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.
When it rains…
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.
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.
Reynold de Mohun
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.
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.
Cutting the cable
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.
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.
A day in the life…on Mars…
Credit for the piece goes to Jonathan Corum and Jeremy White.
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.
Coffee Pie Charts
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.)
Coffee Bar Chart
Much clearer. More concise (I used less than the original’s dimensions).
Credit for the original piece goes to Dan Gentile.
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.
3D movie success
Credit for the piece goes to S. Scarr, G. Cabrera, and K. Pong.
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.
A look at road deaths in Kenya
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.
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.
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.
Using maps to explain maps…
Credit for the selection goes to Mona Chalabi. Credit for each infographic belongs to the infographic’s respective designer.
After two weeks out of the country, I come back and find early this morning (thanks, jet lag) an interactive article published by the New York Times on income mobility. What does that mean? From a medium side, a long narrative interspersed with charts and graphics with which the user can interact to uncover specific data about specific elements in the dataset. From a content side, income mobility means the movement of an individual from one group of money earned to another, e.g. a poor person becoming a millionaire. The piece is fantastic and you should take the time to go read and interact with it.
A map shows the broad context of the data to be looked at in the story
For some time now I have harped on about the need to annotate and contextualise datasets. Too often, large datasets paralyse people; their eyes glaze over and they simply gaze at a graphic without seeing the data, the story, the information. Little notes and blurbs of text can help people synthesise what they see with what they read with what they know to gain better understanding. But in this piece, by combining a lengthy article—very well written although that is not the focus of this post—with powerful interactive maps and graphics, the New York Times has created a powerful piece that states and then proves the point of the article. And while doing all of that, by making the datasets explorable, the Times also allows you to find your own stories.
A story-like piece lets you choose an area and an income to see how the article's topic plays out
Lastly, in the credits section at the end you will see this piece required the input of eight individuals (though I know not in what particular capacities). Clearly, for the Times this is not about to become a regular type of infographic/datavis/journalism piece. But when will skill sets be democratised or dispersed enough that smaller teams can create similar scale projects? That will be interesting to see. However, the Times certainly leads the States if not the world in some of the best information design pieces and undoubtedly this will push other publishers of similar content in this direction.
Ultimately people want to know who's best and who's worse and where they fall and this chart does that at the end of the piece
Credit for the piece goes to Mike Bostock, Shan Carter, Amanda Cox, Matthew Ericson, Josh Keller, Alicia Parlapiano, Kevin Quealy, and Josh Williams.
