If this week’s news cycle cooperates, I am going to try and catch up on some things I have seen over the last several weeks that got bumped because of, well, Trump usually. Today we start with a piece on life expectancy from FiveThirtyEight.
The piece begins with a standard choropleth to identify, at county levels, pockets of higher mortality. But what I really like is this small multiples map of the United States. It shows the changes in life expectancy for all 50 states. And the use of colour quickly shows, for those states drastically different than the national average, are they above or below said average.
Credit for the piece goes to the FiveThirtyEight graphics department.
Today we look at income in American cities and in particular the middle class disappearance. The Guardian published the graphics, but they originate with Metrocosm, LTDB at Brown, and IPUMS National Historical Geographic Information System. So what are we looking at? Well, the big one is a set of small multiples of cities and their income breakdowns as percentages of city census tracts. This screenshot is static, but the original is an animated .gif.
I have a few issues with the design of the graphic, the most important of which is the colour palette. If the goal is to focus on the decline of the middle class—and I admit that may be the point of the Guardian’s authors and not the original authors—why are the most visually striking colours at the top of the income distribution. Instead, you would want to draw attention to the middle of each chart, not the right. And if the idea was that the darker colours represent the higher income groups, well the positioning of each bar on the chart and the axis labelling does that already. After all, if anything, the story is that in a number of cities the middle class has shrunk while the lower income groups have grown. And you can barely see that with the lower income groups coloured yellow.
My other issues are more minor design things such as the city labelling. I kept reading the label as being below the bars, not above as it actually is.
And then I wonder if a different chart form would be more effective at showing the decline in the middle class. Perhaps a line chart plotting the beginning and end points for each cohort?
Then the piece gets into some three-dimensional maps that you can spin and rotate.
Yeah. Shall I count the ways? A more conventional choropleth would have served the purpose far more effectively. The dimensionality hides lower income tracts behind higher ones. The solution? Allow the user to rotate and spin the map? No, get rid of the dimensionality. It offers little to the understanding of the underlying data. Not to mention, are the areas of shadows shadows? Or are they another bin or cohort of income?
And then you have to read the piece to get a fuller understanding of my criticism.
But don’t worry, I can quote it.
Chicago was largely successful transitioning away from manufacturing to a service-based economy. This shift is evident in the bifurcated pattern present in 2015 – a heavy concentration of wealth in the business/financial district and marked decline in the surrounding area.
Those of you who read this blog from Chicago or who have lived in Chicago will pick up on it. The rest of you not so much. The concentration of wealth is not located in the business/financial district. Those dark red skyscrapers are not actual skyscrapers, they are census tracts located not in the financial district, but the areas of River North, Old Town, Gold Coast, &c. Thinking of the issue more logically, yes incomes are up in cities that are doing well. But how many of those very wealthy live on the same block as their office? Not many. Your higher income is going to be concentrated in residential or mixed-residential neighbourhoods near, but not in the business/financial district.
The data behind this work fascinates me. I just wish the final graphics had been designed with a bit more consideration for the data and the stories therein. And a little bit of proper understanding of the cities and their geography would help the text.
Credit for the piece goes to Metrocosm, LTDB at Brown University, and IPUMS National Historical Geographic Information System.
Yesterday, President Trump asked why there had been no discussion about the causes of the Civil War.
No, that is not a joke.
Well, Mr. President, turns out that there has been quite a bit of discussion over the last few years. And the broad consensus?
Note the above, with the darker shaded counties representing those with greater percentages of the population held in slavery. What do most of those states have in common with the Confederacy? That they are in the Confederacy.
To be clear, the Union was not perfect. Delaware, Maryland, Kentucky, and Missouri remained part of the Union, but were states where slavery was legal. In fact both Kentucky and Missouri had two governments. Kentucky provides a great example of the fault line with the pro-Union capital of Frankfort situated in the low-slavery east whereas the Confederate capital was located in western, high-slavery Kentucky.
But the point stands. Slavery was the link between Confederate states and Confederate-aligned parallel governments in Union states. So, Mr. President, when you are asked about the cause of the Civil War, now you know the answer.
Credit for the piece goes to E. Hergeshimer of the US Census Bureau.
Wow do we have a lot to talk about this week. Probably bleeding into next week to be honest. But, last night was the special election for the Georgia 6th.
For those of you not following politics, the congressman representing it was Tom Price; he is now the Secretary of Health and Human Services. Consequently, Georgia needed to elect a fill-in for the Atlanta-suburbs district. That election was between 18 candidates last night. The race could have been won outright, but it would have required a vote total over 50%.
That did not happen—and realistically with 18 people running was not likely. But, Democrats hoped they could get their candidate in at 50+%.
This screenshot is from a nice piece by the New York Times. As you all know by now, I am not a huge fan of choropleth maps. They distort geographic area and population. But, I like the arrangement of these small multiples. It does a nice job of comparing the results for the five major candidates. I particularly like the addition of the 2016 presidential election result. With the cratering poll approvals of Donald Trump, could some of the paler red precincts flip in June?
The above screenshot comes from BuzzFeed, whose coverage I followed via live streaming last night. They used a cartogrammic approach, assuming that cartogrammic is actually a word. The colours could use a bit more sophistication—the best example being the Democratic–Republican margin map where the blues are darker than the reds and have a hopefully unintended greater visual weight.
