The Boston Red Sox hired John Farrell this weekend to be their manager just one season after hiring Bobby Valentine for the role. There is a lot to be said about just who is to blame about the Red Sox’ awful season. But it was pretty awful. How awful? The Boston Globe shows us in this interactive piece.
It’s a series of small multiples of line charts. However, one of the big problems with the infographic is that the labels are entirely absent. As best I can tell the line is the number of games over .500, i.e. an even split between wins and losses. But, it could be more clearly called out if not in the legend or on the axes than in the title.
But over all it does put this past season into a sober perspective.
On Friday we received the monthly jobs report. And the furore that arose with it. Principally the anger stemmed from right-leaning commentators who believed that the non-partisan Bureau of Labor Statistics, a government agency tasked with collecting data on employment among other metrics, “cooked the books”/ “massaged the figures”/ flat-out lied to show a significant drop in the unemployment rate that could not be attributed to people who had stopped looking for work—a cause of some earlier drops over the last few years. As someone who works with data originally collected from national statistics offices across the world on a daily basis, those claims touched a nerve. But I shall leave that rant for another time.
Instead let’s look at the New York Times piece that quickly followed on the outrage of fools. We can look at and analyse the data in different ways—the origin of the phrase lies, damned lies, and statistics—and surely the Republican and Democratic parties would do just that. They did. This New York Times piece shows how that can be—and was—done. It involves points of reference and context.
First the facts:
Then how the Democrats spin them:
Finally how the Republicans spin them:
But the facts themselves do not lie. 114,000 non-farm jobs were added to payrolls. The unemployment rate fell to 7.8%, the lowest rate since January 2009.
Credit for the piece goes to Mike Bostock, Shan Carter, Amanda Cox and Kevin Quealy.
Hannah Fairfield at the New York Times created a great infographic a few years ago that looked at the history of the price of gasoline and how many miles, on average, an American drove in a car per year. The piece told some rather interesting stories starting in the 1950s with the explosion of the suburb, interstate highways, and car ownership. The energy crises of the late 1970s and early 1980s provided a spike that eventually subsided for the 1990s and early 2000s when the United States was the dominant economic power and the only country that really consumed that much gasoline. (I remember those days well for that was when I first started filling my own car’s gas tank. How great $1.xx/gal gas was.)
Earlier this week she returned in a similar fashion to look at driver safety over time. The metrics were average annual miles driven and the number of auto fatalities per 100,000 people. Segments of time characterised by a common theme, story, or technology are highlighted and the annotated to explain the change from the previous time period. It’s a rich story that walks the reader through the history of the American auto experience since World War II.
This is the year of the percentages. From 99 to 47. Earlier this week, Mother Jones revealed via a secretly recorded dinner Mitt Romney as claiming that he doesn’t care about the 47% of people who do not pay income tax. He probably meant that he doesn’t care about getting their vote rather than caring for them as people, but regardless of his intention his statement did not sound good. This 47% are people who are dependent on government, or as Romney’s vice presidential candidate would say, they are “takers”. But is this true?
The great thing about news stories and infographics is that as time progresses, one has more opportunities to find data to back stories and arguments. The day of the breaking news, CNN published this simple pie donut chart. Crude, but given the breaking news it is effective.
It proves that Romney was correct, that 47% of people do not pay income taxes. But, it also proves that Romney was at best glossing over the details or at worst manipulating the people listening into thinking that 47% of people do not pay taxes. It may be only 0.9% of Americans who do not pay any taxes. Furthermore, retirees and young people often do not earn income with which to pay taxes. If retired senior citizens are “takers”, I suppose Romney does not want the elderly vote so often important to the Republican base.
But news stories evolve and more statistics become available. A few days later, the New York Times published a longer infographic piece, with below a cropping of the overall.
