“I see all kinds of benefits (from combining collection and analysis). There are a lot of examples that I can't cite because they are classified. But I will say that I think we will be better able to address an Arab Spring, for example, and better able to anticipate it and respond to it,” (Gen Clapper granted this interview in Geospatial Intelligence Review, Dec 2011. The full interview is available at KMI Media Group (2011-volume-9-issue-7-october)
Earlier, we discussed NGOs and the attempts to monitor and measure instances of freedom of expression. There are several datasets that we can examine for correlations that also might be indicative of Internet censorship. Download the following and examine them for correlations.
- Reporters Without Borders Freedom Index
- Internet Usage and Censorship Index
- TI Corruption Index
- The Quality of Governance Data
- Human Development Index
- Global ICT Statistics from the ITU
As part of this assignment, use the datasets (and any others you might deem appropriate) to select and examine a “worst offender” nation in each. Are the different datasets correlated? Also look at the relative ranking of the US – it may surprise you to see how it was ranked in the datasets. If there is data that you are aware of not mentioned in the list above, please feel free to include it in your analysis. There are several examples in the course material.
- How many more events like the Arab Spring are out there waiting to happen? Select a sub-Saharan African country and perform an analysis on the available data using the methods we have described in this and earlier sections. As a minimum, your analysis should be based on correlation between datasets. A recommended approach would be to look at correlations between datasets, then look at correlations of the measures between countries using the Mahgreb as a reference point. Keep in mind that there are two significant approaches to controlling the free flow of information - deny access completely is one. Extreme filtering of content is another.