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Visualizing Global Risks 2013

Visualizing Global Risks 2013

A year ago we looked at Global Trends 2025, a 2008 report by the National Intelligence Commission. The 120 page document made surprisingly little use of data visualization, given the well-funded and otherwise very detailed report.

By contrast, at the recent World Economic Forum 2013 in Davos, the Risk Response Network published the eighth edition of its annual Global Risks 2013 report. Its focus on national resilience fits well into the “Resilient Dynamism” theme of this year’s WEF Davos. Here is a good 2 min synopsis of the Global Risks 2013 report.

We will look at the abundant use of data visualization in this work, which is published in print as an 80-page .pdf file. The report links back to the companion website, which offers lots of additional materials (such as videos) and a much more interactive experience (such as the Data Explorer). The website is a great example of the benefits of modern layout, with annotations, footnotes, references and figures broken out in a second column next to the main text.

RiskCategories

One of the main ways to understand risks is to quantify it in two dimensions, namely its likelihood and its impact, say on a scale from 1 (min) to 5 (max). Each risk can then be visualized by its position in the square spanned by those two dimensions. Often risk mitigation is prioritized by the product of these two factors. In other words, the further right and/or top a risk, the more important it becomes to prepare for or mitigate it.

This work is based on a comprehensive survey of more than 1000 experts worldwide on a range of 50 risks across 5 broad categories. Each of these categories is assigned a color, which is then used consistently throughout the report. Based on the survey results the report uses some basic visualizations, such as a list of the top 5 risks by likelihood and impact, respectively.

Source for all figures: World Economic Forum (except where noted otherwise)

Source for all figures: World Economic Forum (except where noted otherwise)

When comparing the position of a particular risk in the quadrant with the previous year(s), one can highlight the change. This is similar to what we have done with highlighting position changes in Gartner’s Magic Quadrant on Business Intelligence. Applied to this risk quadrant the report includes a picture like this for each of the five risk categories:

EconomicRisksChange

This vector field shows at a glance how many and which risks have grown by how much. The fact that a majority of the 50 risks show sizable moves to the top right is of course a big concern. Note that the graphic does not show the entire square from 1 through 5, just a sub-section, essentially the top-right quadrant.

On a more methodical note, I am not sure whether surveys are a very reliable instrument in identifying the actual risks, probably more the perception of risks. It is quite possible that some unknown risks – such as the unprecedented terrorist attacks in the US on 9/11 – outweigh the ones covered here. That said, the wisdom of crowds tends to be a good instrument at identifying the perception of known risks.

Note the “Severe income disparity” risk near the top-right, related to the phenomenon of economic inequality we have looked at in various posts on this Blog (Inequality and the World Economy or Underestimating Wealth Inequality).

A tabular form of showing the top 5 risks over the last seven consecutive years is given as well: (Click on chart for full-resolution image)

Top5RisksChanges

This format provides a feel for the dominance of risk categories (frequency of colors, such as impact of blue = economic risks) and for year over year changes (little change 2012 to 2013). The 2011 column on likelihood marks a bit of an outlier with four of five risks being green (= environmental) after four years without any green risk in the Top 5. I suspect that this was the result of the broad global media coverage after the April 2011 earthquake off the coast of Japan, with the resulting tsunami inflicting massive damage and loss of lives as well as the Fukushima nuclear reactor catastrophe. Again, this reinforces my belief that we are looking at perception of risk rather than actual risk.

Another aggregate visualization of the risk landscape comes in the form of a matrix of heat-maps indicating the distribution of survey responses.

SurveyResponseDistribution

The darker the color of the tile, the more often that particular likelihood/impact combination was chosen in the survey. There is a clear positive correlation between likelihood and impact as perceived by the majority of the experts in the survey. From the report:

Still it is interesting to observe how for some risks, particularly technological risks such as critical systems failure, the answers are more distributed than for others – chronic fiscal imbalances are a good example. It appears that there is less agreement among experts over the former and stronger consensus over the latter.

The report includes many more variations on this theme, such as scatterplots of risk perception by year, gender, age, region of residence etc. Another line of analysis concerns the center of gravity, i.e. the degree of systemic connectivity between risks within each category, as well as the movement of those centers year over year.

