My battery is about to die this morning and I don’t have my charger so this is going to be a shorter piece than usual. But I wanted to look back on the 100 Day polling that the New York Times posted. It does paint an interesting picture of somebody so polarising that Trump is probably safe despite being one of the least favourably viewed presidents in modern times. Why? Because his supporters are so fervently loyal.
But that piece is almost a month old now. And so I wanted to point out something that FiveThirtyEight is doing—a running tracker of Trump’s polling. I am sure I will return to it in the future, after all we have over three and a half years to go until the next four year presidential term begins.
Credit for the piece goes to Karen Yourish and Paul Murray for the Times and Aaron Bycoffe, Dhrumil Mehta, and Nate Silver for FiveThirtyEight.
As you know, I am a sucker for military-related things. So here we have a piece from the Wall Street Journal on the leading fighter jets of the world. If you have a bone to pick on which jets were included, please take that up with them and not me.
The screenshot is from the end of an animation where they depict the maximum range and the relative speed of each aircraft against each other.
Credit for the piece goes to Andrew Barnett, Jason French, and Robert Wall.
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.
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.
Well have we got an interesting week this week. Friday begins Trump Time. So hold onto your Twitter accounts, folks. But before we get there, I wanted to do a short week of some data-driven graphics that take a look at the state of things.
Instead of what I had intended for today, let us take a look at a new post from the Wall Street Journal that examines GDP, inflation, industrial production, and the unemployment rate in advanced economies. At its most basic level, the graphics show how many of the 39 advanced economies have a value within a one-percentage point range. The size of the dots indicates how many countries fall within the bin.
What keeps getting me, however, is the colour. Nowhere does the piece explain what the colour represents. Does it represent anything? I think it might only be used to show the ranges in the values, not the number of countries sharing said values. And if that is the case, it is a poor design decision.
My eye goes to the colour first before it goes to the dot density let alone the size of the dots. Like a Magic Eye, when I stare at the piece long enough, I begin to see the overall trend for each metric. But blink and the colours reassert their visual dominance.
I wonder what would happen if the graphic settled on a single colour? My instinct says that the patterns would become far clearer, because colour change would no longer be a visual pattern needing interpretation—even though it needs no interpretation from a data standpoint. By limiting the number of visual patterns, the piece would make the data stand out more clearly and make for clearer communication.
If an editor screams something like “It needz more colourz!!1!”, I would reserve four separate colours and then use one and only one for each of the four metrics.
That all said, what the piece does really well is explain segments of the data. In the above screenshot, you can clearly see and get the overall GDP story. But then from there you read down and get explanations or callouts of the overall to provide more context and information. The designer greys out the remainder of the dots and allows the colour to emphasise those countries in focus. A lightly transparent overlay allows for the background dots to remain faintly visible while the text can clearly be read.
All in all, I am not sure where I fall on this particular piece. It does some things well, others not so much. But either way, the piece does paint an interesting portrait of populism’s potential causes.
In my new role as data visualisation manager at the Philadelphia Federal Reserve, I am learning a lot about what the Fed does and how it does it. Needless to say, this piece from Bloomberg interested me as it displayed how the federal funds rate has changed over time.
What I really enjoy is how they colour-coded the two previous hiking cycles as well as what I think everyone presumes will be a new one. And those colours then move on down the piece into the dot plots. The dot plots show various potential factors in the decision-making process, and just how far off the current hiking cycle is from the two previous.
Credit for the piece goes to Chloe Whiteaker, Jeremy Scott Diamond, and Jeanna Smialek.
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.
Boston Beer Company is the parent company of Sam Adams, which is definitely one of those beers I imbibe when I visit Boston. But, as one of the larger craft brewers in the United States, it finds itself under immense competition. This article from Bloomberg examines the situation the brewery finds itself in from a share price, growth, and revenue standpoint.
Credit for the piece goes to the Bloomberg graphics department.
You clearly didn’t miss this story from two weeks ago, because we all had to change our clocks. But, you might not have thought much about it. Which is fine, because I think there was an election or something a day or two later. Or was I dreaming/nightmaring?
Thankfully Andy Woodruff did think about it and he put together a really nice piece about how the changes to time affect the amount of perceived sunlight. I say perceived because obviously the same amount of sunlight falls upon the Earth, but it’s whether we can see it from underneath the covers or hidden behind our office computer monitors.
His interactive piece lets you examine scenarios based on your preferred inputs. For example, as someone who goes to work a bit later in the morning—I have to write this blog sometime, right?—I would prefer the sun to be up later into the evening. And based on my selections, that means that I should consider the argument for always using Daylight Savings Time.
Whereas if I valued a sunrise with daylight, I might prefer to abolish Daylight Savings Time.
Well this is it. Well at least for you American readers of this blog. It’s Election Day. If you had told me that this is what it would come to almost a year and a half ago, I would have laughed. But it did. And now it comes down to all of us to vote, unless unlike me you live in a state with early voting. And then when the polls begin to close, nerds of the political and data persuasion will be following the results in state, counties, and congressional districts.
And we will be following it all because not all the people on the ballots are named Trump or Clinton. I lived eight years in Illinois. There, you guys are, among others, choosing between Kirk and Duckworth. Here in Pennsylvania, it’s between Toomey and McGinty. Here there is also a referendum on judicial retirement ages. Other districts, counties, and states will have other things upon which to vote.
And while local politics and governance impact us the most, let’s face it. We’re all here for the title fight. The heavyweight class: Trump v. Clinton. So today being Election Day, how is it going to turn out? Well I have my thoughts, check them out here, but who really knows? But who also doesn’t want to try and guess? Enter the New York Times. They have a great interactive decision tree that allows you to experiment. But even without selecting a thing you can see how much more likely a Clinton victory is. She simply has more paths to 270 electoral college votes.
But that all said, a Clinton victory is far from guaranteed. If the narrow polls are wrong in any one of her “firewall” states, Trump can win. And while it may seem forever ago, remember Bernie Sanders in Michigan? The polls had him down by at least five points to Clinton throughout the race. He won the state by two points. Now a seven point swing is a bit extreme, and I am not suggesting any state will be in that much error. But three to four points is very plausible. And Clinton’s leads? In many of these states, they are within that uncomfortable margin. So here is a plausible scenario that makes tiny New Hampshire and its four votes the deciding state.
So remember, if you haven’t already, go vote. And if I learned anything from Chicago, it’s vote once, vote often.
Credit for the piece goes to the New York Times graphics department.