Junk Charts

The hard work of entertaining

Stefan pointed us to his work for the UN GEO (United Nations Global Environment Outlook) data portal.  This set of information posters highlights a vexing issue that crops up on Junk Charts from time to time, that is, the proper balance between information and entertainment value of data displays.  While this blog concerns itself primarily with the former, it does not mean that we are blind to the flashier side of the enterprise.

Let's take Stefan's recycling spiral chart as an example.  One must admit that visually this presentation is more appealing than either a data table or a set of bar charts.  The reader can obtain the primary piece of information, which is the ranking of different countries in terms of the proportion of collected waste that is recycled. 

And if the reader is curious enough, the chart also provides the data on the per-capita amount of waste collected in each of these countries.  (Like the table and bar chart, this display also has the problem that it is one-dimensional, thus the countries can be sorted by proportion of recycling but then the waste collected data will be out of order.)

For those readers who would like to understand the data better, they would want to know some of the following:

• Is there a relationship between amount of waste collected and amount of waste recycled?
• Are there differences in culture resulting in different recycling rates?
• Is the level of development of a country predictive of its recycling rate?
• Why are some countries recycling more of its waste, and others less?

To address these types of questions, one can start with the following scatter plot.

 

With the exception of South Korea, there is a general pattern of positive correlation: the more waste collected per capita, the larger proportion of such waste recycled.  Any dots that are not in the bottom left or top right quadrant are exceptions to the rule.  These countries are labeled in red or blue, the former indicating that the amount of collection is above average while the rate of recycling is below average. 

Because there is sampling error, dots that are close to the average dot (the center of this scatter plot) are probably just average.  Roughly speaking, dots in the gray circle are close enough to the center that I would not consider them exceptional cases.  That leaves Spain and Iceland in the red corner, and South Korea in the blue corner.  If both data series are considered together, these three countries should merit attention; if only the proportion of recycling is considered, then one would pay attention to Italy, Turkey and Slovak Republic on the lower end and South Korea on the high end.

Scatter plots are very versatile.  The following one explores the issue of development level.  Surprisingly, the level of recycling seems to have little to do with development; the countries are quite widely scattered.

 

Technical note: The data on both axes are expressed in "standardized" units.  So the zeroes represent the average per-capita waste collected, and the average proportion of waste recycled (only of those countries depicted in the original chart).  +1 indicates an amount that is one standard deviation above the average.  Think of "standardized units" as measuring how extreme is a particular country with respect to the average. 

Extra reading

The New York Fed has put up some impressive infographics, depicting the credit conditions around the country.

(I was going to praise the producers for this effort - a rarity among government sites but when I went back to the site today, I find that the graphic is loading only occasionally, and try as I might, I could not get rid of the big brown patch that obscures the West coast.  Hope you have better luck!)

As with similar maps, the unresolved problem is that the pattern is strongly dominated by the relative density of people around the country.  As we click through different maps, they all look similar.

But this map -- as do most infographics -- does well to put structure onto unwieldy, large data sets.  It acts as a table of contents to the data set.

Nick Rapp's team at AP produced this great chart on US life expectancy:

Very sensitive axis labels, putting the line labels into the chart (rather than in a legend box far away), showing only the current data, not every data point, subtle coloring to bring out the decades, etc.

I'm not sure about varying the thickness and tinge of the lines.  Is it necessary?  It hinders a bit as we try to compare the slopes of the lines.  Adding the initial data labels (for 1975) would also help us judge the net change over the period depicted.  If it is the change in life expectancy over time that is the main story, consider indicing all lines to the respective 1975 value.

Reference: "CDC says life expectancy in the US is up, deaths not", Miami Herald, Aug 19 2009. 

PS. This is really an awful title for the article.  It reads as if something bad has happened to the death rate while something good happened to the life expectancy, as if there is a paradox but the article makes clear that the death rate declined, which would make the point redundant.