Another set of interesting visualizations comes from the connections between risks. From the report:

Top5Connections

Top10ConnectedRisks

Finally, the survey asked respondents to choose pairs of risks which they think are strongly interconnected. They were asked to pick a minimum of three and maximum of ten such connections.

Putting together all chosen paired connections from all respondents leads to the network diagram presented in Figure 37 – the Risk Interconnection Map. The diagram is constructed so that more connected risks are closer to the centre, while weakly connected risks are further out. The strength of the line depends on how many people had selected that particular combination.

529 different connections were identified by survey respondents out of the theoretical maximum of 1,225 combinations possible. The top selected combinations are shown in Figure 38.

It is also interesting to see which are the most connected risks (see Figure 39) and where the five centres of gravity are located in the network (see Figure 40).

One such center of gravity graph (for geopolitical risks) is shown here:RiskInterconnections

The Risk Interconnection Map puts it all together:

RiskInterconnectionMap

Such fairly complex graphs are more intuitively understood in an interactive format. This is where the online Data Explorer comes in. It is a very powerful instrument to better understand the risk landscape, risk interconnections, risk rankings and national resilience analysis. There are panels to filter, the graphs respond to mouse-overs with more detail and there are ample details to explain the ideas behind the graphs.

DataExplorer

There are many more aspects to this report, including the appendices with survey results, national resilience rankings, three global risk scenarios, five X-factor risks, etc. For our purposes here suffice it to say that the use of advanced data visualizations together with online exploration of the data set is a welcome evolution of such public reports. A decade ago no amount of money could have bought the kind of interactive report and analysis tools which are now available for free. The clarity of the risk landscape picture that’s emerging is exciting, although the landscape itself is rather concerning.

 
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Posted by on January 31, 2013 in Industrial, Socioeconomic

 

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2012 in review

The WordPress.com stats helper monkeys prepared a 2012 annual report for this blog.

Here’s an excerpt:

4,329 films were submitted to the 2012 Cannes Film Festival. This blog had 36,000 views in 2012. If each view were a film, this blog would power 8 Film Festivals

Click here to see the complete report.

 
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Posted by on December 31, 2012 in Uncategorized

 

Circos Data Visualization How-to Book

Earlier this year we have looked at a powerful data visualization tool called Circos developed by Martin Krzywinski from the British Columbia Genome Science Center. The previous post looked at an example of how this tool can be used to show complex connectivity pathways in the human neocortex, so-called Connectograms.

Circos Book Cover

The Circos tool can be used interactively on the above website. In that mode you upload jobs via tabular data- and configuration-files and have some limited control over the rendering of the resulting charts. For full expressive power and flexibility, Circos can also be downloaded freely and used on your computer for rendering with extensive customization control over the resulting charts.

I have been asked to review a new book titled “Circos Data Visualization How-to“, published by Packt Publishing here. It’s main goal is to guide through the above download + installation process and get you started with Circos charts and their modification. Here is a brief review of this book.

Although originally developed for visualizing genomic data, Circos has been applied to many other complex data visualization projects, incl. social sciences. One such study was done by Tom Schenk, who analyzed the relationships between college majors and the professions those graduates ended up in. It appears as if this work inspired the author to write this book to help others with using Circos.

I downloaded the book in Kindle format and read it on the Mac due to the color graphics and the much larger screen size. It’s well structured and around 70 pages in printed form. The book focuses first on the download and install part, then has a series of examples from first chart to more complex ones using customization such as colors, ribbons, heat maps or dynamic binding.

Flow Chart for creation of Circos charts

Flow Chart for creation of Circos charts

Circos is essentially a set of Perl modules combined with the GD graphics library.

