Junk Charts

Data democracy

I have not yet been fully convinced of the direction of infographics until now -- I find too narrow the focus on organizing, structuring and visualizing large datasets; often times, we get pretty pictures with extremely high data-ink ratios but more often than not, these very dense graphics fail to speak directly to readers.  We see a lot of information; we find hardly any insights.

I think I have seen the future.  My friend Adam has been working on a web service called Empirasign, which I will describe as a form of data democracy - he takes boatloads of financial data, runs all sorts of analyses and models, and presents these results in a variety of formats, including on-line reports and tweets.  He does not attempt to visualize all the data, or all possible relationships.  Each analysis or model focuses on specific matters and he presents the result in tables and charts.

For example, a business problem might be as follows (timely for the year-end): in my portfolio, I am carrying some loser stock which I'd like to sell by year end so I can take a tax deduction on the loss, perhaps to cover some investment gains I have realized last year; however, I also believe that the loser stock may be near bottom, and if I sell now, I'd want to buy it back in short order - alas, this may be considered a "wash sale" and prohibited.  What if one can find a hedge (another stock or a portfolio of stocks) that replicates the performance of the loser stock so now I can get the best of both worlds - I sell the loser stock for the tax deduction, but keep the performance by taking a position in the hedge, then unwind when the regulation allows me to buy back into the loser stock?  (If you are interested in this trade, you should consult the experts: Adam's tutorial or wikipedia on "wash sale" or IRS-ese (pdf file).)

There are lots of stocks out there, and lots of possible hedges.  An unsophisticated investor like myself would have to spend a lot of effort to find the right hedge.  Also, it's very unlikely that staring at an infographics chart will uncover such hedges.  What Adam has done is he has collected all the required data and run analyses to find the right hedge for pretty much every (loser) stock out there.  And instead of presenting all the underlying data, he presents the results.  See below.

These data displays are not sexy - and can be improved (the explanation for the columns of the table is found on a separate page, e.g.), but for the target audience looking for trade ideas, they get to the point.  This is the gift of statistical data reduction.

What is also worth noting is through the magic of R, and Web technologies, Adam makes all this run automatically, so the insights from the data are uncovered in real time.  The wash sale avoidance strategy is not the only analysis he provides; there are tons more on the website that implements all sorts of other techniques (of which I am no expert) but it appears that users can pick and choose whatever strategy they like to follow, and Empirasign saves them any of the analytical work.

As I said at the start of this post, I see this as a promising direction for infographics, moving from visualizing data to visualizing insights.

P.S. As with previous years, I have updated my Amazon wish list (click on button on top right).  If you'd like to show your support for this blog, please help me build out my library.  Thanks to those readers who have contributed in past years - since Amazon does not always provide me your contact information, I have not been able to thank each of you personally.  Happy holidays! 

Life-enabling charts

In response to my call for positive examples, reader Merle H. sent in an example of how good charts can make our lives simpler and easier.

All of us have seen the following presentation of air travel data.

Not trying to pick on Travelocity - it's the same format whether you use Expedia or any of the airline sites.  For those customers who are looking to decide what dates to travel so as to minimize their air fare, this format is very cumbersome to use.

What about this fare chart at FuncTravel.com?

As you mouse along the line chart, the average fare for each day is visible.  Clicking on a particular day will fix the departure or return dates.

So much easier, isn't it?

A few caveats, though:

• Instead of just providing the historical averages, they should consider including information on variability, such as bars that indicate the middle 50% or 75% of prices.  Also, what about a sliding control for customers to decide which period of past history the averages should use?  More recent data may be more representative.
• This particular feature appeals to the price-sensitive, date-flexible customer segment.  Not everyone will pick itineraries based on those criteria.  There is an easy fix. If some controls are available for customers to indicate other preferences, e.g. exclude all British Airways flights, include only evening flights, etc., and the chart can update itself based on such selections, then the chart becomes a lot more flexible, and useful to many more customers.
• As with many automatically generated charts, the chosen labels on the vertical axis are laughable.  That should be relatively easy to fix, you'd think.
  

A great start.  I happen to notice that Travelocity has a beta feature that shows a similar chart.  A revolution in how travel sites present data to us is long overdue. 

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. 

A world full of bubbles

As promised, we stick to bubbles.  Like the street artist blowing soap bubbles at passers-by, this map -- published in the Guardian (UK) -- is a gift of bubbles.

And our reader Frederic M. is not amused.  "A tremendous failure", he said.

In terms of conveying the data, a simple bar chart would do a better job in exposing the biggest polluters, as well as the relative magnitude between the biggies and the small fish.

The chart reveals more problems if one clicks on, say, Europe, and sees the following:

For starters, compare the bubbles labeled 858, 468, 586, 418 with those labeled 23, 20, 18.  And look at the little ones in the periphery labeled 133, 174, 128.  Baffling, isn't it?

