Is it worth the drama?
Statistics report raises mixed emotions

More chart drama, and data aggregation

Robert Kosara posted a response to my previous post.

He raises an important issue in data visualization - the need to aggregate data, and not plot raw data. I have no objection to that point.

What was shown in my original post are two extremes. The bubble chart is high drama at the expense of data integrity. Readers cannot learn any of the following from that chart:

  • the shape of the growth and subsequent decline of the flu epidemic
  • the beginning and ending date of the epidemic
  • the peak of the epidemic*

* The peak can be inferred from the data label, although there appears to be at least one other circle of approximately equal size, which isn't labeled.

The column chart is low drama but high data integrity. To retain some dramatic element, I encoded the data redundantly in the color scale. I also emulated the original chart in labeling specific spikes.

The designer then simply has to choose a position along these two extremes. This will involve some smoothing or aggregation of the data. Robert showed a column chart that has weekly aggregates, and in his view, his version is closer to the bubble chart.

Robert's version indeed strikes a balance between drama and data integrity, and I am in favor of it. Here is the idea (I am responsible for the added color).



Where I depart from Robert is how one reads a column chart such as the one I posted:


Robert thinks that readers will perceive each individual line separately, and in so doing, "details hide the story". When I look at a chart like this, I am drawn to the envelope of the columns. The lighter colors are chosen for the smaller spikes to push them into the background. What might be the problem are those data labels identifying specific spikes; they are a holdover from the original chart--I actually don't know why those specific dates are labeled.


In summary, the key takeaway is, as Robert puts it:

the point of this [dataset] is really not about individual days, it’s about the grand totals and the speed with which the outbreak happened.

We both agree that the weekly version is the best among these. I don't see how the reader can figure out grand totals and speed with which the outbreak happened by staring at those dramatic but overlapping bubbles.


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Can you talk a little more about what type of data smoothing you might use, other than weekly bins, on this type of data?


jlb: yes this is a little tricky. I haven't tried this but how about varying the weekly bins by window size (number of days) and shifting, and then some kind of averaging. As far as I know, birds don't follow the human week.

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