Designers fuss over little details and so should you

Those who attended my dataviz talks have seen a version of the following chart that showed up yesterday on New York Times (link):

This chart shows the fluctuation in Arctic sea ice volume over time.

The dataset is a simple time series but contains a bit of complexity. There are several ways to display this data that helps readers understand the complex structure. This particular chart should be read at two levels: there is a seasonal pattern that is illustrated by the dotted curve, and then there are annual fluctuations around that average seasonal pattern. Each year's curve is off from the average in one way or another.

The 2015 line (black) is hugging the bottom of the envelope of curves, which means the ice volume is at a historic low.

Meanwhile the lines for 2010-2014 (blue) all trace near the bottom of the historic collection of curves.

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There are several nice touches on this graphic, such as the ample annotation describing interesting features of the data, the smart use of foreground/background to make comparisons, and the use of countries and states (note the vertical axis labels) to bring alive the measure of coverage volume.

Check out my previous post about this data set.

Also, this post talks about finding real-life anchors to help readers judge size data.

My collection of posts about New York Times graphics.

 

PS. As Mike S. pointed out to me on Twitter, the measure is "ice cover", not ice volume so I edited the wording above. The language here is tricky because we don't usually talk about the "cover" of a country or state so I am using "coverage". The term "surface area" also makes more sense for describing ice than a country.

Three axes or none

Catching up on some older submissions. Reader Nicholas S. saw this mind-boggling chart about Chris Nolan movies when Interstellar came out:

This chart was part of an article by Vulture (link).

It may be the first time I see not one, not two, but three different scales on the same chart.

First we have Rotten Tomatoes score for each movie in proportions:

The designer chopped off 49% of each column. So the heights of the columns are not proportional to the data.

Next we see the running time of movies in minutes (dark blue columns):

For this series, the designer hid 40 minutes worth of each movie below the axis. So again, the heights of the columns do not convey the relative lengths of the movies.

Thirdly, we have light blue columns representing box office receipts:

 

Or maybe not. I can't figure out what is the scale used here. The same-size chunks shown above display $45,000 in one case, and $87 million in another!

So the designer kneaded together three flawed axes. Or perhaps the designer just banished the idea of an axis. But this experiment floundered.

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Here is the data in three separate line charts:

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In a Trifecta Checkup (link), the Vulture chart falls into Type DV. The question might be the relationship between running time and box office, and between Rotten Tomatoes Score and box office. These are very difficult to answer.

The box office number here refers to the lifetime gross ticket receipts from theaters. The movie industry insists on publishing these unadjusted numbers, which are completely useless. At the minimum, these numbers should be adjusted for inflation (ticket prices) and for population growth, if we are to use them to measure commercial success.

The box office number is also suspect because it ignores streaming, digital, syndication, and other forms of revenues. This is a problem because we are comparing movies across time.

You might have noticed that both running time and box office numbers have gone up over time. (That is to say, running time and box office numbers are highly correlated.) Do you think that is because moviegoers are motivated to see longer films, or because movies are just getting longer?

 

 

PS. [12/15/2014] I will have a related discussion on the statistics behind this data on my sister blog. Link will be active Monday afternoon.

A small step for interactivity

Alberto links to a nice Propublica chart on average annual spend per dialysis patient on ambulances by state. (link to chart and article)

It's a nice small-multiples setup with two tabs, one showing the states in order of descending spend and the other, alphabetical.

In the article itself, they excerpt the top of the chart containing the states that have suspiciously high per-patient spend.

Several types of comparisons are facilitated: comparison over time within each state, comparison of each state against the national average, comparison of trend across states, and comparison of state to state given the year.

The first comparison is simple as it happens inside each chart component.

The second type of comparison is enabled by the orange line being replicated on every component. (I'd have removed the columns from the first component as it is both redundant and potentially confusing, although I suspect that the designer may need it for technical reasons.)

The third type of comparison is also relatively easy. Just look at the shape of the columns from one component to the next.

