Another Nobel Prize for visual intelligence!

When Al Gore was awarded the Nobel Peace Prize last year, robert Kosara at Eager Eyes called it "A Nobel Prize for Charts". Now the 2008 Nobel Prize in Chemistry has been awarded Osamu Shimomura, Martin Chalfie and Roger Tsien, for green fluorescent protein (GFP). I think that counts as another prize for graphical display of information:

Using spots and rings in tables

jenmoocat in comments asks about the "spot matrix" table I used to display the scores from one to ten of X options in Y categories. My technique has always been about using bubble charts, in a similar way to this heatmap tutorial at More Information Per Pixel. Chandoo at Pointy Haired Dilbert describes a different way, using a table and the Wingdings 2 font.

This is a great alternative, and would work really well in a dashboard. Five separate score levels is about the maximum that people can easily distinguish anyway. After that, you're relying more on the approximate response to levels of darkness to guide the eye. It becomes less of a table and more of a map.

Those curious about the history of such tables should have a look at page 174 of Edward Tufte's 1983 classic Visual Display of Quantitative Information, where Tufte shows and praises a Consumer Reports small multiple of tables of cars and their repair trouble spots by make and year. Tony Rose of DSA Insights points out that this is a sophisticated version of Harvey Balls, made less qualitative and more quantitative.

Edited to add: Thinking about the design of Chandoo's table some more, if you want to try his technique out in your own tables, bring the spots closer together, so that they appear to be words in a sentence. They'll be easier to read that way. And as there are only five columns in the example, if you bring them still closer, they can be like letters in a word, and "read" at a glance. Narrowing the table may require abbreviating the titles or turning them on their side, but I think it's worth it.

The design philosophy to follow is one similar to Tufte's "sparkline" philosophy, that a tiny picture is like a word, and should be presented at a similar typographical density. Stringing them out is liable to make it harder to see the patterns.

If you want to avoid privileging one orientation, you'll want the lines to be no further apart from each other than the spaces between columns. If you have only one row, consider abandoning spots altogether, and go for a tiny bar-graph sparkline instead. Gauging the relative value of circular spots is a problem, because you're asking the reader to judge areas, which are lower in Cleveland's Hierarchy than lengths. Their symmetry is only an advantage if the table is two-way, where columns would be harder to read up and down.

Reorderable tables II: Bertin versus the Spiders

Chandoo at Pointy Haired Dilbert has a post about the inadequacies of radar, or spider, charts; using a sample of four (software?) options to be selected, and six scores by various criteria, he presents them as a radar chart:
Not very informative. So he tries it as a "petal chart"
Still not showing much. The circular formats of radar and petal charts don't really add value in this case, since we expect to be able to sort best from worst, and we don't really care if the best comes round to meet the worst, like the worm Ouroboros chomping on its own tail.

Bertin says every graph is just a table really: this is the table in question:
So why not just show it as a table?
I've re-ordered the criteria and options to show a rough diagonal trend. The criteria are obviously re-orderable categories, and I assume the options are, even though they're numbered 1-4. I take it that's just anonymising. Clearly Option 1 is the package for you if performance and scalability are what you're looking for, while you should consider option 2 if you really care about usability and flexibility. Sometimes the options in the middle excel at some crieria in the middle, but in this case options 3 and 4 don't really have anything to offer against the all-round competence of option 1.

The spot matrix is a favourite of mine, and does appear in Semiologie graphique, but Bertin more frequently shows his tables as a stacked bar chart:
Finally, has re-ordering helped the spider?
Sadly, no.

Giving in to data loss aversion

D. Kelly O'Day, of the Process Trends web site, has a new Charts & Graphs blog, and his first post comments on the US Census Bureau's Current Population Reports data discussed by Jorge Camoes, and Andreas Lipphardt at xlcubed.com, as an example of a confusing data set with too much detail, or is it just detail in need of sensitive presentation?



(as reader michael rightly points out in comments on the XLCubed post, this can't be showing the rise of income disparity, because it's percentage of households against fixed income level, not percentage of income against fixed household quantiles. Also, the series tops out at $100k, which is peanuts compared to the super-rich levels where the income transfer of the last few decades has really been most spectacular.)

Kelly abandons the time series approach to present the two end points in a dot plot:



However, I think that reducing the curves to a pair of points, one in 1967 and one in 2007, loses a lot of information that the full time series has to tell. So, in the spirit of defending loss aversion, I wondered if a readable time series graph could be constructed that would give real insights into the census table.

First, I made it a cumulative graph, so each income bracket now goes from zero to n(i), not n(i-1) to n(i). This means the lines can't cross any more, which should help:



I eliminated the >$100k line, as this is now by definition 100% of the households, cumulatively. As we see, the percentage of households making under $100k in constant dollars falls steadily from 1967 to 2005, which is just what we would expect from economic growth, and a desirable result. You want more people getting richer. However, this does not carry through to all income levels, which fall by less and less.

Perhaps this is an artifact of the linear scale, as written about by Jon Peltier and Nicolas Bissantz recently. So I tried it with a log scale instead:



(fully accepting Jon's comment that log scales do an equal injustice to the upper values, when the data is values equally distributed within an upper limit instead of proportionally distributed to infinity)

The surprising result is that the <$5k income level contains almost as many households in 2005 as in 1967, and significantly more than in the 70s, after a fall in the 60s. After a modest fall in the 90s, it rises again after 2000, as do all the other income levels below $100k. If we have said a fall is a good thing, then a rise must be bad. So the full time series data do contain a shocking insight, but it's to be found not at the upper levels but the lower ones, and not just by comparing 1967 to 2005, but following the trend down, then up. The dot plot can only tell a story of average improvement between 1967-2007, which masks a story of advance and retreat.