The above chart, when it was unveiled at the end of November last year, got some mileage on my Twitter feed so it got some attention. A reader, Eric N., didn't like it at all, and I think he has a point.
Here are several debatable design decisions.
The chart uses an inverted axis. A tax cut (negative growth) is shown on the right while a tax increase is shown on the left. This type of inversion has gotten others in trouble before, namely, the controversy over the gun deaths chart (link). The green/red color coding is used to signal the polarity although some will argue this is bad for color-blind readers. The annotation below the axis is probably the reason why I wasn't confused in the first place but the other charts further down the page do not repeat the annotation, and that's where the interpretation of -$2,000 as a tax increase is unnatural!
The chart does not aggregate the data. It plots 25,000 households with 25,000 points. Because of the variance of the data, it's hard to judge trends. It's easy enough to see that there are more green dots than red but how many more? 10 percent, 20 percent, 40 percent? It's also hard to answer any specific questions, say, about households with a certain range of incomes. There are various ways to aggregate the data, such as heatmaps, histograms, and so on.
For those used to looking at scientific charts, the x- and y-axes are reversed. By convention, we'd have put the income ranges on the horizontal axis and the tax changes (the "outcome" variable) on the vertical axis.
The text labels do not describe the data patterns on the chart so much as they offer additional information. To see this, remove the labels as I have done below. Try adding the labels based on what is shown on the chart.
Perhaps it's possible to illustrate those insights with a set of charts.
While reading this chart, I kept wondering how those 25,000 households were chosen. This is a sample of households. The methodology is explained in a footnote, which describes the definition of "middle class" but unfortunately, they forgot to tell us how the 25,000 households were chosen from all such middle-class households.
The decision to omit the households with income below $40,000 needs more explanation as it usurps the household-size adjustment. Also, it's not clear that the impact of the tax bill on the households with incomes between $20-40K can be assumed the same as for those above $40K.
Are the 25,000 households is a simple random sample of all "middle class" households or are they chosen in some ways to represent the relative counts? It's also useful to know if they applied the $40K cutoff before or after selecting the 25,000 households.
Ironically, the media kit of the Times discloses an affluent readership with median household income of almost $190K so it appears that the majority of readers are not represented in the graphic at all!
I like the Guardian's feature (undated) on gun violence in American cities a lot.
The following graphic illustrates the situation in Baltimore.
The designer starts by placing where the gun homicides occured in 2015. Then, it leads readers through an exploration of the key factors that might be associated with the spatial distribution of those homicides.
The blue color measures poverty levels. There is a moderate correlation between high numbers of dots (homicides) and deeper blue (poorer). The magenta color measures education attainment and the orange color measures proportion of blacks. In Baltimore, it appears that race is substantially better at explaining the prevalence of homicides.
This work is exemplary because it transcends description (first map) and explores explanations for the spatial pattern. Because three factors are explored together in a small-multiples layout, readers learn that no single factor can explain everything. In addition, we learn that different factors have different degrees of explanatory power.
Attentive readers will also find that the three factors of poverty, education attainment and proportion black are mutually correlated. Areas with large black populations also tend to be poorer and less educated.
I also like the introductory section in which a little dose of interactivity is used to sequentially present the four maps, now superimposed. It then becomes possible to comprehend the rest quickly.
The top section is less successful as proportions are not easily conveyed via dot density maps.
Dropping the map form helps. Here is a draft of what I have in mind. I just pulled some data from online sources at the metropolitan area (MSA) level, and it doesn't have as striking a comparison as the city-level data, it seems.
PS. On Twitter, Aliza tells me the article was dated January 9, 2017.
A reader sent this tip in some time ago and I lost track of who he/she is. This graphic looks deceptively complex.
What's complex is not the underlying analysis. The design is complex and so the decoding is complex.
The question of the graphic is a central concern of anyone who's retired: how long will one's savings last? There are two related metrics to describe the durability of the stash, and they are both present on this chart. The designer first presumes that one has saved $1 million for retirement. Then he/she computes how many years the savings will last. That, of course, depends on the cost of living, which naively can be expressed as a projected annual expenditure. The designer allows the cost of living to vary by state, which is the main source of variability in the computations. The time-based and dollar-based metrics are directly linked to one another via a formula.
The design encodes the time metric in a grid of dots, and the dollar-metric in the color of the dots. The expenditures are divided into eight segments, given eight colors from deep blue to deep pink.
Thirteen of those dots are invariable, appearing in every state. Readers are drawn into a ranking of the states, which is nothing but a ranking of costs of living. (We don't know, but presume, that the cost of living computation is appropriate for retirees, and not averaged.) This order obscures any spatial correlation. There are a few production errors in the first row in which the year and month numbers are misstated slightly; the numbers should be monotonically decreasing. In terms of years and months, the difference between many states is immaterial. The pictogram format is more popular than it deserves: only highly motivated readers will count individual dots. If readers are merely reading the printed text, which contains all the data encoded in the dots, then the graphic has failed the self-sufficiency principle - the visual elements are not doing any work.