What they did was to print the ranks for every country, except the top four in Europe for which the ranks are placed next to the country name (in small font), and the actual amounts are placed in the middle of the bubbles.  The ranks, of course, are pretty useless, and they obliterate the scale of the differences between countries.

Besides, the bigger the polluter, the smaller the rank but the larger the bubble.  This built-in disconnect can also be disorienting.

Every bubble chart typically contains lots of data labels, and the reason is that the bubble form lacks self-sufficiency.  Without the data labels, the reader has trouble comparing the areas.

Reference: "The Carbon Atlas", Guardian, Dec 9 2008.

Pie Cubed

Omegatron also did away with a set of cascading pie charts on Wikipedia, a particularly ineffective use of this chart type.  Whenever there are more than two or three categories, the necessary use of many colors can really make one's head spin.

Here, the cascade is being used like a log scale, to artificially elevate the small pieces, which unfortunately are also the least significant pieces of the energy pie.  There is no reason for nuclear, bio-mass, hydro and "others" to add to 100% except that the author decides to group them together.  The 41% nuclear or the 41% solar heating in the second and third charts, respectively, have no meaning in the larger context.

In deference to the original author, Omegatron's new version preserves the arbitrary three-level cascade.  He converts to stacked bar charts, which brings out the differences better.


He also sensibly exposes the original proportions rather than the arbitrary relative proportions.  For example, nuclear energy accounts for 6% of the total, not 41% of the arbitrary "others" bucket which in turn contains 14% of the total.

I'd prefer an even cleaner presentation with unstacked bar charts.  This can be done in either one chart with all eleven categories, or in two charts, as shown below.  The two-chart version assumes that the reader have two key questions: alternative energy sources as a proportion of the total, and the mix of different sources within the alternative category.

With the ordinary bar chart, many fewer colors are needed, and there is no need to print out each data point, nor a need to use guides to point to labels and data.  The trouble of the latter is its tendency to draw attention to the least important aspects of the data.

With this further example, I continue to find the Wikipedia rule to discourage text annotations on graphs bewildering.   Such a rule apparently does not apply to data labels, as can be seen here.  Of course, a graph without any labeling of categories is robbed of meaning but if labels can be saved, so should annotations!

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? 

Spinning the climate

Mike L. pointed us to this pair of "climate change model pie charts", with the brief comment "Yuck".


What they are doing is to use the spinning wheel analogy to present probabilities (odds).  Not a good use of pies either.  Histograms do the job with minimal fuss:

 

I collapsed the 2-2.5 and 2.5-3 degrees sectors since every other one is a one-degree interval.  We see immediately that the effect of the policy is to shift the probability distribution to changes of fewer degrees.


Reference: "Climate change odds much worse than thought", Science Daily, May 20 2009.

The trouble with two lines

It's never a good idea to put two scales on one chart, and this is another example of what not to do:

The ugliest part of this chart are the duelling gridlines. Because neither axis starts at zero, it is difficult to access whether the number of newspapers was declining at a faster pace than the circulation was. Also, line charts would be better able to trace the evolution over time. The interspersed blue and red columns interfere with each other. Note that the designer lessened our pain by plotting every other year, thus halving the number of columns.

The junkart version also puts two data series on the same chart but on the same scale. Instead of plotting the raw data, we plot indices, with 2008 as 100. This reveals a pattern that was not apparent in the original chart. There appeared to have been four periods of evolution: up till 1980, both the number of newspapers and total circulation were at a plateau; from 1980 to 1990, the circulation stayed stable while the number of papers dropped drastically, indicating perhaps consolidation; then from 1990 to 2003, both series declined at roughly equal rates; and finally, the bottom dropped off the circulation from 2003.

See our previous discussion of dual axes here, here, and here

Reference: "Channel Shift: Online Circulars: the first step by retailers toward web-to-store harmony? ", Internet Retailer (print), May 2009. Data from the Newspapers Association of America.

Bonus 1: Thinking about the column interspersing trick some more, I realize that it is, em, possible, em, that one series was plotted for odd years and the other series plotted for even years!

Bonus 2: Here is the requested scatter plot (still indiced):

Spinning multi-color

New York Times has a great pointer to the Global Warming Art website.  The author Robert Rohde wants to popularize environmental science by visualization of the data.  There are many interesting charts and well worth repeated visits.

These pie charts cry out for some re-dressing:

The pie charts, the colors, the whole works.  Most troubling is that each pie has its own sorting scheme, and because the text labels were not reproduced in the smaller pies, the reader is sent scrambling around to find the right labels.

In addition, these pie charts, as with almost every other pie chart, fail the self-sufficiency test.  Without all the data printed next to each sector, the reader is simply unable to judge the size of each sector.

Further, the aggregate data (larger pie) may not be as relevant after realizing that the smaller pies show very different patterns.  The following junkart version tries to bring out this fact by treating both dimensions (type of greenhouse gas; source of emission) equitably.

While I picked on this particular chart, I must say I support Robert's effort and wish him luck in this very well-intentioned project.