Visualizing the World

The increased ubiquity of the internet has given more and more people greater access to massive amounts of diverse data.  As the amount of data has steadily increased, so too has the processing power of the machines capable of processing this data.  However, Producing reams and reams of data is not enough – you need people who are capable of analyzing this information and visualizing in ways so that other people can also make sense of it.  Last year, the New York Times foretold that statistics will be the next great emerging field, quoting Google’s chief economist Hal Varian as saying, “The sexy job in the next 10 years will be statisticians.  And I’m not kidding.”

Various companies have developed a number of powerful data visualization tools, and the New York Times in particular has been singled out for praise from the blogosphere for its innovative web graphics.  Nevertheless, before descending into the rabbit hole and trying to visualize anything and everything, it’s important to first understand the design principles that make certain data visualizations more or less effective.  The September 1984 edition of the Journal of the American Statistical Association published a paper by William S. Cleveland and Robert McGill that reported on experiments to determine which visualization methods are most easily understood by an audience.  The authors studied what individuals can decode most accurately and ranked the approaches in the following list:

  1. Position along a common scale e.g. scatter plot
  2. Position on identical but nonaligned scales e.g. multiple scatter plots
  3. Length e.g. bar chart
  4. Angle & Slope (tie) e.g. pie chart
  5. Area e.g. bubbles
  6. Volume, density, and color saturation (tie) e.g. heatmap
  7. Color hue e.g. newsmap

It would be very valuable to produce an updated version of this study.  Have things changed in the last 25 years?  Do these trends hold true for all segments of the population?  Perhaps some individuals with special training, such as soldiers, are more attuned to certain visualization methods.   If anyone knows of any additional studies along these lines, please send them our way.  In addition, Strategic Social is going to be attending a data visualization course led by Edward Tufte in Crystal City today; if you’ll also be there, come say ‘hi.’

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Do Working Men Rebel?

The National Bureau of Economic Research recently published a paper by Eli Berman, Jacob Shapiro, and Joseph Felter called “Do Working Men Rebel?” The paper challenges one of the few universal tenets held by Counter Insurgency planners and decision makers: the belief that unemployment drives insurgent violence. To put the traditional view succinctly: give young men a job, and they will throw down their rifle and stop conducting attacks. The authors make a compelling argument, using data from the Iraqi district level and the Philippines equivalent- province level, that in fact the opposite is true. Prosperity brings violence, rather than reducing it.

The purpose of this post is not to explore the statistical models, data sources, or other specific academic concerns, as the two case studies and the types of data used are generally well thought out. There are some questions about the implementation, or operationalization, of the data, from a planner’s perspective.

The following vignette will highlight the operating picture the authors consider statistically in the study: The Iraqi district /Philippine province observed for the study is the source of a major government effort against insurgents. Increased patrols and checkpoints (kinetic operations), increased aid to businesses and community (civil military operations), along with a myriad of other efforts, are being used to reduce insurgent effectiveness. As this occurs, violence increases with no significant relationship to unemployment. At first this glance the policy implication is that efforts to employ young males, the most likely insurgents, are a waste of resources, as they do not reduce violence.  However, further exploration might lead to a different conclusion.  The following issues should be more carefully  considered before concluding that increased employment does not reduce insurgent violence.

1. The data show that the area analyzed is the subject of intense effort by the government security forces. That means that such an effort almost certainly draws insurgents into the area to fight. The study’s authors do not have the ability to build a compelling profile of the insurgents. For Iraq, one immediate question comes to mind: what about foreign fighters? They are potentially one of the most likely elements to “march to the sound of the guns” along with other more professional insurgents. The Syrian elements in Anbar province Iraq prior to the tribal awakening would be a great example of external forces that would skew study data.  Since the study occurred in two very small geographical areas, a better question to ask might be “how did the Iraqi province the district resides in perform overall in terms of reduced violence and higher employment?”

2. The government’s forces cannot be everywhere at once. “Clear, Hold, Build” means that you have to establish a beachhead to work from, as the Marines and the Afghan Army are currently doing in Marja. Marja will draw violence for months as the Taliban tries to disrupt the “Building” that is to follow in the wake of the current “Clearing” and “Holding.” Additionally, Marja may remain a problem, but is the seed planted there really unable to affect Helmand as a whole? Perhaps higher employment reduces the number of insurgents emanating from Marja, ultimately reducing the total number of insurgents in the overall battle space? This does not refute the study data, but calls into question whether the geographical areas studied were large enough to enable operational and strategic level decisions to be made about eliminating programs that provide employment to young males.

A follow-on to this effort that examined a larger geographical area and better examined the question of who is behind attacks would be incredibly insightful and add value to the authors’ study.  While any data can be picked apart, the authors should be commended for challenging the status quo and providing a perspective that may prove to be incredibly invaluable for planners and decision makers.

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