Via the Pew Research Center, the name of the game is concentration:

It is interesting to note that the only two religions that have  spread far wide beyond their place of origin are Christianity and Islam, and this was accomplished through political means, military expansion, colonialism, not because of any voluntary adoption.

I am currently (re)writing our online course on marriage and family (a topic I generally stay away from but them’s the breaks). However, as usual, I decided to integrate a module on data exploration. I stumbled upon this Pew research report on global aging that contained a lot of information and data, so I thought I’d just share some of what I found interesting.

First of all, I find this interactive visual very useful as an introduction the state of the world population by age groups, from 1950 to 2050:

You can either examine data for the US (numbers and percentages) or the world. Focusing on percentages, for the global population, you can clearly see which age group is projected to grow or shrink. So, for instance, the 15 to 64 population is stays pretty much stable from 1950 to 2050 (from roughly 61 to 63%). The under 15 category peaks in 1965 (with 38%, the end of the baby boom) but projected at just over 21% by 2050. However, for the 65 and older age group, the shift is from about 5% in 1950, to 15% projected for 2050. These increases and decreases are clearly visible just by eye-balling the graphic. Switching to the Us, that shift to a “geezerification” of the population is even clearer. as it is for most wealthy countries.

The global overview is nice but only as a starting point. There is some need for some fine-tuning by country and since my main topic here is aging, let’s look at that, for selected countries

Let’s do that below the fold:

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Yup, I’ll be watching the World Cup. And a few media outlets have come up with some great dataviz on the subject. My favorite so far comes from the Economist, and you can spend a lot of time playing with this one:

Goals

It is fully interactive and it visualizes all the World Cup goals with a lot of neat filters (countries, year, stage). You can see which countries are big scorers and try to derive playing style based on that.

Business Insider looks at the average temperatures at kick-off (it will be interesting to see if that correlates with other things, like goals scored, etc.):

wc_temps

More interesting is this dataviz dashboard that comprises information regarding which league players play in during the regular season, as well as the experience v. age factors:

And here is a bunch of various stats on the World Cup (the whole scrolling thing is a bit tedious… simplicity is often better):

And via Le Monde (in French), here is a dataviz video summarizing roughly the same statistical info:


Les chiffres insolites de la coupe du Monde by lemondefr

This is Todd’s turf more than mine but check out this dataviz from the New York Times:

06TK-nat-ARMS-web-Artboard_1

The big question is how and why considering that crime rates are not exactly exploding right now, and it’s not like US law enforcement is fighting the Sinaloa cartel.

Well, first, there is supply to be dumped, according to the article:

“As President Obama ushers in the end of what he called America’s “long season of war,” the former tools of combat — M-16 rifles, grenade launchers, silencers and more — are ending up in local police departments, often with little public notice.

During the Obama administration, according to Pentagon data, police departments have received tens of thousands of machine guns; nearly 200,000 ammunition magazines; thousands of pieces of camouflage and night-vision equipment; and hundreds of silencers, armored cars and aircraft.”

And in a very bureaucratic and Weberian fashion, once the tools are there, they will be used. And sure enough:

“The equipment has been added to the armories of police departments that already look and act like military units. Police SWAT teams are now deployed tens of thousands of times each year, increasingly for routine jobs.Masked, heavily armed police officers in Louisiana raided a nightclub in 2006 as part of a liquor inspection. In Florida in 2010, officers in SWAT gear and with guns drawn carried out raids on barbershops that mostly led only to charges of “barbering without a license.””

And so you end up with a militarized police force even though the crime statistics do not justify it. In addition, the use of militarized gear changes the way police forces approach situations, i.e., they do so more aggressively since the balance of force is more in their favor. And since the equipment is free or would be scrapped if unused, it is easy to see why police chiefs would get stuff that, really, they don’t need. But once they have it, the equipment acquisition has to be rationalized. So, you get jewels like these:

In the Indianapolis suburbs, officers said they needed a mine-resistant vehicle to protect against a possible attack by veterans returning from war.

“You have a lot of people who are coming out of the military that have the ability and knowledge to build I.E.D.’s and to defeat law enforcement techniques,” Sgt. Dan Downing of the Morgan County Sheriff’s Department told the local Fox affiliate, referring to improvised explosive devices, or homemade bombs. Sergeant Downing did not return a message seeking comment.

(…)

Some officials are reconsidering their eagerness to take the gear. Last year, the sheriff’s office in Oxford County, Maine, told county officials that it wanted a mine-resistant vehicle because Maine’s western foothills “face a previously unimaginable threat from terrorist activities.””

