The civil war in Syria rages on. The following graphic from the New York Times accompanies the article and uses a calendar-style timeline to look at the mounting death toll. The visualisation type appears more and more often for time-based data sets shaped around days; we all (usually) understand how calendars work and are shaped.
In this particular case, specific key dates and images are brought out of the timeline and featured on the left. These provide an additional context to the human side of the story that may otherwise be left in the dates and deaths on the right.
A problem with such a design is the length of the year, which might preclude users of small screens from being able to see the entire year in one screen-height. I am left to wonder about whether the user can make an adjustment to a horizontally-scrolling calendar and if in the future such arrangements may better take advantage of widescreen monitors.
Sunday in the New York Times, an article on bicycle delivery had an accompanying infographic. It detailed the dinner route of the article’s main individual. The piece is an interesting use of small multiples to provide a timeline of a route, while each new delivery maintains the old paths for reference. And from a data perspective, I found it good to acknowledge the one instant where the follower lost contact with the delivery man.
In case you were wondering, yes, Virginia, politics in the United States are becoming more polarised. Olympia Snowe, the moderate Republican senator from Maine, is not running for re-election. And so the Senate is left without one more centrist counterweight to an extreme. To try and show how extreme, this graphic from the New York Times plots how often senators voted with their party.
Senators voting with their party
While the chart does not have a marker for the average of each Congress, everyone can see the general trend line. Up. And up means less compromise. And less compromise means getting less done.
Part of the State of the Union was about the administration’s plan to lower the corporate tax rate while closing loopholes and ending subsidies. The goal is to lower the corporate tax rate from 35% to 28% without losing revenue.
Along with the New York Times article about the proposal the Times offers a graphic showing the amount of taxes paid by almost all members of the S&P 500. This includes local, state, federal, and foreign taxes and over earnings from 2005 to 2010. The visualisation is a simple dot plot showing the distribution of tax rates for the various companies, grouped into economic sectors.
Normally, I look forward to the release of the president’s budget proposed budget—fully understanding that it will never pass as proposed. We get to see lots of visualisations trying to show that we really do spend quite a lot on defence. And an awful lot on Social Security, Medicare, and Medicaid. And a little bit on a lot of other varied programs and departments.
Last year was a very nice tree map by the New York Times, see my post about it here to refresh your memory. This year’s, well, frankly, is not so nice. To be fair, the piece is aesthetically pleasing and well designed; the transitions and interactions are all spot on.
What is not so much is the use of circles and bubbles.
In the tree map of last year, all the various leaves fit nicely against each other inside branches as part of the tree. See the below screenshot for a reminder. There were small spaces between the branches and leaves, but no more than necessary. Does the overall shape or size of the tree map represent anything? No, but note how the leaves are grouped by branches. And how, in a pinch, you can compare vertical and horizontal axes of each cell against is neighbours to gain a size comparison.
The 2012 budget proposal tree map
This year’s overall spending graphic shows large gaps between some circles and overlap of others. It is difficult to compare circle to circle and thus gain any true meaning of the size differences between programmes. Furthermore, the spaces do not group like with like, in fact every time I reload that view, the circles are in a new arrangement, making it difficult to return to the programme I had just been viewing. Compare that to the tree map where everything is ordered by department and, because all the changes and filtering happen within the view, the cells remain in place.
All spending in 2013 proposal
This year’s budget proposal has an additional three views presented: types of spending, changes, and department totals. The first moves the circles into two camps: discretionary and mandatory spending. But, the areas of the circles are hard to compare against each other, and the placement of the circles seems arbitrary. Compare that to last year’s which highlighted the types of spending within the tree map and blanking out the other. The cells remained in place and by their positioning against each other, a more accurate sense of scale and relationship was created.
Changes sorts the circles into department, though that part is not entirely clear at first glance. Otherwise, this view makes sense, though I wonder if a more clean scatter plot could not be more useful in plotting size and growth on the x and y axes with colour remaining the change from the previous year. Though one loses the grouping by government department, such a grouping seems less important throughout the 2013 piece except in the by department view.
Sorting by change in spending
That view resorts the circles into a matrix with each department receiving a square-like cell into which its circles are dropped. This was handled much more adeptly and clearly by the tree map of last year.
I appreciate the need to create new and more interesting visualisations every year. But, whereas last year’s was a solid piece, this is a shaky step backward. I would have liked to see a more nuanced and featured improvement to last year’s tree map instead of throwing it out.
Maps are cool. They show the geographic distribution of data. And that is fantastic if there is a story in said distribution. But even if there is a story, sometimes given both the scale of the map and the amount of data encoded in the map, how could you possibly expect to find the story? Which little region of the map do you search to find the interesting nuggets?
