A reader pointed me to this piece of data journalism by the folks at FiveThirtyEight (link). The project examines the impact of the potential ban of abortion clinics in various U.S. states, and how that affects women who want abortions.
I have highlighted Coconino county in Arizona. The nearest clinic accessible to Coconino residents is in nearby Maricopa county, as seen on the map. The distance of travel is about 162 miles. This county is given a purplish-green color, which means the 162-mile distance is considered long distance in the context of the whole country, and the clinic in Maricopa has middling capacity.
Hovering over each county presents the same information about where women go to get abortions today.
Next, the designer presents a series of simulations.
By pressing one of the state buttons, the reader can explore what happens if that state decided to ban abortion clinics. Naturally, we'd expect the counties of that state to be most impacted by the ban.
Here is a close-up of Coconino county after I pressed the Arizona button:
Instead of going to Maricopa county, the women are expected to cross the state line and use the clinic in Clark county, Nevada.
In general, the colors within Arizona are darker, which means either the women have to travel further, or that they have to patronize more crowded clinics. Darker is worse.
This is what the map looks like if I light up all the boxes, i.e. the states deemed by FiveThirtyEight as having a chance of enacting abortion bans.
All in all, I think this dataviz project has many virtues. It addresses a pressing and important issue relevant to many people. The interactive components are well designed, and actually useful. Legends and annotations pop up as readers hover over the map. Lots of calculations have been performed to help answer the question of how much further someone has to go, as well as how much more congested would the facility be.
Nevertheless, the blog reader who told me about this project dislikes the section called "How to Read This Map".
I agree that this color legend is difficult.
I find the three grids confusing. The green one is telling me the first column is green, and darker green represents longer travel to the clinic. The second grid is telling me the first row is pink, and darker pink indicates more congested clinics. Those are not hard.
The third grid is hard to reconcile with the rest. It appears to tell me that the diagonal row is purple, and darker purple indicates high values of both metrics.
I'm trying to juggle those three thoughts in my head, trying to reconcile them, and when I read the map below, I'm seeing a county's specific color, say medium purple, and I want to know what it means without having to refer back to the color legend. It's a fail if I keep having to look up to the legend.
And then, the real problem with the "How to Read This Map" rears its head. Up above, I clicked on Arizona for no reason other than it's the first button on the list. I hovered over Coconino as it's one of the largest counties in Arizona. Here is a close-up of what I see:
Are you noticing what the problem is? The color of this county is a purplish-green mixture. It's not any of the greens, reds, or purples shown in the "How to Read This Map" section! So, I had to find the real color legend, which is elsewhere on the map. This one:
The color for Coconino is the top-middle cell which happens to be one of two cells that were missing in the "How to Read This Map" section. The designer correctly sensed the difficulty of this complicated, two-dimensional legend, and offered help but I feel that the effort hasn't paid off.
For this graphic, I think they can simplify the legend, and make it about congestion only. The reason being that the distance dimension is already captured by the lines that show up on hovering. This alternative design does turn the color to one dimensional, but it is less baffling.
Another idea is to convert distance into travel time, and congestion into service time, and then both can be summed to yield a unidimensional color scale. Quite a bit more analytical work must be done to turn congestion into service time.
I have a few comments on the analytics behind the dataviz; I'll put them on the book blog in the next day or two.