Tag Archives: infographic

Iowa Caucus Results by Demographic Types

Back to the Iowa Caucus results for a moment. A lot of the day-of forecasting for elections is done by entrance and exit polls. So in this piece from the Washington Post, we take a look at entrance poll results. This is basically a two-parter. The first is showing each candidate and the group they won and a number indicates by how much they won the demographic group.

Select the 30–44 age group

Select the 30–44 age group

If you click on any of the demographic groups in particular, you are brought to the part of the page with the actual full results for the demographic. The format is simple a basic heat map with table. Nothing fancy, but nothing fancy is required for that type of data. Interestingly, the colour denotes not the share, but the result. I am not sure I would have done that, but it is a minor quibble.

The 30–44 age group results

The 30–44 age group results

Credit for the piece goes to Lazaro Gamio and Scott Clement.

T-shirt Sizes

It’s Monday, folks. And for most of us that means going back to work. Which means dressing appropriately. And that’s about as far as I’ve got introducing this subject matter, because I wear a dress shirt and tie everyday. Not a t-shirt. But we’re talking t-shirts. Specifically their sizing.

Threadbase is a New York startup looking to do some cool things with data about t-shirts. But that requires having data with which to play. And they are starting to do just that. Their opening blog post has quite a few data visualisations.

Comparing actual sizes via a dot plot

Comparing actual sizes via a dot plot

The dot plot above charts the sizes by dimension for various brands and makes. I might quibble with the particular colours as the red and purple are a bit on the difficult side to distinguish. Symbols could be away around the issue. But the only real issue is that on my monitors the full image runs long and I lose the reference point of the actual dimensions in inches.

But the piece is worth the read for the cyclical changes in dimensions.

Mostly it’s just a pity that I’m not a jeans and t-shirt sort of guy.

Credit for the piece goes to Threadbase.

The Iowa Caucuses

If you did not realise it, today is the first day of the second phase of the American presidential election process. Phase 1 was all the posturing and getting-to-know-me stuff from every candidate. A few dropped out, but now the first votes will be placed in the cold and later tonight snowy town centres of Iowa. The big story for Iowa is can Trump fend off Cruz and can Hillary fend off Bernie. (I like how we can clearly delineate the two parties by whether we use surnames or given names.)

I love election season and in particular the visualisations that go along with them. But I have been making a conscious effort not to go overboard. But that phase is over, so today we look at FiveThirtyEight’s range plots that I have enjoyed for some time now.

Who will be first in Iowa?

Who will be first in Iowa?

They are sort of like a more intuitive version of the familiar box plot. Your highest probability falls within the red—what other colour did you expect—and the average value is denoted. But you can also see that the curves are asymmetric. In short, anybody from Carson up really has a shot. But expect to see Trump or Cruz on top in Iowa.

The race, however, is not quite as exciting on the Democratic side. However, much like I am surprised that Trump is not just still running, but leading, I am surprised about Bernie Sanders’ strength. While he is further behind than Cruz is behind Trump, it is still quite possible for Iowa to “feel the Bern” as they say.

The Democratic plots

The Democratic plots

There are of course other visualisation pieces out there—on this page even—but how about we ease into the commentary? After the presidential election is much more a marathon than a sprint. Anyway, I guess we will all see how accurate these plots are come this time Tuesday.

Credit for the piece goes to the FiveThirtyEight design team.

Urban Homicide

Today we look at a really nice piece from the Washington Post on urban homicide. It combines big, full-width images that use interactivity to promote exploration of data. But as you can see in the screenshot below, the designers took care to highlight a few key stories. Just in case the reader does not want to take the time to explore the data set.

The growth rate is an interactive piece

The growth rate is an interactive piece

But the piece uses scale to provide contrast throughout the article. Because in addition to the three or four big graphics, a similarly well-thought-out and well-designed approach was taken towards smaller, inline supplemental graphics. Here is an example about the homicide rate for New York.

New York's homicide rate as an inline graphic

New York’s homicide rate as an inline graphic

What I really enjoy about these small graphics is the attention paid to highlighting New York against the background averages provided for context. Note how the orange line for the city breaks the grey lines. It is a very nice detail.

Overall, this is a really strong piece marrying written content and data visualisation.

Credit for the piece goes to Denise Lu.

Dude Where Did I Park My Car?

Mother Jones had a lengthy but fascinating piece on urban parking. (I mention the lengthy bit only lest you think it a quick lunch read.) While the design uses a few factettes as sidebars to the main body copy:

Sidebar factette

Sidebar factette

The more interesting piece is the illustrative comparison of a 1.5 vehicle parking space to the size of a 2-bedroom flat. This is the main and really only graphic of the whole piece. However it does a great job comparing the sizes required for humans and for vehicles. We use a lot of space for vehicles.

2 bedrooms vs 1.5 vehicles

2 bedrooms vs 1.5 vehicles

Not that I have any intention of getting rid of my car.

Credit for the piece goes to Chris Philpot.

Blizzard of 2016 Snowfall Totals

You may have heard that the East Coast received a wee bit of snow. Here is the snowfall map from the Wall Street Journal.

Where the snow fell

Where the snow fell

I can report that my family received 30 inches. Which makes sense, because they live somewhere near here. That’s a lot of snow.

My hometown

My hometown

Credit for the piece goes to the Wall Street Journal graphics department.

US Fed Forecasts

Organisations that forecast things are not often inclined to go back and review their forecasts against the actual results. So that makes today’s post from the Wall Street Journal fascinating. They reviewed the Federal Reserve’s forecasts for US GDP growth against the actual growth. And it turns out the Fed consistently overestimated US growth.

US Federal Reserve Forecasts

US Federal Reserve Forecasts

From a design standpoint, what makes this piece interesting is how they presented the range of forecasts. After all, it would otherwise become a plot of squiggly spaghetti lines. Instead, they used colour to group each projection set. A smart idea. Plus a nice literary allusion. I mean if you like Dickens.

Credit for the piece goes to the Wall Street Journal graphics department.

Symbology for Maps

I’m sure the word you were looking for was symbolism. (Points if you get the reference.) Apologies for yesterday, I was a bit under the weather.

Today we deviate from graphs and things and go to another area of conveying information: symbology. I mean iconography. The BBC featured an article about possible new symbols for maps ahead of the 2020 Olympics when, presumably, lots of foreigners will need maps to get around Tokyo. And so you can imagine that the agency behind the proposed ideas has received a backlash about changing customary Japanese symbols for foreigners.

I combined each of the examples from the article. Each row includes the proposed Japanese version and the foreigner version. See if you can identify them without the word. You can imagine, however, that the focus of the article was upon that first row. The answers are after the credits.

Proposed map symbols

Proposed map symbols

Credit for the original work goes to the Geospatial Information Authority of Japan.

The answers, top to bottom: temple, hotel, church, hospital, post office, police station.