In my version, I surface the spatial correlation using maps. The states are classified into sensible groups that allow a story to be told around the analysis. Three groups of states are identified and separately portrayed. The finer variations between states within each state group appear as shades.
Data visualization should make the underlying data easier to comprehend. It's a problem when the graphic is harder to decipher than the underlying dataset.
Ray Vella (link) asked me to comment on a chart about regional wealth distribution, which I wrote about here. He also asked students in his NYU infographics class to create their own versions.
This effort caught my eye:
This work is creative, and I like the concept of using two staircases to illustrate the diverging fortunes of the two groups. This is worlds away from the original Economist chart.
The infographic does have a serious problem. In one of my dataviz talks, I talk about three qualifications of work called "data visualization." The first qualification is that the data visualization has to display the data. This is an example of an infographic that is invariant to the data.
Is it possible to salvage the concept? I tried. Here is an idea:
I abandoned the time axis so the data plotted are only for 2015, and the countries are shown horizontally from most to least equal. I'm sure there are ways to do it even better.
Infographics can be done while respecting the data. Ray is one of the designers who appreciate this. And thanks Ray for letting me blog about this.
Long-time follower Daniel L. sent in a gem, by the Washington Post. This is a multi-part story about the polarization of American voters, nicely laid out, with superior analyses and some interesting graphics. Click here to see the entire article.
Today's post focuses on the first graphic. This one:
The key messages are written out on the 2017 charts: namely, 95% of Republicans are more conservative than the median Democrat, and 97% of Democrats are more libearl than the median Republicans.
This is a nice statistical way of laying out the polarization. There are a number of additional insights one can draw from the population distributions: for example, in the bottom row, the Democrats have been moving left consistently, and decisively in 2017. By contrast, Republicans moved decisively to the right from 2004 to 2017. I recall reading about polarization in past elections but it is really shocking to see the extreme in 2017.
A really astounding but hidden feature is that the median Democrat and the median Republican were not too far apart in 1994 and 2004 but the gap exploded in 2017.
I like to solve a few minor problems on this graphic. It's a bit confusing to have each chart display information on both Republican and Democratic distributions. The reader has to understand that in the top row, the red area represents Republican voters but the blue line shows the median Democrat.
Also, I want to surface two key insights: the huge divide that developed in 2017, and the exploding gap between the two medians.
Here is the revised graphic:
On the left side, each chart focuses on one party, and the trend over the three elections. The reader can cross charts to discover that the median voter in one party is more extreme than essentially all of the voters of the other party. This same conclusion can be drawn from the exploding gap between the median voters in either party, which is explicitly plotted in the lower right chart. The top right chart is a pretty visualization of how polarized the country was in the 2017 election.
When I look at this chart (from Business Insider), I try to understand the decisions made by its designer - which things are important to her/him, and which things are less important.
The chart shows average salaries in the top 2 percent of income earners. The data are split by gender and by state.
First, I notice that the designer chooses to use the map form. This decision suggests that the spatial pattern of top incomes is of top interest to the designer because she/he is willing to accept the map's constraints - namely, the designer loses control of the x and y dimensions, as well as the area and shape of the data containers. For the U.S. state map, there is no elegant solution to the large number of small states problem in the Northeast.
Second, I notice the color choice. The designer provides actual values on the visualization but also groups all state-average incomes into five categories. It's not clear how she/he determines the boundaries of these income brackets. There are many more dark blue states than there are light blue states in the map for men. Because women incomes are everywhere lower than men, the map at the bottom fits all states into two large buckets, plus Connecticut. Women incomes are lower than men but there is no need to break the data down by gender to convey this message.
Third, the use of two maps indicates that the designer does not care much about gender comparisons within each state. These comparisons are difficult to accomplish on the chart - one must involuntarily bob one's head up and down to make the comparisons. The head bobbing isn't even enough: then you must pull out your calculator and compute the ratio of women to men average. If the designer wants to highlight state-level comparisons, she/he could have plotted the gender ratio on a single map, like this:
So far, I infer that the key questions are (a) the gender gap in aggregate (b) the variability of incomes within each gender, or the spatial clustering (c) the gender gap within each state.
(a) is better conveyed in more aggregate form. Goal (b) is defeated by the lack of clear clustering. (c) is not helped by the top-bottom split.
In making the above chart, I discover a pattern - that women fare better in the smaller states like Montana, Iowa, North & South Dakota. Meanwhile, the disparity in New York is of the same degree as Oklahoma and Wyoming.
This chart tells readers a bit more about the underlying data, without having to print the entire dataset on the page.
This chart by Axios is well made. The full version is here.
