Yesterday was the first day of 32º+C (90º+F) in Philadelphia in October in 78 years. Gross. But it made me remember this piece last month from NPR that looked at the correlation between extreme urban heat islands and areas of urban poverty. In addition to the narrative—well worth the read—the piece makes use of choropleths for various US cities to explore said relationship.
As graphics go, these are effective. I don’t love the pure gradient from minimum to maximum, however, my bigger point is about the use of the choropleth compared to perhaps a scatter plot. In these graphics that are trying to show a correlation between impoverished districts and extreme heat, I wonder if a more technical scatterplot showing correlation would be effective.
Another approach could be to map the actual strength of the correlation. What if the designers had created a metric or value to capture the average relationship between income and heat. In that case, each neighbourhood could be mapped as how far above or below that value they are. Because here, the user is forced to mentally transpose the one map atop the other, which is not easy.
For those of you from Chicago, that city is rated as weak or no correlation to the moderately correlated Philadelphia.
Granted, that kind of scatterplot probably requires more explanation, and the user cannot quickly find their local neighbourhood, but the graphics could show the correlation more clearly that way.
Finally, it goes almost without saying that I do not love the red/green colour palette. I would have preferred a more colour-blind friendly red/blue or green/purple. Ultimately though, a clearer top label would obviate the need for any colour differentiation at all. The same colour could be used for each metric since they never directly interact.
Overall this is a strong piece and speaks to an important topic. But the graphics could be a wee bit more effective with just a few tweaks.
Credit for the piece goes to Meg Anderson and Sean McMinn.
This piece from the New York Times isn’t really even a graphic. It’s a factette, or small fact. The article is about how tariffs are raising the price of certain goods, in this case a bicycle. Tariffs do not add money to the US Treasury, they are instead an additional price paid by US consumers on goods—not services—originating from outside the US.
Sometimes a big chart is not as impactful as one big number. And here, in the context of this story, a graphic showing trade flows between the US and Mexico may have been useful. But the real gut punch is showing how the tariffs on Mexico, for this one particular bike, could cost the US consumer an additional $90. A tariff is just another word for a tax paid by the American consumer.
Credit for the piece goes to the New York Times graphics department.
Earlier this month the Economist published an article that looked at a different way of measuring the economic output of North Korea. The state is so secretive that the publicly available data we all rely on for almost every country is not available. Nor would we necessarily believe their figures. So we have to rely on other measures to estimate the North Korean economy.
The article is about how luminosity, i.e. the lights on seen from space at night, can be used as a proxy for economic activity in the reclusive state.
The article is a fascinating read and uses a scatter plot to show the correlation between luminosity and GDP per capita then how that translates to North Korea, comparing it to older models.
Credit for the piece goes to the Economist graphics department.
Yesterday we looked at how China and the European Union are planning their tariff/trade war retaliation to target Trump voters. Today let’s take a look at how those voters are doing as this article from Bloom does.
The article is not terribly complicated. We have four choropleth maps at the county level. Two of the maps isolate Trump-won counties and the other two are Clinton-won. For each candidate we have a GDP growth and an employment growth map.
In the Trump-won maps, the Clinton-won counties are white, and vice versa. Naturally, because the Democratic vote is greatest in the large cities, which, especially on the East Coast, are in tiny counties, you see a lot less colour in the Clinton maps.
Design wise, I should point out the obvious that green-to-red maps are not usually ideal. But the designers did a nice job of tweaking these specific colours so that when tested, these burnt oranges and green-blues do provide contrast.
But I am really curious to see this data plotted out in a scatter plot. Of course the big counties in the desert southwest are noticeable. But what about Philadelphia County? Cook County? Kings County? A scatter plot would make them equally tiny dots. Well, hopefully not tiny. But then when you compare GDP growth and employment growth and benchmark them against the US average, we might see some interesting patterns emerge that are otherwise masked behind the hugeness of western counties.
But lastly. And always. Where. Are .Alaska. And. Hawaii? (Of course the hugeness problem is of a different scale in Hawaii. Their county equivalents are larger than states combined.)
Credit for the piece goes to the Bloomberg graphics department.