It expands upon the nature of the story and breaks down the actual tax burden of the American public. It is far more nuanced that Romney stated back in May (the original recording of the video). The poorest Americans do not pay federal income taxes, they have the Earned Income Tax Credit (EITC) that is designed to deal with poverty, especially amongst families with children. It is, very simply, what one would call a tax credit incentivising work. The poorest Americans, however, still must pay payroll taxes and then state and local taxes. In percentage terms, the poorest Americans pay more to their states and local governments than do the wealthiest Americans, who in turn have the greater burden at the federal level. It is worth noting that many government programmes, local schools for example, are often funded at the state and local level.
The problem with Mitt Romney’s argument on taxes is that he wants to cut taxes at the highest income brackets and cut the social safety net programmes for the lowest brackets. We already have evidence that such policies do not correlate with economic growth. They instead correlate to a worsening gap between the wealthy and the poor. Think the 1920s rather than the 1950s, 60s, and 70s.
Tax policy is worth discussing. It is worth debating. But we should do so on the facts. I cannot recall the politician who spoke these words, but as someone once said, “you are entitled to your own opinion. You are not entitled to your own facts.”
Credit for the pieces go to Susie Poppick (CNN) and the New York Times.
The Globe and Mail of Canada published an infographic that where I work would probably be called a datagraphic. It presents data in a graphic fashion without a lot of context or conclusions that turn data into information. The piece in question looks at Canada’s balance of trade, i.e. how much it imports from other countries vs how much it exports to other countries.
While I appreciate the goal of the overall piece and fully understand that it may have in fact first lived in the print edition, the version shown on their website feels too large for the few data points contained within the graphic. The bars on the right and beneath the timeline are far too wide. The sections could likely have been condensed into a smaller, more compact space that would have given more visual weight to the timeline that clearly tells the story of a more volatile trading period for Canada since the global recession of 2008.
I also would probably change the chart type or simply look at a different data set for the trade balance with principal partners because the data for Japan barely registers. And while the other data can be seen, the minor differences are difficult to read. I would probably shift the emphasis from the actual dollar value of exports and imports to the percentage growth (or decline) of each over the last year.
This graphic from the New York Times looks at the illegal ivory trade out of Africa and into, primarily, the markets of Asia. I think the map works fairly well in showing why certain countries are centres for the illicit industry. But the two donut charts integrated into the graphic as part of the Indian Ocean are a bit weaker.
My main problem is that the shares are a bit difficult to distinguish as arcs, especially when looking at the export countries. But the second chart with the import markets does work a little bit better. In this case there are really only three markets: China, Thailand, and Others. But the chart contains the ambiguous China or Thailand. So in theory, that demarcation could fall anywhere between China and Thailand—a point harder made if comparing simply by bars. This means that the chart really is looking at China vs Thailand that combine to 87% vs. Others. The trick is finding the break between China and Thailand. Is this chart perfect? No, but in this case I think it an acceptable use of the donut—though I likely would have treated it a little bit differently to emphasise that point.
The National Post’s business section, branded separately as the Financial Post, posted a comment about a proposed bridge that would span the Detroit River and add a third major crossing to the Detroit–Windsor area. The comment used a graphic to explain one of the key points of the story, that early 21st century traffic projections haven proven to be very much incorrect. Unfortunately, it took me a little bit of time to realise that in the graphic.
So without access to the raw data provided by United Research Services I have made a quick attempt to improve the graphic within the confines of Coffee Spoons’ main column space, i.e. 600 pixels. The original locator map is quite useful and therefore not included in my effort.
My main issues with the charts are the separation of the estimates from the actuals and the spacing between the estimates. I would have preferred to have seen, as in my example, how the actuals for 2010 fell far short of the 2004 projections. Ideally, I would have liked to have seen the original estimates for the intervening years between 2010, ’20, and ’30, however that data was not provided in the comment if it is even available from the original source. Consequently, unlike the original, I have kept the spacing of the actual data in the estimates with the intervening gaps.
The subtle effect of this increased spacing is to reduce the visual speed, if one will, of the projected growth. Over the original and narrower space the rate of increase appears fairly dramatic. However when given the correct spacing the ‘time’ to reach the projections lengthens and thus the rate ‘slows down’.
