Well, we have arrived at 2017. We all know the big political story in the executive branch. But we also saw elections in the legislative branch. But how different will the 115th Congress look from the 114th? The Wall Street Journal took a look at that in an article.
The article’s graphic does a nice job showing the two different compositions. But if we are truly interested in the growth, we could use a line chart to better showcase the data. So what did I do last night? I made that chart. But as I was playing with the data I saw some numbers that stood out for me. So I compared the proportion of minorities in the original graphic to their proportion of the US national population, per Census Bureau data.
The line charts, broken out into the House vs. the Senate and then into the two parties, do a really good job of showing how the growth is not equally distributed between the two parties. And the reverse of that is that it shows how one party has failed to diversify between the two congresses.
The 115th Congress might be more diverse than ever. But it has a long way to go.
Credit for the original piece goes to the Wall Street Journal graphics department.
Happy Friday after the election. Now that we have had our fill on sweets and bitters, we probably need to move towards a more balanced, more moderate diet. A couple of months ago the New York Times put together this scatter plot from the difference between public and nutritionist opinion on whether certain common foods are healthy.
I normally do not comment on the design of my Friday posts, since I intend them to be on the lighter, more humourous side of things. But this piece interests me, because despite the seriousness of the subject matter I find it lighter and less serious. Why? After studying it, I think it is because of the inclusion of photographs of the items. With the labels still present, I am left thinking that a small dot would be equally effective in communicating what falls where.
But more importantly, look at the sizes of the images relative to the plot. Take the bowls of granola or popcorn, for example. They occupy almost an entire square; the actual value could be anywhere with the 10 percentage point range either vertically or horizontally. And for those two, it does not matter a great deal. Each falls firmly on one side of the line. But what about butter? Kind bars? Cheddar cheese? The large graphic size straddles the line, but because the designers opted for photos over more precise dots, we cannot ascertain whether these foods fall on one side of the line or the other.
The point is that the graphics and design of a piece can influence the perceived seriousness of a piece. An image of a can of Coca-Cola certainly can be more engaging than a 10-pixel dot. But the precision of the dot over the image can also be engaging to the right audience, an audience interested in the data behind the story. There are ways of integrating both, because later on in the same article, we see a means of doing just that.
Here the image provides supplemental information. Just what does a granola bar look like? Well here you can see it. But even here, despite the smaller size and cropped dimensions, the photographs steal a bit of emphasis from the numbers and the charts to the right. (For things like SlimFast, that is no surprise, because the package is designed to capture your attention.)
At the end of the day, the piece interests me because the data interests me. And the story interests me. And I generally like the data visualisation forms the designers chose. But I keep getting hung up the photographs. And not in a good way. What do you think? Do the photos add to the story? Do they make the data clearer?
Credit for the piece goes to Kevin Quealy and Margot Sanger-Katz.
Donald Trump announced how he wants to deport 2–3 million undocumented immigrants that have criminal convictions or that belong to gangs. I read up on the issue at FiveThirtyEight and came across the following graphic from the US Immigration and Customs Enforcement (ICE).
However, when I review the graphic, I found it difficult to understand the FiveThirtyEight article’s point that President Obama has lessened the focus on deportation, but those deported are those convicted of serious criminal offences. So I expanded the size of the y-axis and broke apart the stacked bar chart to show the convicted criminals vs. the non-criminal immigration violators. This graphic more clearly shows the dramatic falloff in deportations, and the emphasis on those with criminal convictions.
Credit for the original goes to the graphics department of the US Immigration and Customs Enforcement. The other one is mine.
A few weeks ago the Wall Street Journal published a graphic that I thought could use some work. I like line charts, and I think line charts with two or three lines that overlap can be legible. But when I see five in five colours in a small space…well not so much.
So I spent 45 minutes attempting to rework the graphic. Admittedly, I did not have source data, so I simply traced the lines as they appeared in the graphic. I kept the copy and dimensions and tried to work within those limitations. Clearly I am biased, but I think the work is now a little bit clearer. I also added for context the Great Recession, during which credit tightened, ergo more properties would have been likely purchased with cash. It’s all about the context.
And my take:
Credit for the original work goes to the Wall Street Journal graphics department.
Well the election is next Tuesday, and last Friday and this past weekend was…interesting. So one(ish) week to go, and we are going to turn to a few posts that use data visualisation and graphics to explore topics related to the election.
Today we start with the latest tracking polls, released on Friday. The piece comes from the Washington Post and highlights the closing gap between Clinton and Trump with a sudden spike in Republican candidate support. But what I really like about the piece is the plot below. It displays the 0 axis vertically and plots time with the most recent date at the top. And then support for the various demographics can be filtered by selectable controls above the overall plot.
Of course the really interesting bit is going to be how much this changes in the next seven days. And then what that means for the results when we all wake up on Wednesday morning.
Credit for the piece goes to Chris Alcantara, Kevin Uhrmacher, and Emily Guskin.
70+ million people watched the debate last week. But, 2.5 million people have already voted. Me? Well in Pennsylvania there is no early voting, so you queue up on Election Day. But that also means I will have had the full election season to brush up on candidates for president and all the other offices. But what about early voters? Well the Washington Post put together an article last week about the numbers of early voters—hence my figures in the opening—and the amount of information they might have missed.
