Well maybe not so much the space. Anyway, Nicolas Rapp, who does a lot of work for Fortune Magazine and previously the AP, created his first connected scatter plot. I have been a fan of them for quite some time and have been able to use them fromtimetotime. Rapp’s scatter plot looks at the profits and revenues of the Fortune 500 in the last 20 years. But what I think makes his piece particularly strong are the two annotations he provides to explain the “loops” in the data: the two big recessions.
Earlier this month the Federal Reserve Bank of New York published a report on household debt. Among the findings was the story that student debt is rising to problematic levels as it may act as a brake on economic recovery. In short, without an economy creating jobs for the young (recent university graduates) it becomes increasingly difficult for the young to pay pack the loans for the sharply rising costs of university tuition.
The report made this argument by use of interactive choropleth maps and charts. The one below looks at
Which consumers have how much debt
But another chart that talks about the rising levels of student loan debt misses the mark. Here we see some rather flat lines. Clearly student loans are growing, but without a common baseline, the variations in the other types of debt muddle that message.
The NY Fed's presentation of non-housing debt
I took the liberty of using the data provided by the New York Fed and charting the lines all separately. Here you can clearly see just how in less than ten years, student loans have risen from $200 billion to $1,000 billion. This as credit card debt is falling along with other forms of debt (non-automotive).
My take on non-housing debt
The New York Fed did some great work, but with just one tweak to their visualisation forms, their story is made much more powerful and much more clear.
Credit for the original work goes to the Federal Reserve Bank of New York.
Here’s an older, March graphic from the New York Times that looks at Alaska Airlines. This exemplifies what maps do well; it maps relevant data onto a map. Perhaps that reads silly, but too often people map data just because most things are tied to a geography; things that happen in the world happen somewhere, ergo everything could be mapped.
The growth of Alaska Airlines
In this graphic, however, mapping the tight and Alaska-focused network with tendrils sneaking off-map to distant cities. The map supports the article that tells how after decades of focusing on Alaska, the airline has begun to expand to Midwestern cities in the US, cities in Mexico, and Hawaii.
I am not terribly keen on the stacked bar chart. It highlights the steady Alaska market over the decades at the cost of showing dynamism in those Midwestern, Mexican, and Hawaiian markets.
Credit for the piece goes to the New York Times Graphics Department.
Strikeouts are an important part of baseball. They are the moments where the pitcher wins the duel between pitcher and batter that is the essential element of baseball. But over the years the game has seen more and more batters striking out more often. Earlier this year the New York Times looked at the rising rates of strikeouts in a story supported by interactive data visualisation components.
Strikeouts on the upswing
Like the piece on Bryce Harper, this piece on strikeouts is more of a narrative with the interactive graphics supporting the written words. It is not as lengthy as the Washington Post’s piece, but this one is far more interactive as the user can select his or her favourite teams and follow their performance over time.
Credit for the piece goes to Shan Carter, Kevin Quealy and Joe Ward.
Bryce Harper is undoubtedly one of the best baseball players in the game today. To put it simply, he hits. And he hits well. And he hits well often. So the Washington Post put together an interactive, long form piece about Harper’s swing and hitting.
Pitching to Bryce Harper
The piece begins with a narrated video outlining the science behind Harper’s swing. Then the reader can down into the piece and learn more about Harper’s history and development and how he compares to other hitters. Statistics and data visualisation pieces show just how impressive Harper is as a hitter and how pitchers are trying to combat that.
Interactive long form articles are appearing more and more often online. But this is perhaps the most data- and science-intensive piece I have seen thus far. What is particularly nice about the format is that, as I have often noted, annotations and explanations are what make good infographics and what move data visualisation from presentational to informational. That this piece in particular happens to be about baseball, well, all the better.
Credit for the piece goes to Adam Kilgore, Sohail Al-Jamea, Wilson Andrews, Bonnie Berkowitz, Todd Lindeman, Jonathan Newton, Lindsay Applebaum, Karl Hente, Matthew Rennie, John Romero, and Mitch Rubin.
The Boston Red Sox are in Chicago this week to play the other Sox, i.e. the White Sox. So this week we have a bunch of baseball-related pieces. The first is this recent interactive graphic from the New York Times. It is a daily-updated graphic that looks at the payroll of all Major League teams that is tied up on players on the Disabled List, i.e. those unable to play because of injuries.
Comparing all MLB teams
Clearly the Yankees are paying a lot of money for no production. You can go down the list and compare each team’s total spending. But if you want intra-team details, the piece offers you the ability to look at player-by-player salary details. Interestingly one of Chicago’s baseball teams ranks just above the Red Sox while Milwaukee sits just below.
Red Sox players on the Disabled List
Credit for the piece goes to Shan Carter, Kevin Quealy and Joe Ward.