We are going to have a busy week this week. From the CBO release on Trumpcare costs and coverage to the elections in the Netherlands. Oh, and it might snow a wee bit here in Philadelphia and the East Coast. So let’s dive straight into today’s post, an article all the way from the West Coast and the LA Times.
It looks at a comparison between Trumpcare and Obamacare.
The clearest takeaway is that they are using some pretty good colours here. Because purple.
But in all seriousness, the takeaway from this graphic is that Trumpcare as proposed will cost more for the poor and the elderly. And it will cost especially more for those who live in rural and more isolated areas. And that basically comes down to the fact that Trumpcare will not factor in the local cost of insurance, which generally costs more in non-urban areas.
But for the fullest understanding of the differences, you should read the full piece as it offers a point-by-point comparison.
Credit for the piece goes to Noam N. Levey and Kyle Kim.
Alternatively known as the zombie food map. Sorry, but I couldn’t resist that one. Today we look at a piece from Bloomberg that maps brain drain across the country. What is brain drain? Basically it is the exodus of people with advanced degrees and education employed in science-y industries and fields. So this map shows us where the brains are moving from and where they are moving to.
Credit for the piece goes to Vincent Del Giudice and Wei Lu.
By just a hair under 20 percentage points, Italian voters—with a 70% turnout rate—voted down the reform package of soon-to-be-former Prime Minister Matteo Renzi. While the election was focused narrowly on a set of political reforms for Italian government, e.g. reducing the number of senators, the vote was unofficially seen by many as a test of the strength of anti-establishment populists in Europe. Note wins by such groups in Brexit and Donald Trump. In Europe this is a particularly important barometer reading because of 2017 elections in the Netherlands, France, and then Germany.
I had been looking for some online results trackers, in English, last night but found little. There was, however, this page from Bloomberg. The key thing for me is the link between the regions on the map and the section on the bar chart.
Credit for the piece goes to Bloomberg’s graphics department.
Today’s post is a choropleth map from the Washington Post examining diversity in the United States and how fast or slow diversity is expanding. Normally with two variables one goes instantly to the scatter plot. But here the Post explored the two variables geographically. And it holds up.
The colours are perhaps the only part holding me up on the piece’s design. Are blue and yellow the best two colours to represent level of diversity and growth? I lose some of the gradation in the yellows, especially between the big increases in diversity. Can I offer a better solution? No, and maybe there is not. But I would love the chance to explore different palette options.
As you well know, I am not a big fan of always plotting things on maps. I call them the silver bullet. However, in this instance, there are clear geographic patterns to the four different scenarios. Of course this soon after the election I would love adding a third variable: how the counties voted in the presidential election. Maybe next time.
Credit for the piece goes to Dan Keating and Laris Karklis.
In politics, it is really easy and often popular to bash the federal government. Especially when it comes to its penchant for collecting taxes to pay for things. And sometimes those things are in other states than your own. But do you know how much federal money goes back to your own state? Well now you can thank the Pew Charitable Trusts for putting together this piece that explores what percentage of state budgets is comprised of federal grant money.
While the piece also includes a donut chart—because why not?—my biggest gripe is with the choropleth and the choice of colour for the bins. If you look carefully at the legend, you will see how both the lowest and highest bins use a shade of blue. That means blue represents states that receive less than 25% of their budget from federal grants and also states that receive more than 40% of their budget from the same federal grants. But if your state is between 25% and 40%, your state suddenly turns a shade of green. It really makes no sense. I think the same colour, either blue or green, could be used for the entire spectrum. Or, if the designers really wanted a divergent scheme, they could have used the national average and used that as the breakpoint to show which states are above and which are below said average.
Credit for the piece goes to the Pew Charitable Trusts graphics department.
One of the things I like about Chicago’s WGN network is its weather blog. They often include infographic-like content to explain weather trends or stories. But as someone working in the same field of data visualisation and information design, I sometimes find myself truly confused. That happened with this piece last Friday.
The map in the upper-right in particular caught my attention and not in the good way. The overall piece discusses the heavy rainfall in the Chicago area on Thursday and the map looks at the percentage increase in extreme weather rainfall precipitation. All so far so good. But then I look at the map itself. I see blue and thing blue > water > rainfall. The darker/more the blue, the greater the increase. But, no—check out Hawaii. So blue means less rainfall. But also no, look at the Midwest and Southeast. So does green mean anything? Beyond being all positive growth, not that I can tell. As best I can tell, the colour means nothing in terms of rainfall data, but instead delineates the regions of the United States—noting of course they are not the standard US Census Bureau regions.
So here is my quick stab at trying to create a map that explains the percentage growth. I have included a version with and without state borders to help readers distinguish between states and regions.
And what is that at the bottom? A bar chart of course. After all, with only eight regions, is a map truly necessary especially when shown at such an aggregate level? You can make the argument that the extreme rainfall has, broadly speaking, benefitted the eastern half of the United States. But, personally speaking, I would prefer a map for a more granular set of data at the state or municipality level.
Credit for the piece goes to Jennifer Kohnke and Drew Narsutis.