Economists Banerjee and Duflo are celebrated for their work on global poverty but they won't be winning awards for the graphics on the website that supports their book "Poor Economics" (link). Thanks to a reader for the pointer.
Here is one view ("radial") of the data:
Here is the so-called linear view of the same data:
And here is what the data really look like:
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The linear view contains a host of misleading features. The length of the row of bubbles does not indicate how many total hours the individual spends doing the selected chores, not even directionally. Instead, the length is a proxy for the number of bubbles per person, which is a measure of the variety of chores - the more chores, the longer the chain of bubbles.
And then, you ask where the color legend is. This information is hidden in the mouse-over effect, or in the drop-down menu by clicking "compare daily activities". It's a bit of wasted energy to have to press on various bubbles in order to understand which color is mapped to which chore, isn't it?
The chart also contains a brainteaser. What is the logic behind the order of the individuals?
And finally, what to make of the little orange circles dancing around the chart? (They also decorate the radial view.) Go to the page and try clicking.
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Our version tries to do "less is more". One of the tricky features of this dataset is the profusion of little categories capturing daily activities that occupy 0.5 to 1 hour of someone's time. Instead of printing every activity, I chose to bundle all activities requiring less than 1 hour into a single "Others" category.
The "total" category is a reference level. One can also choose to print white boxes around every single bar and eliminate that row.
Just as there are counterfeit handbags that look like the real thing, there are fake data graphics that look like the real thing. Reader San C. shows us an example of this (found on the All Things D blog here):
At first sight, this appears to be a bubble chart. Further, the legend is telling us that the colors are meaningful. So, the bubbles correspond to different types of data, grouped by color, and the size of the bubbles represents the relative level of concern expressed by respondents.
That would be true if we were looking at the "real thing". But this is a counterfeit. How do we know it's fake?
First, the size of the bubbles was not sized to scale. Just look at the Social Security Number versus credit card number (shown on the right). A 1% difference shouldn't be visible on this sort of chart but the credit card bubble is clearly smaller.
Second, the legend gives the impression that the tint of the color carries information. However, it really doesn't. I lined up all of the green bubbles in order of decreasing data, and couldn't find any pattern to the tints.
There also isn't a clear pattern in the location of specific bubbles. Were they randomly scattered onto the chart?
In summary, this is the ultimate non-self-sufficient chart. If we remove the actual printed data from this chart, we're left with nothing.
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This data can be put onto a grouped bar chart dot plot.
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Aside from the graphical aspects, we should pay attention to some statistical issues.
The article does not stress enough the potential bias of this survey. The survey is an online survey of Internet users. Their average opinion about Internet-related issues should not be used to represent the opinion of the average American without careful consideration. There is a good possibility that people who have concerns about Internet privacy are less likely to be found on the Internet.
I also wonder if survey takers understand clearly the poll question. What does it mean by a company "accessing my personal information"? Does it mean I give the company the information (such as my credit card number) because I need to complete a transaction? Does it mean the company purchases such information from an information exchange? And if so, with or without informing me?
In particular, I don't understand the 28% who say they are not concerned about companies accessing their social security number.
Reader Irene R. was asked by a client to emulate this infographic movie, made by UNIQLO, the Japanese clothing store.
Here is one screen shot of the movie:
This is the first screen of a section; from this moment, the globes dissolve into clusters of photographs representing the survey respondents, which then parade across the screen. Irene complains of motion sickness, and I can see why she feels that way.
Here is another screen shot:
Surprisingly, I don't find this effort completely wasteful. This is because I have read a fair share of bore-them-to-tears compilation of survey research results - you know, those presentations with one multi-colored, stacked or grouped bar chart after another, extending for dozens of pages.
There are some interesting ideas in this movie. They have buttons on the lower left that allow users to look at subgroups. You'll quickly find the limitations of such studies by clicking on one or more of those buttons... the sample sizes shrink drastically.
The use of faces animates the survey, reminding viewers that the statistics represent real people. I wonder how they chose which faces to highlight, and in particular, whether the answers thus highlighted represent the average respondent. There is a danger that viewers will remember individual faces and their answers more than they recall the average statistics.
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If the choice is between a thick presentation gathering dust on the CEO's desk and this vertigo of a movie that perhaps might get viewed, which one would you pick?
That sounds like a silly question. Isn't the answer self-evident? Am I suggesting that we banish the discipline of charting?
