Last week parts of Ohio voted for a special election in the 12th Congressional District. Historically it has been a solidly Republican district by margins in the double digits. However, last week Republicans barely managed to hold the seat by, at the latest count I saw, less than one percentage point. Why? Well, it turns out that Republican support is bleeding away from one of the traditional strongholds: suburban counties.
I saw this data set late last week on Politico and I knew instinctively that it needed to be presented in another form than a table. Consequently I sketched out how it could work as small multiples of area charts to highlight just how Republican the district is. This is the digitisation of that take. Unfortunately my original sketch also featured a map of the district to show how this falls to the north and east of the city of Columbus. But I did not have time for that. Instead, I sketched up something else, but I need time to work on that. So for now, this concept will have to suffice.
I am a millennial. That broadly means I am destroying and/or ruining everything. It also means I am obsessed with things like avocado toast. It also means I am not buying a house. Thankfully the Economist is on top of my next fad: indoor houseplants.
Your author will admit to having a few: a hanging plant, an Easter lily, an aloe plant and its children, and a dwarf conifer. Just don’t ask me how they’re doing. (Hint: not well.) Turns out I am not a plant person.
In terms of the graphic, though, what we have is a straight up set of small multiples of line charts. The seasonality mentioned in the article text appears quite clearly in a number of plants.
But is Swiss Cheese really a plant?
Credit for the piece goes to the Economist Data Team.
We are now less than 100 days away—95 to be exact—from the 2018 midterm elections here in the United States. As we get closer and closer we not only get more information from polls, but also campaign finance reports. Those can sometimes serve as a proxy for support as lots of grassroots support can dump lots of cash in a candidate’s war chest. Wheras a candidate who drums up little support might find him or herself with scant funds to fight the campaign.
So what does that funding tell us right now? Well last week Politico posted an article looking at that data. They broke the dataset into chunks by the likelihood of the results. This screenshot is of Pennsylvania’s 1st Congressional District.
Each district is represented by a dot plot, with the total money raised by each candidate plotted, the distance in grey being the amount by which the Democrat outraised the Republican.
This is a nice piece as the hover state provides a nice grey bar behind the district to focus the user’s attention. Then for the secondary level of information in terms of cash on hand for the Democrats, i.e. who has cash now, we get the dot filled in versus the open state for simply money raised. Then of course the hover state reveals the actual numbers for the two candidates along with the difference between the two.
The funny thing with this particular district, the Pennsylvania 1st, is that Wallace is not necessarily raising a lot of money. He is a self-funding millionaire. He also is not the most electable Democrat in a competitive seat. It will be fascinating to watch how this particular district performs over the next few months, but most importantly in November.
The weather in Philly the past week has been just gross. It reminds of Florida in that it has been hot, steamy, storms and downpours pop up out of nowhere then disappear, and just, generally, gross. I do not understand how people live in Florida year round. Anyway, that got me thinking about this piece from a month ago in the New York Times. It looked at the impact of climate change and living conditions in South Asia. Why is South Asia important? Well, it is home to nearly a billion people, a large number of whom are poor and demanding resources, and oh yeah, has a few countries that have fought several wars against each other and are armed with nuclear weapons. South Asia is important.
The map from the piece—it also features a nice set of small multiples of rising temperatures in six countries—shows starkly how moderate emissions and the high projection of emissions will impact the region. Spoiler: not well. It notes how cities like Karachi, for example, will be impacted as hotter temperatures mean lower labour productivity means worse public health means lower standard of living. And it doesn’t take a rocket scientist to see how things like demand for water in desert or arid areas could spark a conflict between Pakistan and India. Although, to be very clear, the article does not go there.
As to the design of the graphic, I wonder about the use of white for no impact and grey for no data. Should they have been reversed? As it is, the use of white for no impact makes the regions of impact, most notably central India, stand out all the more clearly. But it then also highlights the regions of no data.
Credit for the piece goes to Somini Sengupta and Nadja Popovich.
A few months ago I covered an editorial piece from the New York Times that looked at all the action, by which I mean inaction, the federal government had taken on gun violence in the wake of some horrific shootings. Well on Saturday the Washington Post published an article looking at how there has been action on the state level.
It used a series of small multiple maps of the United States with states represented as tiles or boxes. States are coloured by whether they took action in one of six different categories. It is a pretty simple and straightforward design that works well.
The only thing I am unsure about is whether the colours are necessary. A single colour could be used effectively given that each map has a clear title directly above it. Now, if the dataset were to be used in another chart or graphic alongside the maps where the types of action were combined, then colours could be justified. For example, if there was a way to see what actions a state had taken, i.e. pivot the data display, the different colours could show what from the set the state had done.
And in Pennsylvania’s case, sadly, that is nothing.
