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.
Cicadas are loud. And while some are around every year, there is at least one species that lives for up to seventeen years. They mate every seventeen years. In 2013 we are witnessing the emergence of Brood II, one of the numerous clusters that are synchronised to each other. But when and where have other broods been spotted over time? The New York Times looked at this question last week via an interactive graphic.
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.
Well, actually, your author is driving back from Ganister today. Unfortunately, while on holiday I was not working (nor was I planning to.) So while I could of run silent today, I wanted to share with all of you again a project I created last year about my return drive from Ganister. For all of who familiar with the piece, I apologise for my re-posting of previous work. For those of you unfamiliar with the work or with Ganister and its distance/remoteness, enjoy. (It’s full-size, so no click-for-higher-resolution.)
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.)
2048 Results
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.
Today I have more immigration-related information graphics and data visualisation for you. Earlier this week the New York Times looked at immigration to California, but this time the focus was on Asian population growth and not Hispanic. The graphic here supports an article looking at where the growth has been focused in California. And given that emphasis, the map accompanying the article makes sense. And as the reader can clearly see, much of that growth has been centred in the San Gabriel Valley and Orange County.
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.
The GOP's Tricky Terrain
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.
The other day I posted an example of a good cartogram, actually a pair of good ones from the New York Times. Today, I wanted to share another good example. The Economist created this cartogram, map of Great Britain’s constituencies. What is perhaps most effective in this chart, even more so than in the Times’, is its use of a “traditional” map form for comparison. You quickly get a sense of how large rural Britain’s constituencies are compared to those of London.
After the capture this weekend of the second suspect in the Boston Marathon bombing, the Washington Post published an interactive piece that looked at the entirety of the story. It captured the bombing, looked at the investigation, then the manhunt, and finally the capture of one of the suspects.
The piece incorporated static diagrams along with video and interactive navigation to tell the story in day-sized chunks on the screen. When taken together as one whole piece, it is quite impressive.
The capture of one of the suspects
Credit for the piece goes to what may well be the entire graphics staff of the Washington Post: Wilson Andrews, Darla Cameron, Emily Chow, Alberto Cuadra, Kat Downs, Laris Karklis, Todd Lindeman, Katie Park, Gene Thorp, and Karen Yourish.
The Kepler observatory is responsible for finding Earth-like planets in distant solar systems. It was launched only in 2009, but has been incredibly successful. Earlier this week scientists announced the discovery of Kepler 62, a star system that has five planets. Two of those planets exist within the Goldilocks zone, where conditions are just right for habitable planets (for Earth-like organisms) to form and exist. Of course, not all planets in such zones are habitable, look at Venus and Mars for examples. But still, the news is quite significant.
Over at the New York Times, Jonathan Corum plotted all the data on all the systems so far discovered by Kepler, including that new information on Kepler 62. The result is a mesmerising view of star systems beyond our own. The stars are planets are enlarged for visibility and the orbits are made a bit more circular, but the overview is still fantastic.
The chart shows the relative sizes of the stars and their temperatures and allows you to compare the orbits of the planets so far known. You can also sort the chart either by size or time of discovery. It also shows the relative times of the planets’ orbits. That is, they move…
The Kepler star systems
Fans of this will remember that in 2011, the New York Times used a similar, albeit static, method to explain the discovery of planets at Kepler 20, whose planets all orbit closer to their star than Mercury to ours.