Maybe I won't go so far. But it's difficult not to have such a destructive thought when one stares at charts like this:
Now, compare the above with this version shown on the right ... and it's clear all the squares and bubbles and colors gave us nothing. Readers have to read the fine print in order to take in the unequal distribution of income. This chart violates the notion of self-sufficiency we often speak about.
Peering back at the original chart, we find that the entire square grid edifice only serves to explain that 0.01% is one-tenth of 0.1%, which is one-tenth of 1%, etc. On the other hand, the part that has a chance of conveying the main message -- the relative size of the biggest bubble versus the smallest bubble -- is shoved off the screen. The gigantic yellow bubble being mostly off the chart, readers are essentially asked to read the data labels.
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The same article (via Yahoo!) contains other charts that are well executed.
This one, for instance, shows the increasing inequality very well. (The legend is on the left panel which I did not include here: the top red line is the top 1%, the other five lines are the quintiles or 20% buckets). At least four-fifths of the country is worse-off now than in 1980 in terms of their share of after-tax income.
Like Australia-based reader Ken B., I don't understand why many chart designers insist on using charts to deliver lessons to the public on map geography. Here is a recent example from Down Under, on earthquakes: (click on this link for the interactive version)
Was there a quake that shook the middle of the Pacific? Did a new geological formation give New Zealand a Pinocchio nose? No and no. The ugly presentation of the 2010 and 2011 Christchurch earthquakes -- as two ends of a dumbbell -- makes clear the straitjacket that maps are when it comes to delivering quantitative information.
Besides, the bubbles represent the relative magnitude of the quakes when one would hope that their sizes represent the geographical extent of the damage; at least, that would be information that has a spatial dimension.
The location of the quake is the only data with a spatial dimension surfaced on this plot. The only purpose of the map background is to tell us where Christchurch, Sichuan, etc. are on a map. In order to deliver this map lesson, the designer has to hide all of the more interesting data, like the relative magnitudes, the time-lines, the extent of the damage, the mortality rates, etc. In my mind, that is a very poor tradeoff.
Today we look at an example of a powerful visualization of some unstructured data. The data team at Guardian (UK) organized the Wikileaks data concerning reported incidence of IEDs in Afghanistan.
A scatter plot on a map provides an overview of the intensity of attacks from a spatial perspective. (A part of this map is shown on the right.) The background data -- the relief map of Afghanistan, and the major thoroughfares -- add to our understanding of why attacks were concentrated in certain parts of the country. It is always a great idea to add (con)textual data to help readers grasp the information shown on the chart.
Readers may want to understand the temporal pattern of attacks as well. The designer chose a small-multiples format to show this data, disaggregated by year of occurrence. This graphical construct is very versatile, and illustrates this data well... even though there has been little change over time, apart from a general increase in the number of reported attacks across the country.
It is a good idea to track the total number of attacks over time -- but not with those bubbles! The bubble chart almost always fails the self-sufficiency test; our eyes are not equipped to read relative areas of circles, and so any information we obtain about the aggregate number of attacks comes from reading the data directly. Switching to a bar chart, or removing the bubbles, leaving just the data, is recommended.
The major problem with a dataset like this is reporting bias: only attacks that were reported by U.S. personnel were included. The following chart helps close the gap a little by also showing the number of defused attacks, reported in the U.S. database. I'd have preferred a stacked column chart here since the total of defused (gray) and detonated (red) IEDs is an interesting statistic.
A stock trading volume type chart would also be nice, something like this:
I could have filed this one under Light Entertainment but it's too good a chart to lay to waste:
(The chart is from Internet Retailer.)
Let's focus on the (mis)match between the question being addressed and the data collected to address it. The intention of the analyst is fully divulged in the title of the accompanying article: "Don't sell Twitter short: those 140-character messages reach an affluent and engaged audience". The chart supports this claim by showing that Twitter users disproportionately represent the types of consumers that marketers most covet, i.e. those with advanced education, and those earning higher incomes.
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Up top, the concept "user" is very pliable. Is it someone who has a Twitter feed or is it someone who reads Twitter feeds or is it someone who subscribes to Twitter feeds? Is it someone who is registered or everyone who visits? Is it someone who has visited the site in the last x months? or posted a tweet in the last x months? If a writer, does it include someone with no subscribers? no page views? What about people who simultaneously publishes multiple feeds (like John Cook who writes one of my recommended blogs)?