Yesterday I looked at the aboriginal Canadian identity infographic and wondered if bubbles in a bubble suffice for understanding size and relationship. Today we look at an interactive graphic from the Los Angeles Times where I do not think the bubbles suffice.
California Budget 2013–4
In this graphic, I cannot say the bubbles work. Besides the usual difficulty in comparing the sizes of bubbles, too many of the bubbles are spaced too far apart. These white gaps make it even more difficult to compare the bubbles. Furthermore, as you will see in a moment, it is difficult to see which programmes receive more than others because there is no ranking order to the bubbles.
Below is a quick data sketch of the state funds only data for 2013 and 2012.
California Budget 2013–14
While I did not spend a lot of time on it, you can clearly see how simply switching to a bar chart allows the user to see the rank of programmes by state funding. It is not a stretch to add some kind of toggle function as in the original. One of the tricky parts is the percent growth. You will note above that my screenshot highlights high speed rail; the growth was over 3000%. That is far too much to include in my graphic, so I compared the actuals instead. That is one of the tradeoffs, but in my mind it is an acceptable one.
Credit for the original goes to Paige St. John and Armand Emamdjomeh.
Recently the National Post looked at the results of a Canadian census that identified significant growth in people identifying with the aboriginal populations of Canada. As an American, I am not terribly familiar with Canadian native populations, but if I recall, they are broken into the three groups examined in the infographic: First Nations, Inuit, and Metis. The First Nations are the original tribes of Canada, the Inuit are the natives from northern Canada, and the Metis are the mixed-race persons of native and early European colonisation.
Aboriginal Canada
I find interesting the National Post’s use of network diagrams (the bubbles with lines) to show how the subcomponents form the whole. This as opposed to perhaps a more common form of a tree map or bubbles within a bubble. I would be curious to see or learn about which is the most effective at showing the relationship both in terms of structure (hierarchy) and size (without the datapoints included as labels).
Credit for the piece goes to Andrew Barr, Mike Faille, and Richard Johnson.
Today’s post comes from xkcd. It looks at how much time can be spent improving efficiency before you become an inefficient efficiency person. It is important to note that this is over a five year span. And while I do not know about my readers, I can barely stick doing one thing for more than a year.
Yesterday both the New York Times and the Washington Post published fascinating pieces looking at the difference in the cost of medical procedures. But each took a different approach.
I want to start with the New York Times, which focused at the hospital level because the data is available at that level of granularity. They created a geo-tagged map where hospitals were colour-coded by whether their bills were below, slightly above, or significantly above the US average.
Hospitals across the United States
The ability to search for a specific town allows people to search for their hometown, state, country and then compare that to everyone else. My hometown of West Chester, Pennsylvania is fortunate—or perhaps not—to have several hospitals in the area that charge at different rates. That makes for an interesting story. But I am from the densely populated East Coast and someone from say rural Montana might not have the same sort of interesting view.
Hospitals near West Chester, Pennsylvania
Regardless of the potential for uninteresting small-area comparisons, once you find your hospital, you can click it to bring up detailed statistics for procedures, costs, and comparisons to the average.
Brandywine Hospital's data
All of this makes for a very granular and very detailed breakdown of hospital versus hospital coverage. But what if you want something broader? What good is comparing Brandywine Hospital to some medical centre in Chicago? Neither is reflective of the healthcare industry in the Philadelphia area or the Chicago area, let alone Pennsylvania or Illinois. The Washington Post tackles this broader comparison.
The Post leads off with a hospital-level example from Miami. Two hospitals on one street have vastly different prices. If we knew about this in Miami we could surely find that in the New York Times map. Instead, the Post guides us to that kind of example.
Comparing two hospitals in Miami
But the broader view is the centre of the piece. Using dot plots and filters, the user can compare the state averages for 10 different medical procedures. Fixed to the plot are the minimum and maximum averages along with the national average. And given the Post’s smaller circulation area—the New York Times is national, the Post is less so—there are quick links to states of particular interest: DC, Maryland, and Virginia.
Pennsylvan's averages
The ability to pick different states from the drop down menu allows the user to quickly see differences between states. What is lacking is perhaps a quick view of where all the states are visible so that the user does not have to click through each individual state.
California's averages
Both pieces are very successful at their narrowly-focused aims. Neither tries to do everything all at once, but nor would their designs allow for it. Plotting and filtering all the hospitals could be done in the Post’s style, but it would be messy. The state averages could all be made to colour state shape files, but you would lose the inter-procedure differences, the minimums, maximums, and the averages. In short the two pieces from the two teams complement each other very well, but a weird and hybrid-y cross of the two would be large, cumbersome, and potentially difficult to use without spending a lot of time to design and develop the solution. (Which I imagine they did not have.)
Credit for the piece from the New York Times goes to Matthew Bloch, Amanda Cox, Jo Craven McGinty, and Matthew Ericson.
Credit for the piece from the Washington Post goes to Wilson Andrews, Darla Cameron, and Dan Keating.