On Sunday, the New York Times published an interesting solution to that very quandary. The context is an article looking at the anger and resentment felt by some towards government assistance via the social safety net, and yet how these very same people depend upon that safety net through programmes like Social Security, Medicare, Medicaid, &c. The map, a choropleth, examines several different metrics that comprise government assistance, e.g. Medicaid payments as a percentage of income.
Government benefits overview
One can easily toggle through the various metrics at the scale of the entire United States. This is a rather standard feature for such maps. However, in the upper-left corner, the designers placed a ‘guide’ that provides context and stories for each metric. But, not only does the guide provide text to support the map, but it zooms in on specific areas and regions that then support the text and best exemplify the point.
Here we see the map of the whole US for Medicaid, which appears to be scattered pockets of higher percentages. Interesting perhaps, but the user likely has few ideas as to what that visualisation actually means.
Medicaid's broad overview
Compare that to the guide’s view of the map, which focuses on the large cities on the East Coast.
Medicaid's eastern core context
Providing context and guiding a reader/user through the stories contained in the map, or at least those deemed interesting by the designers and editors, is an interesting solution to the problem of finding the story in maps such as these. However, by moving away from a strict visualisation of the data, the New York Times and others that try similar avenues introduce human biases in the story-telling that may otherwise be unwanted or distracting.
Credit for the piece goes to Jeremy White, Robert Gebeloff, Ford Fessenden, Archie Tse and Alan McLean.
Mitt Romney lost badly last night. No way around that. But as I watched the results come in through various sources, I noticed two interesting design decisions that made me think; one from the Guardian (the British perspective), and the other from the New York Times.
Using only Colorado as an example, here is the map of county results by the Guardian.
Results map from the Guardian
Note how the map is presented in 3-D that therefore allows the use of height as another encoded variable, in this case the size of the lead. Now compare that to the map used by the New York Times.
Results map from the New York Times
Note how this map is flat. So much less cool, right? But try to compare the results in Denver County. When I look at the Times’ map, I see blue; Mitt Romney won. When I look at the Guardian’s map, I see…actually, I can’t. That label is in the way. And then even when I begin to interact with the map, Denver County is hidden by the height of Arapahoe County.
But what about the size of the lead? I cannot see that encoded in the New York Times map. No, one cannot. However, they added a toggle function to change the data displayed on the map—though the utility of that view can be left for another discussion.
And now to a minor point about comparing the totals.
Again, a look at the Guardian’s presentation.
Results table from the Guardian
And now the New York Times. Numbers are numbers and faces are faces. But look at the graphic element representing the percentage. With the Guardian, I can just barely discern that the size of the circles for Santorum and Romney are not the same. And the same goes for Gingrich and Paul. But when I look at the Times’ presentation, I see a simple bar chart that more clearly shows the relationships between the results.
Results table from the New York Times
So interesting design decisions lead to one view that I find far more successful in showing the data: the New York Times.
Bridges are vital parts of infrastructure networks connecting two separate pieces of territory, but often they can be choke points. Damage to a bridge can result to isolation at worst and at best long, circuitous reroutes that add significant time to travel. In the San Francisco area authorities are building a new bridge to replace the current Bay Bridge. But as everyone knows, buildings and infrastructure in that area can be significantly damaged during earthquakes. And the area is waiting for the ‘Big One’ that shall come some day or another.
So how to build a new bridge for the long-term that will also survive a major earthquake? The New York Times explains it in an interactive piece accompanying an article. The interactive piece includes an animation with voiceover explaining the details of the design, with diagrams illustrating the components placed next to the video player. At the bottom, anchoring the piece (pun intended), is a photo-illustration of the new bridge’s design.
Diagram explaining the Bay Bridge replacement
Credit for the piece goes to Mika Gröndahl and Xaquín G.V.
So apparently there was a game last night? I didn’t get the chance to watch it, I was busy updating this blog here. The changes ought to make it easier to be more social, since that’s the thing these days.
But, so about that game, apparently New Jersey won. Congratulations to the New Jersey Giants of East Rutherford, New Jersey. You have prevailed. The newspaper in the nearby city of New York had a graphic to explain the progress of the game, this being a cropping of the ending. Which is probably all anybody really needed to see anyway, right?
Houses are meant to be lived in. Which is good to know if you’re a real estate investor because the housing market in the US is still not so good. According to an article in the New York Times, we’re back to 2003 levels (on average of course) for single-family homes.
Accompanying the article is an interactive chart that lets users view the full breadth of the survey while highlighting specific markets of interest and showing actual values along the length of the chart.
The Case-Shiller Index for Housing Prices
Credit for the piece goes to Kevin Quealy and Jeremy White.
