In discussing the bar-density and pie-density charts with a buddy (thanks LB!), it became obvious that the labeling is a challenge. And he's right.
Here is the pie-density chart for the Youtube views with the labels as originally conceived.
These labels are trying too hard to provide precise data to the reader.
Here are some simplified labels that get at the message rather than the data:
Here is a slightly different version:
I came across this older chart in the Financial Times, which is a place to find some nice graphics:
The key to success here is having a good story to tell. Blackpool is an outlier when it comes to improvement in life expectancy since 1993. Its average life expectancy has improved, but the magnitude of improvement lags other areas by quite a margin.
The design then illustrates this story in two ways.
On the right side, one sees Blackpool occupying a lone spot on the left side of the histogram. On the left chart, the gap between Blackpool and the national average is plotted over time. The gap is clearly widening; the size of the gap is labeled so the reader immediately knows it went from 1.8 to 4.9.
Although they're not labeled, the reader understand that the other two lines are the best and worst areas. The comparison between Glasgow City and Blackpool is also informative. Glasgow City, which has the worst life expectancy in the U.K. is fast catching up with Blackpool, the second worst.
I also like color-coded titles. It draws attention to Blackpool and it links the conclusion to both charts in an efficient manner.
At first glance, this graphic's message seems clear: what proportion of Americans are exceeding or lagging guidelines for consumption of different food groups. Blue for exceeding; orange for lagging. The stacked bars are lined up at the central divider - the point of meeting recommended volumes - to make it easy to compare relative proportions.
The original chart is here, on the Health.gov website.
The little icons illustrating the food groups are cute and unintrusive.
It's when you read further that things start to get complicated. The last three rows display a flipping of the color scheme, with orange on the right, blue on the left. Up to this point, you may understand blue to mean over the recommended value, and orange is under. Suddenly, the orange is shown on the right side.
The designer was wrestling with a structural issue in the data. The last three food groups - sugars, fats and sodium - are things to eat less. So, having long bars on the right side is not good. The orange/blue colors should be interpreted as bad/good and not as under/over.
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The problem with this design is that it draws attention to this color flip - that is to say, it draws attention to which food groups are favored and which ones are to be avoided. This insight is actually in the metadata, not what this dataset is about.
In the following chart, I enforce the bad/good color scheme while ignoring the direction of good. The text is adjusted to use words that do not suggest direction.
Dieticians are probably distressed by this chart, given that most Americans are lagging on almost all of the recommendations.
In a final edit, I re-ordered the categories.
Knife stabbings are in the news in the U.K. and the Economist has a quartet of charts to illustrate what's going on.
I'm going to focus on the chart on the bottom right. This shows the trend in hospital admissions due to stabbings in England from 2000 to 2018. The three lines show all ages, and two specific age groups: under 16 and 16-18.
The first edit I made was to spell out all years in four digits. For this chart, numbers like 15 and 18 can be confused with ages.
The next edit corrects an error in the subtitle. The reference year is not 2010 as those three lines don't cross 100. It appears that the reference year is 2000. Another reason to use four-digit years on the horizontal axis is to be consistent with the subtitle.
The next edit removes the black dot which draws attention to itself. The chart though is not about the year 2000, which has the least information since all data have been forced to 100.
The next edit makes the vertical axis easier to interpret. The indices 150, 200, are much better stated as + 50%, + 100%. The red line can be labeled "at 2000 level". One can even remove the subtitle 2000=100 if desired.
Finally, I surmise the message the designer wants to get across is the above-average jump in hospital admissions among children under 16 and 16 to 18. Therefore, the "All" line exists to provide context. Thus, I made it a dashed line pushing it to the background.
A WSJ chart caught my eye the other day – I spotted someone looking at it in a coffee shop, and immediately got a hold of a copy. The chart plots the ebb and flow of GE’s revenues from the 1980s to the present.
What grabbed my attention? The less-used chart form, and the appealing but not too gaudy color scheme.
The chart presents a highly digestible view of the structure of GE’s revenues. We learn about GE’s major divisions, as well as how certain segments split from or merged with others over time. Major acquisitions and divestitures are also depicted; if these events are the main focus, the designer should find ways to make these moments stand out more.
An interesting design decision concerns the sequence of the divisions. One possible order is by increasing or decreasing importance, typically indicated by proportional revenues. This is complicated by the changing nature of the business over the decades. So financial services went from nothing to the largest division by far to almost disappearing.
