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.
Somalia is beset by a bevy of problems; from an Islamist insurgency that holds great swathes of the south, to the de facto independent regions of Somaliland and Puntland in the north, to the pirates operating off the coast, to the barely functional government in Mogadishu that controls only sections of the capital through the backing of an African Union peacekeeping force, to the recent famine that devastated the south of the country.
The famine, which ended formally ended only earlier this month, is the focus of an interactive piece by the Guardian. It examines how the tragedy unfolded, especially when early indicators pointed to the likelihood of a famine. Through a timeline, the piece marks out what happened when—probably important as not all readers may be familiar with the details of the disaster—atop a chart that visualises the aid given to Somalia. Other line charts describe who donated and when.
The most interesting, however, is an investigation into what (perhaps) spurred the donations. Using the same timeline as a common base, it charts when donations were made against mentions in six US and UK media outlets against Twitter mentions and Google Search Insights.
With this last bit in particular, the Guardian has attempted to use data visualisation to support an argument made in accompanying text. Often times data visualisation and infographics will simply document an event or provide facts and figures. Here, however, an attempt was made to link the aid effort to media coverage (90% of aid came to Somalia after the story broke in the media), perhaps to show causation. But, the writer admits that ultimately the visualisation can only show the overlap or correlation, which the writer further notes is itself consistent with academic debate over the existence of the “CNN effect”.
Credit for the piece goes to Claire Provost, Irene Ros, Nicola Hughes, and the Guardian Interactive Team.
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.
Yo, Philly, apparently Pew did a survey on what Philadelphians think about Philadelphia. And what better way to talk about a survey than through an infographic. So thanks to the Inquirer, that is what we have.
The interesting bit is that while there is a black-and-white, presumably print version, the website broke the whole graphic into its components and made them larger for web viewing. But, if you look at this example from the segment on immigration and diversity, they ought to have left colour alone. The two segments Bad Thing and No Difference use the same colour when they clearly do not mean the same thing. The black-and-white version keeps those two as separate greys.
Credit for the piece goes to John Tierno.
The previous two entries have been about visualisations of the administration’s budget proposal for 2013. Today’s will be (probably) the last in such a theme. Perhaps some wonder if not the bubbles and circles of the Times’ visualisation, what?
Some might answer bar charts. Because we all love bar charts. But, as in this example from the Philadelphia Inquirer, sometimes we are left wanting more.
The graphic captures the size of the budget by general spending and revenue areas, but misses the story on how each has changed on account of this new era of austerity. What colour was in the previous examples, here instead we see it used to group the different categories of spending. From an aesthetic standpoint, the depth in the third dimension is distracting and the space between the two stacked bars (and the line separating them) does not aid in comparison.
In brief review, of the three visualisations presented over the past three days, I have to say that the Washington Post’s tree maps are the most useful from a design perspective, but sadly lacks in the granularity we see—regardless of the clarity or lack thereof in presentation—in the piece from the New York Times.
Credit for the piece goes to John Duchneskie.
Yesterday’s post was about the administration’s 2013 budget proposal as visualised by the New York Times. This morning we have a brief comparison to the visualisation of the Washington Post.
The main visualisation shows spending by department compared against revenue, the difference between being the grey box of deficit. Of note is that this piece also contains the revenue, and not just the spending, unlike the New York Times version. You can also see that the level of granularity is different; the Post looks only at department-level data while the Times delves into specific programmes. Critically, the arrangement of the budget components in this graphic makes it easier to attempt comparisons of area and thus weigh Education against Defence.
If you click a particular department, you swap out the revenue side of the budget equation with the details of previous spending in that area, broken down into presidential administrations that are coloured by party. The same holds true for revenue, clicking on those reveals the amount of revenue taken in by administration. Of some note is the deficit, which shows how we did briefly have a budget surplus back in the 1990s and how that compares to the deficits of today.
All in all, while the level of detail is not present in the Post’s visualisation, I find that the comparison at the departmental level stands strongly in the favour of the Post. The Post also benefits from presenting the other side of the budget story, revenue. Unfortunately, if you care to dig any deeper into any particular part of the budget, say weapons procurement or education grants, you cannot in the Post. That leaves space for a nicely designed, detailed, clear, and informative piece should someone or some organisation be so inclined to build it.
Credit for the piece goes to Wilson Andrews, Dan Keating and Karen Yourish.
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.
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.
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.
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.
Credit for the piece goes to Shan Carter.
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.
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.
Compare that to the guide’s view of the map, which focuses on the large cities on the East Coast.
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.
Often we think of graphs, charts, and other forms of data visualisation as a means to exploring the economic growth of so and so, or visualising traffic patterns, of explaining the complexities of science, or the reporting of yesterday’s news. But, we can all use data visualisation in our own lives to help make better decisions.
While I normally opt not to post links to other data visualisation blogs—I figure most people are also already checking those out—Nathan Yau posted about why he wants to cut the cable, i.e. lose his cable television subscription. He has two separate charts that are simple but effective in driving home the point that he really ought to think about cutting cable out.
The article, while a bit longer than usual, is well worth the read. The charts with the explanation make for a compelling argument.
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.
Credit for the piece goes to Kevin Quealy and Jeremy White.
