Junk Charts

The real climategate

Climategate is all the rage at the moment. What interests me about this episode is not the integrity of certain scientists, or science in general, nor the culture of academia, and certainly not the evidence of climate change. For me, the real climategate is the woeful state of statistical education.  Let me explain.

Here is the infamous email: (via Nathan Silver, with my highlights)

From: Phil Jones
To: ray bradley ,mann@[snipped], mhughes@
[snipped]
Subject: Diagram for WMO Statement
Date: Tue, 16 Nov 1999 13:31:15 +0000
Cc: k.briffa@[snipped],t.osborn@[snipped]
Dear Ray, Mike and Malcolm,

Once Tim’s got a diagram here we’ll send that either later
today or first thing tomorrow. I’ve just completed Mike’s Nature
trick of adding in the real temps to each series for the last 20
years (ie from 1981 onwards) amd [sic] from1961 for Keith’s to
hide the decline. Mike’s series got the annual land and marine
values while the other two got April-Sept for NH land N of 20N.
The latter two are real for 1999, while the estimate for 1999
for NH combined is +0.44C wrt 61-90. The Global estimate for
1999 with data through Oct is +0.35C cf. 0.57 for 1998.

Thanks for the comments, Ray.

Cheers, Phil

What concerns me is Phil Jones' describing what he did as a "trick" to "hide the decline".  He apparently thought that he was doing something shameful. But when is it shameful to extend the plot of a time series so as to display the long-term trend, and not be misled for short-term fluctuations? This is providing statistical context to the data being examined. Lots of people are condemning this as a willful act to mislead the public but if they have some statistical literacy, they will understand that finding the appropriate time scale to look at the data is one of the most important tasks of analyzing time series data. It's a problem when even prominent scientists do not comprehend why they should be doing this.

I have always wondered why in climatology as well as in economics, we rarely see decomposed time-series plots (at least not in the public's eye). 

On the right, I found on-line a plot of a decomposition of beer sales that separates out seasonality, trend and other parts of a time series. The original data is shown up top. In practice, newspapers and blogs give us such plots all the time when they should show us the third plot down (the trend with the seasonal factor removed), unless the story is about seasonality.

Note to self: should include basic time-series decomposition in the intro stats syllabus; much too important a topic to leave to a second course.

 

Reading

Here are some of my favorite links from other places:

A spatial journey illustrating a very long scale, created by the Genetic Science Learning Center (here)

Long scales are very difficult to deal with in charts; I have never been satisfied with log scales since it addresses the designer's challenge of trying to fit everything onto one page, bu does not deal with the reader's need to compare the elements accurately

Not sure how this helps but perhaps some of you will figure it out

Tommi left a comment about this conceptual chart by xkcd, which has been making the rounds.  Fits into our Light Entertainment category.

Says there is no optimal chart type.  A type that works very well for one data set may get hopelessly cluttered for another, similar data set.

From fellow bloggers (especially Jorge), a whole series of views of the U.S. unemployment figures by state over time.  Alternatives that are much more interesting to look at than the typically line chart. Jorge even found something in Excel that looks good.

A second lease on life

I hinted at it in the last post, and some readers also made similar suggestions.  What happens if we plot the U.S. life expectancy data in relative terms (indiced) rather than in absolute terms?

The result is highly revealing, and that is why we should always look at the data many ways.  While in the original chart, the differences in the race/gender segments were essentially obscured by the overall slowly-growing trend, in our new chart, we took out the trend, isolating the growth rates.



The reconstructed chart showed that:

• Between 1970 and roughly 1990, blacks of both genders gained in life expectancy at a rate higher than the national average, while white females lagged behind white males
• However, almost all of the gain by blacks were attained between 1970 and 1984, and in the 10-15 years following, this excess gain was wiped out so that by 1992 or so, the black male, black female and white male lines again converged.
• Starting in 1995, black males again achieved significant improvement in life expectancy.  This time, black females did not follow their male counterparts.  Meanwhile, white females continue to lag behind.

Not being a health care specialist, I can't say what happened to the cohorts of the 1970s, the 1980s and 1995.  One thing is for sure: these insights are hard to glean from the original.

 

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

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.

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.

Sore-thumb graphics

A particular genre of graphics is designed to induce awe: certain bits are allowed to stick out like a sore thumb.  Via reader Andre L., and an archive of US Army medical photos and illustrations:

This is a small multiples graph designed to display the somewhat seasonal pattern of deaths due to influenza over years.  Basically, we see a U shape in almost every year; however, the height of the peak, and the timing of the peak shows quite a lot of variation.  Further, some years exhibit more of an L-shape than U-shape.

But the attention grabber here is the massive peak that occurred between 1918 and 1919.  It was unusual in many ways... it was the second big peak during 1918, it occurred late in the year and ellided with the next year's peak.  The designer allowed these two components to bleed into the other charts.

From the perspective of scale, readability, cleanliness, this bit sticks out like a sore thumb!  But one has to say it is effective.  

A log scale is often used to deal with data containing such outliers.  But while this makes neater charts, the impact of the orders-of-magnitude difference is lost on the reader, except in her imagination.