The first part is on Installing Circos, with a chapter each on Windows 7 and on Linux or Mac OS. Working on MAC I went the latter route. I ended up right in the weeds and it took me about 4 hours to get everything installed and working. The description is derived from a Linux install and is generally somewhat terse. It assumes you have all prerequisite tools installed on your Mac or at least that you are savvy enough to figure out what’s missing and where to get it. I had to dust off some of my Unix skills and go hunting for solutions via Google to a list of install problems:

  • directory permissions (I needed to warp the exact instructions with sudo)
  • installing Xcode tools from Apple for my platform (make was not preinstalled)
  • understanding cause of error messages (Google searches, Google group on Circos)
  • locating and installing the GD graphics library (helpful installing-circos-on-os-x tips by Paulo Nuin)
  • version and location issues (many libraries are in ongoing development; some sources have moved)

Others may find this part a lot easier, but I would say there should be an extra chapter for the Mac with tips and explanations to some of these speed bumps. On the plus side, the Google group seems to be very active and I found frequent and recent answers by Circos author Martin Krzywinski.

The next part of the book is easy to understand. One creates a simple hair-to-eye color relationship diagram. Then configuration files are introduced to customize colors and chart appearance. All required data and configuration files are also contained in the companion download from the Packt Publishing book page.

Chart of relationship between hair and eye colors

Chart of relationship between hair and eye colors

The last part of the book goes into more advanced topics such as customizing labels, links and ribbons, formatting links with rules, reducing links through bundling, and adding data tracks as heat maps or histograms. This is the meat for those who intend to use Circos in more advanced ways. I did not spend a lot of time here, but found the examples to be useful.

Contributions by State and Political party during 2012 U.S. Presidential Elections

Contributions by State and Political party during 2012 U.S. Presidential Elections

This section ends abruptly. One gets the feel that there are other subtleties that could be explored and explained. A summary or outlook chapter would have been nice to wrap up the book and give perspective. For example, I would have liked to hear from the author how much time he spent with various features during the college major to professions project.

In summary: This book will get you going with Circos on your own machine. Installing can be a challenge on Mac, depending on how familiar you are with Unix and the open source tool stack. The examples for your first Circos charts are easy to follow and explain data and configuration files. The more advanced features are briefly touched upon, but require more experimentation and time to understand and appreciate.
Circos author Martin Krzywinski writes on his website: “To get your feet wet and hands dirty, download Circos and a read the tutorials, or dive into a full course on Circos.” The How-to book by Tom Schenk helps with this process, but you still need to come prepared. If you are a Unix power user this should feel familiar. If you are a Mac user who rarely ever opens a Terminal then you might be better off just using Circos via the tableviewer web interface.
Lastly, I would recommend buying the electronic version of this book, as you can cut & paste the code, leverage the companion code and documents. A printed version of this book would be of very limited use.

 
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Posted by on December 6, 2012 in Education, Scientific

 

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2012 Election Result Maps

2012 Election Result Maps

The New York Times has covered the 2012 U.S. presidential election in great detail, including the much heralded fivethirtyeight Blog (after the 538 electoral votes) by forecaster Nate Silver. His poll-aggregation model has consistently produced the most accurate forecasts, and called 99 of 100 states correctly in both the 2008 and the 2012 elections.

A popular visualization is the map of the 50 states in colors red (Republican) and blue (Democrat) plus green (Independent). Since most states allocate all their electoral votes to the candidate with the most votes in that state, this state map seems the most important.

2012 Election Result By State (Source: NYTimes.com)

This map hardly changed from 2008, only Indiana and North Carolina changed color. Hence the electoral vote result in 2012 (332 Dem206 Rep)  is similar to that of 2008 (365 Dem173 Rep). The visual perception of this map, however, is that there is roughly the same amount of red and blue, with slightly more red than blue. This perception becomes even stronger when looking at the results by county.

2012 Election Results By County (Source: NYTimes.com)

Why is the outcome so strongly in favor of the blue (Democrat) when it looks like the majority of the area is red? The answer is found in very uneven population density of the 50 states. Although roughly the same size, California’s (slightly more blue) population density is about 40x higher than Montana’s (mostly red). On the extreme end of this scale, the most densely populated state New Jersey has about 1000x as many people living per square mile as the least densely populated state Alaska. Urban areas have a much higher density of voters than rural areas. The different demographics are such that urban areas tend to vote more blue (Democrat), rural areas tend to vote more red (Republican). The size of the colored area in the above chart would only be a good indicator if the population density was uniform. A great way to compensate visually for this difference can be seen in the third chart published by the NYTimes.