Credit for the original piece goes to Richard Johnson. The reinterpretation and any errors therein are entirely my own.
Oil, sweet oil. How we depend upon you for modern civilisation. BP published a report on world energy that Craig Bloodworth visualised using Tableau.
The piece has three tabs; one is for production, another consumption, and a third for reserves. (The screenshot above is for production.) But when I look at each view I wonder whether all the data views are truly necessary?
In production for example, is a map of a few countries truly informative? The usual problem of Russia, Canada, the US, and China dominating the map simply because they are geographically large countries reappears. Furthermore the map projection does not particularly help the issue because it expands the area of Siberia and the Canadian arctic at the expense of regions near the Equator, i.e. the Middle East. That strikes me as counter-intuitive since some of the largest oil producers are actually located within the Middle East.
A map could very well be useful if it showed more precisely where oil is produced. Where in the vastness of Russia is oil being sucked out of the ground? Where in Saudia Arabia? In the US? Leave the numbers to the charts. They are far more useful in comparing those countries like Kuwait that are major producers but tiny geographies.
Lastly about the maps (and the charts), the colour is a bit confusing because nowhere that I have found in my quick exploration of the application does the piece specify what the colours mean. That would be quite useful.
Finally, about the data, the total amount of oil produced, but more importantly consumed, is useful and valuable data. But seeing that China is the second largest consumer after the US is a bit misleading. Per capita consumption would add nuance to the consumption view, because China is over three-times as large as the US in population. Consequently, the average Chinese is not a major consumer. The problem is more that there are so many more Chinese consumers than consumers in any other nation—except India.
A bit of a hit and miss piece. I think the organisation and the idea is there: compare and contrast producers and consumers of oil (and consumers of other energy forms). Alas the execution does not quite match the idea.
Credit for the piece goes to Craig Bloodworth, via the Guardian.
This piece is doing some interesting things within the framework of the donut chart I generally dislike. We do get to see the levels of detail for different departments or areas of spending. For example, one can see that costs for building Australia’s new destroyers and how that fits into the whole budget. Or, by clicking on a slice of the donut, one can zoom in to see how pieces fit at the selected level.
But the overall visual comparison of pieces and then identifying them through colour is less than ideal.
Found via the Guardian’s datablog, credit for the piece goes to Prosple and OzDocsOnline.
This falls under the just-because-it’s-about-geographies-doesn’t-mean-it-should-necessarily-be-visualised-as-a-map category. The Guardian has taken data from the African Economic Outlook, specifically real GDP growth rates, and charted them as a map. This caught my interest initially because of some work I have been doing that required me to read a report on African economic development in coming years. So I figured this could be interesting.
But it’s a map. That’s not to say there is anything inherently wrong about the map. Though the arrangement of the legend and size of each ‘bin’ of percentage values is a bit odd. I would have placed the positive at the top of the list and tried to provide an equal distribution of the data, e.g. 3–10 for both positive and negative values. But, without looking in any depth at the data, the designer may have had valid reasons for such a distribution.
That said, two finer points stick out to me. The first is Western Sahara. Long story short, it is a disputed territory claimed by different factions. I am not accustomed to ever seeing any real economic data coming out of there. But, according to the map, its growth is 0–3%. When one looks at the data, however, one finds that as I would have expected the data says “no data”. Ergo the green colour on the map is misleading. Not necessarily incorrect, for the growth could have been between those two points, but without any data one cannot say for sure.
The second concern for me is South Sudan—remember that story? For starters one cannot find it on the map; South Sudanese territory is depicted as part of Sudan. While South Sudan is one of the poorest countries on the earth, its split from Sudan is rather important. Looking at the data, one can see Sudan’s growth went from 8 to 4.5 to 5 to 2.8. Why the sudden drop? Probably because Sudan’s economic boom has largely been built on the boom in oil prices over the past decade or so. But, most of that oil is no longer in Sudan, Not because its been pumped dry, but rather most of the oil fields can now be found in South Sudan.
These are some of the contextual stories that make sense of a data set. But these are the stories lost in a simple, interactive map.