The fourth type of comparison is where the challenge lies for any small-multiples construction. This is also a secret of this chart. If you mouse over any year on any component, every component now highlights that particular year's data so that one can easily make state by state comparisons. Like this for 2008:

You see that every chart now shows 2008 on the horizontal axis and the data label is the amount for 2008. The respective columns are given a different color. Of course, if this is the most important comparison, then the dimensions should be switched around so that this particular set of comparisons occurs within a chart component--but obviously, this is a minor comparison so it gets minor billing.

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I love to see this type of thoughtfulness! This is an example of using interactivity in a smart way, to enhance the user experience.

The Boston subway charts I featured before also introduce interactivity in a smart way. Make sure you read that post.

Also, I have a few comments about the data analysis on the sister blog.

A great visual of complicated schedules

Reader Joe D. tipped me about a nice visualization project by a pair of grad students at WPI (link). They displayed data about the Boston subway system (i.e. the T).

The project has many components, one of which is the visualization of the location of every train in the Boston T system on a given day. This results in a very tall chart, the top of which I clipped:

I recall that Tufte praised this type of chart in one of his books. It is indeed an exquisite design, attributed to Marey. It provides data on both time and space dimensions in a compact manner. The slope of each line is positively correlated with the velocity of the train (I use the word correlated because the distances between stations are not constant as portrayed in this chart). The authors acknowledge the influence of Tufte in their credits, and I recognize a couple of signatures:

• For once, I like how they hide the names of the intermediate stations along each line while retaining the names of the key stations. Too often, modern charts banish all labels to hover-overs, which is a practice I dislike. When you move the mouse horizontally across the chart, you will see the names of the unnamed stations.
• The text annotations on the right column are crucial to generating interest in this tall, busy chart. Without those hints, readers may get confused and lost in the tapestry of schedules. If you scroll to the middle, you find an instance of train delay caused by a disabled train. Even with the hints, I find that it takes time to comprehend what the notes are saying. This is definitely a chart that rewards patience.

Clicking on a particular schedule highlights that train, pushing all the other lines into the background. The side panel provides a different visual of the same data, using a schematic subway map.

 Notice that my mouse is hovering over the 6:11 am moment (represented by the horizontal guide on the right side). This generates a snapshot of the entire T system shown on the left. This map shows the momentary location of every train in the system at 6:11 am. The circled dot is the particular Red Line train I have clicked on before.

This is a master class in linking multiple charts and using interactivity wisely.

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You may feel that the chart using the subway map is more intuitive and much easier to comprehend. It also becomes very attractive when the dots (i.e., trains) are animated and shown to move through the system. That is the image that project designers have blessed with the top position of their Github page.

However, the image above allows us to  see why the Marey diagram is the far superior representation of the data.

What are some of the questions you might want to answer with this dataset? (The Q of our Trifecta Checkup)

Perhaps figure out which trains were behind schedule on a given day. We can define behind-schedule as slower than the average train on the same route.

It is impossible to figure this out on the subway map. The static version presents a snapshot while the dynamic version has  moving dots, from which readers are challenged to estimate their velocities. The Marey diagram shows all of the other schedules, making it easier to find the late trains.

Another question you might ask is how a delay in one train propagates to other trains. Again, the subway map doesn't show this at all but the Marey diagram does - although here one can nitpick and say even the Marey diagram suffers from overcrowding.

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On that last question, the project designers offer up an alternative Marey. Think of this as an indiced view. Each trip is indiced to its starting point. The following setting shows the morning rush hour compared to the rest of the day:

 I think they can utilize this display better if they did not show every single schedule but show the hourly average. Instead of letting readers play with the time scale, they should pre-compute the periods that are the most interesting, which according to the text, are the morning rush, afternoon rush, midday lull and evening lull.

The trouble with showing every line is that the density of lines is affected by the frequency of trains. The rush hours have more trains, causing the lines to be denser. The density gradient competes with the steepness of the lines for our attention, and completely overwhelms it.

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There really is a lot to savor in this project. You should definitely spend some time reviewing it. Click here.

Also, there is still time to sign up for my NYU chart-making workshop, starting on Saturday. For more information, see here.

