The Olympics are now fully underway and we can begin to see some patterns about who is doing well and who is, well, not. This infographic has a lot more to say about who had been doing well up through 2008. That is important because that was the last year before the fiscal/financial crisis brought about the first global recession since World War II. Stay tuned for the post-Olympic piece where I look at medal performance in 2012 compared to GDP per capita. Some interesting stories appear to be happening.
One can clearly see that GDP per capita is (generally speaking) a good variable for estimating Olympic success. So the countries in this graphic are three major economic regions. The G7, BRIC, and the Future-7. The G7 are the world’s richest/most productive countries. BRIC are supposed to become the next G7. And the Future-7 is a Euromonitor International grouping that looks at a group of countries that are expected to become the next BRIC-like group of countries.
It is probably worth noting that despite this being an infographic for work, where I generally am not allowed to write analysis, the written analysis is mostly mine with some key ideas brought to my attention by co-workers.
Mexico has some serious problems. Primarily with the drug cartels. About two weeks ago the National Post created an infographic that looked at the northern spread of Mexican drugs into the United States. The infographic also included details on the transit and transportation networks the different drugs take along with the geographic spread of the various cartels from the Tijuana, Federation, Juarez, and Gulf Cartels as reported by US cities.
Foreign Policy magazine rates countries as to how close they may or may not be to becoming failed states. Mexico is among those that have fallen into the “Warning” category over the recent years. The second half of the infographic looks at why. In short, in the past few years over 50,000 people have been killed in drug-related homicides and several more thousand have simply disappeared. The police, military, civilian officials, journalists, &c. have become targets of the cartel if they oppose the cartels.
Mexico has some serious problems. Sadly problems have a tendency to spill over borders.
Credit for the piece goes to Jonathan Rivait and Richard Johnson.
Census data fascinates me from a data visualisation perspective; one can look at it so many different ways. Last week I looked at some of the Slovakian census data on the Carpatho-Rusyns that live in the northeastern mountains of Slovakia. But yesterday, the British Office of National Statistics released the results from their census of England and Wales (Scotland reports later and Northern Ireland did so already, yay devolution.) One of the big news stories was that England and Wales had 500,000 more people than had been expected. That doesn’t sound like a lot of people, but to put it roughly into American proportions, that would be like finding that there was a whole new city the size of Chicago somewhere in the United States.
But while many organisations and individuals will certainly be looking at the census data in the coming days, weeks, and months, the ONS released its own interactive application. Basically it looks at the population pyramid for England and Wales from 1911 to 2011, a century’s worth of data. But what makes this different from the GE population pyramids, for example, is the context that the ONS has added that strict data pulls lack.
Here in 1921, rolling over a particular cohort reveals the details of those aged 30 in 1921. There is a clear difference between the number of men and women. But why? The text block’s first note details how 700,000 men aged 20–40 died during World War I and thus altered the basic structure of the English and Welsh population.
And in 1951 we begin to look at the British baby boom in the post-war era. Again, while the Baby Boom might be expected, the ONS also points out that the NHS, the British National Health Service, had also recently started and was positively affecting life expectancy and the general health of the British public. These are again things that would not likely appear in more data-focused pieces.
But everybody loves to compare things to other things. So, the ONS also released a more data-focused application that allows the user to select two different census geographies and compare them. This is more as one would expect, comparing overlays vs. side-by-side looks at different population pyramids. The example below compares London to Birmingham.
Credit for the pieces go to the ONS Visualisation Centre.
As the Supreme Court is likely to scrap the mandate provision of the health care law—without which sick people are left to pay higher premiums if they can get coverage at all—later today, the New York Times looks at the impact of removing the health care law changes the number of people without health insurance.
It appears as if the Greeks, who voted in parliamentary elections for the second time in as many months, have narrowly voted for pro-bailout parties. But whether the pro-bailout parties can put aside their other political differences and form a coalition government remains to be seen.
I appreciate the mirror approach, but wonder if the comparisons might not have been clearer if measured directly? Or what would have happened without the mirror approach and compared the two countries in single but slightly larger charts? Regardless, one can easily see that Greece has some serious problems.
Credit for the piece goes to Andrew Barr, Mike Faille, and Richard Johnson.
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.