What it does though, is turn police officers into soldiers in occupied territories where all civilians are potential enemies and neighborhoods into potential war zones. And we all know which neighborhoods will face militarized police forces, of course, because we already know who bears the brunt of heavy policing.

The World Cup is about to start, so, unsurprisingly, football is in the news a lot and I have noticed the publication of quite a bit of dataviz relating to sport in general, and football in particular.

First, the Guardian’s great Datablog has a series of quick charts on the statistics of the different teams.

Top and bottom five by average team age (not age by players where the oldest is 43 and the youngest is 18):

The oldest teams are in Latin America and the youngest two are in Africa but the spread is pretty narrow (about three years).

Top and bottom five by caps:

Spain won in 2010, so, it kinda makes sense that they would use experienced players. However, France, for instance (we all remember their great “performance” in 2010) has a comparatively low number of caps for a country that won 16 years ago and was finalist after that. Maybe there’s a generational change at work. I am surprised that Japan would rank so high. Japan is not a big football team on the international scene. Or maybe there is a limited pool of players, so, they get to accumulate more caps.

Top 10 by number of international goals:

The top three are really not surprising at all. In number of goals, Spain and Germany completely outclass every other team. But where are Brazil, Argentina, or Italy?

Clubs with 10 or more players in the World Cup:

No surprise here either. It is the Big teams (those that can afford these kinds of players) that top the list. Note the large representation of the English Premier League. Speaking of which…

Leagues with the most players featured:

The wealthy leagues top the list. Again, no surprise here.

Top and bottom teams with players based in home country:

We know the big leagues extract players out of other countries (especially African countries). That’s another resource flow from the periphery to the core.

But football is a global sport and the migration of players outside their own countries during regular season is also well illustrated by this dataviz (I’ll put the ginormous but interesting version below the fold) by the Pew Research Center:

World_Cup_Migration_Map

But sports also involve spectatorship and the Economist has done some data work on that as well:

Sports_Attendance

Even though, the US tops the list with attendance figures for the NFL, that is a relatively small percentage of the population. Surprisingly (at least, for me) is the largest percentage that goes to the Australian football league. Also note that Canada is counted with the US except for football. One could argue that the US has three popular sports (football, baseball, and basketball, then, to a smaller extent, hockey) where other countries tend to have one largely dominant sport (often football).

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Pro Publica (a very worthwhile news organization that you should all read) has published a very nice data visualization regarding imports and exports of guns by states (and nationwide) based on tracing done after criminal activity. You can go state by state and look at where guns came from and where they went across state lines.

So, first, the national map:

National_exports

Ok, I decided to pick Illinois since this is where I am located and in, every discussion on guns, someone will claim to have the decisive argument that Chicago is not murder-free despite a gun ban. So, let’s look at Illinois imports:

IL_imports

About half of the guns traced by the authorities came from out of state. Who would have thought that somehow, things, like guns, would make their way to Chicago and that somehow, Chicago does not live under a dome, Stephen-King style.

For the most part, these imported guns came from Indiana, Wisconsin, and Mississippi. The dataviz also has these imports (and exports) broken down by state.

Anyhoo, Illinois also exports guns to other states:

IL_exports

But check out New York state:

NY_imports

So, close to 70% of guns traced in NY state came from out of state, and you can see that, for the most part, they came from other East Coast states.

But compare that to Texas:

TX_imports

Only 18% of the traced guns came from out of state.

No big surprise here. If a state has strict gun laws, one can expect more imports. But if a state has fewer restrictions on guns, then, by definition, imports will be less necessary.

Get check out the whole thing.

With the recent shootings at UCSB, there has been a lot of talk about the gendered nature of homicides, where the gender of shooters is almost invariably male, and the gender of the victims is largely male but also female.

I would like to pursue the point that, indeed, homicide is a gendered social fact. So, first, check out this visualization of the proportion of homicide by gender:

Let me note that ratio is not the appropriate term here. The map actually represents the proportion of males in the sum of homicides in a country. For instance, if you take Zambia, about 78% of murder victims are men, hence the dark blue color.

Now, you can notice that any country in red, orange, or yellow would be a country where the proportion of women victims of homicide is higher than that of men. The light yellow color would represent rough equality in the gender of victims (between 45 and 55%). So, congratulations, Iceland, on being the only country where 100% of murder victims were women. Anyone care to guess the murder rate in Iceland? The most recently published data show 1 murder in the last year (2012). I presume the unfortunate victim was a woman.

