Junk Charts

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.

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. 

Degrees of likeness 2

We left off the other day with an interactive graphic with the ability to peer into subgroups.  This feature assumes implicitly that the overall average obscures differences within subgroups.  What statisticians do with this type of data is to compare the subgroups, and identify the factors that make someone different from the average.

For example, there is a clear distinction between the employed and the unemployed in how they spend the day (not surprising).

This happens to be what NYT printed in the paper edition that day.  (Note, though, that the graphic loses quite a bit without the interactivity.)

On the other hand, there appears to be little differentiation between men and women.

Nor is there much difference between blacks and whites.

One factor that matters is age.  Older people are not exactly like the young.  A lot of these factors (for example, age and employment status) are correlated, by the way.

I showed all these in order to talk about the statistical concept of "aggregation".  We noted that the distribution of time use of the employed is different from that of the unemployed.  Thus, we cannot use the "average" distribution to describe both groups, and so we show the data in disaggregated form.  Similarly for time use and age.  

But there is not much gain in disaggregating race and gender: the "average" is representative of the subgroups for these two factors.  This is one distinction I see between information graphics and statistical graphics: the former typically shows all possible subgroups while in the latter, the designer zooms in on the factors that matter.

Degrees of likeness 1

The NYT team just put up a fantastic visualization of the American Time Use Survey data, which purports to measure how the average American spends the time of a day.  (Apparently, thousands of people recalled what they did every minute of an average day.)  The amount of data collected is massive, and this graphic allows readers to explore the data in intuitive ways.

The chart shows for each minute of the day (horizontal axis) the proportion of people doing specific activities.  Not surprisingly, we spend more time sleeping than any other type of activity.  The axis and data labeling as well as gridlines are very restrained.

Normally, I am not a big fan of these proportional area charts because the only relevant dimension to look at is the vertical distance from one curve to the next but the focus on areas put equal weight on the horizontal and vertical distances.   The horizontal distance is meaningless, and thus the area is meaningless.

These designers found a solution to the problem, and good for them!  Because of the mouse-over effect, I could not save the actual appearance -- here, I show what it looks like.

By mousing over different parts of the graph (say, moving vertically), we can compare the actual proportions.  Terrific!

The key interest of this graphic is the following legend.

While the above graphic shows the use of time by all Americans in aggregate, this panel allows us to zoom in on specific groups of Americans.  How alike are Americans?

Reference: "How Different Groups Spend Their Day", New York Times, July 31 2009.

 

Visual analogies

Many a startling faux pas in graphics start with the desire to utilize visual analogies: the recent shopping mall chart is a good example.  There is something inviting about making a chart about shopping malls look like a shopping mall, or a chart about breakfast foods look like a donut, and so on.

The New York Times (or maybe OECD) makes a chart about economic cycles look like, you guess it, cycles.  Don't really have too much to say about this one - except these two points:

• Much of the validity of the chart depends on the theory behind it.  Do the two variables being tracked (amount produced relative to trend; change in production in last 6 months) capture everything?
• The focus on generating cycles visually left out one of the most important dimension - that of time scale.   For this chart to work, we would like to know not just where we are in the cycle but how much time we'd spend at each point in the cycle.  The interactive charts (graphs 4, 5, 6, 7) show us that the cycles are traversed not at constant speed.  A proper rendering of this data needs to play up the time scale.

If you have ideas of how to improve this, feel free to comment.

Reference: "Turning a corner?", New York Times, July 2 2009.  

Colorful maps

Bernard L. loved the recent NYT take on immigration in America.

The very pretty maps are found here.

An amazing amount of data is being visualized here.  Mousing on the map will pick up the specific data for each county.  There is a bar up top for discovering the evolution over time.  It would be great if there is an animation button so the map can be played out without clicking.  An animated gif will also do (similar to the disease map we featured some time ago).

The colors on the first map represent the origin of the top ethnic group in each county.  Within each group, the tint of the color further displays the percentage of the population that group accounts for.  The subgroups appear to be 0-2%, 2-5%, over 5%.  The last subgroup is very wide.

Not so keen on the second map with all those bubbles.  They show the number of people from each country by county.  The bubble size is proportional to population.  Every version of this map looks the same because the population is concentrated in the cities and the interior is sparsely populated, no matter what ethnic group.

Regardless, this is another laudable effort by the crew at the Times.

Reference: "Immigration Explorer", New York Times, March 10 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.

Eternal optimist

Chris P pointed us to the "Financial Comeback" calculator, surely a well-meaning joke from the folks at the Times.  Here is how one gets to make a 40% loss back in just six years!


Surely, someone has to tell them about simulation.  They have to assume a probability distribution on the annual returns, and show us some sample paths.  Using the average annualized historical return in essence wipes out all variability and no wonder it's smooth sailing upwards.  Eternal optimist.  

Here is Chris' comment:

The bad news is that the range of values it offers does not include the return on the market from last year (-31% to -36%).  I guess they are optimistic.

The interactive features of this chart, however, impressed me.  The smooth adjustment to the chart as one slides the control, including the automatic choice of appropriate axis labels, is very nice indeed.

