A reader sends me to Adam Obeng, who did the dirty work deconstructing a set of charts by the U.S. National Highway Traffic Safety Administration on his blog. Here's an example of these charts:
Aside from the sneaker chart, they concocted a pop stick, a pencil, a tower of Hanoi, etc. These objects are ones I think should be evaluated as art. Adam gamely tells us that the proportions are totally off, and they are both internally and externally inconsitent.
I'll add two small points to Adam's post.
First, these charts pass my self-sufficiency test, that is to say, they did not print the entire data set (just one number here) on the page. Alas, given the distortion identified by Adam, not printing the data means everyone is free to create their own data. Herein lies the problem: there is an argument for allowing a small degree of distortion in exchange for "beauty" but these charts without any data have gone too far.
Second, see Adam's last point (the footnote). The original data is something quite convoluted: “3 out of 4 kids are not as secure in the car as they should be because their car seats are not being used correctly.” (How would they know this, I wonder.) This is a statistic about kids while the picture shows a statistic about their parents (or drivers).
Felix linked to a set of charts about guns in the U.S. (and elsewhere). The original charts, by Liz Fosslien, are found here.
I like the clean style used by Fosslien. Some of the charts are thought-provoking. Many of them may raise more questions than they answer. Here are a few that caught my eye.
A simplistic interpretation would claim that banning handguns is futile, and may even have an adverse impact on murder rate. However, this chart does not reveal the direction of causality. Did some countries ban handguns because they are reacting to higher violence? If that is the case, this chart is confirming that the countries with handgun bans are a self-selected group.
The U.S. is an outlier, both in terms of firearm ownership and firearm homicides. This makes the analysis much harder because the U.S. is really in a class of its own. It's not at all clear whether there is a positive correlation in the cluster below, and even if there is, whether we can draw a straight line up to the U.S. dot is also dubious.
Fosslien is being cheeky to deny us the identity of the other outlier, the country with few firearms but even higher death rate from intentional homicide. These scatter plots are great by the way to show bivariate distributions.
I'd still prefer a line chart for this type of data but this particular paired bar chart works for me as well. The contents of this chart is a shock to me.
A reader, Stephen M., who's a high school math Information Technology teacher in Australia, assigned the following chart to his class as a Junk Charts style assignment. (link to original here)
We have seen racetrack charts before (e.g. here or here), and we have dual racetracks here.
Stephen's class identified the following problems with the chart:
- The group agreed this should be better called a data visualisation than an infographic
- The purpose of the 'infographic' seems to be more on the design/form,
than the function of conveying an understanding of the data
- There seems to be a bit of an optical illusion with the lower upper circle
for the US appearing larger than the upper lower one (we checked, there isn't)
- There are no clear labels to assist. It is an assumption that because
in the heading and the figures, population is on top of donations, that
the lines are the same. The class agreed that country labels would help
to the left of each line start.
- No scale on the lines and where do you measure from/to (especially as the US line is a single line for a proportion of the way
- It's too abstract and the spatial separation of the curves makes comparison difficult.
Wow, that's great critique from the 16-year-olds. They are working on ways to re-make this graphic. One good idea is to collapse the two dimensions into one: per-capita donations.
Another issue with this chart is that the countries are sorted in different ways from one chart to the next. It's really difficult to compare one country to another.
It is also instructive to discuss what the key message is in this data. Why those six countries? What kinds of donations are being counted? Do the counting methodology differ by country? How comparable is the data?
Finally, is this art or is this science?
P.S. [12/2/2012] Stephen noted that another deficiency identified by the students is the lack of sourcing. Indeed, where did the data come from? They think it's the CIA Factbook.
Note: This post is by Aleksey Nozdryn-Plotnicki, who blogs at ThinkDataVis.
On my way to Crete recently, I was flipping
through the in-flight magazine when I stumbled upon this treat. This full-page
piece was about Claire Cock-Starkey’s upcoming (at the time) book, Seeing the
The book sells itself as “Global
Infographics” and the article says it is “swapping dry words for colourful
illustrated visuals”. The baby and the iPhone are pure decoration, but there
are also some information graphics here at the top and the bottom which bear a
Above we have what at first looks
innovative, but is actually a disguised bar chart. That’s fine, but:
Bars have been arched,
challenging our ability to compare them
Outer bars actually have
further to go as the radius and therefore circumference increases. So while
Japan has the lowest percentage, its bar appears to be equally as long as that
of Norway, the largest. In fact, since the values are sorted, for the most part
all bars are the same length and size.