Two good maps, considered

A Relection on the past year:

Thanks to you for continuing to make this blog a success. Writing it has given me much enjoyment over the years, and I have learned much from your comments as well as from the visualization projects of many colleagues. 2013 also saw the publication of my new book Numbersense: How to Use Big Data to Your Advantage (link). I thank those of you who have purchased the book, and supported my writing. For those who haven't, please check it out. I have also been speaking at various events, mostly about interpreting data analyses published in the mass media, and building effective data analytics teams. In addition, I am heavily involved in the new Certificate in Analytics and Data Visualization at New York University (link). While the frequency of posting has suffered a little due to my other projects, I hope you found the contents as engaging, fun, and constructive as before.

Looking forward to 2014, I have as usual a basket of projects. Besides the two blogs, I will be expanding my teaching at NYU, including a visualization workshop that I'll be writing about here soon; taking on consulting projects; evangelizing better communications of data and analytics; and prospecting several book projects. I continue to spend most of the week at Vimeo, where my team analyzes data.

This will be my last post in 2013. It is an extra-long post to tie you over to the New Year. Happy New Year!

Kaiser

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A short while ago, I was in correspondence with Thomas Rhiel who created a lovely map depicting the age of buildings in Brooklyn (link). In this case, it's the data that intrigues my interest. I haven't seen this type of data visualized before. The map type is exquisitely aligned to the data: buildings are geographically located and the age is a third, non-geographical dimension which is encoded in the colors. Red-orange is the most recent while green-blue is the oldest.

 

 

The data is at the level of individual buildings. If you hover over a building, you find the raw data including the address and the year of construction. The details seem to show that even the shape of each building is depicted. This really impressed me since a lot of manual labor must have been applied (according to Rhiel, there is a source for this type of data). Here is the map at its most magnified:

I came across this starry patch near the Manhattan Bridge, in which the buildings show up as red asterisks. (Rhiel said the shape came from the data. I am not sure I believe the data. Anyone lives near Sands Street?)

 

The map is useful if you are interested in questions such as "where are the new developments" (look for the deep red buildings) or "what's the average age of the buildings in a specific block" or "what's the age distribution of the buildings in a set of blocks". At the magnified level shown above, the street names are available to help readers orient themselves. The light gray color keeps the roads and the names safely in the background.

Now, zoomed to the other extreme, we get the image of the whole of Brooklyn:

 

I have a couple of suggestions for Rhiel. As someone who is not familiar with the geography of Brooklyn, this view presumes knowledge that I don't have. Unlike the magnified view, there are no text labels to help us decipher the different sections of Brooklyn. It would be nice if there is a background map to indicate the better-known areas like Williamsburg or Brooklyn Heights or Red Hook, etc.

The other concern is the apparent lack of pattern shown here. At this level, an appropriate question is which sections of Brooklyn are being redeveloped and which sections have older buildings. I see sprinkles of colors everywhere, giving the impression that everything is average. I suggested to Rhiel that aggregating the data would help bring out the pattern.

In data visualization, there is an obsession of plotting the "raw data" at its most granular level. Sometimes, this strategy backfires. It's the classic signal versus noise problem. Aggregation is a noise removal procedure. If for example, Rhiel gives up the data for individual buildings, including those beloved building shapes, and looks at the average age of buildings within each block, or even Census tracts, I suspect that the resulting map would be more informative.

It turns out that the Graphics team at the New York Times just published an interactive map that illustrates exactly what I suggested to Rhiel. Since this post is getting long, please go to the next post to continue reading.

 

The exception to the rule against dual axes

Dual axes are almost always a bad idea. But there is one situation under which I'd use it.

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Last week, Alberto Cairo (link) engaged in a Twitter/blogging debate about a chart that first appeared in Reuters concerning the state of the woman CEO in the Fortune 500 companies. Here is the chart under discussion:

This chart already is cleaner and more useful than the original original, which came from a research report from Catalyst (link):

Jonathan Keller re-made the Reuters chart as follows:

 

Cairo Jorge Camões contributed this version:

 

The Voila blog (link) has yet another take:

Then Chris Moore, responding to Cairo, created this view and also left some insightful comments:

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What's at stake here? There are really three related topics of discussion.

