An Ailing Graphic on the Healthcare Labour Force

I know I have said it before, but I like the increasing number of graphics-led articles published by Politico. Many policy and politics stories are driven—or should be driven—by data. But, myself included, we cannot hit it out of the park at every plate appearance. And that is what we have from Politico today, actually last week.

The graphic focuses on the healthcare industry and its need for a larger labour force in coming years as the baby boomers continue to age and start to retire. If their own doctors retire along with them, who will be their new doctors?

But there are two components of the graphic on which I want to focus. The first is the projection of the number of registered nurses (RNs) in 2024 compared to a 2014 baseline.

We need more. Just more.
We need more. Just more.

The story focuses on the future condition, but that colour is set to the lighter green thus drawing the reader’s eyes to the 2014 data point. Flipping those two colours would shift the focus of the chart to the 2024 timeframe, which would better match the text above.

Then we have the design decision to include a line chart for the growth rate, presumably total, for each category of RN from 2014 to 2024. The problem is that the chart itself does not sit on any baseline. While I do not care for the dual axis chart, that format at least keeps an axis legend on the right side of the chart. (You still have the problem of implying certain things based on what scale you choose to use relative to the first data series.) Here, because there is no chart lines associated with the growth data, I wonder if a table below the x-axis labels would be more efficient? Home health care, a very small category, will have the highest growth (a small change from a small base will beat the same small change or even slightly bigger changes from a far larger base) but the eye has the furthest to travel to reach the 61% number from the top of the bars or the labelling.

The other component I wanted to discuss is the scatter plot that compares the number of jobs to their average salary.

Bursting these bubbles…
Bursting these bubbles…

But this is a bubble chart, not a scatter plot, and so we have a third variable encoded in the size of the dot/bubble. The first thing I looked for was a scale for the size of the circles. What magnitude is the RN circle vs. the Personal Care Aides circle? There is none, but unfortunately that seems to be a common practice with bubble chart. But after failing to find that, I noticed that the circles decrease in size from right to left. That was when I looked to the legend and saw the y-axis in numbers of jobs and the x-axis in average salary. But then the circles are sized in proportion to the average salary of each profession to the other. In other words, the circles are basically re-plotting the x-axis. The physical therapist circle should be roughly twice as large, by area, than the vocational nurses. But we can also just see by the x-axis coordinates. The bubble chart-ness of the chart is unnecessary and the data could be told more clearly by stripping that away and making a straight-up scatter plot where all the circles are sized the same.

Credit for the piece goes to Christina Animashaun.

Trump’s Wall

Another day, another story about the administration to cover with data-driven graphics. We are approaching Trump’s 100th day in office, traditionally the first point at which we examine the impact of the new president. And well, beyond appointing a Supreme Court justice, it is hard to find a lot of things President Trump has actually done. But on his 99th day, he will also need to approve a Congressional bill to fund the government, or else the government shuts down on his 100th day. Not exactly the look of a successful head of state and government.

Why do I bring this up? Well, one of the many things that may or may not make it into the bill is funding for Trump’s wall that Mexico will pay for, but at an undetermined later date, because he wants to get started building the wall early, but late because he promised to start on Day 1.

Several weeks ago the Wall Street Journal published a fantastic piece on the current wall bordering Mexico. It examines the current state of fencing and whether parts of the border are fenced or not. It turns out a large portion is not. But, the piece goes on to explain just why large sections are not.

The wall today
The wall today

You should read the full piece for a better understanding. Because while the president says building the wall will cost $10 billion or less, real estimates place the costs at double that. Plus there would be lawsuits because, spoiler: significant sections of the border wall would cross private property, national parks, and Native American reservations. Also the southern border crosses varied terrain from rives to deserts to mountains some lengths of which are really difficult to build walls upon.

But the part that I really like about the piece is this scatter plot that examines the portion of the border fenced vs. the number of apprehensions. It does a brilliant job of highlighting the section of the border that would benefit most significantly from fencing, i.e. a sector with minimal fencing and a high number of apprehensions: the Rio Grande Valley.

Where would more fencing make more of a difference
Where would more fencing make more of a difference

And to make that point clear, the designers did a great job of annotating the plot to help the reader understand the plot’s meaning. As some of my readers will recall, I am not a huge fan of bubble plots. But here there is some value. The biggest bubbles are all in the lower portion of fenced sectors. Consequently, one can see that those rather well-fenced sectors would see diminished returns by completing the wall. A more economical approach would be to target a sector that has low mileage of fencing, but also a high number of apprehensions—a big circle in the lower right of the chart. And that Rio Grande Valley sector sits right there.

Overall, a fantastic piece by the Wall Street Journal.

Credit for the piece goes to Stephanie Stamm, Renée Rigdon, and Dudley Althaus.

 

Hans Rosling Has Died

It’s easy to miss the news these days. But as a designer who does a lot of work—and writes a blog about—data visualisation and information design, I was fortunate to catch the word that Hans Rosling died. You might know him best from his TED talks, but I became familiar with him through his Gapminder project.

