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.
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.
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.
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.
Continuing this week’s map theme, we have an example of a cartogram from the New York Times. This piece supplements an article about how some manufacturing companies are starting to look away from China as a place for their facilities. There are two maps, the first (not shown here) looks at economic output overall. The second (below) takes that output and accounts for population.
GDP per capita
Hexagons are used instead of the more familiar squares to represent 500,000 people and the colour is the GDP per capita. The text accompanying the graphic explains how this is a measure of economic potential being (or not being) realised. But what the hexagons allow the map to do is better represent the shapes of the countries. Squares, more common in cartograms, create awkward box-like outlines of countries. That would be fine if countries were often shaped like squares, but they are not.
I am not often a fan of cartograms, but I find this one well executed and the annotations and explanatory text make what might otherwise be confusing far simpler to understand. All in all, a solid piece.
Credit for the piece goes to Mike Bostock and Keith Bradsher.
Keeping with maps, they can be useful, but all too often people fall back upon them because it is a quick and easy way of displaying data for geographic entities. This graphic from the New York Times on ADHD is not terribly complex, but it uses a map effectively.
The article discusses how ADHD rates among states vary, but are still higher in the South. The map supports that argument. Consider how it would be different if every other state were darkened to a different shade of purple. There would be neither rhyme nor reason as to why the map was being used.
A map well done
A subtle point worth noting is that only the states falling into the highest bin are labelled. Those are the states that best support the story. The remainder of the states are left unlabelled so as not to distract from the overall piece.
Today’s map comes from the Texas Tribune out of Austin, Texas. The map displays the location of disposal wells, i.e. the sites where the waste water from fracking and related drilling operations are dumped. Firstly, the map hints that the fracking industry is not spread equally across the state.
But secondly, the map does this through the use of hexagons that represent well density. So at a broad, state-wide view, the user sees almost a traditional choropleth. The difference is that these are not natural or political boundaries but rather data constructs designed to aggregate highly granular data points.
Well locations state-wide are aggregated into coloured hexagons
Even nicer, however, is that if you want to see where disposal wells are in your county or town, the map lets you do that too. Because as you zoom in ever closer, the individual wells appear within the hexagons that they colour. It’s a very solid piece of work.
Individual wells colour the hexagons, but are only visible up close
The US imports a lot. But it does not export quite as much. The difference between those two figures is what is known as the balance of trade. Quartz looks at the US balance of trade not at an overall level, but between individual countries.
US Balance of Trade
This is not one of my favourite pieces. For starters, while the overall figures are in the accompanying text, it would be useful to include total US imports and exports alongside the graphic as a point of reference.
Secondly, a long-standing issue I have is area comparisons. Sometimes they are needed and useful, a good example is a tree map. But in this piece, the circles do not add up to a recognisable whole. They also do not help when looking at individual countries and their historical trade values. A dotted outline of a circle shows the previous year’s trade. But more often than not, the trade level was so similar that the circles nearly overlap exactly.
The grouping and highlighting functionality hints at a useful application to explore US trade data, but the clumsiness of the circles renders that usefulness moot. .
First things first, the verb is to sequester. The noun is sequestration. 1 March is not when the sequester begins. It is when the sequestration begins.
Now that we have the preliminaries out of the way, much is made of high government spending relative to revenue. However, this conversation still misses the point that government spending has fallen significantly. The New York Times charted that recent fall in spending in this graphic. This contraction is the largest drop in over 50 years. Along with the bars indicating recessions, I perhaps would have indicated major US military conflicts given the emphasis the introduction places on those events.
Government spending
The piece also looks at government employment, which has been atypically lower than pre-recessionary figures. Taken in sum, the two sets of data point to an extant condition of austerity that shall only be worsened by…c’mon everyone…that’s right, the sequestration.
The New York Times looks at who controlled the redistricting of US congressional seats because of the 2010 census. It then showed an example in North Carolina where Republican control led to the state being less competitive in the past for Democrats. In 2010, Democrats held 7/13 seats in North Carolina. But after the redistricting, in 2012 the Democrats held only 4/13. And all of this is done in a small, compact space. This is a very effective graphic.