2012 Election Delta By County (Source: NYTimes.com)

Now the size of the colored circles is proportional to the number of surplus votes for that color in that county. The few blue circles around most major cities are larger and outweigh the many small red circles in rural areas – both optically intuitive and numerically in total. The original map is interactive, giving tooltips when you hover over the circles. For example, in just Los Angeles county there were about 1 million more blue (Democrat) votes than red (Republican).

2012 Election in Los Angeles County

This optical summation leads to intuitively correct results for the popular votes. The difference in popular vote was about 3.5 million more blue (Democrat) votes or roughly 3%. We see more blue in this delta circle diagram.

Of course, the president is not elected by the popular, but by the electoral votes per state. So no matter how big the Democrat advantage in California may be, there won’t be more than the 55 electoral votes for California. This winner-take-all dynamic of electoral votes by state leads to the outsized influence of swing states which are near the 50%-50% mark on the popular votes. A small lead in the popular vote can lead to a large gain in electoral votes. In extreme cases, a candidate can win the electoral vote and become president despite losing in the popular vote (as happened in 2000 and the very narrow win of Florida by George W. Bush).

Another variation on this theme of visually combining votes and population density information comes from Chris Howard. (This was referenced in an article on theatlanticcities.com by Emily Badger on the spatial divide of urban vs. rural voting preferences which has other election maps as well). The idea is to use shades of blue and red with population density increasing in darker shades of the color, used on a by county map.

2012 Election by county with shading by population density (Source: Chris Howard)

A final visualization comes from Nate Silver’s Blog post on November 8. While the % details of this at the time preliminary result may be slightly off (not all votes had been counted yet), the electoral vote counts remain valid.

2012 Election By State Cumulative (Source: Fivethirtyeight Blog)

It shows which swing state [electoral votes] put the blue ticket over the winning line (Colorado [9]) and which other swing states could have been lost without losing the presidency (Florida [29], Ohio [18], Virginia [13]). It also gives a crude, but somewhat telling indication of where you might want to live if you want to surround yourself by people with blue or red preferences.

 
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Posted by on November 15, 2012 in Socioeconomic

 

Superstorm Sandy – Visualizing Hurricanes

Superstorm Sandy – Visualizing Hurricanes

Time-lapse animation of Sandy Oct-28 from geostationary orbit, 1 frame per minute, 11 hours of daylight. Although “only” a category 1 hurricane, this superstorm has enormous size. Tropical storm force winds extend out over an area 900 miles in diameter.

Living in South Florida makes you alert to tropical storms during hurricane season from May to November. Exactly 7 years ago, at the end of October 2005, the eye of category 3 hurricane Wilma swept over our home in West Palm Beach in South Florida – the most powerful natural weather event I have ever witnessed. After avoiding a direct hit since then, we got a massive rain event from Isaac earlier this year, but again avoided a direct hit. To be sure, often the flooding associated with hurricanes is worse than the wind damage. For example, when hurricane Katrina hit New Orleans in August 2005, most of the devastation came from flooding after the levees were breached. But the first question is always where the storms will make landfall and how strong they are when they hit your area.

Tropical storms are being tracked and forecast in great detail, in particular by the National Hurricane Center of the National Weather Service. There are many great visualizations illustrating the path, windspeed, rainfall, extent of tropical storm force winds, etc. Due to the convenience for browsing, I have almost completely switched to following hurricane or weather updates from the iPad. (In this case I’m using the Hurr Tracker app from EZ Apps.)

Last week a new tropical storm emerged in the Carribean and was named ‘Sandy’. A few days ago with Sandy’s center over the Bahamas, the path looked like this:

Path of hurricane Sandy as of Oct-25 (Hurr Tracker iPad app)

Note the use of color for wind speed and the cone of uncertainty in the lower segment, as well as the rings around the center indicating the size of the area with storm-force winds.

Naturally curious whether South Florida was likely to get hit, another image gave us some relief:

5 Day tracking map for hurricane Sandy

Now a few days later, while we did get some strong northerly winds and pounding surf leading to beach erosion, Sandy was not a particularly disturbing event for South Florida. At the same time, however, Sandy is forecast to make landfall on the Jersey shore within about 24 hours during the night from Monday to Tuesday.