Late last week we heard a lot about contributions to NATO. Except, that was not true. Because the idea of spending 2% of GDP on NATO is actually about a NATO member spending 2% of its GDP on its military. And within that 2%, at least 20% must be spent on hardware or R&D. There is a separate operating budget to which countries actually contribute funds. But before we look at all of this as a whole, I wanted to explore the burden sharing, which is what NATO terms the 2% of GDP defence expenditures.
I did something similar a couple of years ago back in 2014 during the height of the Russia–Ukraine crisis. However, here I looked at a narrower data set from 2011 to 2018 and then across all the NATO members. In 2014, NATO met in Wales and agreed that over the next ten years all members would increase their defence spending to 2% of GDP. We are only four years into that ten year plan and so most of these countries still have time to reach that level.
Brexit is bad for Britain. Here is some proof from an article by Bloomberg that looks at where London-based banking jobs are headed post-Brexit. Spoiler alert, not elsewhere in Britain. The article purports to be more of a tracker in that they will add on data about jobs moving places when news breaks. But I cannot verify that part of the piece.
What I can verify is a sankey diagram. Underused, but still one of my favourite visualisation forms. This one explores where companies’ London-based banking jobs are moving. Right now, it clearly says Frankfurt, Germany is winning.
As sankeys go, this one is pretty straightforward. Aesthetically I wonder about the colour choice. I get the blues and that the banks are coloured by their ultimate destination. But why the gradient?
But conceptually the big question would be what about London? I probably would have kept London in the destination set. While many jobs are likely to leave Britain, some will in fact stay, and those lines will need to go somewhere in this graphic.
The piece also makes nice use of some small multiple maps and tables. All in all, this is a really solid piece. It tells a great—well, not great as in good news—story and does it primarily through visuals.
Credit for the piece goes to Gavin Finch, Hayley Warren and Tim Coulter.
Yesterday the Economist posted a graphic about Chinese urban clusters, of which the Chinese government is planning to create 19 as part of a development strategy. In terms of design, though, I saw it and said, “I remember doing something like that several years ago”.
The Economist piece looks at just the geography of the Chinese clusters. It highlights three in particular it discusses within the article while providing population numbers for those clusters. Spoiler: they are large.
The Economist graphic does little else beyond labelling the cities and the highlighting of the three features clusters. But that is perfectly okay, because that was probably all the graphic was required to do. I am actually impressed that they were able to label every city on the map. As you will see, we quickly abandoned that design idea.
So back in 2015, using 2014 data, my team worked on a series of graphics for a Euromonitor International white paper on Chinese cities. The clusters that the analysts identified, however, were just that, ones identified by researchers. Since the Chinese government had not yet created this new plan.
We also looked at more cities and added some vital context to the cluster map by working to identify the prospects of the various Chinese provinces. Don’t ask me what went into that metric, though, since I forget. The challenge, however, was identifying the four different tiers of Chinese city and then differentiating between the three different cluster types while overlaying that on a choropleth. Then we added a series of small multiples to show how now all provinces are alike despite having similar numbers of cities.
Credit for the Economist piece goes to the Economist Data Team.
Credit for the Euromonitor piece is mine. I would gladly give a shoutout to those that worked with me on that project…but it’s been so long I forget. But I’m almost certain both Lindsey Tom and Ciana Frenze helped out, if not on that graphic, on other parts of the project.
I found myself doing a bit of summer cleaning yesterday and I stumbled upon a few graphics of interest. This one comes from a September 2016 Wall Street Journal article about the changes in the S&P 500, a composite index of American stocks, some of the 500 largest.
In terms of the page design, if it were not for that giant 1/6 page advert in the lower right corner, this graphic could potentially dominate the visual page. The bulk of it sits above the page’s fold and the only other competing element is a headshot to the upper-right. Regardless, it was clearly enough to grab my attention as I was going through some papers.
As for the graphic itself, I probably would have some done things differently.
To start, are these actual tree maps? Or are they things attempting to look like tree maps? It is difficult to tell. In an actual tree map, the rectangles are not just arranged by convenience, as they appear to be here. Instead, they are in descending—or perhaps occasionally ascending—area, within groupings.
The groupings would have been particularly powerful here. Imagine instead of disparate blue boxes for industrials and utilities in the latter two years that they were combined into a single box. In 2001, that box may have been larger than the orange financials. Then by 2016, you would have seen those boxes switch places—in both years well behind the green boxes of 2001 debuts. If instead the goal was to show the percentages, as it might be given each percentage is labelled, a straight bar chart would have sufficed.
I am not always a fan of the circle for sizes along the bottom. But the bigger problem I have here is the alignment of the labelling and the pseudo-tree maps. One of my first questions was “how big are these years?”. However, that was one of the last points displayed, and it is separated from the tree maps from the listing of the largest company in the index from that year. I would have kept the total market cap closer to the trees, and perhaps used the whole length of line beneath the trees and instead pushed the table labels somewhere between the rather large gap from 1976 and 2001.
Credit for the piece goes to the Wall Street Journal 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.