The sequencing need not be data-driven; it can be design-constrained. The merging and splitting of business units are conveyed via linking arrows. Longer arrows are unsightly, and meshes of arrows are confusing.
On this chart, the long arrow pointing from the orange to the gray around 2004 feels out of place. What if the financial services block is moved to the right of the consumer block? That will significantly shorten the long arrow. It won’t create other entanglements as the media block is completely disjoint and there are no other arrows tying financial services to another division.
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To improve readability, the bars are spaced out horizontally. The addition of whitespace distorts the proportionality. So, in 2001, the annotation states that financial services (orange) accounted for “about half of the revenues,” which is directly contradicted by the visual perception – readers find the orange bar to be clearly shorter than the total length of the other bars. This is a serious deficiency of the chart form but this chart conveys the "ebb and flow" very well.
Mike A. pointed me to two animated maps made by Caltech researchers published in LiveScience (here).
The first map animation shows the rise and fall of water levels in a part of California over time. It's an impressive feat of stitching together satellite images. Click here to play the video.
The animation grabs your attention. I'm not convinced by the right side of the color scale in which the white comes after the red. I'd want the white in the middle then the yellow and finally the red.
In order to understand this map and the other map in the article, the reader has to bring a lot of domain knowledge. This visualization isn't easy to decipher for a layperson.
Here I put the two animations side by side:
The area being depicted is the same. One map shows "ground deformation" while the other shows "subsidence". Are they the same? What's the connection between the two concepts (if any)? On a further look, one notices that the time window for the two charts differ: the right map is clearly labeled 1995 to 2003 but there is no corresponding label on the left map. To find the time window of the left map, the reader must inspect the little graph on the top right (1996 to 2000).
This means the time window of the left map is a subset of the time window of the right map. The left map shows a sinusoidal curve that moves up and down rhythmically as the ground shifts. How should I interpret the right map? The periodicity is no longer there despite this map illustrating a longer time window. The scale on the right map is twice the magnitude of the left map. Maybe on average the ground level is collapsing? If that were true, shouldn't the sinusoidal curve drift downward over time?
The chart on the top right of the left map is a bit ugly. The year labels are given in decimals e.g. 1997.5. In R, this can be fixed by customizing the axis labels.
I also wonder how this curve is related to the map it accompanies. The curve looks like a model - perfect oscillations of a fixed period and amplitude. But one suppose the amount of fluctuation should vary by location, based on geographical features and human activities.
The author of the article points to both natural and human impacts on the ground level. Humans affect this by water usage and also by management policies dictated by law. It would be very helpful to have a map that sheds light on the causes of the movements.
The Thai cave rescue was a great story with a happy ending. It's also one that lends itself to visualization. A good visualization can explain the rescue operation more efficiently than mere words.
A good visual should bring out the most salient features of the story, such as:
In terms of what made the rescue challenging, some of the following are pertinent:
There were many attempts at visualizing the Thai cave rescue operation. The best ones I saw were: BBC (here, here), The New York Times (here), South China Morning Post (here) and Straits Times (here). It turns out each of these efforts focuses on some of the aspects above, and you have to look at all of them to get the full picture.
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BBC's coverage began with a top-down view of the route of the rescue, which seems to be the most popular view adopted by news organizations. This is easily understood because of the standard map aesthetic.
The BBC map is missing a smaller map of Thailand to place this in a geographical context.
While this map provides basic information, it doesn't address many of the elements that make the Thai cave rescue story compelling. In particular, human beings are missing from this visualization. The focus is on the actions ("diving", "standing"). This perspective also does not address the water level, the key underlying environmental factor.
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Another popular perspective is the sideway cross-section. The Straits Times has one:
The excerpt of the infographic presents a nice collection of data that show the effort of the rescue. The sideway cross-sectional section shows the distance and the up-and-down nature of the journey, the level of flooding along the route, plus a bit about the headroom available at different points. Most of these diagrams bring out the "horizontal" distance but somehow ignore the "vertical" distance. One possibility is that the real trajectory is curvy - but if we can straighten out the horizontal, we should be able to straighten out the vertical too.
The NYT article gives a more detailed view of the same perspective, with annotations that describe key moments along the rescue route.
If, like me, you like to place humans into this picture, then you have to go back to the Straits Times, where they have an expanded version of the sideway cross-section.
This is probably my most favorite single visualization of the rescue operation.
There are better cartoons of the specific diving actions, though. For example, the BBC has this visual that shows the particularly narrow part of the route, corresponding to the circular inset in the Straits Times version above.
The drama!