One interesting set of maps with a color code displaying the probability of an area experiencing winds of a certain speed, say at least tropical storm force winds (>= 39 mph). The following map was issued this afternoon and indicates the very large area (mostly offshore) with near 100% probability of exceeding tropical storm force winds in purple.

Tropical storm force wind speed probabilities for hurricane Sandy as of Oct-28

This indicates how large Sandy is – an area the size of Texas with tropical storm force winds! Meteorologists are concerned for the Northeast due to Sandy converging with two other weather events, a storm from the West and cold air coming down from the North. This is expected to intensify the weather system, similar to the Perfect Storm of 1991. Due to the timing around Halloween this is why Sandy was also called a ‘Frankenstorm’.

One of the most chilling pictures is this animated GIF from WeatherBELL. A story in the Atlantic earlier today writes this:

Dr. Ryan Maue, a meteorologist at WeatherBELL, put out this animated GIF of the storm’s approach yesterday. “This is unprecedented –absolutely stunning upper-level configuration pinwheeling #Sandy on-shore like ping-pong ball,” he tweeted. It shows how cold air to the north and west of the storm spin Sandy into the mid-atlantic coastline.

(Click the image if the animation doesn’t play in your browser.)

Animation of hurricane Sandy moving into the NorthEast (Source: WeatherBELL)

Understandably this forecast of superstorm Sandy has the authorities worried. The full moon tomorrow exacerbates the tides and New York City is expecting up to 11 ft storm surge. Cities across the Northeast are taking precautions as of this writing. For example, the New York City subway metro transit system is shutting down tonight and several hundred thousand people in low-lying coastal areas are under mandatory evacuation order. More than 5000 flights to the area on Monday have been cancelled. Take a look at the expected 5 day precipitation forecast in the Northeast. Some areas may get up to 10 inches of rain and/or snow!

5 day precipitation forecast with Sandy’s impact for the Northeast

The first priority is to use such visualizations to communicate the weather impact and allow people to take necessary precautions. One can use similar hurricane charts to visualize other uncertain events, such as the future outcomes of development projects. We will look at this in an upcoming post on this Blog.

 

Addendum 11/4/12: The NYTimes has provided some interactive graphics detailing the location and size of power outages caused by superstorm Sandy in the New York and New Jersey area. The New York City outages have been summarized in this chart, normalized to the percentage of all customers. As can be seen, the efforts to restore power over the first 6 days have been fairly successful, especially in Manhattan and Staten Island, less so in Westchester.

6 day tracking map of power outages caused by Sandy in New York City

 
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Posted by on October 28, 2012 in Recreational, Scientific

 

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Trends in Health Habits across the United States

Trends in Health Habits across the United States

This week Scientific American published an interesting article about trends in health habits across the United States. The article includes both a large composite chart as well as a page with an interactive chart. Both are well done and a great example of using a visualization to help telling a story. I personally find the most useful part of the graphic to be the comparison column on the right with shades of color indicating degree of improvement (blue) or deterioration (red).

US health habits 1995 vs. 2010 (Source: Scientific American)

From the article:

Americans are imbibing alcohol and overeating more yet are smoking less (black lines in center graphs).

Some of the behaviors have patterns; others do not. Obesity is heaviest in the Southeast (2010 maps). Smoking is concentrated there as well. Excess drinking is high in the Northeast.

Comparing 2010 and 1995 figures provides the greatest insight into trends (maps, far right). Heavy drinking has worsened in 47 states, and obesity has expanded in every state. Tobacco use has declined in all states except Oklahoma and West Virginia. The “good” habit, exercise, is up in many places—even in the Southeast, where it has lagged.

A more detailed visual analysis is possible using the interactive version of these graphs on the related subpage Bad Health Habits are on the rise. Here one can compare up to three arbitrary states against top, median, and bottom performing states by health habit.

The following examples show tobacco use, exercise and obesity by state with line charts for the three arbitrarily selected states of Florida, California and Hawaii.

