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.
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.
Using only Colorado as an example, here is the map of county results by 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.
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.
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.
So interesting design decisions lead to one view that I find far more successful in showing the data: the New York Times.
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?
The Guardian has an interactive piece that details payments to and from European Union member states to institutions, determining whether each state is a giver or receiver.
The concept sounds all well and good. However, the piece itself feels clumsy with too much scrolling and whipping about to pan across the whole EU. The charts look a tad heavy—which could have been remedied for a more concise piece—and the callouts beg for a level of interactivity that is otherwise lacking.
Lastly, I have concerns about the list of countries at the top, although these may stem only from the point of view of an American not too familiar with Europe. Flags are not circles, they are, in most cases, rectangular in shape. Does cropping a symbol or icon of a country make it more or less useful of a symbol or icon? Furthermore, do the British recognise the flags of their fellow EU member states?
The country icons/flags call for some type of sorting function, to compare payments and receipts and their balance. But, instead, they sit there in unalterable silence, providing only an economic overview when clicked. An overview that through its staid design feels more like an afterthought.
The riots in the UK earlier this year prompted questions about British society and the causes behind the riots. The Guardian has been reporting on different elements of the riots for some time now and has released the results of their work on discovering those causes. And naturally, survey results should be visualised for more awesomeness.
The discrepancies between the causes should be interesting. However, the number of bars and their tight spacing along with contrasting colours makes me wonder if the chart would be more effective not if it plotted the value of the responses, but rather the value of the difference in the responses.
I don’t know about you, but to me, it’s beginning to look a lot like campaign season. At least from what I read on the internet. Because, according to this interactive piece by the Washington Post, there has been little local campaign spending on ads in the Chicago television market.
By clicking on the left, you are able to see the spending amounts and spending places of ads by both personal campaigns and interest groups. For national ad campaigns, there is a small outline of the continental US in the bottom left.
Above the map you have some facts about the spending and spending over time and a curious bit about whether the ads are positive or negative. Already if you move from the beginning to now, you can watch the positive ad number slip.
Via Fareed Zakaria, an interactive piece by Food Service Warehouse that looks at the leading nations of food consumption in calories—and what people spend for their food.
The map is not entirely useful, although it does at least hint at the geographic locations of the largest consumers (the West) and the smallest consumers (the Rest of the World). More interesting is the simple bar chart at the bottom of the interactive piece.
Forbes released Jon Bruner’s latest map of migration in the United States. It uses IRS figures to show inbound and outbound movement from counties across the United States. The work itself is an improvement from his map from last year, which was a bit more difficult to read. Beneath is the new version, and at the end, for comparison, the old.
Firstly, the colour palette is far more sophisticated. Secondly, and most crucially, the user can hide the lines on the map, which obscures a key part of the story of migration in urban areas—higher income people moving out of the city and into the suburbs. Thirdly, the map data now includes additional years, which are available by clicking the small chart in the upper right—a welcome addition that allows the data from last year’s map to become accessible this year. Fourthly, and to be fair this may have existed previously but not that I can recall, the new map is accompanied by essays.
These essays use the map and its data to tell stories and explain what one sees going on with the data. It is (relatively) easy for one to put together a piece of data visualisation from a data set. But, without knowing where to look, users may not actually find anything valuable in the visualisation. By pointing to these essays, the map—already much improved from a design perspective—takes on a much more rounded and mature character and becomes more about generating information and knowledge than simply figures and statistics.