So You Want To Be a Data Journalist: Hollywood Style

Static, two-dimensional maps are so early 2000s. It’s all about the interactive visualizations now. I think I can speak for the ADD-induced smartphone generation when I say that data journalists should visualize information to grab the audience’s attention. I find myself easily distracted if the visualization doesn’t stimulate me even if the data is important.

Golden rule: Bring in the “Hollywood style” effects.

Bubble tree maps are a great way of making data interactive by allowing users to make sense of the relationships between data, according to Richard Boardman, a former Google programmer.

Gregor Aisch (hello again), helped to redesign a well-known bubble map, “Where Does My Money Go” that tracks government spending. Aisch created a video for his driven-by-data blog:

Bubble tree maps use useful for data that has a hierarchical format. The information is cleanly presented, the relationships are made obvious with the sub formatting, and it’s fun for the user to make the connections on her own.

It was difficult to find journalistic examples of bubble tree maps aside from Aisch’s OpenSpending project. There may be two reasons for this:

1. I’m inept when it comes to Advanced Google search. (Huge possibility)

2. Bubble tree maps, although clean and readable, can’t display enough information without overwhelming the user. Adding too much detail may make bubble tree maps too complicated – a huge turn off for most readers.

I found one bubble tree map, which made me realize that it’s probably reason #2. Here’s why: The Guardian created a bubble tree map on government spending by department.

It’s initially visually interesting but overwhelming. It’s frustrating to zoom in and out of the page to read the tiny writing. This may lead users to ignore the smaller notes at the bottom of the graphic.

It’s an informative piece but not so much journalistically.

Elaborate visualizations like the Guardian‘s bubble tree probably benefit from a stronger interactive element, such as clickable animation features.

The New York Times created a 2010 budget prediction graph that is successful for two reasons:

1. It translates and clarifies bureaucratic jargon for users to not only help them understand the graph but probably makes them want to understand it.

2. It allows users to take control of the graph by giving them the chance to choose how they would like to view the graph based on the timeline.

The map also shows the relationship between the budget forecasts and what the results really were. Users don’t necessarily need further detail to realize that there is a significant discrepancy in the White House predications of the federal budget. The step-by-step account is enough to guide users without overwhelming them.

The problem that many interactive visualizations run into is that, unlike the Times graph, they lack a guide that can help users digest the information. It may be a simple tool for programmers, but if the Guardian was visualized in a similar way like a Prezi, it could probably have contained the same amount of information while still attracting users’ attention.

Still, sometimes the “Hollywood” data visualizations don’t work because beneath the hoopla, the main point is unclear. Design and style are important but when not when they compromise the data. It’s like when great books are turned into crappy screenplays for even crappier movies.

The Times fell victim to the style>content trap in May, following Osama Bin Laden’s death. It’s visually stimulating to see the graphic load but it leads to high expectations. It’s possible that the graphic made more sense when/if the comments populated in real time. But now that the graphic is longer accepting comments, I’m not so sure the graphic makes a relevant point. It definitely needs to be accompanied by the article. But even then, questions linger. How did the designers assess where the comments were positioned on the map? Was there specific key terms that designated positions? It seems subjective to decide which comment is more negative than the other.

It’s also unclear how multiple comments are tagged together in one box or why the lighter blue boxes don’t have pop-ups when my mouse scrolls over them.

Still, the interactive feature of this graphic probably held my attention longer than the Guardian bubble tree because I tried to figure out how to find more information.

I think that, if possible, most data visualizations need an interactive “Hollywood” glamorized quality to compete with the decreasing attention span of most users. Doing so would also allow data journalists to squeeze in as much information as they can by using the interactive animations to display or hide the information, when necessary, while still keeping the graphic visually appealing.

I hate to sound like a data snob, especially since I’m still learning the basics, but simple visualizations are like Internet memes. Anyone can create a meme but only a few go viral because they know how to demand attention from the multitasking, smartphone-owning, YouTube generation:


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