Community outreach

Omegatron re-cycled some Wiki charts and we are happy to report that they are great improvements over the originals.  I welcome other readers to alert us when you have done your bit of community outreach, by ridding the world of chartjunk.  The email address is the name of the blog at gmail for any submissions.

Not surprisingly, the originals were pie charts.

The recycled art: (can be seen here and here)

Why are pie charts such poor tools for communications?  Think about where we can place the message, and you'll find that this chart type is far too rigid.  In a pie chart, the key resource is the relative size of the sectors, followed by the number of sectors, and sometimes the size of the total pie.  Other than those, there are little else useful in a pie chart.

The histograms used by Omegatron are much more flexible.  There can be information encoded in the height of the bars, the width of the bars, the total area, the relative distribution of bar areas, the existence and location of peaks and troughs, etc. etc.

For comparing data collected in slightly different formats, the pie charts are hopeless.  Notice that the lowest category on the left (pink) corresponds to 8 weeks or less, which would include two and a half sectors on the right plus potentially a missing sector for 4 weeks or less.  The histograms below handle this easily.

Omegatron asked for some feedback.  I think the new ones are significantly better.  A few minor points:

• Instead of coloring the background to the chart, I'd color the bars themselves into green/yellow/orange according to the trimester
• I'd put the trimester labels under the horizontal axis, close to the "week" labels 
• The charts obviously need to identify the country and year of the data (which I added).  Omegatron pointed me to an inexplicable Wiki convention of not putting text inside charts (see here).  I must disagree with this convention.  Annotations on charts are some of the most useful things.
• If these two charts are to be placed side by side for comparison, then we need to sort out the vertical scale.  It cannot be the absolute number of abortions but some kind of relative scale in proportion to the population size, or some similar metric. 
• In addition, if comparison is the point, I'd suggest an overlapping histogram with bars having no fill.  

Great work!  And I love to see more of it! 

Space and time

When it comes to space or time in graphics, old habits die hard.   When we have spatial data, the default is to put it on a map.  When we have a time series, the default is to plot time along the horizontal axis.  Sometimes, these defaults work; other times, breaking up the map or straight-time-line works better.

Thanks to a reader, I noticed that Google put up a "Flu Trends" website to help us track the flu season.  They use two main charts to plot the data, as shown below.




On the right side is the time series, showing the severity of flu cases from month to month.  There are many great things about this chart and one serious flaw.  I love the fact that they did not plot time on the horizontal axis; they realize the seasonality and they create overlapping lines.  They make good use of foreground and background; it's easy for us to compare year to year differences.

The serious flaw: no vertical scale.  This was a problem with Google Trends from day one (see my post here).  They still haven't fixed it.  Because of this, we don't know if the peak shown was 5 cases or 5000 cases.  While for Google key word searches, one can excuse them for trying to protect commercial secrets.  I would imagine that this public health data is, well, public.  Since the apparent purpose of this chart is to allow citizens to declare a flu epidemic (say, when they see the current trend depart from the historical norm), not having the scale is a huge problem.

I also disagree with shifting the months around for the Northern Hemisphere so that the peaks of the graphs are aligned towards the middle.  It is better for the peaks to appear on the left and let the order of the months conform to our expectation.  (The "peak" would be split on the sides and the chart would look like a valley, which presumably is why they did it this way.)

The charts on the left side plot the spatial data, not surprisingly on maps.  Sadly, the standard exhibited on the time-series charts is nowhere found on these maps.

First, the legend is seriously deficient.

Second, the gradation of the colors is not fine enough, or put differently, the aggregation to the state/province level is much too coarse for any interesting pattern to be seen.

This poorly aggregated map becomes a farce when applied to the U.S.  There is not much left to be said, is there? 

Don't mess with the scale

My friend Patrick pointed out the single biggest issue with the chart below -- that the designer chose a scale that precisely undermines the message of the chart.  Undermine may be too mild a word to use here; annihilate may be more apt.