Mind the gap, please.
Mind the gap, please.

Do I agree with the design decisions? Of course not, just ask anyone who has asked me anything about bubble charts. But that is not the point. He and others laid the groundwork for myself and those newer to the field to work on the presentation of data, and its integration into analysis.

Unfortunately his death comes at a time when the field of data visualisation comes under threat. Not from the Chinese stealing our jobs, or robots doing them better for cheaper, but from those who assail the veracity of data and fact itself.

It’s easy to joke about alternative facts and alternative data—I do it on an almost daily basis now. But, as Rosling knew that accepting facts, even if unpleasant or challenging to your view on things, was critical to public discourse. To quote from Claire Provost of the Guardian, who interviewed him in 2013:

“Rosling stood for the exact opposite – the idea we can have debates about what could or should be done, but that facts and an open mind are needed before informed discussions can begin.”

Hans Rosling, dead at the age of 68.

Credit for the piece goes to Hans Rosling.

Basketball Finals

So the basketball finals begin tonight with the Cleveland Cavaliers taking on the Golden State Warriors. This is also the part of the post where I fully admit I know almost nothing about basketball. I did, however, catch this so-labelled infographic from ESPN contrasting the two teams.

Point differential
Point differential

What I appreciate at this piece is that ESPN labelled it an infographics. And while the data might be at times light, this is more a data-rich experience than most infographics these days. Additionally the design degrades fairly nicely as your browser reduces in size.

The chart formats themselves are not too over-the-top (that seemed like a decent basketball pun when I typed it out) with bars, line, and scatter plots. Player illustrations accent the piece, but do not convey information as data-encoded variables. I quibble with the rounded bar charts for the section on each team’s construction, but the section itself is fascinating.

I might not know most of the metrics’ definitions, but I did not mind reading through the piece.

Go Red Sox.

Credit for the piece goes to Luke Knox and Cun Shi.

Growth of Inland Cities

Some of the nation’s fastest growing cities are inland, away from the coast where housing prices are high. To support an article about the demographic shift, the New York Times created this map. Circle size represents growth over a six-year period while the colour of the bubble represents housing prices.

Fastest growing cities
Fastest growing cities

Credit for the piece goes to the New York Times graphics department.

Coal vs. the Great Barrier Reef

Your humble author is away this week. But the Great Barrier Reef in Australia is still here. For now. The Guardian takes a look at the growing threat to the World Heritage site from the coal industry in Queensland, Australia. The author takes you through the narrative in a chapter format, using charts and maps to illustrate the points in the brief bit of text. A really nice job altogether.

Major ports and their volume
Major ports and their volume

Credit for the piece goes to Nick Evershed.

The Curse(s) of the CEOs

It’s Friday, so we should try to take things a bit lighter. For me that usually means knocking back a drink or two and a swear-y exultation about it being the end of the work week. But, it turns out, I’m just trying to emulate our captains of industry. Bloomberg has gone through company conference calls and tabulated the number of swear words used and charted the results. And for fun, you can read some of the excerpts.

They'll swear by it
They’ll swear by it

Credit for the piece goes to David Ingold, Keith Collins, and Jeff Green.

How Africa Tweets

Today’s piece is hit and miss. It comes from the World Economic Forum and the subject matter is the use of Twitter across Africa. I think the subject matter is interesting; mobile communication technology is changing Africa drastically. The regional trends shown in the map at the core of the piece are also fascinating. Naturally I am left wondering about why certain countries. Does spending on infrastructure, GDP per capita, disposable income levels have any sort of correlation if even only on a national and not city level?

How Africa tweets
How Africa tweets

But what really irks me is the content that wraps around the map. First the donut chart, I think my objections to donuts—at least the non-edible kind—are well known. In this case, I would add—or sprinkle on—that the white gaps between the languages are unnecessary and potentially misleading.

Secondly, the cities are eventually displayed upside down. Thankfully the labels are reversed so that city names are legible. However, the continually changing angle of the chart makes it difficult to compare Douala to Luanda to Alexandria. A neatly organised matrix of small multiples would make the data far clearer to read.

In short, I feel this piece is a good step in the right direction. However, it could do with a few more drafts and revisions.

Credit for the piece goes to Allan Kamau.

Ivory Poaching

The South China Morning Post had a fantastic infographic detailing the hunting of elephants for their ivory. Despite bans to make such hunting illegal, the problem continues and is worsening because of the Asian trade in ivory.

Cropping of the infographic
Cropping of the infographic

Credit for the piece goes to Adolfo Arranz.

The Young and the Educated

Today’s piece comes from the National Journal. It is an interactive bubble chart that compares the educated class of cities in 1980 to those in 2010 (educated meaning the share of population with at least a bachelor’s degree).

College graduate cities
College graduate cities

Not a whole lot to say about this one, in a good way. A nice summation at the top with clearly presented data below while annotations on the plot call out particular objects in the series worth noting. And then for those who want to find themselves, a drop down filter at the top allows users to select a particular city.

Credit for the piece goes to Brian McGill and Nancy Cook.