison of downloads at the end could have used some more love, but overall it’s surprisingly effective and well-paced.

Hans Rosling: Human Development Index

Finally, no posting about video would be complete without the grand-daddy of all data-based communication videos, Hans Rosling’s famous talk at TED 2006 using the gapminder tool. The amount of information he gets across, the steps, the pacing, and the enthusiasm are still unmatched. If you have seen this before, it’s worth watching again. If you haven’t seen it, you have to watch it now.

Published by

Robert Kosara

Robert Kosara is Senior Research Scientist at Tableau Software, and formerly Associate Professor of Computer Science. His research focus is the communication of data using visualization. In addition to blogging, Robert also runs and tweets.

3 thoughts on “Data Storytelling in Video”

  1. Hi Robert:

    Thanks for providing the info vis video examples and for candidly expressing your skepticism about video.

    Your comments about video are interesting. I look at video and its important to visualization from a completely different (if not perhaps opposite) viewpoint.

    My first contributions to visualization were in the arena of scientific visualization for air pollution problems The key issues focused on trying to understand pollution transport. While geographic maps of pollution data were helpful, it was difficult to understand the pollution transport with static images of maps changing over time. For this situation computer generated video was critical to understanding the air pollution transport. At our US EPA Scientific Visualization Center in the 1990s, we generated computer generated visualizations showing the hourly average distribution of reactive pollutant emissions. The design of the visual display remained constant through all of the frames, so that attention was devoted completely to shifts in the data. We used discrete color scales to show pollution values. Twenty-four hours or more of an air pollution data model run would be organized into the time series visual display to view the changes in the pollutant concentrations over time. A map of the geographic region of the air pollution transport would be overlayed over the visualization to understand in what direction the air pollution was moving. We basically had weather model type movies of air pollution concentrations. These videos were (and likely continue to be) critical to helping environmental scientists understand pollution transport. Video of time series displays provided something that static geographic (GIS) systems could not.

    Tufte refers to this concept as the small multiple On page 170 of The Visual Display of Quantitative Information book, Tufte notes “Small multiples resemble the frames of a movie: a series of graphics, showing the same combination of variables, indexed by changes in another variable. Twenty-three hours of Los Angeles air pollution are organized into this display, based on computer generated video tape. Shown is the hourly average distribution of reactive hydrocarbon emissions. The design remains constant through all of the frames, so that attention is devoted entirely to shifts in the data.”

    This type of scientific data visualization allowed us to tell the following kind of story: In the summer months with typical high temperatures and wind conditions, high air pollution concentrations in the Washington DC area could be transported to the Boston, Massachusetts area quickly, perhaps in a matter of a few days. It was a dramatic display of regional pollution transport that demonstrated the need for air pollution control at the federal level since the air pollution crossed state boundaries easily. The visualization work supported policy setting activities like the Clean Air Act at the United States Federal Government Level. Issues like Acid Rain and Regional Oxidant concentrations were depicted with these time series animations and visualizations.

    We were not skeptical about the value of video and time series animation to tell the story of air pollution issues in the USA. On the contrary, we were in constant demand to produce the environmental science visualizations.

  2. Thanks for your awesome article!

    I wonder if next generation visualization tools will offer pre-scripted transitions and animations like in these videos, but in combination with the ability for users to at any point stop the script and use the tool interactively to explore the data on their own, possibly drawing new conclusions, and possibly recording their own new script and sharing it on the Web. Food for thought ;)

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