Presentation by Phil Smith from the National Geospatial-Intelligence Agency for the ESRI Users Conference, July 22, 2015.

1. UNCLASSIFIED
Where the Hammer-Strokes Fall
Hardest: Using Human Terrain to
Facilitate Anticipations of
Violence in South Sudan
Phil Smith

2. 2
• Republic of South Sudan, the world’s newest country, suffers from
historical catalysts of violence
– Tribal tensions, cattle raiding, political instability, rebellions, conflict with Sudan
• Between 2008 and 2012, at least 10,000 South Sudanese died in violent
incidents; hundreds of thousands displaced
– Humanitarian demands stretch UN, NGO, and government of South Sudan (GoSS) resources
Introduction
Jane’s Intelligence Weekly – 14 March 2014; Copyright © IHS Global Limited, 2014

3. 3
Implications and Research
Opportunities
• East African stability important to U.S. foreign policy
– Peace fosters an environment suitable for sustainable development
• Emergence of free, openly available spatial datasets presents
opportunity for focused analyses
– Human terrain datasets: demography, ethnicity, boundary, historical data
Research Question: to what extent can spatially-enabled human terrain
datasets assist in anticipating locations of violence in South Sudan?
Hypothesis: spatially-enabled human terrain datasets can assist in
anticipating where violence is most likely to occur
-provide warnings of humanitarian plight and instability

4. 4
Datasets
• Violent incidents: Armed Conflict Location and
Event Dataset (ACLED)
– Events categorized by actor and impact (injuries, deaths,
property destruction)
– Thesis utilized eight years of violence data (2005-2011); time
period between end of Sudanese Civil War and South Sudan’s
independence in July 2011. Data from July 2011 to July 2012
tested model.
• Population density: LandScan
• Tribal borders: Columbia University GULF/2000
Project
• Seasonal migration routes and grazing land
• Roads
• Demographics: focused on young males
• Water resources: rivers, water holes, boreholes
• Commerce: village and urban markets
• Sudan-South Sudan international border
http://gulf2000.columbia.edu/images/maps/Sudan_Ethnic_Linguistic_lg.jpg
Author: Dr. Michael Izady; host: GULF/2000 Project; Copyright © M. Izady 2007, 2011

5. 5
Dataset Limitations
• Only geospatially-referenced datasets used; eliminates potentially pertinent
information (political treaties, seasonal variations, leadership intentions)
• Broadly described violent incidents (tied to larger area– county or state)
unsuitable for identifying smaller-scale patterns
• Risk of incomplete, inaccurate, or biased datasets
• Reporting inconsistencies (UN/NGO presence = more reporting)
• Geographic dataset cutoffs: impact of surrounding countries
• Impact of non-human terrain factors on human terrain data (military)
Bottom Line: thesis focused on qualitative assessments from quantitative data;
research time, dataset availability/accuracy, and analytical judgments precluded
statistical assessment. Hypothesis cannot be proven, but alternative hypotheses can
be weakened

6. 6
Area Limitation Model Inputs

7. 7
Anticipatory Mapping/Area
Limitation Model Inputs
Area Limitation Model Inputs

8. 8
Introduction
Area Limitation Model Results

9. 9
Conclusions
• Overlaid ACLED violence data from July 2011 to July 2012 to test model (data not
included in area limitation model)
– Approximately 80-percent of violent incidents fell within 30-percent of South Sudan territory
designated as extremely, moderately, or possibly likely to experience violence
– Approximately 40-percent of violent incidents within six-percent of South Sudan designated as
extremely or moderately likely to experience violence
– Instances where model conflicted with data– highlights potentially missing or overlooked data/factors
• UN/NGO focus on humanitarian plight near Sudan-South Sudan border
• A model is exactly that– a simplified view of reality, limited by inputs and analytic
methods
• Arms the international community and policymakers with an anticipatory tool to
highlight locations at a potentially higher risk of violence
– Improve planning and understanding of violence and subsequent humanitarian impacts

10. 10
“2013 UNHCR country operations profile – South Sudan.” UNHCR.org. January 2013. http://www.unhcr.org/pages/4e43cb466.html (accessed 13
February 2013).
“About ACLED.” Armed Conflict Location & Event Dataset. http://www.acleddata.com/about-acled/ (accessed 13 February 2013).
Attree, Larry. “China and conflict affected states: Between principle and pragmatism.” Saferworld.org.uk. January 2012.
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Eldridge, Erik and Andrew Neboshynsky. “Quantifying Human Terrain.” NPS.edu. June 2008.
http://www.nps.edu/programs/ccs/Docs/Pubs/Eldridge_Nebo_Thesis.pdf (accessed 13 February 2013): 18-19.
“Georef-all-africa.xls.” Armed Conflict Location & Event Dataset. January 2013. http://www.acleddata.com/wp-content/uploads/2013/01/georef-all-
africa.xlsx (accessed 13 February 2013).
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Hart, Timothy and Paul Zandbergen. “Effects of Data Quality on Predictive Hotspot Mapping.” NCJRS.gov. October 2012.
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Izady, Michael. “Ethnic Groups in Former (united) Sudan.” Columbia University
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Sources

11. 11
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