Similarly, you will notice rough equality in Germany and a couple of Scandinavian countries (Finland and Norway), and a couple of smaller European countries, Japan and South Korea. These are all countries with low murder rates.

Otherwise, the rest of the map is solidly blue, that is, there are more men victims of homicides than women, sometimes, dramatically so. And these darker blue countries are also countries where the murder rate is higher. Let me explore that a bit further with some more UNODC data I have used before.

Let’s look at where the homicides are, compared to population size, so we can get a rough sense of over/under:

Homicide compared population

Clearly, Africa and the Americas are the continents where homicides are a major issue. No surprise here. But when one adds the gender aspect of this, we see two different dynamics at work:

Homicides compared gender

Women are more likely to be killed by a spouse (or ex), a relative, or an acquaintance. Men, on the other hand, are more likely to be killed by someone they do not know (a rival gang member, for instance) or an acquaintance. For women, victimization is an intimate thing. Not so for men.

Let’s add another layer to this:

Homicides compared locations

As you can see, high-homicide rate countries have more men victims, and the murders are more likely to take place in public places. On the other hand, in low-homicide rate countries, the proportion of women victims increases and these homicides become privatized, taking place at home.

This goes to the larger context: countries where homicide rates are high are countries where governments have a hard time exercising legitimacy and authority, and therefore, obtaining and retaining a monopoly over the use of force (see; Weber). So, such a country might have a very big gang / drug cartels / paramilitary groups problem. These groups are composed largely young men, who might end up killing each other. And the internal culture of these organizations is very much hegemonically masculine. Moreover, when a group like the Zetas engage in mass murder, they do not just as a tactical matter, but also as a public statement of power (hence the gruesome stagings). These killings are a form of “public policy” for these groups.

On the other hand, in countries with low homicide rates, governments tend to be stable and able to exercise their authority over their entire territory. As such, there is less public violence and less challenging of governmental authority. Therefore, murders become more private matters and women are more likely to be the victims.

The Washington Post has a couple of very detailed data visualizations regarding the death penalty in the US. Here is the first one (click on the image for a larger view):

DP1

This graph reads like a population pyramid and clearly shows the racial distribution between the executed population (on the left) and the victim population (on the right). Apparently, the victims are factored in at the time of execution rather than actual year of death (see 2001, where the victims of Timothy McVeigh were counted when he was executed). It is easy to see the overrepresentation of African Americans on the executed side as well as the greater prevalence of whites on the victim side, which conforms what we know about the application of the death penalty.

Here is the second dataviz:

DP2

Here, one can clearly see that executions peaked in 1999 (the article explains why), then steadily declines after that. By method, one can see when lethal injections became prevalent, at the expenses of electrocution (although that my be changing has drugs needed for executions are becoming more difficult to obtain, see Todd’s post on this).

The age category might be affected by the length of appeals. Men (as they are mostly men, as one can see on the gender chart) may be sentenced while in one age group and actually executed in another. The chart is based on age of execution, not sentencing. So, the bulk of execution takes place for men in their 30s and 40s.

More shocking, yet not surprising, is the regional distribution. Check out the South. It dwarves all the other region by at least a factor of 10. And Texas alone dwarves the rest of the country by at least a factor of five. If the death penalty really worked, the South, and especially Texas, should be a crime-free heaven.

Seriously, am I the only one who sees a problem here?

DSC00127

The 37% for biofuels is the only one that looks ok to me. But food crops are listed as 25% in the paragraph but on the chart, it looks more like 12%. Livestock reads as 3% in the paragraph but barely registers on the chart. Non-food crops read as 5% in the paragraph, but at about 1.5 / 2% on the chart.

I re-read this several times, just to be sure, but the paragraph clearly refers to fig 2,1 (which it is, even if it does not show up on the photo).

This is from a serious book, published by a serious academic publisher, and written by one of my favorite (really serious) sociologist, so, this bothers me a lot even though it looks like first world problem.

If you do not subscribe to the US Census Bureau updates, you are missing out on a lot of great information. Yesterday, the update was about poverty, how to measure it, and the latest data on the subject. So, if you teach poverty (and don’t we all), the following are some pretty interesting and discussion-starting visualizations.