Tobacco Trend By State

Exercise Trend By State

Obesity Trend By State

Leading the exercise statistics are citizens in states offering attractive outdoor sports opportunities, like Oregon or Hawaii. Such correlation seems intuitive in both causal directions: People interested in exercise tend to move to those states with the most attractive outdoor sports. And people living in those states may end up exercising more due to the opportunity.

When looking at the average trend line, exercise seems to have leveled off after a bump in the early 2000’s, whereas the decline in smoking over the last decade continues unabated.

15 years is half a generation. During that time, Americans have in almost every state smoked less, exercised more in many states, but obesity is sharply on the rise in every state! From a health and policy debate the latter seems to be the most alarming trend. Most people want the next generation to be better off than the previous one. This has to some extent been true with wealth, at least until the great recession of 2008. But these data show that at population levels, more wealth is not necessarily more health.

 
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Posted by on October 19, 2012 in Medical

 

Inequality and the World Economy

Inequality and the World Economy

The last edition of The Economist featured a 25-page special report on “The new politics of capitalism and inequality” headlined “True Progressivism“. It is the most recommended and commented story on The Economist this week.

We have looked at various forms of economic inequality on this Blog before, as well as other manifestations (market share, capitalization, online attention) and various ways to measure and visualize inequality (Gini-index). Hence I was curious about any new trends and perhaps ways to visualize global economic inequality. That said, I don’t intend to enter the socio-political debate about the virtues of inequality and (re-)distribution policies.

In the segment titled “For richer, for poorer” The Economist explains.

The level of inequality differs widely around the world. Emerging economies are more unequal than rich ones. Scandinavian countries have the smallest income disparities, with a Gini coefficient for disposable income of around 0.25. At the other end of the spectrum the world’s most unequal, such as South Africa, register Ginis of around 0.6.

Many studies have found that economic inequality has been rising over the last 30 years in many industrial and developing nations around the world. One interesting phenomenon is that while the Gini index of many countries has increased, the Gini index of world inequality has fallen. This is shown in the following image from The Economist.

Global and national inequality levels (Source: The Economist)

This is somewhat non-intuitive. Of course the countries differ widely in terms of population size and level of economic development. At a minimum it means that a measure like the Gini index is not simply additive when aggregated over a collection of countries.

Another interesting chart displays a world map with color coding the changes in inequality of the respective country.

Changes in economic inequality over the last 30 years (Source: The Economist)

It’s a bit difficult to read this map without proper knowledge of the absolute levels of inequality, such as we displayed in the post on Inequality, Lorenz-Curves and Gini-Index. For example, a look at a country like Namibia in South Africa indicates a trend (light-blue) towards less inequality. However, Namibia used to be for many years the country with the world’s largest Gini (1994: 0.7; 2004: 0.63; 2010: 0.58 according to iNamibia) and hence still has much larger inequality than most developed countries.

World Map of national Gini values (Source: Wikipedia)

So global Gini is declining, while in many large industrial countries Gini is rising. One region where regional Gini is declining as well is Latin-America. Between 1980-2000 Latin America’s Gini has grown, but in the last decade Gini has declined back to 1980 levels (~0.5), despite the strong economic growth throughout the region (Mexico, Brazil).

Gini of Latin America over the last 30 years (Source: The Economist)

Much of the coverage in The Economist tackles the policy debate and the questions of distribution vs. dynamism. On the one hand reducing Gini from very large inequality contributes to social stability and welfare. On the other hand, further reducing already low Gini diminishes incentives and thus potentially slows down economic growth.

In theory, inequality has an ambiguous relationship with prosperity. It can boost growth, because richer folk save and invest more and because people work harder in response to incentives. But big income gaps can also be inefficient, because they can bar talented poor people from access to education or feed resentment that results in growth-destroying populist policies.

In other words: Some inequality is desirable, too much of it is problematic. After growing over the last 30 years, economic inequality in the United States has perhaps reached a worrisome level as the pendulum has swung too far. How to find the optimal amount of inequality and how to get there seem like fascinating policy debates to have. Certainly an example where data visualization can help an otherwise dry subject.

 
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Posted by on October 15, 2012 in Socioeconomic

 

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