Okay, they want to trademark the name of the calculator so perhaps this is serious.

Reference: "Calculate your financial comeback", Jan 6 2009.

How to read a graph

Via Gelman, here is a nifty book-buying map from Amazon, displaying the split between "red books" and "blue books" bought by Amazon users in each state in the months leading up to the 2004 and 2008 presidential elections.



Gelman noted the similarity between the Amazon map and the red-blue split of rich voters.

This post is about how to read a graph.  Here are some things that come to mind looking at the map:

• Sampling bias: how does Amazon's customer base compare with the U.S. population, or rich voters?  It would be prudent to check this before making generalizations.  Gelman's point may be that Amazon customers behave like rich voters.

• Sampling period: is the period long enough to capture the average inclination of the book buyers?  As is well known, book sales follow a long-tail distribution (Chris Anderson wrote an entire book based on this observation.)  Best-sellers have a disproportionate influence on average values.  If the time period is too short, the data may only represent the best-sellers.  Consider the following two maps in successive periods in 2004:

Much of the red in the first map was due to John O'Neill's "Unfit for Command", published in August 2004, and much of the blue in the second map was due to John Dean's "Worse Than Watergate", published in April 2004.  If one of these two-month periods was used to draw conclusions, we would make big mistakes!

• Classification: The long-tailed nature of book sales has wide-reaching implications on interpreting the data.  The most essential feature is that single books (bestsellers) have a disproportionate impact on average sales.  Since the key metric here is proportion of red (or blue) books, it follows that whether a best-seller is classified as red or blue makes a huge difference. 
  

Thus, one of the first things to look at is Amazon's helpful explanation of how they classified books as "red" or "blue".  We learn that they also have "purple" books which are those they could not decide if it's red or blue.  Each red or blue book is given equal weight but it appears that purple books are not tallied.  Glancing at the list of purple books, I see some hugely important books, e.g. Ron Paul's "The Revolution: A Manifesto" (Amazon rank #56  among all books), Tom Friedman's "Hot, Flat and Crowded" (#15).

If the purple books include best-sellers, then the decision to call it purple rather than red or blue causes an influential book to be excluded from the calculation.  We often forget that the decision to exclude is not a neutral decision; it is an active decision that says the excluded data contains no useful information.
 
This is not to say that excluding those books is the wrong decision.  We must make these decisions with considerable care, and realize that excluding best-sellers when book sales have a long-tailed distribution must not be taken lightly.

• Causality: Lets say we are sufficiently satisfied that we can make a statement about book buying habits and voting behavior.  Then we need to think about the direction of causality.  Is the map saying that red book buyers are likely to vote red?  Or that red voters are likely to buy red books?  No prolonged staring at this data set will resolve this issue as other data would be needed to address it.

The more data is used to create a graph, the harder our task is to interpret it.  But the pay-off for spending the time is all the sweeter.  Happy graph-reading!

One final note: there is no doubt that this interactive map feature is a brilliant marketing move by Amazon.  This is a great and fun way for readers to find interesting books.

Reference: "Amazon, U.S.A.", Gelman blog, Oct 5 2008.

Olympic tallies

Andrew N., a reader from Australia, wasn't too impressed with the way National Nine News presents the Olympic medal table on its home page.  To the extent that we want to venture beyond the typical tabular presentation, this bar chart is in fact quite appropriate.  Let me explain.

Lets take a tour around the world.  It's the battle of the data tables. 

 

The Boston Globe's is the cleanest of the bunch.  I especially like the way they set up the USA count at the top; the use of country codes is inferior to spelling out country names, as done in all of the other examples.  The New York Times is the only one to utilize colors to set aside gold, silver and bronze, which lets readers easily assess the two dominant metrics, total golds and total medals.  A small touch but very nice.

The biggest design issue here is the existence of the two different metrics.  In any tabular presentation, the countries can be ranked by only one metric so the designer must make a choice.  The American papers present ranking by total medals; the French paper by total golds; the two Canadian ones shown here are split.  The American papers also choose to carry the ranking implicitly while the others explicitly provide a numerical rank.  Le Monde and Globe and Mail provide ranks that are consistent with ordering of countries, both by total golds.  The Star, by contrast, wants it both ways: the order reflects total medals while the "POS" column shows total golds.  This extra column does help the readers who prefer ranking by golds but the primacy of the other ranking has not been overcome.

So what about National Nine News?  I have not been a fan of stacked bar charts but surprisingly, this is a great application.  Stacked bars have the disadvantage that the stacked segments don't share the same base and thus it is difficult to compare their lengths.  Here, though, our two metrics are total medals and total golds so readers should be drawn to compare the total lengths, and the lengths of the first segments.  Those wanting to compare silvers and bronzes must make a stronger effort but they will be in the minority.

What can be improved are the distracting data labels, especially the gold circles.  Instead, one should provide a scale, or use symbols such as one circle per medal.  (See this old post.)  Here is a version with a scale:

One cannot end this post without mentioning the attempt by NYT editors to insert levity into these proceedings with first a cartogram and then a bubble chart.