A gem among the snowpack of Olympics data journalism

It's not often I come across a piece of data journalism that pleases me so much. Here it is, the "Happy 700" article by Washington Post is amazing.

 

When data journalism and dataviz are done right, the designers have made good decisions. Here are some of the key elements that make this article work:

(1) Unique

The topic is timely but timeliness heightens both the demand and supply of articles, which means only the unique and relevant pieces get the readers' attention.

(2) Fun

The tone is light-hearted. It's a fun read. A little bit informative - when they describe the towns that few have heard of. The notion is slightly silly but the reader won't care.

(3) Data

It's always a challenge to make data come alive, and these authors succeeded. Most of the data work involves finding, collecting and processing the data. There isn't any sophisticated analysis. But a powerful demonstration that complex analysis is not always necessary.

(4) Organization

The structure of the data is three criteria (elevation, population, and terrain) by cities. A typical way of showing such data might be an annotated table, or a Bumps-type chart, grouped columns, and so on. All these formats try to stuff the entire dataset onto one chart. The designers chose to highlight one variable at a time, cumulatively, on three separate maps. This presentation fits perfectly with the flow of the writing. 

(5) Details

The execution involves some smart choices. I am a big fan of legend/axis labels that are informative, for example, note that the legend doesn't say "Elevation in Meters":

The color scheme across all three maps shows a keen awareness of background/foreground concerns. 

When your main attraction is noise

Peter K. asked me about this 538 chart, which is a stacked column chart in which the percentages appear to not add up to 100%. Link to the article here.

Here's my reply:

They made the columns so tall that the "rounding errors" (noise) disclosed in the footnotes became the main attraction.

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The gap between the highest and lowest peaks looks large but mostly due to the aspect ratio. The  gap is only ~2% at the widest (101% versus 99%) so it is the rounding error disclosed below the chart.

The lesson here is to make sure you suppress the noise and accentuate your data!

 

 

Two nice examples of interactivity

Janie on Twitter pointed me to this South China Morning Post graphic showing off the mighty train line just launched between north China and London (!)

Scrolling down the page simulates the train ride from origin to destination. Pictures of key regions are shown on the left column, as well as some statistics and other related information.

The interactivity has a clear purpose: facilitating cross-reference between two chart forms.

The graphic contains a little oversight ... The label for the key city of Xian, referenced on the map, is missing from the elevation chart on the left here:

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I also like the way New York Times handled interactivity to this chart showing the rise in global surface temperature since the 1900s. The accompanying article is here.

When the graph is loaded, the dots get printed from left to right. That's an attention grabber.

Further, when the dots settle, some years sink into the background, leaving the orange dots that show the years without the El Nino effect. The reader can use the toggle under the chart title to view all of the years.

This configuration is unusual. It's more common to show all the data, and allow readers to toggle between subsets of the data. By inverting this convention, it's likely few readers need to hit that toggle. The key message of the story concerns the years without El Nino, and that's where the graphic stands.

This is interactivity that succeeds by not getting in the way. 

 

 

 

A chart Hans Rosling would have loved

I came across this chart from the OurWorldinData website, and this one would make the late Hans Rosling very happy.

If you went to Professor Rosling's talk, he was bitter that the amazing gains in public health, worldwide (but particularly in less developed nations) during the last few decades have been little noticed. This chart makes it clear: note especially the dramatic plunge in extreme poverty, rise in vaccinations, drop in child mortality, and improvement in education and literacy, mostly achived in the last few decades.

This set of charts has a simple but powerful message. It's the simplicity of execution that really helps readers get that powerful message.

The text labels on the left and right side of the charts are just perfect.

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Little things that irk me:

I am not convinced by the liberal use of colors - I would make the "other" category of each chart consistently gray so 6 colors total. Having different colors does make the chart more interesting to look at.

Even though the gridlines are muted, I still find them excessive.

There is a coding bug in the Vaccination chart right around 1960.

 

A look at how the New York Times readers look at the others

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.

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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.

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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!

 

Excellent visualization of gun violence in American cities

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.

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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.

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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.