Expert handling of multiple dimensions of data

I enjoyed reading this Washington Post article about immigration in America. It features a number of graphics. Here's one graphic I particularly like:

Wpost_smallmultiplesmap

This is a small multiples of six maps, showing the spatial distribution of immigrants from different countries. The maps reveal some interesting patterns: Los Angeles is a big favorite of Guatamalans while Houston is preferred by Hondurans. Venezuelans like Salt Lake City and Denver (where there are also some Colombians and Mexicans). The breadth of the spatial distribution surprises me.

The dataset behind this graphic is complex. It's got country of origin, place of settlement, and time of arrival. The maps above collapsed the time dimension, while drawing attention to the other two dimensions.

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They have another set of charts that highlight the time dimension while collapsing the place of settlement dimension. Here's one view of it:

Wpost_inkblot_overall

There are various names for this chart form. Stream river is one. I like to call it "inkblot", where the two sides are symmetric around the middle vertical line. The chart shows that "migrants in the U.S. immigration court" system have grown substantially since the end of the Covid-19 pandemic, during which they stopped coming.

I'm not a fan of the inkblot. One reason is visible in the following view, which showcases three Central American countries.

Wpost_inkblot_centralamerica

The main message is clear enough. The volume of immigrants from these three countries have been relatively stable over the last decade, with a bulge in the late 2000s. The recent spurt in migrants have come from other places.

But try figuring out what proportion of total immigration is accounted for by these three countries say in 2024. It's a task that is tougher than it should be, and the culprit is that the "other countries" category has been split in half with the two halves separated.

 


When should we use bar charts?

Significance_13thfl sm

Two innocent looking column charts.

These came from an article in Significance magazine (link to paywall) that applies the "difference-in-difference" technique to analyze whether the superstitious act of skipping the number 13 when numbering floors in tall buildings causes an inflation of condo pricing.

The study authors are quite careful in their analysis, recognizing that building managers who decide to relabel the 13th floor as 14th may differ in other systematic ways from those who don't relabel. They use a matching technique to construct comparison groups. The left-side chart shows one effect of matching buildings, which narrowed the gap in average square footage between the relabeled and non-relabeled groups. (Any such gap suggests potential confounding; in a hypothetical, randomized experiment, the average square footage of both groups should be statistically identical.)

The left-side chart features columns that don't start as zero, thus the visualization exaggerates the differences. The degree of exaggeration here is tame: about 150 got chopped off at the bottom, which is about 10% of the total height. But why?

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The right-side chart is even more problematic.

This chart shows the effect of matching buildings on the average age of the buildings (measured using the average construction year). Again, the columns don't start at zero. But for this dataset, zero is a meaningless value. Never make a column chart when the zero level has no meaning!

The story is simple: by matching, the average construction year in the relabeled group was brought closer to that in the non-relabeled group. The construction year is an ordinal categorical variable, with integer values. I think a comparison of two histograms will show the message clearer, and also provide more information than jut the two average values.


Is this dataviz?

The message in this Visual Capitalist chart is simple - that big tech firms are spending a lot of cash buying back their own stock (which reduces the number of shares in the market, which pushes up their stock price - all without actually having improved their business results.)

Visualcapitalist_Magnificent_Seven_Stock-Buybacks_MAINBut is this data visualization? How does the visual design reflect the data?

The chart form is a half-pie chart, composed of five sectors, of increasing radii. In a pie chart, the data are encoded in the sector areas. But when the sectors are of different radii, it's possible that the data are found in the angles.

The text along the perimeter, coupled with the bracketing, suggests that the angles convey information - specifically, the amount of shares repurchased as a proportion of outstanding share value (market cap). On inspection, the angles are the same for all five sectors, and each one is 180 degrees divided by five, the number of companies depicted on the chart, so they convey no information, unless the company tally is deemed informative.

Each slice of the pie represents a proportion but these proportions don't add up. So the chart isn't even a half-pie chart. (Speaking of which, should the proportions in a half-pie add up to 100% or 50%?)

What about the sector areas? Since the angles are fixed, the sector areas are directly proportional to the radii. It took me a bit of time to figure this one out. The radius actually encodes the amount spent by each company on the buyback transaction. Take the ratio of Microsoft to Meta: 20 over 25 is 80%. To obtain a ratio of areas of 80%, the ratio of radii is roughly 90%; and the radius of Microsoft's sector is indeed about 90% of that of Meta. The ratio between Alphabet and Apple is similar.

The sector areas represent the dollar value of these share buybacks, although these transactions range from 0.6% to 2.9% as a proportion of outstanding share value.

Here is a more straightforward presentation of the data:

Junkcharts_redo_vc_buybacks

I'm not suggesting using this display. The sector areas in the original chart depict the data in the red bars. It's not clear to me how the story is affected by the inclusion of the market value data (gray bars).


The radial is still broken

It's puzzling to me why people like radial charts. Here is a recent set of radial charts that appear in an article in Significance magazine (link to paywall, currently), analyzing NBA basketball data.

Significance radial nba

This example is not as bad as usual (the color scheme notwithstanding) because the story is quite simple.

The analysts divided the data into three time periods: 1980-94, 1995-15, 2016-23. The NBA seasons were summarized using a battery of 15 metrics arranged in a circle. In the first period, all but 3 of the metrics sat much above the average level (indicated by the inner circle). In the second period, all 15 metrics reduced below the average, and the third period is somewhat of a mirror image of the first, which is the main message.

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The puzzle: why prefer this circular arrangement to a rectangular arrangement?

Here is what the same graph looks like in a rectangular arrangement:

Junkcharts_redo_significanceslamdunkstats

One plausible justification for the circular arrangement is if the metrics can be clustered so that nearby metrics are semantically related.

Nevertheless, the same semantics appear in a rectangular arrangement. For example, P3-P3A are three point scores and attempts while P2-P2A are two-pointers. That is a key trend. They are neighborhoods in this arrangement just as they are in the circular arrangement.

So the real advantage is when the metrics have some kind of periodicity, and the wraparound point matters. Or, that the data are indexed to directions so north, east, south, west are meaningful concepts.

If you've found other use cases, feel free to comment below.

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I can't end this post without returning to the colors. If one can take a negative image of the original chart, one should. Notice that the colors that dominate our attention - the yellow background, and the black lines - have no data in them: yellow being the canvass, and black being the gridlines. The data are found in the white polygons.

The other informative element, as one learns from the caption, is the "blue dashed line" that represents the value zero (i.e. average) in the standardized scale. Because the size of the image was small in the print magazine that I was reading, and they selected a dark blue encroaching on black, I had to squint hard to find the blue line.