From a design standpoint, it is a really nice article that blends together large centre-piece graphics such as the above to smaller in-line graphics to margin graphics. None are interactive; all are static. But in these cases, users do not need the freedom to interact with the charts. Instead, the designers have selected the points in time or data points more relevant to the story.
Overall the piece is solid work.
Credit for the piece goes to Kevin Uhrmacher and Lazaro Gamio.
Last week the Washington Post published a fascinating article on the data visualisation work of the Donald Trump media campaign. In my last job I frequently harped on the importance of displaying the baseline and/or setting the baseline to zero. When you fail to do so you distort the data. But maybe that is the point of this, for lack of a better term, political data visualisation.
My favourite author is George Orwell of 1984 and Animal Farm fame. But Orwell also penned numerous essays, one of which has struck me as particularly relevant in this election cycle: Politics and the English Language. In concluding the essay Orwell wrote:
Political language…is designed to make lies sound truthful and murder respectable, and to give an appearance of solidity to pure wind.
And so political data visualisation? Well I believe it exists to serve the same purpose. The article goes into detail about how the designers behind the graphics fudged the numbers. Now did the campaign intend to mislead people with the data visualisation graphics? It is hard to say, because some of their graphics actually diminish leads that Trump has among certain demographics. Could it be the designer behind the graphics simply does not understand what he or she is doing? Perhaps. We clearly cannot know for certain.
Either way, it points to a need for more understanding of the importance and value of data visualisation in the political discourse. And then the natural follow-up of how to best design and create said visualisations to best inform the public.
But I highly recommend going to the Post and reading the entirety of the article.
Credit for the original work goes to the Trump campaign graphics department, the criticism to John Muyskens of the Washington Post.
In politics, it is really easy and often popular to bash the federal government. Especially when it comes to its penchant for collecting taxes to pay for things. And sometimes those things are in other states than your own. But do you know how much federal money goes back to your own state? Well now you can thank the Pew Charitable Trusts for putting together this piece that explores what percentage of state budgets is comprised of federal grant money.
While the piece also includes a donut chart—because why not?—my biggest gripe is with the choropleth and the choice of colour for the bins. If you look carefully at the legend, you will see how both the lowest and highest bins use a shade of blue. That means blue represents states that receive less than 25% of their budget from federal grants and also states that receive more than 40% of their budget from the same federal grants. But if your state is between 25% and 40%, your state suddenly turns a shade of green. It really makes no sense. I think the same colour, either blue or green, could be used for the entire spectrum. Or, if the designers really wanted a divergent scheme, they could have used the national average and used that as the breakpoint to show which states are above and which are below said average.
Credit for the piece goes to the Pew Charitable Trusts graphics department.
One of the things I like about Chicago’s WGN network is its weather blog. They often include infographic-like content to explain weather trends or stories. But as someone working in the same field of data visualisation and information design, I sometimes find myself truly confused. That happened with this piece last Friday.
The map in the upper-right in particular caught my attention and not in the good way. The overall piece discusses the heavy rainfall in the Chicago area on Thursday and the map looks at the percentage increase in extreme weather rainfall precipitation. All so far so good. But then I look at the map itself. I see blue and thing blue > water > rainfall. The darker/more the blue, the greater the increase. But, no—check out Hawaii. So blue means less rainfall. But also no, look at the Midwest and Southeast. So does green mean anything? Beyond being all positive growth, not that I can tell. As best I can tell, the colour means nothing in terms of rainfall data, but instead delineates the regions of the United States—noting of course they are not the standard US Census Bureau regions.
So here is my quick stab at trying to create a map that explains the percentage growth. I have included a version with and without state borders to help readers distinguish between states and regions.
And what is that at the bottom? A bar chart of course. After all, with only eight regions, is a map truly necessary especially when shown at such an aggregate level? You can make the argument that the extreme rainfall has, broadly speaking, benefitted the eastern half of the United States. But, personally speaking, I would prefer a map for a more granular set of data at the state or municipality level.
Credit for the piece goes to Jennifer Kohnke and Drew Narsutis.
Another day in Philadelphia, another post of Philly data visualisation work. Here we have a piece from 2015 that was updated earlier this spring. It looks at overdose rates in the Philadelphia region, including parts of New Jersey. It does include a map, because most pieces like this typically do. However, what I really find interesting about the piece is its use of small multiple line charts below to take a look at particular counties.
The piece overall is not bad, and the map is actually fairly useful in showing the differences between Jersey and Philadelphia (although why New Jersey is outlined in black and the Philly suburban counties are not I do not know). But I want to take a look at the small multiples of the piece, screenshot below.
You can see an interesting decision in the choice of stacked line charts. Typically one would compare death rates like for like and see whether a county is above, below, or comparable to the state, local, or national averages. But combining the three gives a misleading look at the specific counties and forces the user to mentally disentangle the graphic. I probably would have separated them into three separate lines. And even then, because of the focus on the counties, I would have shifted the colour focus to the specific counties and away from the black lines for the national average. The black is drawing more attention to the US line than to the county line.