Stepping away from both the Brexit drama and the aircraft drama of the week, let’s look at US political drama. Specifically, the Democratic field and some of the early support for candidates and assumed-to-be candidates.
This piece comes from an article about the bases of various candidates. From a data visualisation perspective it uses a scatter plot to compare the net favourability of the candidate to the share of people who have an opinion about said candidate.
But what if you don’t know who the candidate is? As in, you don’t know what they look like. Well, then it might be difficult to find Bernie or Elizabeth Warren. This kind of graphic relies on facial recognition. I’m not certain that’s the best, especially when one is talking about a field in which people may not know or have an opinion on the candidates in question.
Another drawback is that the sizes of the faces are large. And, especially in the lower left corner, this makes it easier to obscure candidates. Where exactly is Sherrod Brown? Between a unidentified face and that of Terry McAuliffe.
I think a more simplistic dot/circle approach would have worked far better in this instance.
Credit for the piece goes to the FiveThirtyEight graphics department.
As many of you know, genealogy and family history is a topic that interests me greatly. This past weekend I spent quite a bit of time trying to sort through a puzzle—though I am not yet finished. It centred on identifying the correct lineages of a family living in a remote part of western Pennsylvania. The problem is the surname was prevalent if not common—something to be expected if just one family unit has 13 kids—and that the first names given to the children were often the same across family units. Combine that with some less than extensive records, at least those available online, and you are left with a mess. The biggest hiccup was the commonality of the names, however. It’s easier to track a Quinton Smith than a John Smith.
Taking a break from that for a bit yesterday, I was reminded of this piece from the Economist about two weeks ago. It looked at the individualism of the United States and how that might track with names. The article is a fascinating read on how the commonness or lack thereof for Danish names can be used as a proxy to measure the individualism of migrants to the United States in the 19th century. It then compares that to those who remained behind and the commonness of their names.
The scatter plot above is what the piece uses to introduce the reader to the narrative. And it is what it is, a solid scatter plot with a line of best fit for a select group of rich countries. But further on in the piece, the designers opted for some interesting dot plots and bar charts to showcase the dataset.
Now I do have some issues with the methodology. Would this hold up for Irish, English, German, or Italian immigrants in the 19th century? What about non-European immigrants? Nonetheless it is a fascinating idea.
Credit for the piece goes to the Economist Data Team.
On Tuesday the San Diego Padres signed Manny Machado to a guaranteed contract worth $300 million over the next ten years—though he can opt out after five years. Machado was one of two big free agents on the market, the other being Bryce Harper. One question out there is whether or not these big contracts will be worth it for the signing teams. This piece yesterday from the New York Times tries to look at those contracts and how the players performed during them.
Like the piece we looked at Tuesday, this takes a narrative approach instead of a data exploratory approach—the screenshot above is halfway through the read. Unlike the Post piece, this one does not allow users to explore the data. Unlabelled dots do not reveal the player and there is no way to know who they are.
Overall it is a very strong piece that shows how large and long contracts are risky for baseball teams. The next big question is where, for how long, and how much will Bryce Harper sign?
Credit for the piece goes to Joe Ward and Jeremy Bowers.
Yesterday we looked at the wildfire conditions in California. Today, we look at the Economist’s take, which brings an additional focus on the devastation of the fires themselves. However, it adds a more global perspective and looks at the worldwide decline in forest fires and both where and why that is the case.
The screenshot here focuses on California and combines the heat and precipitation we looked at yesterday into a fuel-aridity index. That index’s actual meaning is simplified in the chart annotations that indicate “warmer and drier years” further along the x-axis. The y-index, by comparison, is a simpler plot of the acres burned in fires.
This piece examines more closely that link between fires and environmental conditions. But the result is the same, a warming and drying climate leaves California more vulnerable to wildfires. However, the focus of the piece, as I noted above, is actually on the global decline of wildfires.
Only 2% of wildfires are actually in North America, the bulk occur in Africa. And the piece uses a nice map to show just where those fires occur. In parallel the text explains how changing economic conditions in those areas are lessening the risk of wildfire and so we are seeing a global decline—even with climate change.
Taken with yesterday’s piece with its hyper-California focus, this provides a more global context of the problem of wildfires. It’s a good one-two read.
Credit for the piece goes to the Economist Data Team.
