Today’s post looks at an interactive graphic from the Los Angeles Times. The subject matter is piracy and the piece has three distinct views, the second of which is displayed here.
Generally speaking, the package is put together fairly well. My biggest concern is with the first graphic. It uses circles to represent the number of attacks by locale over time. I would have either included a small table for each geographic area noted, or instead used a bar chart or line chart to show the progress over time.
Credit for the piece goes to Robert Burns, Lorena Iñiguez Elebee, and Anthony Pesce.
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
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.
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).
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.
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.
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.
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.
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.
Credit for the piece goes to Shan Carter, Kevin Quealy and Joe Ward.
On Tuesday I shared with you some work by Jonathan Corum at the New York Times on the 17-year cicadas now starting to emerge back east. (And as I recall from my childhood, I assure you that they are quite loud.) Today we look at an illustration of the cicada life cycle via the Washington Post.
As I discussed the other day about other graphics, there are differences in how the two newspapers are presenting the same topic or subject matter. The New York Times piece concerns itself with the emergence over time of cicadas across the United States and links to historical articles about those events. Here, however, the Washington Post instead explains just how you get a seventeen-year period between emergences.
Additionally, the Washington Post maps near the end are not interactive as in the New York Times piece. But what this allows the Post to do is focus on those broods that impacted the Washington area instead of all those areas likely outside the Post’s core readership.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Of 2048. Well, kind of. Lately the country has been talking a lot about immigration and its impacts because of this bipartisan desire to achieve some kind of result on an immigration bill working its way through the Senate. One of the common thoughts is that if we legalise a whole bunch of illegals or document most of the undocumented (I’ll leave the language for you to decide), the new American citizens will overwhelmingly vote Democratic and there goes the Republic(an Party).
Nate Silver—yes, that Nate Silver who accurately predicted the presidential results and a whole bunch of other stuff too—looked at a more complex and more nuanced set of demographic variables and found that the aforementioned argument greatly oversimplifies the results. The problem is not entirely the entry of newly documented or illegal workers. Instead, there are systemic demographic issues.
So here comes the New York Times with an excellent data explorer and forecast modeller. You can set the year to examine and then set the results of the immigration debate with how many immigrants are made legal/documented and then how many of them vote. After that you can begin to adjust population growth, voting patterns, &c. to see how those affect the elections. (The obvious caveats of acts of god, party platforms, candidates, &c. all hold.)
The fascinating bit is that if you keep the demographic patterns as they are currently, adjusting the immigration factors at the outset have very little impact on the results. The country is moving towards the current Democratic platform. Even if 0% of the undocumented/illegal immigrants become documented/legal, and if 0% of 0% vote, the result is still a landslide for the Democrats. The real changes begin to happen if you adjust the population growth rates of the legal/documented citizens and voters. But those patterns/behaviours are a lot more difficult to adjust since you can’t legislate people to have more babies.
All in all a fascinating piece from the New York Times. The controls are fairly intuitive, drag sliders to adjust percentages. The sliders have clear labels. And the results on the map are instantaneous. Perhaps the only quirk is that the ranges of the colours are not detailed. But that might be a function of forecasting the data so far into the future and having growing ranges of certainty.
Credit for the piece goes to Matthew Bloch, Josh Keller, and Nate Silver.
Following on last week’s posts onimmigration comes today’s post on how that might impact Republican politics. Well I say might but pretty much mean definitely. The graphic comes from the Wall Street Journal and it takes a look at the demographic makeup of states, House congressional districts and then survey data on immigration broken into Republicans vs. Democrats.
I think the piece is a good start, but at the end of the introductory paragraph is the most salient point about the piece. And unfortunately the graphic does not wholly embody that part. Of course within limited time and with limited resources, achieving that sort of completeness is not always possible. That said I think overall the piece is successful, it just lacks that finishing graphical point.
Credit for the piece goes to Dante Chinni and Randy Yeip.