First, there is the matter of the upper limit of the vertical axis. Three solutions were suggested: 100 percent, 50 percent, and 4 percent. (Cairo at one point suggested 25 percent, which can be wrapped into the 50 percent bucket.) In reality, this is an argument over which of two key messages should be emphasized. The first message is that women still comprises a pathetically small proportion of Fortune 500 CEOs. The second message is more hopeful, that the growth in this proportion has been quite rapid since 1995.

All versions of the chart actually display both messages. In the Reuters chart (as well as Moore and Cairo), the message about the absolute proportion of women is given as an annotation while the Keller and Voila versions extend the vertical axis, thus encoding this message directly to the chart. Conversely, the Keller and Voila versions deemphasize the growth in proportions, and so I'd have preferred to see a note about that growth when using their versions.

Voila selectes a 50% upper limit because the 50/50 split has an intuitive meaning in the context of gender balance. Because the resulting chart is so visually arresting, and so biased to one of the two key messages, I'd only consider it if the point of the display is to draw attention to the female deficit.

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The second disagreement is in using absolute counts versus relative proportions. Moore chose absolute counts. I am in this camp as well. This is primarily because we are talking about Fortune 500 and the 500 number is an idee fixe. In Moore's version, I find the data labels distracting since all the numbers are small and insignificant.

Finally, the linkage between the absolute and the relative numbers also produces multiple solutions. Cairo's post pinpoints this issue. His solution is to include an inset pie chart with an arrow to explicitly link the two views. Moore likes the inset idea, but experimented with a donut chart or a partition in place of the pie chart. He also removes the explicit guiding arrow.

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It turns out this dataset is perfectly made for the dual axes. The absolute counts and relative proportions are in one to one correspondence because it's really only one data series expressed twice. This happy situation leads to one line that can be cross-referenced on two axes, one side showing counts and the other side showing proportions. This is shown in my version below (the orange line).

In addition to having two axes, I have plotted two related data series. The second series (in red) shows the incremental change in the number of women CEOs from the previous year (also shown in both counts and proportions).

The first series (the same one everyone plotted) draws attention to the first message, that the growth rate of women CEOs is quite strong since 1995. The second series is a bit of a downer on that message, suggesting that from the absolute count perspective, the progress (only one or two additions per year) has been painfully slow, and not that impressive.

Thanks again to Alberto for making me aware of this discussion. This has been fun!

 

PS. I have left out the other chart and may return to it in a future post.

Beyond the obvious

Flowing Data has been doing some fine work on the baby names data. The names voyager is a successful project by Martin Wattenberg that has received praise from many corners. It's one of these projects that have taken on a commercial life as you can see from the link.

Here is a typical area chart presentation of the baby names data:

The typical insight one takes from this chart is that the name "Michael" (as a boy's name) reached a peak in the 1970s and have not been as popular lately. The data is organized as a series of trend lines, for each name and each gender.

Speaking of area charts, I have never understood their appeal. If I were to click on Michael in the above chart, the design responds by restricting itself to all names starting with "Michael", meaning it includes Michael given to a girl, and Michaela, for example. See below.

What is curious is that the peak has a red lining. At first thought, one expects to find hiding behind the blue Michael a girl's name that is almost as popular. But this is a stacked area chart so in fact, the girl's name (Michael given to a girl, if you mouse over it) is much less popular than the boy Michael (20,000 to 500 roughly).

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Nathan decides to dig a layer deeper. Is there more information beyond the popularity of baby names over time?

In this post, Nathan zones in on the subset of names that are "unisex," that is to say, have been used to name both boys and girls. He selects the top 35 names based on a mean-square-error criterion and exposes the gender bias for each name. The metric being plotted is no longer pure popularity but gender popularity. The larger the red area, the greater the proportion of girls being given that name.

You can readily see some interesting trends. Kim (#34) has become almost predominantly female since the 1960s. On the other hand, Robbie (#18) used to be predominantly female but is now mostly a boy's name.

 One useful tip when performing this analysis is to pay attention to the popularity of each name (the original metric) even though you've decided to switch to the new metric of gender bias. This is because the relative proportions are unstable and difficult to interpret for less popular names. For example, the Name Voyager shows no values for Gale (#29) after the 1970s, which probably explains the massive gyrations in the 1990s and beyond.