The legend is far larger than
the chart itself, and is what really delivers the information at all. Using
that space for a larger chart and labelling the bars directly (like in a usual
bar chart) might be better.
There is no axis with any ticks
The chart has too many
categorical colours, so knowing what any colour represents requires looking it
up in the legend where the raw data is anyway.
Why this circular shape? I
suspect it was a clock-face for time, but the decoration, presumably informing
our sense of “leisure activity” has removed the clock hands, so the metaphor is
Why does the Norway bar go only
90 degrees around? This seems equivalent to not properly scaling the Y-axis on
a bar chart and leaving copious empty space above. Maybe this is meant to
indicate that even the most leisurely Norwegians only have time for gardening,
being a kite, and drinking at a table.
Consolation points, however,
for taking the time to clearly state what leisure time was defined as in this
At first this looks more like a traditional
bar chart, until you realise that:
Larger data is at the top and
smaller at the bottom, so the data is tied to the blue lines on the left,
rather than the visually-weighty bars on the right. Or maybe the height of the
pyramid is meant to be tied to age at marriage?
Bars are artificially grouped
and forced to be the same length, i.e. Sweden 34.3 and Germany 33.7. This leads
to a “lie factor”.
In any event the data is so
loosely encoded that it can hardly be considered encoded at all. The lines and
the data are both sorted.
It has a non-zero baseline at
roughly 20 or so, a “sin” in bar charts, though you could argue for a non-zero
baseline of around 18 for marriage since you would never expect to see values
Ultimately, what I think we have here
belongs in a genre of its own, perhaps “popcorn infographics”. At the time of writing the one review on
amazon.co.uk reads “Bought this for my 14 yr old - absolutely loves it and
showed friends who were also suitably impressed. Thank you” which says a
lot, and not all negative. Perhaps there
is room for popcorn infographics in this world or perhaps it’s just junk.
Aleksey Nozdryn-Plotnicki an analyst/consultant and data
visualisation blogger at ThinkDataVis.com. He is @alekseynp on Twitter.
Reader Steve S. sent in this article that displays nominations for the "Information is Beautiful" award (link). I see "beauty" in many of these charts but no "information". Several of these charts have appeared on our blog before.
Let's use the Trifecta checkup on these charts. (More about the Trifecta checkup here.)
The topic of this chart is both tangible and interesting. As someone who loves books, I do want to know what genres of books typically win awards.
However, both the data collection and graphical design make no sense.
The data collection problem presents a huge challenge and it's easy to get wrong. The problem is how narrow should a theme be. If it's too narrow, you can imagine every book has its own set of themes. If it's too wide, each theme maps to lots of books. The challenge is how to select the themes such that they have similar "widths". For example, "death" is a very wide theme and lots of books contain it, as indicated by the black lines. "Nanny trust issues" is a very narrow theme, and only one of those books deals with this theme. When there is such a theme, is its lack of popularity due to its narrow definition or due to writers not being interested in it?
The caption of this chart said "Cover stars: Charting 50 years up until 2010, this graphic shows The Beatles to be the most covered act in living memory." If that is the message, a much simpler chart would work a lot better.
Since the height of the chart indicates the number of covers sold in that year, the real information being shown is the boom and bust cycles of the worldwide economy. So, a lot more records were sold in 2005, and then the market tanked in 2008, for example.
That's why the data analyst should think twice before plotting raw data. Most data like these should be adjusted. In this case, you could either compare artists against one another in each year (by using proportions) or you have to do a seasonal and trend adjustment. I also don't see the point of highlighting year-to-year fluctuations. Nor do I understand why only in certain years is the top-rated cover identified by name and laurel wreath.
I talked about this stream graph of 311 calls back in 2010. See the post here.
I featured this set of infographics/pie charts back in 2011. See the post here.
This chart is a variant of the one from New York Times that I discussed here. I like the proper orientation on the NYT's version. The color scheme here may be slightly more attractive.
An email lay in my inbox with the tantalizing subject line: "How to Create Good Infographics Quickly and Cheaply?" It's a half-spam from one of the marketing sites that I signed up for long time ago. I clicked on the link, which led me to a landing page which required yet another click to get to the real thing (link). (Now, you wonder why marketers keep putting things in your inbox!)