Now, we don't expect the analyst to describe fully how a "user" is defined but while interpreting this chart, we should ask appropriate questions of the data.
Next, the analyst establishes a reference level called "general population". Is this the right metric? This depends on what the chart is used for. If you are choosing between spending money on Twitter, and say spending the money on national TV advertising, then perhaps this comparison is valid. If you are selecting between Twitter and say Google, then absolutely not. For most readers, I think a more relevant point of comparison would be the general Web user, rather than the general population. This is an important distinction because the general Web user also earns higher incomes and has higher educational attainment than the general population, thus the current set of data exaggerates the "value" of Twitter exposure.
Finally, if you are a marketer looking to spend with Twitter, you are also worried about "reach". Say, 50% of website ABC's users are rich people compared to 10% of your reference population. That sounds like an amazing opportunity. Well, only if website ABC has sufficient number of users! If ABC is a niche website serving only 1% of your reference population, then despite the benefit of targeting, its scale is too small for your need.
Don't take a chart on its surface. Read behind the chart!
Stefan S., whose team created the inkblot charts featured here, has updated his page of charts with a bunch of new ones, including some bubble charts. I had fun looking at a few of his experiments, and learnt a bit from them.
This chart deals with the geographical distribution of CO2 emissions and of wealth, and their correlation. The "standard" format would be to put pairs of columns onto a world map. That format has various weaknesses, and it is great to see Stefan try to reimagine the chart.
I especially love how he broke up the map into continents and subregions and arranged these pieces in a clean manner. He also recognized that stacked columns are not great for comparisons as the two pieces being compared don't usually sit at the same level. So in this chart, most pieces are level at their base. The solution is not perfect though, as for instance in the European section, it was very hard to put everything level without introducing white space.
Stefan also realized that you can't make it easy to compare distribution within a continent and across the globe in one chart, so he created the right-side column to solve this problem. Again, it's a good effort, not entirely successful but a very good start.
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Another chart I like is the inkblot chart that deals with all levels of the data simultaneously. These, I think, manage to both engage our brains and entertain us.
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Stefan specifically asked for feedback on this bubble chart:
I must say the map legend is a lovely touch.
As a whole, the scatter plot is effective at showing the inverse positive [oops, see comment below] correlation between development and per-capita emissions.
The effort is enormously ambitious in terms of stuffing as many dimensions as possible onto the same chart. I feel like the data can benefit from being shown in a set of charts, rather than one.
For example, having the continental averages plotted with all the individual countries doesn't work for me. I'd rather see individual plots for each continent, with the continental average plotted against a background of all the individual countries. This proposed view is reminiscent of the Gapminder presentation from what seems like eons ago.
Also, many metrics are found here: population, per-capita emissions, total emissions, human development index. Anyone familiar with this business knows that there are controversies around different pairs of metrics, e.g. per-capita versus total emissions, total emissions v. population, per-capita emissions v. population. Thus, a panel of scatter plots that focuses on different pairs of metrics would encourage readers to ponder these practical questions. Otherwise, we may feel like being dumped in the deep end.
I'm sure Stefan would appreciate other comments on this or other charts.
When Mike K. sent this in, he had a few comments, including "This is, of course, from the Chronicle of Higher Education", and "talking about a math course", which mean "very naively", we would "impose a higher standard." Should scientists be held to a higher standard? Lead by example, perhaps? I had the same feeling when I wrote the post on "Unscientific American" about the charts that'd flunk Ed Tufte's intro class, published in Scientific American.
In one word: confusion. Mike couldn't understand the relationship between the first row of bubbles and the second row of bubbles. It is as if the one course taken at Bronx Community College results in credits recognized everywhere! (You basically have to read all the footnotes to get some clues.)
Also note the usual confusion about areas and diameters.
In addition, the zero-bubbles prove themselves to be the nothing that is. They expose the folly of using bubbles when the data series contain zeroes (not to mention negative numbers). We can visualize this problem:
It gets worse.
For those mathematically inclined, we actually have an impossible situation: the size 4 bubble really contains the zero bubble plus a size 4 bubble; that is, 0 + 4 = 4 but if 0 has positive area, then the area of the 4 on the left hand side of the equation must be smaller than the area of the 4 on the right side. So, basically, don't use bubble charts if your data has zeroes.
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