The lines in this chart are anchored at the zero point on the time line (horizontal axis) used to indicate the bottoms of various bear markets in the Dow from 1929 to 2007.  From that anchor, time runs to the left showing the amount of time for the Dow to go from peak to bottom (the decline); time runs to the right showing the amount of time for the Dow to climb back to the prior peak (the recovery).  As the caption said, the point of the chart is "if the decline was fast, the recovery took a considerable time".

Funny thing then that the distances from the zero point are roughly comparable on the left as on the right.

This illusion resulted from some very convoluted and perplexing messing around with the horizontal scale.  First, the left-of-center scale is in months while the right-of-center is in years.  Second, the left-of-center scale has normal spacing while the right-of-center seemingly was suffering from spasms.  Take a closer look:

The first five years (0-5) took up about half the scale while the next five (5-10) took maybe one-eighth.  The first year (0-1) took about as much space as the next two years (1-3).

I am not quite sure what is the logic behind this but since the message of the chart has everything to do with the time duration, it is most unfortunate to introduce such distortions.

There is yet another "innovation" in this chart.  Notice that on the right side, the axis labels are irregular (more spasms)... 0,1,3,4,5,10,15,20, 25...  This is as if the designer is posing one of those IQ questions requiring readers to figure out the next number in the sequence.  The specific time intervals selected may have meaning: note that all the lines are straightened out in between these tick marks.  Given that each line represents a different historical sequence, it is difficult to comprehend the regularity of these intervals across history.  Perhaps this will prove to be the key to unlocking the secret of this chart.  Please comment below if you are able to unravel this mystery.

Besides, the same type of "innovation" was not applied to the left side of the chart.  Here, the designer opted to throw out all the data between the peak and the bottom and straightened out all the intermediate fluctuations.

Below are two different versions of this chart, basically restoring the time scale to the normal, equally spaced, symmetric appearance.  The top one used monthly Dow returns where the volatility obstructed our understanding of the trends, requiring the use of color to differentiate the lines.  In the next version, I used R to generate the loess estimates (a type of smoothing) and the trends became clearer.  (There was a prior discussion of loess on Junk Charts here.)

 

Now, these pictures are very different from the original graph!

I'd be very cautious about reading into these charts anyway.  This question is not one suitable for statistical analysis.  The sample size of six is far too small.  Each recession is different in terms of causes, remedies and context.  The fact that we call them recessions do not make them comparable.  Further, it is also impossible to know at this stage if the 2007 decline has reached bottom.  The chart designer essentially assumed this to be the case but who knows?

PS. Nick Rapp, one of the designers of the chart, responds in the comments.  He has started a blog to feature the work of his graphics team at AP.  His colleague has created an interactive version.  More than anything, this post highlights an aspect of the chart that Nick and his team clearly spent a lot of time doodling over.  The concept of the chart itself is wonderful actually, if I didn't say so already; it is essentially the same chart as the oft-printed chart where the anchor point is the start of each recession, only here the anchor is the bottom of each recession.

Pure delight

 My favorite Bumps chart in the New York Times ...

For the purist, this is the original rank-based version.

With judicious use of color and background/foreground, this makes for a good story.

The color scheme here, however, is a bit bland.  Green for improvement, blue for decline and orange for USA.

Note, for example, New Zealand and England both suffered similar drastic drops as the US.

It would be better to (for example) split out the large improvements and large declines, or to split out the developed world versus the developing world.

This chart is created like this probably because the accompanying piece makes only passing reference to this chart so there is not a clear message to the creator what to do with the data.  

Interestingly, there were no ties in 1960 but quite a few ties in 2004.  I wonder why.  I'd shift the dot to the mid-point between ranks rather than move them up to the higher rank.

All in all, a much more engaging way to present this data than the reams of table found in say the UN World Development Report.

Reference: "Vital Statistics: U.S. Still Struggling With Infant Mortality", New York Times, April 6 2009.

Exemplary

From Bernard L., another exemplary effort by the Times.  This one really got me excited.  

The set of line graphs shows how demographics of students in American schools have evolved in the last two decades.  Here, I selected New York City schools, and the tool sensibly decided to compare those with New York State schools (gray line).

