Here in Pennsylvania this week, the state Supreme Court will hear arguments on the legality of congressional districts drawn by Republicans in 2010. The state is rather evenly split between Republicans and Democrats, e.g. Donald Trump won by less than one percentage point or less than 45,000 votes. But 13 of its 18 congressional districts are represented by Republicans, roughly 72%.
This graphic is from the New York Times Upshot and it opens a piece that explores gerrymandering in Pennsylvania. The graphic presents the map today as well as a nonpartisan map and an “extreme” gerrymander. The thing most noticeable to me was that even with the nonpartisan geography, the Democrats are still below what they might expect for a near 50-50 split. Why? One need only look at Philadelphia and Pittsburgh where, using the Times’ language, the Democrats “waste” votes with enormous margins, leaving the suburban and rural parts of the state open for Republican gains.
Credit for the piece goes to Quoctrung Bui and Nate Cohn.
Last week I covered a lot of Red Sox data. And your feedback has been fantastic. I think you can look forward to more visualisation of sportsball data. But since this is not a sports blog, let us dive back into some other topics. Like today’s piece on economic growth.
It comes from the Economist and explores the development history of national economies relative to that of the United States. The point of the chart was to illustrate what the researchers determined was the middle income trap, a space in which countries develop and become semi-rich, but then can never quite escape.
The Economist makes the point that the definition of middle income matters. The range is enormous and one statistic says that it could take 48 years to graduate at a healthy rate of economic growth. I wonder is this bit, however, could also have been charted. The show don’t tell mantra works well here for setting up the problem, but a chart or two showing that exact range could have supplemented the text and perhaps made it more digestible.
Credit for the piece goes to the Economist Data Team.
Less than a week after posting about the satellite views showing entire villages razed to the ground, we have a piece from the Economist looking at refugee outflows. And they are worse than the outflow of refugees during the Rwandan genocide back in 1994.
To be clear, they are not saying that nearly a million people have been killed—though there is quite a bit of evidence to say the Burmese security forces are cleansing the state of Rakhine of one of its primary ethnic groups.
But when it comes to the chart, I am not quite sure what I feel about it. It uses both the x and y axis to show the impact of the refugee outflow. But the problem is that we are generally rubbish at comparing areas. Compounding that, we have the total number of refugees represented by circles, another notorious way of displaying areas. (Often people will confuse the circle’s area with its radius or diameter and get the scale wrong.)
I wonder, would a more straight forward display that broke the dataset into two charts would be clearer? What if the designers had kept the Marimekko-like outflow display, but represented each crisis and its total outflow as a straight bar chart to the right of the timeline? (I do think the timeline is particularly good context, especially since it highlights the earlier persecution of the Rohingya.)
Credit for the piece goes to the Economist’s Data Team.
Well it finally happened. While the Great Recession spared Philadelphia for several years, Phoenix has finally moved up into the rank of fifth-largest city in the United States.
There are some notable differences that this graphic captures. The big one is that Philly is relatively small at 135 square miles. Phoenix is half the size of Rhode Island. What the graphic does not capture, however, is that Philly is still growing, albeit more slowly than southern and western cities. Because also in the news is the fact that Chicago has shrunk and lost people. Personally I count as a -1 for Chicago and a +1 for Philly.
Credit for the piece goes to the Philly.com graphics department.
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.
Quite a few things to look at this week. But I want to start with something that caught my attention last Friday. The Economist produced this graphic about the top-50 cities by the always pleasant metric of homicide. I bring it up because of the oft mentioned capital of carnage here in America: Chicago. (To which I’m briefly returning late this week.)
Note which city is not on that list: Chicago.
Some countries, sadly El Salvador, Honduras, and Mexico, are among those expected on that list. But the United States is the only rich, industrialised nation present. Unfortunately this is not a list on which we should aspire to be.
The graphic itself does a few nice things. In particular, I like the inclusion of the small multiple national rate to the left of the cities. Because, obviously, high murder rates are not great in El Salvador, but on the plus side, they are down of late. And the same small multiples do go a long way to show that, in general, despite what the administration says, homicide rates in the United States are quite low by these standards.
My quibble with the graphic? Breaking out cities by country. Yeah, it does make a lot of sense. But look at that country listed two spots below the United States: Puerto Rico. I am not here going to get into the whole Puerto Rican statehood vs. sovereignty argument, but suffice it to say that it is a part of the United States.
Credit for the piece goes to the Economist’s graphics department.
As much as I like trains…we need to get back to Trumpcare. As you probably know, it will cover fewer people than Obamacare. Just how many fewer people? Somewhere in the ten to twenty million range. The poor, the elderly, and the sick are those who will be worse off. Because the poor, the elderly, and the sick are the ones who clearly do not need healthcare. Higher-income young people, your subsidies are about to go up.
But I digress, the Los Angeles Times looked at county electoral and tax data to see just where the pain falls geographically, and more importantly where it falls politically. So they took a look specifically at the bracket that will be hurt the most: the poor and elderly, 60 and earning $30,000.
Well, it looks like all those people who voted against the idea of Obamacare just voted themselves to get even less assistance. Trumpcare’s going to be great, guys. Unless you’re old. Or poor. Or sick.
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.
Well, so about that whole Michael Flynn furore thing I wrote about yesterday…. Time to add another name to the list of people to be appointed—as I said, that post isn’t confirmed, merely appointed.
But today is Valentine’s Day. So for all you lovebirds out there, here are some graphics showing how rate of marriages has declined in the United States.
It does a real nice job of presenting the overall national view, but then breaking that down into a state-by-state comparison over time, the small multiples shown below.
My critique would be the labelling. Note how the state label appears above the chart, but how when stacked in a row, the label for the state below appears far closer to the chart above. The first few times I looked at this, I saw the label for the chart as being below. And I was therefore curious why Kansas was so different from the rest of the plains state. It just goes to show you how important spacing and layout can be on the page.