The article was surprisingly sane. The author, Carrie Hill, suggests that the first thing to do is to ask "who cares?" This is the top corner of my Trifecta Checkup, asking what's the point of the chart. Some of us not so secretly hope that answer to "who cares?" is no one.
Carrie then lists a number of resources for creating infographics "quickly and cheaply".
Easel.ly caught my eye. This website offers templates for creating infographics. You want time-series data depicted as a long, hard road ahead, you have this on the right.
You want several sections of multi-colored bubble charts, you have this theme:
In total, they have 15 ready-made templates that you can use to make infographics. I assume paid customers will have more.
infogr.am is another site with similar capabilities, and apparently for those with some data in hand.
Based on this evidence, the avanlanche of infographics is not about to pass. In fact, we are going to see the same styles repetitively. It's like looking at someone's Powerpoint presentation and realizing that they are using the "Advantage" theme (one of the less ugly themes loaded by default). In the same way, we will have a long, winding road of civil rights, and a long, winding road of Argentina's economy, and a long, winding road of Moore's Law, etc.
But I have long been an advocate of drag-and-drop style interfaces for producing statistical charts. So I hope the vendors out there learn from these websites and make your products ten times better so that it is as "quick and cheap" to make nice statistical charts as it is to make infographics.
The Guardian (via Graphic News) has put out some fantastic infographics posters, so we can't say they are all bad. This is a big collection created in anticipation of the London Olympics. Here's one illustrating the 10,000m race: (link)
It's nice that they give an overview of the race, plus the calendar. The evolution of men and women times is shown on the same scale. In order to stress the improvement over time, they omitted those years in which the times did not improve (I think, although there are some mysterious omissions of data labels).
They have charts for all the different events and also in water sports, gymnastics, etc.
PS. I do not know why the women's times were omitted from some of the charts (100m, 200m etc.) In those charts, the lines for men are better colored blue to align with the dots on the calendar.
NYC mayor Michael Bloomberg is getting mixed reviews for his proposal to ban super-sized sugary drinks. Reader John O. wasn't impressed with this graphical effort (link):
The key problem: this picture is not scary at all. The reason it's not horrifying is that there is no context. People who have knowledge about healthy eating habits will get the message but that's preaching to the choir.
If you know that the recommended consumption of daily sugars for adults is roughly 20-36 grams, then you can see that one sugary drink of 12 ounces or higher would take you over the daily limit. A 64-ounce drink would give you more than 7 times what you need in a day. That's a powerful message but you won't know it from this chart. Not from the sugar cubes doubling as shadows, which is a cute, creative concept.
Also, make use of the chart-title real estate! Instead of "Sugar & Calories per Fountain Drink", say something memorable. "Fountain drinks make you fat and sick".
There is something else fishy about this graphic. What are the most prominent data being displayed?
You got it. They're 7, 12, 16, 32, 64. Where have we seen this type of data display?
Yup. This format is lifted from a menu in a Starbucks or a McDonald's (without prices).
Is this a health warning? Or a restaurant menu?
Also slightly confused about the slightly non-linear relationship between calories and drink size. Maybe volume of ice is held constant...
It is in fact a proportional relationship. The confusion arises from the non-linear increase in cup size from 7 to 64 ounces. The math is roughly 11 calories per ounce, and 3g of sugar per ounce. I wonder if it is better to show those two numbers instead of the ten not-very-memorable numbers shown on the chart itself.
In case you're wondering, the heights (thus areas) of the cups have no relationship with any of the data, not calories, not sugars, and not the cup size.
PS. John also wrote: "The soda cup graph reminds me of the chart from Pravda that Tufte cites in 'Cognitive Style of Powerpoint'. " If you know what he's talking about, please post a link to the chart. Thanks.
Where would this chart fall in my "return on effort matrix"? It is an extremely high-effort chart; I got tired trying to figure out what all those dimensions mean.
Is it a high-reward or a low-reward chart? It depends on why you're reading the chart. For most readers, I suspect it's low-reward.
In my view, the best charts are high-reward, low-effort. I'd emphasize that by effort, I mean effort by the reader. In general, the effort by the chart designer is inversely proportional to that by the reader.
In some special cases, high-effort charts may have high reward justifying the destruction of some brain cells.