Junk Charts

Baby names and success

While we speak of baby names, David F. nominates this set of 6 charts from WSJ.  Compare this with Wattenberg's names voyager, and the benefit of interactive graphics is immediately evident.

In David's words:

They show graphs of six different names, but the two on the bottom use a dramatically different scale (from 1st to ~20th, instead of from 1st to 1000th). The introductory text notes the difference, but it is still a shock.

We like the use of "small multiples" but their impact is compromised if we don't keep the background material constant so that readers can compare between charts.  By having  different scales, the message was distorted: Mary has had a much larger drop than David, and it's easily missed in these charts.

Lines should take the place of areas which carry scant meaning in this context.

The use of blue and red is a nice touch but dovetailing the male and female charts strikes us as excessive fun.  It would have been clearer to give the sons and the daughters their own columns.

The article itself relates the anguish of modern parents in naming their babies.  Much of this angst can be traced to serious econometric studies that claim to have found cause-and-effect relationships between someone's name and their eventual success in life.  Some of this research was highlighted in Freakonomics, for example.  My stance is that all such studies are dubious, there being innumerable confounding factors (socio-economic, genetic, cultural, luck, etc. etc.).  In addition, the measured response can range from "happiness" to income to many other metrics.  The danger of finding something because one looks hard enough is very real.  We don't currently have tools powerful enough to substantiate this sort of studies.

Source: "The Baby-Name Business", Wall Street Journal, June 22, 2007

Less is more

Derek pointed me to the style.org site which also parses political speeches.  Their preferred graphic is not the tag cloud but a labeled bar chart.

From top to bottom, each bar represents a sentence; the length of each bar is the length of each sentence.  Further, the user can specify word pairs for comparison.  Here the red bars are sentences containing the word "freedom"; the blue bars, "security".

It's a good illustration of the "small multiples" principle in constructing comparative graphics.

However, the choice of dimensions is perplexing.  I'd be much more interested in the timing of mentions of those words, rather than which sentence they appeared in.  I also find the length of each sentence to be irrelevant.

Here's one concept that brings out the point better.  It uses less space and voluntarily gives up some of the data (the sentence structure).

Information gain and loss

The previous two posts indicated that CNN, TWC and Intellicast had the best on-line weather forecasting accuracy by looking at the median and mean error in predicting daily low and high temperatures over 41 days.  Is it possible to differentiate between those three?

For that, we need more data so I switched from summary statistics back to the data.  In this new chart, the day by day errors were plotted.  The gridlines labelled errors within 5 degrees, which is an arbitrary guideline for acceptable / unacceptable.  The three scatters looked remarkably similar although CNN appeared to hit the bull's eye (the middle square) with less bias (errors more evenly distributed) but not much better accuracy overall (similar number of unacceptable errors).

Horrid stuff

Small multiples can work wonders when data are replicated, as in this case.  The chart accompanied an Economist article on pollution levels in several European cities, as indicated by the concentration of nitrogen dioxide and particulates.

In the junkart version, I plotted the data series side by side, rather than one over the other.  Further, the order of cities was according to decreasing levels of NO2, which seemed to be the worse pollutant.  All gridlines are removed except the 30 line which worked pretty well to separate out the highly polluted cities.

An odd pattern has now surfaced.  Namely, there is some degree of negative correlation between the concentration of the two pollutants.  Environmental scientists may be able to tell us why.

Reference: "The Big Smoke", Economist, Feb 3 2007.

Rip tide

As if a rip tide has torn through, this chart drowned the data in the depths of colors, scales and graffiti.

Scales - every chart has its own scale, rendering it impossible to read across charts.

Colors - every brand has its own color.  This feature is redundant since the data labels already serve the purpose of linking the two columns of charts.

Compression - it is impossible to judge the growth or decline of individual companies, especially since only the current market share is provided.

If anyone has access to the data, please send them over so we can remake this chart.  Or just send in your charts and I'll put them up here.

Reference: "Now Playing in Europe: The Future of Detriot", New York Times, Oct 28 2006.