NYT also has a set of cartoons. Here's one:
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There is one perspective that curiously has been underserved in all of the visualizations - this is the first-person perspective. Imagine the rescuer (or the kids) navigating the rescue route. It's a cross-section from the front, not from the side.
Various publications try to address this by augmenting the top-down route view with sporadic cross-sectional diagrams. Recall the first map we showed from the BBC. On the right column are little annotations of this type (here):
I picked out this part of the map because it shows that the little human figure serves two potentially conflicting purposes. In the bottom diagram, the figurine shows that there is limited headroom in this part of the cave, plus the actual position of the figurine on the ledge conveys information about where the kids were. However, on the top cross-section, the location of the figure conveys no information; the only purpose of the human figure is to show how tall the cave is at that site.
The South China Morning Post (here - site appears to be down when I wrote this) has this wonderful animation of how the shape of the headroom changed as they navigated the route. Please visit their page to see the full animation. Here are two screenshots:
This little clip adds a lot to the story! It'd be even better if the horizontal timeline at the bottom is replaced by the top-down route map.
Thank you all the various dataviz teams for these great efforts.
Yesterday, I pulled this graphic from a journal paper, and said one should not copy and paste this into an oral presentation.
So I went ahead and did some cosmetic surgery on this chart.
I don't know anything about the underlying science. I'm just interpreting what I see on the chart. It seems like the key message is that the Flowering condition is different from the other three. There are no statistical differences between the three boxplots in the first three panels but there is a big difference between the red-green and the purple in the last panel. Further, this difference can be traced to the red-green boxplots exhibiting negative correlation under the Flowering condition - while the purple boxplot is the same under all four conditions.
I would also have chosen different colors, e.g. make red-green two shades of gray to indicate that these two things can be treated as the same under this chart. Doing this would obviate the need to introduce the orange color.
Further, I think it might be interesting to see the plots split differently: try having the red-green boxplots side by side in one panel, and the purple boxplots in another panel.
If the presentation software has animation, the presenter can show the different text blocks and related materials one at a time. That also aids comprehension.
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Note that the plot is designed for an oral presentation in which you have a minute or two to get the message across. It's debatable as to whether journal editors should accept this style for publications. I actually think such a style would improve reading comprehension but I surmise some of you will disagree.
I've attended a number of talks in the last couple of days at the Joint Statistical Meetings. I'd like to offer some advice to presenters using graphics in their presentations.
Here is an example of the style of graphics that are being presented. (Note: I deliberately picked an example from a Google image search - this graphic was not used in a presentation but is representative of those I've seen.)
Here are some tips to make your graphic much more impactful:
The larger lesson is that the chart that is perfect for publication in a journal is less than perfect for an oral presentation.
PS. Please see here for an example of how one can remake the above chart for use in a conference presentation.
NPR has this chart, which I like:
It's a small multiples of bumps charts. Nice, clear labels. No unnecessary things like axis labels. Intuitive organization by Major Factor, Minor Factor, and Not a Factor.
Above all, the data convey a strong, surprising, message - despite many high-profile gun violence incidents this year, some Democratic voters are actually much less likely to see guns as a "major factor" in deciding their vote!
Of course, the overall importance of gun policy is down but the story of the chart is really about the collapse on the Democratic side, in a matter of two months.
The one missing thing about this chart is a nice, informative title: In two months, gun policy went from a major to a minor issue for some Democratic voters.
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I am impressed by this Financial Times effort:
The key here is the analysis. Most lazy analyses compare millennials to other generations but at current ages but this analyst looked at each generation at the same age range of 18 to 33 (i.e. controlling for age).
Again, the data convey a strong message - millennials have significantly higher un(der)employment than previous generations at their age range. Similar to the NPR chart above, the overall story is not nearly as interesting as the specific story - it is the pink area ("not in labour force") that is driving this trend.
Specifically, millennial unemployment rate is high because the proportion of people classified as "not in labour force" has doubled in 2014, compared to all previous generations depicted here. I really like this chart because it lays waste to a prevailing theory spread around by reputable economists - that somehow after the Great Recession, demographics trends are causing the explosion in people classified as "not in labor force". These people are nobodies when it comes to computing the unemployment rate. They literally do not count! There is simply no reason why someone just graduated from college should not be in the labour force by choice. (Dean Baker has a discussion of the theory that people not wanting to work is a long term trend.)
The legend would be better placed to the right of the columns, rather than the top.
Again, this chart benefits from a stronger headline: BLS Finds Millennials are twice as likely as previous generations to have dropped out of the labour force.
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