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withdrawn from the battlefield.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
» “Central Statistics Organization, Islamic Republic of
Afghanistan,” http://cso.gov.af/en
» “The Asia Foundation, Visualizing Afghanistan: A Survey of
the Afghan People 2012’”
http://afghansurvey.asiafoundation.org/
