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.
This piece in the Globe and Mail of Toronto looks at smartphone usage by operating system through a comparison of Canada to both the United States and Japan.
While I understand the need for aesthetic distinction from having an entire page of bar charts, these ring or donut charts are a touch misleading. Because of the space between rings, the radius of each circle from the central Android icon is significantly increased. This of course proportionally scales up the length of each segment within the rings. In short, it becomes difficult to compare segments of each ring to the corresponding segments on the other rings without looking at the datapoint. And if you need to look at the datapoint, one could argue that the infographic has failed from the standpoint of communication of the data.
Beneath is the original (with the legend edited to fit into my cropping) with two very simple (and hasty) reproductions of the data as straight pie charts placed next to each other and then as clusters of bar charts grouped beneath each other. I leave it to you the audience to decide which is easiest to decode.
Credit for the Globe and Mail piece goes to Carrie Cockburn.
The crisis in Syria now resembles more of a civil war. The UN General Assembly has condemned the conflict and passed a resolution calling for Bashar al-Assad to step aside along with a host of other steps to resolve the conflict. However, nothing can happen until the Security Council agrees on a measure, which is still unlikely given the previous vetoes by China and Russia.
This piece from the Guardian chronologically explains what has been happening—at least as best as can be determined in the not-so-media-friendly country. As this story focuses on dates and places, a map feels natural. The designers have added some crucial details from the backstory about the ethnic complexities of the country and denoted the larger and more important urban centres.
When one clicks on a date, coloured by what part of the story is taking place, markers with text boxes overlay the original city markers and provide the user information on what happened in that city on that date. Or, if the event is more general, the box appears outside the borders.
The interface is rather simple, but works in focusing a person in on a time. Unfortunately, since much of this story can be seen through the lens of locale, e.g. the city of Homs has borne the brunt of al-Assad’s wrath, one cannot focus in on a place and then add time. For example, clicking on the marker for Homs and then seeing a chronological list of events that occurred there would also be quite useful.
Another slight improvement would be more clearly signifying the date being viewed. It does appear in the text box, but with the visual prominence of the main navigation at the top, on a few occasions when I was going through the piece, I did forget what date I was on and had brief moments of confusion.
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.
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.