I like to think that becoming a good designer requires lots of work. And that means different types of work. Work pushing you to learn new skills. So this graphic by Jessica Hagy over on Indexed makes perfect sense. How good you at something ties into how much you work at it.
Today is the semifinal match between England and Croatia. I could have posted this yesterday, but the US Supreme Court selection seemed more important. But today’s post is a simple scatter plot from FiveThirtyEight. It is part of a broader article comparing the four semifinalists of the World Cup. (Spoiler alert, France won its match.)
In terms of design, we can contrast this to yesterday’s dot plot about Kavanaugh. There the highlighted dot was orange with a black outline. Here, same deal. But yesterday, the other justices were shown with black dots and an empty dot for retiring Justice Kennedy. Here all the other countries in the World Cup are orange dots.
I wonder, given the orangeness of the other countries, maybe a solid black dot would have worked a little better for the four semifinalists. Or to keep the orange with black outline dots, maybe a lighter orange or grey dots for the other World Cup teams. (I think black would probably be too strong in this case.)
Overall, it shows that today’s match between England and Croatia will be tough. And should England advance, a match against France will be even tougher.
As most of you know, I am what would have been called a loyalist. That is, I disagree with the premise of the American Revolution. People often mistake that as saying I think Americans should be British. No, although I personally would not mind that. Instead, America would likely have been a lot more like Canada and it would have obtained its independence peacefully through an organic, evolutionary process leading to, likely, some kind of parliamentary democracy.
Every year, somebody digs up articles people have written about why the Revolution was a bad idea. I have seen a lot of them. But I had not seen this Washington Post article that looked at constitutional monarchies. It was published during the whole royal baby buzz back in 2013. It examines why constitutional monarchies are not so bad, and might even be better than presidential republics.
The above graphic is far from great. The same goes for the other graphic in the article. I probably would have added more emphasis on the constitutional monarchies as they get overwhelmed by the number of non-constitutional monarchies s in the scatter plot. That could be through a brighter blue or keeping the pink and setting the rest to a light grey. I perhaps would have added a trend line.
Today is Friday. We all made it through yet another week. So let us look up into the evening sky tonight and see the Hertzsprung–Russel diagram in action. Or, we can take xkcd’s expanded version and just enjoy ourselves.
Last week we talked a lot about trade—and we will get back to it. But the World Cup is now in full swing and I want to take a look at a couple of things this week. But to begin, the Economist published an article about the difficulty of predicting the outcome of World Cups. It looks at the quirks of random events alongside more quantitative things like ranking systems and their differences.
But one graphic in particular caught my attention. It explore the difference between the ranking in individual players versus the teams as a whole. In short, some teams are valued more highly than their constituent players and others vice versa. The graphic is fairly straightforward in that it plots the team value on the y-axis and the players’ on the x.
Personally? I would never bet against Germany. Or Brazil.
But if your author is lucky, he’s going to enjoy the England–Tunisia match this afternoon for lunch—rooting for England, of course. Though thanks to some online tools that’s not the only team I’m rooting for this year. But more on that later this week.
Credit for the piece goes to the Economist graphics department.
Here in Philadelphia, I think yesterday was the first day it had not rained in over a week. Not that everyday was a drenching storm, but at least showers passed through along with some downpours and definitely grey skies. But what about my old home, Chicago?
Well, FiveThirtyEight turned to a longer-term look and examined how over the century the amount of rainfall in the upper Midwest has been increasing. We are actually looking at the same places the Post looked at a few days ago. But instead of political maps, we have rainfall maps.
This one in particular is weird.
I get why they have the map, to show the geographic distribution of the rain gauges that collect the data. And those are site specific, not statewide. But did the designer have to choose area?
We know that area is a less than ideal way of allowing users to compare data points. And as I just noted, a choropleth, even at say the county level, is out of the question. But what about little squares? Or circles? Could colour have been used to encode the same data instead of size? And then we would likely have fewer overlapping triangles.
I suppose the argument is that the big triangles make a bigger visual impact. But they do so at the cost of comparable data points across the Midwest. Maybe the designer chose the area of triangles because there were too few gauges across the country. I am not sure, but for me the triangles are not quite on point.
That said, the graphics throughout the rest of the article are quite good, especially the opening scatterplots. They are not the sexiest of charts, but they clearly show a trends towards a wetter climate.