1. By: Matthew Gulino For: Penn State, GEOG 594A Date: May, 2015

2. » The question that I will endeavor to answer is “where are terrorist attacks likely to occur in Afghanistan?” » After reviewing the geospatial precision of terrorist attack data available, this translates into “what districts in Afghanistan are likely to have terrorist attacks?” » Wikipedia defines terrorism as “violent acts (or threat of violent acts) intended to create fear (terror), perpetrated for a religious, political, or ideological goal, and which deliberately target or disregard the safety of non-combatants (e.g., neutral military personnel or civilians).” » The key factor is that terrorism typically targets non- combatants. Any predictive analysis of terrorist attacks should disregard attacks against opposing military/police units (in this case the International Security Assistance Force (ISAF) and the Government of the Islamic Republic of Afghanistan (GIROA)) as predictors of future locations of attacks.

3. » I will analyze past terrorist attack locations in Afghanistan to determine what geospatial trends I can detect. » The locations of civilian populations will not likely change in the near future and certain civilian population centers will be more likely than others to be the focus of terrorist attacks.

4. » Assumption 1: The locations of future terrorist attacks are predictable. » Assumption 2: Terrorist attack locations can be predicted despite the lack of knowledge of military/police force activities (this data is highly sensitive in nature). » Assumption 3: Terrorist attacks will continue in Afghanistan despite the fact that ISAF has withdrawn from the battlefield.

5. The analytic process is described as “A Notional Model of Analyst Sensemaking,” with the cognitive task analysis indicating that the bottom-up and top-down processes shown in each loop are “…invoked in an opportunistic mix.” The graphic below illustrates this process:

6. » My hypothesis is that locations of past terrorist can be used as a guide to determine geospatial trends of terrorist attacks. » Limitations: Much of the data only specifies which district an attack occurred in, so any geospatial prediction made using this data can only be specific to the district level.

7. You can see that it is impossible to analyze this data by simply placing dots on a map.

8. It is much more effective to create a chloropleth map in which the number of terrorist attacks are reflected in the color of each district. This map shows that three districts by far had the most terrorist attacks between 2002 and 2013. - Kabul - Khost - Kandahar

9. » The previous slide is a good decision aid for the Afghan government in deciding the top three districts to protect from terrorist attacks (Kabul, Khost, and Kandahar). However, it does not provide good guidance on what other areas that need additional protection. » One way to help determine what other areas to protect is to display the same attack data but to divide the number of attacks by the number of people in the districts. ˃This will help to display where people are more likely to be attacked by terrorists.

10. This map shows that although there are many more total terrorist attacks in the Afghan Capital Kabul district, you are much more likely to be attacked by a terrorist if you are a civilian living in Wazakhan and Qalat Districts.

11. » I wanted to analyze the most recent data to determine terrorist attack trends, so I used the data for 2013. » I evaluated two different methods for displaying the trends of terrorist attack locations. ˃Kernel Density Analysis found in ArcGIS 10 ˃Spatial and Temporal Analysis of Crime (STAC) found in the Crime Stat IV software

12. This map is the result of a kernel density analysis of terrorist attacks in 2013. It shows that portions of Hilmand and most of Nangarhar provinces have the highest density of terrorist attacks. The weakness of using this approach appears to be its lack of precision. It does not appear to be effective in narrowing its scope to district level analysis at this scale.

13. The STAC hotspots identified in this map are much more precise than the hotspots identified in the kernel density analysis. The STAC routine only identifies the statistically significant hotspots. It appears to be a much more precise method of identifying geospatial trends in terrorist attacks.

14. Interestingly, the center of the major kernel density hotspots correspond with the STAC hotspots. The STAC analysis appears to have an advantage because it can identify districts of concern rather than portions of provinces. If you distil the kernel density hotspots so that only the purple and blue are visible, you lose visibility of other less prominent hotspots (in red, orange, and yellow). The STAC analysis does not have this drawback.

15. This map displays some of the characteristics of Afghan districts that may affect where terrorist attacks occur. Ironically, only in Kashrod district are you likely to be attacked by a terrorist in a Taliban controlled district. In contrast, living in a district with high poppy growth appears to increase your chances of being attacked by a terrorist.

16. » “U.S. commander predicts more Afghan suicide attacks,” http://archive.militarytimes.com/article/20140123/NEWS08/3012 30009/U-S-commander-predicts-more-Afghan-suicide-attacks » “RC-East commander predicts hike in insurgent attacks in Afghanistan,” http://www.stripes.com/news/rc-east-commander- predicts-hike-in-insurgent-attacks-in-afghanistan-1.235336 » “Why The Predictions Of Catastrophic Terror Attacks At The Sochi Olympics Didn’t Come True,” http://thinkprogress.org/world/2014/02/24/3322141/sochi-terror- attacks-happen/ » “Researchers try to develop a methodology for predicting terrorist acts,” http://www.homelandsecuritynewswire.com/dr20150122- researchers-try-to-develop-a-methodology-for-predicting-terrorist- acts » “Afghanistan: At Least 21,000 Civilians Killed,” http://costsofwar.org/article/afghan-civilians

17. » “Attempts to Predict Terrorist Attacks Hit Limits,” http://www.scientificamerican.com/article/attempts-to-predict- terrorist-attacks-hit-limits1/ » “Afghan War Games: Computer Scientists Accurately Predict Attacks,” http://www.motherjones.com/mojo/2012/07/afghan- war-games-researchers-predict-conflicts » “Math Can Predict Insurgent Attacks, Physicist Says,” http://www.npr.org/2011/07/31/138639711/math-can-predict- insurgent-attacks-physicist-says » “A Computer Program That Predicts Terrorist Attacks,” http://www.fastcoexist.com/1680540/a-computer-program-that- predicts-terrorist-attacks » “Terrorism Expert Predicts a Record 15,000 Terror Attacks Around the Globe in 2014,” http://www.cnsnews.com/news/article/penny- starr/terrorism-expert-predicts-record-15000-terror-attacks- around-globe-2014

18. » “ESOC Empirical Studies of Conflict,” https://esoc.princeton.edu/file-type/gis-data » “GISTPortal,” https://gistdata.itos.uga.edu/user » “USGS PROJECTS IN AFGHANISTAN,” http://afghanistan.cr.usgs.gov/geospatial-reference-datasets » “AIMS: Afghanistan Information Management Services,” http://www.aims.org.af/ssroots.aspx?seckeyt=295 » “GTD: Global Terrorism Database,” http://www.start.umd.edu/gtd/ National Consortium for the Study of Terrorism and Responses to Terrorism (START). (2013). Global Terrorism Database [Data file]. Retrieved from http://www.start.umd.edu/gtd http://www.start.umd.edu/gtd/ http://www.start.umd.edu/gtd » “Central Statistics Organization, Islamic Republic of Afghanistan,” http://cso.gov.af/enhttp://cso.gov.af/en » “The Asia Foundation, Visualizing Afghanistan: A Survey of the Afghan People 2012’” http://afghansurvey.asiafoundation.org/