Admittedly, I only read today’s piece because of the photograph on the Washington Post’s homepage. It featured a giant banner saying Lordstown (Ohio) was the home of the (Chevy) Cruze. Every single time I drove between Philadelphia and Chicago I would see that sign. It was also near the halfway point, so whichever way I was headed I only had about six or so hours to go.
But the article itself is about the trials of people working in the area where that plant is located, near Youngstown, Ohio. GM, who owns Chevy, is shutting down the plant as it moves away from the manufacture of cars and focuses on trucks and SUVs. The story is about the people, but it did have this nice little map.
It does a nice job of showing that while manufacturing has, in fact, rebounded since the Great Recession in 2009, that rebound has been uneven. There are some areas of the country, like Youngstown, that have seen manufacturing continue to disappear.
I mentioned this this time last year, but I used to make a lot of datagraphics about GDP growth. The format here has not changed and so there is nothing new to look at there. But, the content is still interesting. And the accompanying Economist article makes the point that high growth rates are not always what they seem. After all, Syria’s high growth rate is because its base is so small.
Credit for the piece goes to the Economist Data Team.
Happy Friday, everybody. We made it to week’s end. But wouldn’t you know it? Millennials are still terrible. Admittedly this piece is over a year old, but I could not remember ever seeing it before.
If you do not recall, last year there was a debate about the spending habits of millennials and why they are not out there buying homes and properties. The point was that we waste our money on experiences like expensive coffees and, most specifically, avocado toast. So amidst all this, the BBC decided to look at how many pieces of avocado toast would be needed to purchase an apartment in 10 global cities. Neither Philadelphia nor Chicago were on that list, but New York is.
Ultimately, I have never had avocado toast. But it sounds pretty good. But I find it a stretch to think the reason I do not own a home is because I am trying to eat 12,135 slices of avocado toast.
Last Thursday, the US entered its longest bull market in history. And the New York Times covered the story on the front page, which makes this another episode of covering graphics when they land on the Times’ front page. Of course, last week was a big news week away from the economy and so it is no surprise that the above-the-fold coverage was on the scandals besetting the president and those of his team who have pleaded guilty or been convicted of crimes by juries.
But you will note that below the fold is that nice little graphic. Here we see it in more detail.
What I like about the graphic is how it uses the blue fill to draw attention to the bull markets but then also labels how long each was. Those keen on the story will note there is a debate whether a particular 19.9% drop qualifies for the 20% drop usually used to benchmark the beginning and ending of a bull market. That is why there is that second label with the black arrows on the graphic.
It also uses the negative space created by the shape of the graphic to contain its title, text, and caption information.
Even the Washington Post admits there sort of is no such thing, because standards vary across the world. But broadly speaking, you have enough for the essentials and then a little extra to spend discretionarily. The concept really allows us to instead benchmark global progress in development. Regardless, yesterday the Post published a calculator that allows you to compare household income across the world to that global middle class.
The catch, however, is that income is priced in US dollars, which is the currency of very few countries. But thankfully, the Post gives the methodology behind the calculator at the end of the piece so you can understand that and the other little quirks, like rural vs. urban China.
From a design standpoint, there is not much to quibble with. I probably would not have opted for red vs. green to showcase global middle and global lower-than-middle class. But the concept certainly works.
Credit for the piece goes to Leslie Shapiro and Heather Long.
We have been looking at tariffs a little bit this week, but unfortunately one of the side effects of tariffs is job losses. And of course when it comes to people losing jobs, not all countries in the developed world handle them the same. Last month the Washington Post published an article examining how those countries compare in a number of related metrics such as unemployment compensation, notice for termination, and income inequality.
It uses a series of bar charts to show the dataset and reveal how the United States fares poorly compared to its peers. The chart above looks at the earning needed for termination from employment and the differences are stark. The outlined bar chart shows longer tenured employees and the full bars as coloured. Of course this makes it look like a stacked bar chart or filled bar chart. Instead I wonder if a dot plot would be clearer. It would eliminate the confusion in determining what if any share of the empty bar is held by the full bar.
The chart for unemployment insurance versus assistance is a bit better. Here the bar represents insurance and the lines assistance. I like how the lines continue off beyond the margins to indicate an unlimited timeframe for assistance. However, for those countries where assistance is short-lived, the bars versus lines again begin to look like an instance of a share of a total, which they are not.