First off, of course, we are all familiar with the official poverty line and its shortcomings. So, the USCB uses a supplemental measure designed to overcome these shortcomings. The differences between the two measures are summarized in this nifty infographic (click on all the images for a larger view):

poverty_measure-how

How many people are poor and how long do they stay that way? According to the Census Bureau summary:

“31.6 percent of Americans were in poverty for at least two months from 2009 to 2011, a 4.5 percentage point increase over the prerecession period of 2005 to 2007. Poverty was a temporary state for most people; however, 3.5 percent of Americans were in poverty for the entire three-year period.”

Visually, things look like this (pdf version here):

cb14-05_poverty_001

These data and visualizations are related to a new report on poverty from the Census Bureau. The basic concepts it uses are as follows:

Poverty definitions

So, using these different measures, here are some of the results.

First off, the overall picture:

Poverty overall

No big surprise here. Poverty rates were higher after the recession than before. Note that the use of a three-year panel overestimates episodic poverty compared to calendar year measures, and underestimates chronic poverty compared to calendar year measures. It’s pretty obvious why (people in poverty for more than 2 months but less than three years).

Now, let’s look at chronic and episodic poverty with various demographics:

Poverty chronic and episodic

No surprise here either: blacks and female-householder families lead the pack on chronic poverty,followed by Hispanics and under 18.  On the other hand, whites, seniors, and married-couple families have lower rates of chronic poverty. These trends are the same for episodic poverty, but with higher rates.

More specific data show the same trend as well:

Poverty by demographics

Now looking more specifically at poverty entry rates:

Poverty entries by demographics 2009 2011

The same categories of people as above entered poverty in higher than the overall rate between 2009 and 2011.

Now, poverty exit rates:

Poverty exits by demographics 2009 2011

Again, this time, one would look at rate below the overall rate to determined which categories of people have a harder time escaping poverty, basically, any rate below 35.4. The pattern we identified above persists.

How long does poverty last? How long is a poverty spell?

Poverty spells

This lopsided trend is interesting. Either a spell is short or it is long, but the middle durations show lower percentages. But the vast majority of spells are a year or less.

Looking at median poverty spells:

Poverty median spells

Here, the only surprise is the rate for 65 and over. Remember, that category had a lower poverty rate but for those who are poor, then, the median spells are longer than the overall median.

This is confirmed by the data on poverty persistence between 2009 and 2011:

Poverty duration

This is all pretty interesting stuff. You can read the full report, if you are so inclined.

I should note though, that poverty spells may be short, but insecurity and precarity are not and take a toll.

Take a look at this great visualization of life expectancy around the world. You’ll need to do a lot of zooming in and out, but it’s worth it. And it’s a visually appealing way of presenting it. I like the organization by continent and by color. Don’t forget to scroll all the way down to the bottom for the time series.

Good visualizations on this and related topics can also be found using the Human Development Report visualization tool:

The US Census Bureau has released a series of recent maps showing the wealthiest and poorer counties, nationwide, using data from the Small Area Income and Poverty Estimates program.

First, median incomes (for all, click on the images for larger view):

Median Income_001

The Northeast metropolitan corridor is pretty striking: where the power elite is. As the report notes:

“The U.S. Census Bureau reports that five of the counties or county-equivalents nationwide with the highest median household income in 2012 were located in Northern Virginia. Among them were Arlington County, at $99,255, Fairfax County, at $106,690, Falls Church (an independent city), at $121,250, Loudoun County, at $118,934, and Stafford County, at $95,927. Falls Church and Loudoun also had among the lowest poverty rates in the country.”

Then, poverty rates:

Poverty Rates_001

Now, one can see a Southeastern corridor of high poverty, with a few other spots (the tips of Texas, and parts of South Dakota).

Thirdly, child poverty:

Children Poverty_001

The patterns are a bit harder to distinguish (partly because of the color scheme), but you can clearly see that the east coast wealthy corridor is very white and that the same Southeastern corridor is there as well.

Finally, shifts in median income:

Change in Median Income_001

Now, one can see a red (as in increase) crossing the central part of the country, from North to South. If I remember correctly, this was also the region least affected by the economic recession and high unemployment (especially the Dakotas). The Southwest is impressive in its decrease (the West overall, but really, the Southwest, especially).

As the report states, again:

“he findings also show that median household income is higher in nearly half of the counties in the Dakotas now than it was before the recession began in 2007. Between 2007 and 2012, 55 of the 119 counties in North and South Dakota experienced a statistically significant increase in median household income. In contrast, of the remaining 3,023 counties or equivalents nationwide, the same was true of only 56 of them. Of all the U.S. counties with a statistically significant change in income relative to 2007, 89 percent experienced a decline.

Emphasis mine.