Food art

Adam, who is the designer behind the Wired graphics special on "The Future of Food", asked about the rest of the series.  We previously made some comments on a set of mini donut charts.

The first thought that came to mind after browsing through all the charts was: what a great job they have done to generate interest in food data, which has no right to be entertaining.  Specifically, this is a list of things I appreciated:

• An obvious effort was undertaken to extract the most thought provoking data out of a massive amount of statistics collected by various international agencies.  There weren't any chart that is overstuffed, which is a common problem.
• It would be somewhat inappropriate to use our standard tools to critique these charts.  Clearly, the purpose of the designer was to draw readers into statistics that they might otherwise not care for.   Moreover, the Wired culture has long traded off efficiency for aesthetics, and this showed in a graph such as this, which is basically a line chart with two lines, and a lot of mysterious meaningless ornaments:
• 
  
• A nice use of a dual line chart, though.  It works because both data series share the same scale and only one vertical axis is necessary, which is very subtly annotated here.
• The maintenance of the same motifs across several charts is well done.  (See the pages on corn, beef, catfish)
  

Further suggestions:

• It would be nice if Wired would be brave enough to adopt the self-sufficiency principle, i.e. graphs should not contain a copy of the entire data set being depicted.  Otherwise, a data table would suffice.  The graphical construct should be self-sufficient.  This rule is not often followed because of "loss aversion"; there is the fear that a graph without all the data is like an orphan separated from the parents.  Since, as I noted, these graphs are mostly made for awe, there is really no need to print all the underlying data.  For instance, these "column"-type charts can stand on their own without the data (adding a scale would help).
• Not sure if sorting the categories alphabetically in the column chart is preferred to sorting by size of the category.  The side effect of sorting alphabetically is that it spreads out the long and the short chunks, which simplifies labelling and thus reading.
• Not a fan of area charts (see below).  Although it is labelled properly, it is easy at first glance to focus on the orange line rather than the orange area.  That would be a grave mistake.  The orange line actually plots the total of the two types of fish rearing, not the aquaculture component.  The chart is somewhat misleading because it is difficult to assess the growth rate of aquaculture.  Much better to plot the size of both markets as two lines (either indiced or not).
  
•  

Reference: "The Future of Food", Wired, Oct 20 2008.

Loss aversion

Loss aversion manifests itself in chart-making, as it does in economics.  In chart-marking, loss aversion can be defined as the tendency to avoid losing data at any cost.  Given a rich data set, designers often make the mistake of cramming as much data into the chart as possible.  This is taking Tufte's concept of maximizing data-ink ratio to the extreme, and it often leads to awkward, muddled charts.

Gelman provided a great example of this recently.  See here. 


Every piece of data is given equal footing, which results in nothing standing out.  The reader gasps for air.


Here is a recent example from the New York Times, in which the designer showed admirable restraint.

  The best evidence is the set of small multiples shown at the bottom.  These give the amount of phosphorus flowing into the lake annually since 1973, as measured from four locations.

The point is that the pollution has been most serious on the northern shores, especially in recent years.  Thus, the Florida plan focusing on the southern region is likely to make limited impact.

The choice of vertical lines is smart, as the typical time-series connected-line chart would jump up and down crazily.  A simple vertical axis marks the amounts, avoiding the temptation to print all the data.  The designer realizes it is the trend, rather than individual values, that is the issue.

Taken together, the three components tell a good story.  This is a well-executed effort.  The Times once again proves itself the leader in developing sophisticated graphics.



Reference: "Florida Deal for Everglades May Help Big Sugar", New York Times, Sep 13 2008.

Maps and dots

Happy New Year

The cosmos of university ranking got more interesting recently with the advent of the "brain map" by Wired magazine.  This new league table counts the total number of winners of five prestigious international prizes (Nobel, Fields, Lasker, Turing, Gairdner) in the past 20 years (up to 2007); and the researcher found that almost all winners were affiliated with American institutions.

As discussed before, the map is a difficult graphical object; it acts like a controlling boss.  In this brain map, the concentration of institutions in the North American land mass causes over-crowding, forcing the designer to insert guiding lines drawing our attention in myriad directions.  These lines scatter the data asunder, interfering with the primary activity of comparing universities.

The chain of dots object cannot stand by itself without an implicit structure (e.g. rows of 10).  This limitation was apparent in the hits and misses chart as well.  Sticking fat fingers on paper to count dots is frustrating.  Simple bars allow readers to compare relative strength with less effort.

In the junkart version, we ditched the map construct completely,  retaining only the east-west axis.  [For lack of space (and time), I omitted the US East Coast and Washington-St. Louis.]  With this small multiples presentation, one can better contrast institutions.

To help comprehend the row structure, I inserted thin strikes to indicate zero awards. A limitation of the ranking method is also exposed: UC-SF has a strong medical school and not surprisingly, it has received a fair share of Nobel (medicine), Lasker and Gairdner prizes; but zero Lasker and Gairdner could be due to less competitive medical schools or none at all!

Reference: "Mapping Who's Winning the Most Prestigious Prizes in Science and Technology", Wired magazine, Nov 2007.