Unless you avoid the news, we all heard a lot about tariffs this weekend. So this morning, instead of going with some other things I found, I decided I wanted to look and see just what the data is on tariffs. Turns out Trump is wrong on the data about tariffs. In short, in 2016 the US had a slightly higher average tariff for all products at 1.61%. The EU was at 1.6%. And the Canadians? They charged an outrageous 0.8%.
The data comes from the World Bank.
And over breakfast, I did not really have the time to clean this graphic up, so it shows the whole world. Though it goes to show you, the western countries against which Trump raged this weekend generally have low tariffs, some lower than what the US.
Today is primary day and everyone will be looking to the California results. Although probably not quite me, because Eastern vs. Pacific time means even I will likely be asleep tonight. But before we get to tonight, we have a nice primer from last Friday’s New York Times. It examines the California House of Representatives races that we should be following.
Like most election-related pieces, it starts with a map. But it uses some scrolling and progressive data disclosure. The map above, after a bit of scrolling, finally reveals the districts worth following and their 2016 vote margins.
From there the article moves onto a bit of an exploration of those few districts. You should read the full article—it’s a short read—for the full context on the California votes today. But it does make some nice of bar and line charts to plot the differences in presidential race vs. congressional race margins and the slow Democratic shift.
Credit for the piece goes to Jasmine C. Lee and Karen Yourish.
We are inching ever closer to the US midterm elections in November. In less than a week the largest state, California, will go to the polls to elect their candidates for their districts. So late last week whilst your author was on holiday, the Economist released its forecast model for the results. They will update it everyday so who knows what wild swings we might see between now and the election.
I will strike out against the common knowledge that this is a wave election year and Democrats will sweep swaths through Republican districts in an enormous electoral victory. Because while Democrats will likely win more overall votes across the country, the country’s congressional districts are structurally designed to favour Republicans as a result of gerrymandering after the 2010 Census redistricting. The Economist’s modelling handles this fairly well, I think, as it prescribes only a modest majority and gives that likelihood as only at 2-in-3. (This is as of 30 May.)
But how is it designed?
The big splashy piece is an interactive map of districts.
It does a good job of connecting individual districts to the dots below the map showing the distribution of said seats into safe, solid, likely, leaning, and tossup states. However, the interactivity is limited in an odd way. The dropdown in the upper-right allows the user to select any district they want and then the district is highlighted on the map as well as the distribution plot below. Similarly, the user can select one of the dots below the map to isolate a particular district and it will display upon the map. But the map itself does not function as a navigation element.
I am unsure why that selection function does not extend to the map because clearly the dropdown and the distribution plot are both affecting the objects on the map. Redeeming the map, however, are the district lines. Instead of simply plopping dots onto a US state-level map, the states are instead subdivided into their respective congressional districts.
But if we are going so far as to display individual districts, I wonder if a cartogram would have been a better fit. Of course it is perfectly plausible that one was indeed tried, but it did not work. The cartogram would also have the disadvantage of, in this case, not exhibiting geographically fidelity and thus being unrecognisable and therefore being unhelpful to users.
Now the piece also makes good use of factettes and right-left divisions of information panels to show the quick hit numbers, i.e. how many seats each party is forecast to win in total. But the map, for our purposes, is the big centrepiece.
Overall, this is solid and you better bet that I will be referencing it again and again as we move closer to the midterms.
Credit for the piece goes to the Economist Data Team.
This piece will make a ton of sense to my British and European readers, likely less so to those of you from the States. The English Premier League has been not so great at finishing well let alone winning in the Champions League.
Super briefly, English football—soccer—has a whole bunch of teams that play at different levels. Kind of like the US minor leagues, but without the affiliation of minor league teams to major league teams. That is, every team for itself. The Premier League is the top rung. (Every year, the worst teams in the Premier League are dropped into the minors and the very best from the minors move up into the Premier League.) This league includes the ones even Americans have heard of: Manchester, Arsenal, Chelsea. And maybe even Liverpool. Liverpool is playing today to make it into the Champions League finals.
(Full disclosure: I always say if I had to pick an English team to follow it would be Liverpool. Why? Because they are owned by Fenway Sports Group, the same group that owns the Boston Red Sox.)
The thing is that as well known as many of these teams are, they have been faring not well in the Champions League, which is like the Premier League but of all European football. That is, the best teams from every top league in all of Europe compete for a European trophy. FiveThirtyEight explored some reasons why, but also included a nice graphic to showcase the relative failures of the Premier League teams.
The chart makes nice use of grouped bar charts showing the number of teams from each league at each stage of the playoffs. The designers made good use of labelling, especially at the top to indicate to which country each league belongs. My only question would be is whether these make sense from the top down, as they presently are, or if they would work better bottom up, in that the winning team has to climb their way to victory.
To be honest, I am not really sure which approach would work best. I think it might be even odds. Either way, Liverpool plays Roma later today.
Just a neat little piece today from FiveThirtyEight. They take a look at the potential impact of the Trump administration’s proposed tariffs on the farm vote in the United States. The screenshot of the table shows how the farm population compares to Trump’s margin of victory in 2016.
The three states at the top? The very same Pennsylvania, Wisconsin, and Michigan about which we hear so often. Yes, Pennsylvania does have large cities like Philadelphia and Pittsburgh, but agriculture is an important part of its economy. So if the tariffs or the reprisals to the tariffs have any significant impact on the livelihood of farmers, that could be enough, all things being equal, to flip those states.
