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.
The Globe and Mail has been working on a story about immigration to Canada because apparently not all immigrants come to America. The story has its section headers running down the side column of the page, like many other segmented stories you’ll see posted online these days, but also uses graphics to make and supplement its arguments.
This one chart from the piece is an example of how the simple format of a line chart can clearly express and visualise an interesting trend. Immigrants from the past two decades earn less than immigrants to Canada in the 1970s. Those from the early 90s, however, do appear to have a faster rate of income growth that approaches parity with Canadian-born income-earners.
On Sunday the New York Times featured a small graphic highlighting the disparity in growth rates across the G-20 if broken into the ‘core’ G-8 and then what one might call the emerging markets of the G-11.
The charts are small yet compelling in telling the story of how the two different groups are performing. However, I was left wanting to better understand the comparisons between the sizes and growth of the various countries. The areas of circles are difficult to compare and aggregates mask interesting outliers. So, using what I imagine to be the same data from the IMF, I took a quick try at the data to create my own infographic.
Indeed, interesting stories began to appear as I plotted the data. Russia is a member of the G-8, but perhaps has more in common with the G-11. After all, Russia’s growth was nearly 500%. Similarly interesting were Canada and Australia. The former, a G-8 country, was the only G-8 country besides Russia to have greater than 100% growth. And Australia, certainly not an emerging market in most senses, experienced nearly 300% growth. Whereas the emerging markets of Mexico and South Korea lag behind the rest of the G-11.
Then, when plotting the sizes of the economies, China was no surprise as the second-largest economy. However, that Brazil has managed to already surpass the G-8 economies of Italy, Russia, and Canada was a bit shocking. And Brazil looks nearly ready to surpass the UK, but for its apparent recent downturn. Also interesting to note are the Financial Crisis dips in GDP across most countries. Some countries, like China, unsurprisingly did not suffer greatly. However, that Japan and South Africa kept on a steady pace of growth was unexpected.
All of that would have been missed but for a slightly deeper dive into the IMF data. And a few hours of my time.
This weekend the New York Times looked at segregation in New York City schools by mapping the least (and most) diverse and offering quick comparisons to other large cities. (Is it really a surprise that the country’s largest cities also would need the largest demographic shifts to create diverse education environments?) Probably the best thing, seemingly as always, in the piece is the annotations that provide stories and context and explain the outliers that are all otherwise visualised in the infographic.
Mariano Rivera of the New York Yankees is(was?) one of the best closers in baseball history. I’ll give him that. So when a freakish accident brought to an end his season—and possibly his career—the New York Times of course had an(other) infographic about his historic numbers.
I don’t normally do the re-posts to the other blogs I follow, but this post on Flowing Data is a link to an interesting piece of analysis on the political groups in the US Senate. It’s worth a(nother) look.
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.
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.