Detroit’s Housing Market

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.

The original:

The original graphic
The original graphic

And my take:

My take on it all
My take on it all

Credit for the original work goes to the Wall Street Journal graphics department.

Climate Change

So this is the last Friday before the election next Tuesday. Normally I reserve Fridays for less serious topics. And often xkcd does a great job covering that for me. But because of the election, I want today’s to be a bit more serious. Thankfully, we still have xkcd for that.

Recent temperature history
Recent temperature history

The screenshot above gets to the point. But the whole piece is worth a scroll-through and so it goes at the end. Credit for the piece goes to Randall Munroe.

Earth's global average temperature
Earth’s global average temperature

The National Debt

One of the things discussed during the election season—though very minorly compared to other things—is the national debt. Debt itself is not scary. Look at student loans, home loans, auto loans, &c. Look at the credit cards in your wallet. But running a country is far more difficult and complex than a household budget. That said, our national debt is high, though of late it has been trending in a positive direction, i.e. flattening out its growth curve.

So what would electing either Clinton or Trump do to the debt? Well, nothing great. According to this piece from the Washington Post, we would be talking about increasing the debt because of plans that are not fully funded or revenue cuts that fail to match spending cuts. But as the graphic shows with a really nice piece of layout between text and image, one option is far worse than the other for the issue of the national debt.

The graphic is clear, and emphasised by the layout of the text
The graphic is clear, and emphasised by the layout of the text

The opening graphic above draws the reader into the overall piece, but the remainder of the piece breaks down policies and implications with additional graphics. If you want to understand the differences between the candidates and the impact of those differences, this is a good read.

Credit for the piece goes to Kevin Uhrmacher and Jim Tankersley.

Pennsylvania’s Polls

Again, the election is next week. And since I have moved from Chicago to Philadelphia, I now find myself in a contested state. This piece comes from the New York Times and explores the polling results across the blue-leaning-but-still-a-swing-state. I find it particularly interesting just how much red and purple there is in the suburban counties of Delaware, Chester, Montgomery, and Bucks all surrounding Philadelphia. But that will only make my vote matter more than it would have had were I still living in Chicago.

But you should also check out the piece for some updates on the Senate race we have going on here. The Republican Pat Toomey is running for re-election against the Democrat Katie McGinty. The race can be described as a tossup as the polls seem to be flipping back and forth. But there is some interesting polling data to be found in the article.

Pennsylvania's pre-election support
Pennsylvania’s pre-election support

In about a week we will see just how Pennsylvania goes for both the presidential election and the Senate election.

Credit for the piece goes to Nate Cohn.

Tracking Polls One Week Out

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.

Saturday's polling numbers
Saturday’s polling numbers

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.

Early Voting So Far

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.

The number of early votes cast
The number of early votes cast

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.

Beating Ted Williams

Last week the Red Sox’s season came to an end after being swept by the Cleveland Indians and with the sweep so too ended David Ortiz’s career. He is one of the best Red Sox hitters of all time, but Ted Williams was the best. And so last week FiveThirtyEight ran a piece on how one manager from the Cleveland Indians—hence the relevance, right?—beat Ted Williams by “inventing” what we all know in baseball as the shift.

The below photo comes from the game and shows what we baseball fans now think of as routine was at the time almost brand new. (Although to be fair, the shift in this case left only one fielder on the left side of the field—the left fielder. Typically today both the shortstop and left fielder both remain.) Anyway, for those baseball fans, the article is worth a quick read.

Who's on first? Not Ted Williams after his at bat.
Who’s on first? Not Ted Williams after his at bat.

Credit for the piece goes to an unknown photographer ca. 1946.

UK Performance at the Olympics

The Olympics are over and Team GB did rather well, coming in second in the medals table with 27 gold medals, more than they won back in 2012 when they hosted the Olympics. (See my piece four years ago where a colleague of mine and I accurately predicted the UK’s total medal count.)

Consequently the BBC put together an article with several data-driven graphics exploring the performance and underpinnings of Team GB. This screenshot captures a ranking chart that generally works well.

How the Olympics rankings have changed over the years
How the Olympics rankings have changed over the years

However, the use of the numbers within the dots is redundant and distracting. A better decision would have been to label the lines and let the eye follow the movement of the lines. A good decision, however, was to label the grey lines for those countries entering and falling out of the Top-5.

Credit for the piece goes to the BBC graphics department.

Pill Popping Power

But not likely. As this FiveThirtyEight piece explains, steroids are not likely the cause of the increased power exhibited this year by Major League Baseball. The article goes into a bit of detail, but this set of small multiples does a nice job comparing several other factors that could be at play.

How different factors increased power or not
How different factors increased power or not

What I like about the piece is how each line chart is centred on the year where the factor came into play. And then to the right and left are ten years before and after. Maybe a little bit more could have been done to highlight the differing years—I admit that I missed that at first—but the concept itself is solid.

Credit for the piece goes to the FiveThirtyEight graphics department.

OD’ing in Philly

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.

Philly area counties
Philly area counties

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.

Credit for the piece goes to Don Sapatkin.