Broadly speaking, data comes in two flavors: quantitative and qualitative. Quantitative data includes numerical values (you know, like quantity). Qualitative data, on the other hand, is non-numerical – as in the quality of the information.
Quantitative data is pretty straightforward. You’ve got numbers, you graph the numbers. Done and done.
Qualitative data, on the other hand, can be tricky. How do you graph a quote? How do you compare changing feelings over time? In many cases we cheat a little by counting words for word clouds and gauging feelings on scales of 1-10 – essentially turning qualitative data into quantitative.
But what happens if we go the other way?
What if we take numbers out of the equation?
Now hang on, you might say, if you already have numerical data, why would you abandon that? Don’t you want information to be MORE precise?
Usually yes… but sometimes no.
Remember that the ultimate goal is to communicate an idea to your audience. So if your audience isn’t comfortable with numbers, then perhaps that’s not the best way forward.
This week we’re looking at one such case where less was more.
A study in salmon
The salmon of British Columbia, Canada are iconic to say the least. So too are they contentious: First Nations, commercial, and recreational groups all stake a claim to portions of the annual migration. To advocate effectively, each group needs effective access to data. Yet data are often provided in spreadsheets and data formats that are difficult to work with and may in fact alienate the very people wanting to use them.
The goal of this project was to present over a century’s worth of salmon harvest data in a more accessible way.
Out of this effort came two illustrations of the same dataset: one quantitative, one qualitative.
Quantitative: The Bar Graph
Alright, so the bar graph isn’t the most innovative visualization in the world. But what it lacks in originality it makes up for in effectiveness. People are very good at judging the length of a shape. The bar graph uses this innate ability to facilitate comparisons between years.
But that can also be the bar graph’s downfall.
When you first looked at the graph above, what did you do? If you’re anything like me, you probably scanned for the largest and smallest bars, then started comparing neighboring bar segments to see how they stacked up. Bar graphs don’t just invite comparisons – they demand them. If you don’t want your readers diving into the details, then perhaps a different option would be better.
Qualitative: The Streamgraph
The streamgraph is essentially an area graph that has been detached from the x-axis and allowed to float in mid-air. There are definitely pros and cons to doing this. One major con is that you lose the ability to read values using the y-axis, the standard way of reading data in a bar graph. But what you lose in detail you gain in seeing the big picture – the streamgraph essentially forces the reader to take a step back from the year-to-year data and look at the overall trend.
In this way, the streamgraph makes the quantitative harvest data much more qualitative. Sure the graph is still generated by numbers, but they don’t take center stage. Where you once had precise numbers, you now have more fluid trends.
This type of graph has proven quite effective with groups who may not be as comfortable with math and typical data visualizations. If someone has had a bad experience with school or thinks they’re bad at math, then flashing a bar chart isn’t exactly welcoming them with open arms. An unusual graph type may skirt around the math issue and instead connect with your reader in their own terms.
The importance of getting feedback
In my previous work with Ecotrust Canada, I showed these two graphs at a community meeting on fisheries and salmon harvests. Unsure of myself, I put the bar graph up first. After all, it was the “right” way to show this sort of harvest data. The bar graph was met with general disinterest; people glanced at it and immediately looked away.
On a whim, I switched to the streamgraph. I had been messing around with graphs the night before and thought it looked pretty cool. And besides, I had never seen a streamgraph presented to an audience and I wondered how they might react. To my surprise, participants immediately engaged with it. People saw patterns in the graph and could relate the data to their own experiences in the fishery. One gentleman even ran up and used it as a visual aid for his own presentation.
To be honest, if I hadn’t changed graphs at that meeting I probably wouldn’t be so comfortable trying weird new data visualizations today. Years of science classes had taught me the “right” ways to display data. But the 180-degree change in reaction that I witnessed was undeniable; by making the information more human and presenting it in a different way, I was able to reach people that the bar graph might have left in the cold.
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