It's easy to identify all the Cat 5 hurricanes. Only important ones are labeled. The other labels are hidden behind the hover. The chart provides a good answer to the question: what time of the year does the worst hurricanes strike. It's harder to compare the maximum speeds of the hurricanes.
I wish there is a way to incorporate geography. I'd be willing to trade off the trajectory of wind speeds as the max speed is of most use.
Reader Berry B. sent in a tip quite some months ago that I just pulled out of my inbox. He really liked the Washington Post's visualization of the electoral college in the Presidential election. (link)
One of the strengths of this project is the analysis that went on behind the visualization. The authors point out that there are three variables at play: the population of each state, the votes casted by state, and the number of electoral votes by state. A side-by-side comparison of the two tile maps gives a perspective of the story:
The under/over representation of electoral votes is much less pronounced if we take into account the propensity to vote. With three metrics at play, there is quite a bit going on. On these maps, orange and blue are used to indicate the direction of difference. Then the shade of the color codes the degree of difference, which was classified into severe versus slight (but only for one direction). Finally, solid squares are used for the comparison with population, and square outlines are for comparison with votes cast.
Pick Florida (FL) for example. On the left side, we have a solid, dark orange square while on the right, we have a square outline in dark orange. From that, we are asked to match the dark orange with the dark orange and to contrast the solid versus the outline. It works to some extent but the required effort seems more than desirable.
I'd like to make it easier for readers to see the interplay between all three metrics.
In the following effort, I ditch the map aesthetic, and focus on three transformed measures: share of population, share of popular vote, and share of electoral vote. The share of popular vote is a re-interpretation of what Washington Post calls "votes cast".
The information is best presented by grouping states that behaved similarly. The two most interesting subgroups are the large states like Texas and California where the residents loudly complained that their voice was suppressed by the electoral vote allocation but in fact, the allocated electoral votes were not far from their share of the popular vote! By contrast, Floridians had a more legitimate reason to gripe since their share of the popular vote much exceeded their share of the electoral vote. This pattern also persisted throughout the battleground states.
The hardest part of this design is making the legend:
Reader Matt F. contributed this confusing chart from Wired, accompanying an article about Netflix viewing behavior.
Matt doesn't like this chart. He thinks the main insight - most viewers drop out after the first episode - is too obvious. And there are more reasons why the chart doesn't work.
This is an example of a high-effort, low-reward chart. See my return-on-effort matrix for more on this subject.
The high effort is due to several design choices.
The most attention-grabbing part of the chart is the blue, yellow and green bars. The blue and yellow together form a unity, while the green color refers to something else entirely. The shows in blue are classified as "savored," meaning that "viewers" on average took in less than two hours per day "to complete the season." The shows in yellow are just the opposite and labeled "devoured." The distinction between savored and devoured shows appears to be a central thesis of the article.
The green cell measures something else unrelated to the average viewer's speed of consumption. It denotes a single episode, the "watershed" after which "at least 70 percent of viewers will finish the season." The watershed episode exists for all shows, the only variability is which episode. The variability is small because all shows experience a big drop-off in audience after episode 1, the slope of the audience curve is decreasing with further episodes, and these shows have a small number of episodes (6 to 13). In the shows depicted, with a single exception of BoJack Horseman, the watershed occurs in episode 2, 3, or 4.
Beyond the colors, readers will consider the lengths of the bars. The labels are typically found on the horizontal axis but here, they are found facing the wrong way on pink columns on the right edge of the chart. These labels are oriented in a way that makes readers think they represent column heights.
The columns look like they are all roughly the same height but on close inspection, they are not! Their heights are not given on top of the columns but on the side of the vertical axis.
The bar lengths show the total number of minutes of season 1 of each of these shows. This measure is a peripheral piece of information that adds little to the chart.
The vertical axis indicates the proportion of viewers who watched all episodes within one week of viewing. This segmentation of viewers is related to the segmentation of the shows (blue/yellow) as they are both driven by the speed of consumption.
Not surprisingly, the higher the elevation of the bar, the more likely it is yellow. Higher bar means more people are binge-watching, which should imply the show is more likely classified as "devoured". Despite the correlation, these two ways of measuring the speed of consumption is not consistent. The average show on the chart has about 7 hours of content. If consumed within one week, it requires only one hour of viewing per day... so the average show would be classified as "savored" even though the average viewer can be labeled a binge-watcher who finishes in one week.
[After taking a breath of air] We may have found the interesting part of this chart - the show Orange is the New Black is considered a "devoured" show and yet only half the viewers finish all episodes within one week, a much lower proportion than most of the other shows. Given the total viewing hours of about 12, if the viewer watches two hours per day, it should take 6 days to finish the series, within the one-week cutoff. So this means that the viewers may be watching more than one episode at a time, but taking breaks between viewing sessions.
The following chart brings out the exceptional status of this show:
PS. Above image was replaced on 7/19/2017 based on feedback from the commenters. Labels and legend added.