About the design, I think the inclusion of the mini-bar chart helps tremendously. Tables are great for organising information, but scanning over and through cell after cell of black text can hide patterns. The visualisation of those patterns at the end of each row helps the user tremendously, by making it very clear why those three states were highlighted.
Credit for the piece goes to Rebecca Shimoni Stoil.
Monday was the birth of the newest British prince. We covered that here. Interestingly, the Economist then covered the cost of giving birth. No surprise, those involving royals tend to be costly. But I did not think that the average American birth actually cost more.
Credit for the piece goes to the Economist’s graphics department.
On Monday I read, in print, part of a page one article in the Times. I ran out of times given the whole new royal baby coverage, and opted to read the rest digitally. Originally, this was just for my own enjoyment as there were no graphics in the article.
But this one appeared online.
I clearly have nothing to compare it to in print, which is a shame because this is a nice graphic with one thing I really wanted to point out. Although, maybe a print version would not have had the thing I will get to. But maybe there just wasn’t space in the print edition or they tried to make it work, but the colours or layout wasn’t working. Who knows.
When I saw the digital version, the line chart struck me as particularly nice. Now, maybe the Times has been doing this for a little while and I have missed it, but notice the highlighted line, Rural public. Yes the line is thicker or bolder than the others, but more importantly it has a thin white stroke attached that helps separate it from the lines behind it. Those lines are important for context, but not necessarily to tell the story of how rural public servant jobs have been hit the hardest.
You often see this kind of approach taken with maps. Don’t believe me? Take a look at Google Maps as one example. Their text often has a thin white outline to make it stand out from the content of the map. I just have never seen the logic applied to a line chart.
I doubt the design would hold up in a number of other scenarios. For example, a straight line chart with no line highlighted in particular, the spaghetti-ness mess would make the above a largely white line chart. Too much overlap. And a simple comparison, say of two lines, probably is clear enough that the approach is not necessary. But in scenarios like these where the highlighted series is important, the choice clearly works.
On a much smaller note, check out the x-axis labels. They are used only once for the first chart. And then because the bar charts and line charts align, they carry through straight down the rest of the piece. Very efficient.
I only wish I knew how this would have appeared in print…
Credit for the piece goes to the New York Times graphics department.
So two weeks ago I posted about the graphics in a BBC article about how London has surpassed New York in terms of murders, due to a spate of stabbings in the British capital. Well, somehow I missed this: an article from the Economist that rebuts that point. And it does it brilliantly.
Lies, damned lies, and statistics.
I think everybody who works with data knows that adage. Now, I am not using it to say that the BBC—or the numerous other media outlets that ran the story—lied. Just that it is easy to change the story based on the data, how it is presented, or which subsets of the data are selected.
The Economist’s article points out that the surpassing of New York is a short term data point, a worrying short term trend, definitely, but they then look at the data. They select two timeframes and look at them side-by-side.
And that is what I love about this piece. It shows the long-term context of New York having a far-higher medium-term history of murder (some 28 years of data is shown). When I was growing up in the 90s, murders in New York—and to be fair almost all large American cities—was just something that was a known fact. During that time, London hovered below 200 or so, compared to the 2000+ in early 90s New York.
But then they also show the short term, which does point to a steady rise in London murders. But, the data could also show a one-time dip in the murders in New York. But they also show that the total number of deaths is still higher in New York than London, despite the three months of data.
Murder is not good. But these graphics are a good example of how selecting different time series for the same data set, and then showing which parts of the data to show. The earlier BBC piece, and my revision of it, did not show the total deaths. Nor did either piece show the longer timeline of data.
Credit for the piece goes to the Economist graphics department.
Yesterday we looked at the shrinking Denver Post. Today we have a graphic from a related story via Politico. The article explores the idea that President Trump performs better in what the article terms “news deserts”, those counties with a very low level of newspaper circulation. (The article explains the methodology in detail.) This piece we are looking at here shows how those counties performed against the circulation rate and their 2016 presidential election result.
Overall, the work is solid. But I probably would have done a few things differently. First, the orange overlay falls in the middle of one column of dots. Do those dots then fall inside or outside the categorisation of news desert?
Secondly, the dots. If this were perhaps a scatter plot comparing the variables of circulation rates and, perhaps, election vote results as a percent, dots would be perfect. Here, however, they create this slightly distracting pattern in the the main area of counties. When the dots are stacked neatly and apart from other columns, as they are more often on the right, the dots are fine. But in the packed space on the left, not as much.
As I was reading through the article I had a couple of questions. For example, couldn’t the lack of newspapers be reflective of the urban–rural split or the education split, both of which can be seen in the same election results. Thankfully the article does spend time going through those points as well. It is a bit lengthy of a read—with a few other perfectly fine graphics—but well worth it.
I know I’ve looked at the Times a few times this week, but before we get too far into the next week, I did want to show what they printed on Saturday.
It is not too often we get treated to data on the front page or even the section pages. But last Saturday we got just that in the Business Section. Two very large and prominent charts looked at federal government borrowing and the federal deficit. Both are set to grow in the future, largely due to the recently enacted tax cuts.
The great thing about the graphic is just how in-the-face it puts the data. Do two charts with 14 data points (28 total) need to occupy half the page? No. But there is something about the brashness of the piece that I just love.
And then it continues and the rest of the article points, at more normal sizes, to treasury bill yields and car loan rates. The inside is what you would expect and does it well in single colour.