Graph redesign is hot

Joe D., a long time reader, points us to a few blogs that have been active creating redesigns of charts, similar to how we do it here.

First up, here are some examples from Storytelling With Data (link).

This example transformed a grouped bar chart into a line chart, something that I have long advocated. I'm still waiting for the day when market research companies start to switch from bars to lines.

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Jorge Camoes, also a long-time reader, produced a redesign of a chart on military spending first printed in Time magazine. (link)

 

Dual-axis plots have been pilloried here often, especially when the two axes have different and incompatible units, as in here. As usual, transforming to a scatter plot is a good first step, which is what Jorge has done here. He then connected the dots to indicate the time evolution of the relationship. This is a smart move here just because the pattern is so stark.

The chart now illustrates an "inflexion point" in 2000. Prior to 2000, troop size was decreasing while the budget was stable. After 2000, budget increased sharply while troop size remained relatively stable.

Now peer back at the original chart. You can discern the sharp decrease in troop size over time, and the sharp increase in budget over time, but separately. The chart teases a cross-over point around 1995 which turned out to be misleading. This is a great illustration of why dual-axis plots are dangerous.

Once more, superimposing time series creates silly theories

After I wrote the post about superimposing two time series to generate fake correlations, there was a lively discussion in the comments about whether a scatter plot would have done better. Here is the promised follow-up post.

The contentious issue is that X and Y might appear correlated but in fact, what we are observing is that both data series are strongly correlated with time (e.g. population almost always grows with time), and X and Y may not be correlated with each other.

Indeed, the first thing a statistician would do when encountering two data series is to create a scatter plot. Economists, by contrast, seem to prefer two line charts, superimposed.

The reason for looking at the scatter plot is to remove the time component. If X and Y are correlated systematically (and not individually with the time component), then even if we disturb the temporal order, we should still be able to see that correlation. If the correlation goes away in an x-y plot, then we know that the two variables are not correlated, and that the superimposed line charts created an illusion.

The catch is that the scatter plot analysis is necessary but not sufficient. In many cases, we will find strong correlation in the scatter plot. But that does not prove there is X-Y correlation beyond each data series being correlated with time. By plotting X and Y and ignoring time, we introduce time as an omitted variable, which can still be controlling both X and Y series.

The scatter plot (right) shows the per capita miles driven against the civilian labor force participation rate. Having hidden the time dimension, we still see a very strong correlation between the two data series.

This is because time is still the invisible hand. Time is running from left to right on the chart still. This pattern is visible if we have line segments connecting the data in temporal order, as in the chart below.

 

 

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One solution to this problem is to de-trend the data. We want to remove the effect of time from each of the two data series individually, then we plot the residual signals against each other.

Here is the result (right). We now have a random scatter of points that average about zero. If anything, there may be a slightly negative correlation, meaning that when the labor force participation rate is above trend, the per-capita miles driven tend to be slightly below trend; this effect if it exists is small.

What I have done here is to establish the trend for each of the two time series. The actual data being plotted is what is above/below trend. What this chart is saying is that when one value is above trend, it gives us little information about whether the other value is above or below trend.

 

 

Superimposing time series is the biggest source of silly theories

Business Insider (link) published the following chart and declared "the end of the car age in one chart". The chart superimposed the monthly motor vehicle miles driven per capita and the labor force participation rate.

This is the conclusion of the post:

There's a logical connection between the two. Not in the workforce? You're less inclined to drive.

It's strange that they chose to show a time series going back to the 1970s. The conclusion is logical only for the last five years of the data. Looking back even another decade, to the last recession (2001), one finds the exact opposite conclusion: as the work force participation rate fell, the per-capita miles driven went up.

The other problem is causation creep, about which I have written on the sister blog (link). This chart merely shows correlation (and that is questionable). The conclusion of cause and effect is purely theory. Another theory would be the rise in telecommuting and work-from-home situations. A counter-theory would be that the unemployed may have more free time to drive. Another theory is that gas prices have gone up:

Any time series you can find that has a peak during the 2000s can be similarly interpreted as having caused people to stop driving. Here's a chart of real house prices from Calculated Risk.

Falling house prices causes people to stop driving. Or perhaps falling house prices causes people to lose jobs.