There is so much to learn from one simple chart:

• The blue and gray lines are almost parallel everywhere, which tells us that in terms of the change in demographic composition, New York City pretty much resembled New York State during this entire period.
• However, in terms of demographic composition, rather than the change in composition, New York City schools are very different from the rest of the state, in that the proportion of white is lower by a third while that of minorities are much higher, especially black and Hispanic students.
• State-wide (as well as city-wide), black and white students have been declining as a proportion while Hispanics and Asians have increased. 
• The extent of the change is immediately visible, Asians have jumped from 7% to 14% for example.  

  
From a graph design perspective, the execution is very clean.  Data labels are limited to the first and last values.  A small multiples concept is used with the ethnic groups placed side by side.  A great awareness of foreground and background as well.  And imagine how much data has been visualized here, and be impressed.  You can look at any county in the country.

Here's one where the county change does not exactly mirror the state change (Napa in California):

Reference: "Diversity in the classroom", New York Times, March 12 2009.

Deep dive

The chart on the right which compares the unemployment picture across past recessions has many good features.



It uses a very sensible metric, counting the percentage change from peak employment.  I have mentioned the superiority of this type of presentation compared to plotting a time series of unemployment rates.  The following is an example of a standard graphic using gray bands to indicate recessions.  The difference is obvious.

Here is the previous post that dealt with the drop in market capitalization of banks since the peak which screamed out for this type of treatment.

It handles the foreground-background issue very well.  A number of similar charts is circulating in which every single line has a different color.  Here, the designer clearly tells us the current recession is the foreground and all the past recessions form the background.  It looks as if the 1981-3 recession is slightly highlighted with a darker orange to draw attention to the fact that it is most similar to the current situation.  I find this unnecessary because the association is clear even without the darker hue; however, the designer does this with a very light touch so this is just a question of taste.

I would label the horizontal axis starting at 0. 


Reference: "Job Losses Hint at Vast Remaking of Economy", New York Times, Mar 8 2009.

Dealing with skew

Bernard L pointed us to this income distribution chart printed in the Economist.

The accompanying paragraph points to the range of the bars, that is, the gap between the top decile average and the bottom decile average, as evidence of income disparity, concluding that the US and Britain are among the worst.

Bernard likes the use of vertical sections to represent the average incomes by decile and dislikes the USA-Today style background image.  Agreed.  But why plot the middle deciles at all when the only worthy data involve the endpoints of the bars?

A close examination of the spacing of the middle deciles leads to more befuddlement.  There does not appear to be much difference between the countries.

The answer to this is that decile statistics are not appropriate for data as skewed as incomes.  At the high end, the 10% intervals are too coarse.

One clue to this is that the top 10% in the US only earns $90,000 on average but we have all heard of the billion-dollar hedge fund managers and Wall Street bankers and $30 million a movie celebrities.  The problem is that within the top decile, the income distribution is also tremendously skewed. 

The neat idea of plotting the vertical sections indicates an awareness that the red dots (average income) are insufficient because of the skew.  Alas, there remains a lot of skew above the top decile and the designer inadvertently falls back into the same trap by considering the average income within the top 10%.  Thus, the amount of disparity on the right side of the chart is grossly underestimated.  Roughly speaking, we are looking at 10 samples of the distribution, nine of which at the low end of the range and only one at the top end (long tail).  Here is the idea:

Reference: "Spreading the wealth", Economist, Oct 21 2008.

From bad to worse

Pie charts can range from bad to worse.  Brent L. pointed us to a few on the right end of that spectrum.

Brent wrote: "The background image makes it almost totally unreadable.  And what does the forest scene have to do with programming?  *sigh*"

That's not to mention the oval rather than circle, the dizzling array of colors, the Excel-style legend that inverts the order of importance ("Other" at the top), etc. etc.

Again, a column chart would have been much clearer.  Since the total number of famous programmers is arbitrary, a chart of counts would work at least as well as one that plots proportions.

More here.

Reference: "Famous programmers from Adleman to Zimmerman", grokcode.