Your SlideShare is downloading. ×
  • Like
Aaron DeVera  - Demo North Africa Security
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply

Aaron DeVera - Demo North Africa Security

  • 102 views
Published

This is a demonstration of some of the simple methodology I used when doing research for term paper in Comparative Cultures 3351. …

This is a demonstration of some of the simple methodology I used when doing research for term paper in Comparative Cultures 3351.

The final report was titled Evaluating Security Risks in North Africa and their Links to Regional Climate Change and can be found on my website at aaronsdevera.com.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
102
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
3
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Demonstration of Methods - Evaluating Security Risks in North Africa and their Links to Regional Climate Change Aaron S. DeVera, FCRH16 Fordham University Department of Economics Updated April 17, 2014
  • 2. Introduction This is a demonstration of some of the simple methodology I used when doing research for term paper in Comparative Cultures 3351. The final report was titled Evaluating Security Risks in North Africa and their Links to Regional Climate Change and can be found on my website at aaronsdevera.com.
  • 3. Key Takeaways The amount of farmable land is negatively correlated at a degree of -.46 and a error margin of .11 The increase of population in urban populaces and the frequency of terror attacks is positively correlated at a degree of .47 and a margin of error of .1 While the correlations values around -.5 and .5, and the margins of errors valued around .1, a valid arguement can be made for terror being a function of regional climate change. What’s more, the correlation decreased once behavior that mitigated population growth was introduced and policies that helped farmers were undertaken.
  • 4. Hypothesis From ”Abstract”, page 1: The purpose of this paper is to provide a comprehensive evaluation of al-Qaida in the Islamic Maghreb, in the context of the group’s exploitation of the regional victims of climate, famine, migration, and political crises.
  • 5. Demonstration: Data Three datasets, observing occurences from 2000 to 2012 (when data available). Arable land is the amount of land in an area that is fertile and convertible for agriculture. Representated as a percentage of total land. Population in urban agglomerations is the term used to define the mass buildup in population in urban environments where the population is close to or exceeds 1 million people. Terror attacks are defined using START UMD’s criteria, which shares similarities in incident characteristics with the National Counterterrorism Center’s (NCTC) own benchamrks. My sources include Trading Economics and START, University of Mayland’s terrorist incident database.
  • 6. Arable Land Year Arable Land (% of total land) 2000 5.82 2001 5.90 2002 5.96 2003 6.02 2004 5.96 2005 5.91 2006 5.88 2007 5.92 2008 5.94 2009 5.89 2010 5.88 2011 5.85 2012 5.80 Arable land suitable for agriculture (1) ¯x = 5.902 σ = .578
  • 7. Population Increases Year Pop. Increase (% of total population) 2000 20.15 2001 20.14 2002 20.17 2003 20.16 2004 20.06 2005 19.97 2006 19.89 2007 19.81 2008 19.98 2009 20.01 2010 20.05 2011 20.58 2012 20.67 Increases in urban settlements 1mil+ (2) ¯x = 20.126 σ = .237
  • 8. Increase in Organized Terror Year Organized Terror Attacks 2000 1 2001 1 2002 4 2003 5 2004 1 2005 2 2006 11 2007 25 2008 29 2009 18 2010 6 2011 12 2012 73 Attacks in scope countries (3) ¯x = 14.462 σ = 19.113
  • 9. Normalizing Data z = x − µ σ In R Studio, the variable arrays zara, zpop, and zter are set to the following values in order to render normalized z-score arrays (hence the prefixed ”z” in the variable names: (ara - mean(ara)) / arasd (4) (pop - mean(pop)) / popsd (5) (ter - mean(ter)) / tersd (6)
  • 10. Normalizing Data Year zara zpop zter 2000 -1.424 0.101 -0.704 2001 -0.039 0.058 -0.704 2002 0.998 0.185 -0.547 2003 2.036 0.143 -0.495 2004 0.998 -0.279 -0.704 2005 0.133 -0.659 -0.652 2006 -0.386 -0.996 -0.181 2007 0.306 -1.334 0.551 2008 0.652 -0.616 0.761 2009 -0.213 -0.490 0.185 2010 -0.386 -0.321 -0.443 2011 -0.905 1.914 -0.129 2012 -1.770 2.294 3.063
  • 11. Comparing Data Plots rendered with R Studio and the GoogleVis package.
  • 12. Comparing Data Visual Observations: Elevated inverse correlation between Arable Land and Urban Population Increases as well as Terror Attacks from years 2003-2004 and from years 2010-2012. Surges in Terror Attacks follow losses in Arable Land. Inverted behavior from years 2006 to 2010 creates anomalous region within the otherwise fairly correlated plots of Urban Population and Terror Attacks. Droughts in early 2000s and 2010 in Sahel plausible causality for plot correlations, as well as the normalization seen in 2007-2009.
  • 13. Comparing Data Pearson Correlation Tests data: arable land and urban population t = −1.7353, df = 11, p − value = 0.1106 (7) alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval : −0.8081462 0.1173845 (8) cor = -0.4635841 (9)
  • 14. Comparing Data Pearson Correlation Tests data: urban population and terror attacks t = 1.7805, df = 11, p − value = 0.1026 (10) alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval : −0.1054949 0.8122822 (11) cor = 0.4729831 (12)
  • 15. Prediction Models Prediction Model 1 (y = a + bx) data: arable land and urban population regression coeeficients: a=-6.726e-15 b=-4.636e-01 Arable Land +/- Urban Population +/- -.5 .232 -.4 .185 -.3 .139 -.2 .093 -.1 .046 0 -6.7260e-15 .1 -.046 .2 -.093 .3 -.139 .4 -.185 .5 -.232
  • 16. Prediction Models Prediction Model 2 (y = a + bx) data: urban population and terror attacks regression coeeficients: a=-6.726e-15 b=-4.636e-01 Urban Population +/- Terror Attacks +/- -.5 -.237 -.4 -.189 -.3 -.142 -.2 -.095 -.1 -.047 0 -6.907e-15 .1 .047 .2 .095 .3 .142 .4 .189 .5 .237