charting – Page 25 – Coffee Spoons

Whip Counts to Authorise Force in Syria

I was catching up on some news tonight and I came upon an unhelpful graphic that was supposed to be helping me understand the whip count (who is voting yes or no) for authorising the use of force in Syria. Below is the original graphic from Think Progress.

The whip count as of 15.30 on 3 September

I struggled, however, to directly compare the yes and no votes. While I certainly understand that the splits within both parties are a fascinating subplot to the greater issue of will we bomb Syria, the traditional congressional arc visualisation is not helpful here. So using the same numbers, I simply plotted what is essentially a stacked bar chart. In truly heretical, i.e. independent, fashion I mixed the two parties together and placed them at either ends of the chart. The first to reach 50% wins. (If I were updating this live of course.)

Credit for the original goes to Igor Volsky and Judd Legum.

When it Rains…

Today’s interactive piece comes from Axis Philly and it looks at the total amount of rainfall in Philadelphia (1990–2013) to find both which months and what time of day receive the most rainfall.

As it turns out, evenings in the summer months receive the most rainfall. And since 1990, the most rain has fallen between 19.00 and 20.00 in July. A nice complementary piece would have been a small graphic showing total distribution of rain over the months, without segregating the data into hourly chunks. But again, that would have been a nice complement to the piece as it is far from necessary.

Credit for the piece goes to Jeff Frankl, Casey Thomas, and Julia Bergman.

All in the Family (and the Friends and the Neighbours)

Recently my hobby of my family’s history has focused on my Rusyn (or Ruthenian) roots. However, this recent work out of Stanford University piques my interest in my English heritage, even though much of it is very far back in time. Using my 23 × great-grandfather Reynold de Mohun you can begin to see how it links persons within families, how those lives intersected over time, and the geographical areas where that person lived. In Reynold’s case, it was the 12th–13th centuries in Somerset, England.

But as the title kindred implies, this piece is not just about direct family connections, but also the marriages and close cultural links between certainly the elite of British society. Below is how Reynold is connected to King William I, better known as William the Conqueror.

Connecting Reynold de Mohun to William I

Family history or genealogy is a topic ripe for data visualisation and information design because it is all about connections. But I have found beyond the common family tree diagram little interesting has been created. This work is a solid start in the right direction.

Credit for the piece goes to Nicholas Jenkins, Elijah Meeks, and Scott Murray.

Cutting the Cable

We have all heard talk about cutting cable, i.e. unsubscribing from cable television. But the question is what is replacing it if anything? Fortunately, this really nice graphic produced by Quartz shows the market over the course of the last five years.

It is a really nice use of small multiples and the power of not overlapping size and growth charts, or combo-charts, just because you can. Different metrics deserve different charts. The important part is placement, and that’s where a good designer can make sure to place relevant data near its partner.

Credit for the piece goes to Ritchie King.

Mars or Bust…Wait a Minute…

We already got to Mars. At the end of a week of maps and map-related things. Here’s a map of Mars. Well, sort of. It’s more of a map of Mars as explored by Curiosity. (Remember that guy?)

It’s an interactive piece from the New York Times that charts out just where the rover has driven and photographs of the stops along the way. There’s also a nice little chart that shows just how much of the trip has consisted of driving.

Credit for the piece goes to Jonathan Corum and Jeremy White.

Coffee Pie Charts

Fear not, this graphic makes about as much sense as the title. The concept is actually a worthwhile exploration of the variation in caffeine across cups of coffee from different cafes and coffee shops. But, this visualisation fails at showing it.

Remember, pie charts show the piece amongst the whole. What is the whole in this case? A cup of coffee? No, the data labels indicate milligrams per fluid ounce. It appears as if 60mg./fl. oz. is the whole. A bit arbitrary that. So what happens if you lose the trite pie as a cup of coffee device and simply chart the values. Oh wait, that’s not very hard to do. (I also threw in what I believe to be the benchmark for an average cup of brewed coffee, though I could be wrong.)

Much clearer. More concise (I used less than the original’s dimensions).

Credit for the original piece goes to Dan Gentile.

3D Movie Success

Today’s graphic comes from Thomson Reuters. It charts the success of 3D movies that have been released and how long those movies stayed in the theatres.

Credit for the piece goes to S. Scarr, G. Cabrera, and K. Pong.

Road Kill

Driving can be dangerous. But perhaps most so in the developing world. The Pulitzer Center created this interactive map to allow users to explore just how dangerous driving can be.

Little windows provide details on countries the user rolls over. This data looks at deaths per 100,000 people, killer/victims, and lastly a rating of law enforcement across several different issues. The map also includes links to stories on the website as well as an information panel that related small bits of information about selected countries.

Credit for the piece goes to Tom Hundley and Dan McCarey.

Overpaying for Underachievers

Major League Baseball is set to suspend Alex Rodriguez this morning—if the news reports are true. That will all but end the season for Rodriguez, though he could well play through his appeal so you never really know. But what does this mean for the Yankees and their offense?

The New York Times put together an interactive scatter plot charting the annual salary against the number of hits (roughly a measure of offensive production throughout the year) with benchmark lines for the league average of both. First, the user can see the team averages.

Comparing baseball teams salaries vs. offensive production

At the team-level, one can see that, roughly speaking, the more money a team pays to hitters, the more productive the team. Production it should be noted, does not necessarily equal wins. Look at the Angels, who have some of the most hits, but are in fourth place (out of five) and in a difficult place to make the playoffs.

Quick comparison of the Red Sox's hitters to the Yankees' hitters

But then the user can switch to the top-10 paid hitters on each team. (Four presets are offered beneath the piece, but click on a player from any team and his compatriots will appear.) You can see how the Yankees are hitting poorly in comparison to the Red Sox. (The only reason the Yankees are not truly awful is because their pitching has not been horrible.)

So if Rodriguez is suspended for this year and next, maybe they can use his salary for next year to buy a one-year free agent that isn’t at the bottom right of the this chart.

Credit for the piece goes to Mike Bostock and Joe Ward.

16 Useless Infographics

Happy Friday, everyone. Today’s post comes via colleagues of mine in London, who shared with me the Guardian’s selection of 16 useless infographics. They are shit infographics. Well, at least one is. Check them out and you’ll understand.

Credit for the selection goes to Mona Chalabi. Credit for each infographic belongs to the infographic’s respective designer.