Bart McDonough
UEP232
5/9/14
Exploring the Nature of Conflict: Mapping Environmental Conditions and Transnational
Ethnic Populations in Africa’s Great Lake Region
Project Description
This GIS analysis seeks to display what environmental conditions promotes a higher rate of
armed conflict inside ethnic groups whose populations are allocated within adjacent countries.
The project focuses on the countries located in Africa’s Great Lakes region: Burundi, the
Democratic Republic of Congo, Rwanda and Uganda. To better understand why conflict was so
prevalent in this region, a brief segment for historical context is needed. The early 1960s saw
colonialism’s rapid decline throughout Africa. The exiting European powers created new
countries predicated off their former colonies’ borders, which they originally assembled in an
geographically arbitrary manner during colonialism’s expansion in the late 19th and early 20th
centuries; disregarding any cultural delineations outright. The newly created countries
separated traditional tribal lands and established ethnic enclaves along the borders of
neighboring countries. In essence, the subsequent conflicts that originated from one country
easily permeated into adjacent countries that shared ethnic groups.
Another damaging legacy of colonialism that had tremendous consequences in this region was
the establishment of the racial caste system. The colonial government favored one tribe over
the other in order to create an antagonizing environment between different ethnic groups. The
logic behind the system was to divert anger from the Europeans and redirect it towards native
peoples in order to avoid an unified rebellion against the colonial government; this most
notably occurred in Belgian Rwanda between the Hutu and Tutsi ethnic groups. In Rwanda’s
case, the caste system was systematically inserted into the Hutu and Tutsi psyche that
cultivated a profound hatred between the two groups. Tragically, the extreme enmity between
the two tribes culminated with the Rwanda genocide in 1994 when Hutu militia and military
forces decimated the Tutsi population, resulting in 800,000 Tutsi deaths.
The variables I chose to include in my analysis were ethnic territories, environmental
conditions, and the rate of conflict that existed from 1997-2013 in the region. I wanted to
investigate conflict’s prevalence within different environmental classifications, determine
where it occurred most frequently, and identify the ethnic groups that were more prone to
conflict.
Literature Review
Spittaels, S., & Hilgert, F. (2008). Mapping conflict motives: Eastern DRC. Antwerp: IPIS.
This study explores the key rebel, militia and military groups who occupy the Petit Nord, Grand
Nord and northern South Kivu regions of the DRC and investigates the primary indicators that
exacerbating conflict in those regions. The authors’ focus is centered on four groups that are
prevalent in these areas: the National Congress for the Defense of the People (CNDP), Forces
Démocratiques de Libération du Rwanda (FDLR), Armed Forces of the Democratic Republic of
Congo (FARDC), and the Mayi-Mayi. In summary, the authors attempt to extricate the complex
relationships between these four groups by revealing each group’s ultimate goals, how they
survive, and the common denominators they share with one another. They further provide
evidence through a GIS analysis that connects economic motives to the land the rebel,
government and militia groups occupy.
Wimmer, Andreas, Lars-Erik Cederman, and Brian Min. "Ethnic politics and armed conflict: a
configurational analysis of a new global data set." American Sociological Review 74.2 (2009):
316-337.
This study investigates the elements that drive ethnic conflict juxtaposition to the consolidation
of political power. The authors suggest when a certain portion of the population is marginalized
and barred from the political process due to their ethnicity, the chances of armed insurrection
or ethnic conflict increases. I agree with the author, in that, often ethnic exploitation is a
political mechanism utilized by a select few to consolidate power. Interestingly, the authors
conclude that the prevalence of ethnocentric politics surrounding the state will increase the
probability of ethnic conflict. I find this study relevant to my analysis, in that, it provides a
possible hypothesis that connects conflict in the Great Lakes Region to the marginalization of
ethnic groups within their respective political system.
Cederman, Lars-Erik, Halvard Buhaug, and Jan Ketil Rød. "Ethno-nationalist dyads and civil
war a GIS-based analysis." Journal of Conflict Resolution 53.4 (2009): 496-525.
This study focus on the use of GIS datasets to provide more clear understanding of the dyadic
relationship between excluded and included groups. The authors wished to investigate conflict
beginning in 1946 in order to establish trends that lead up to ethnic violence. To determine
where different ethnic groups were located, the authors digitized an old soviet atlas, Atlas
Narodov Mira, and used it as a base map to create datasets. Their hypothesis stated that
demographic and geographic mechanisms together provide opportunities and incentive for
political violence. This is true for the most part in my analysis, in that, the conflicts that did
occur were primarily located far-beyond the capital’s influence and within neglected and
difficult to reach communities.
Mesev, Victor, Peter Shirlow, and Joni Downs. "The geography of conflict and death in
Belfast, Northern Ireland." Annals of the Association of American Geographers 99.5 (2009):
893-903.
This study used GIS to quantify and facilitate an understanding between location of violence
and the various factors that exasperated conflict in Belfast, Northern Ireland. It established a
correlation between neighborhood segregation percentages between impoverished Catholics
and Protestants and the percentage of fatalities. The higher the segregation rate within a
poorer community, the more fatalities that community experienced. I found this study
interesting because it develops a greater understanding of the populations that suffer
considerably more in a sectarian conflict. Furthermore, the analysis Mesev, et al. conducted is
related to my study, to the extent that I seek to locate populations that suffered great harm
during periods of conflict.
Data Sources
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Armed Conflict Locator and Event Date (ACLED)
o A detailed excel file that provided information on conflicts occurring across the globe
from 1997-2013
o Source: Armed Conflict Location and Event Date Project
o Metadata: http://www.acleddata.com/wp-content/uploads/2014/01/ACLED-userguide.pdf
Geo-Referencing of Ethnic Groups (GREG)
o A shapefile that demarcates the geographic presence of different ethnic populations
from 1997-2013
o Source: Eidgenossen Technische Hochschule Zürich
o Metadata: http://worldmap.harvard.edu/data/geonode:Naradov_Mira_GREG
Global Land Cover by National Mapping Organizations (GLCNMO)
o A raster file displaying the various vegetation on the planet from 1997-2013
o Source: International Steering Committee for Global Mapping
o Metadata: http://www.iscgm.org/GM_glcnmo.html
Global Administrative Areas (GADM)
o Shapefiles that delineate countries and provide additional domestic information from
1997-2013
o Source: Global Administrative Areas
o No metadata available
Data Preparation and Analysis Steps
1. Converted the ACLED excel data into vector data and imported all shape files into an
Africa Lambert Conformal Conic projection.
2. Merged all administrative boundary shape files for the purpose of forming a Great Lakes
region dataset.
3. Clipped GREG, ACLED and GLCNMO to Great Lake Region dataset.
4. Selected by location all ethnic groups that crossed international borders and created a
new shape file out of the selected features.
5. Clipped ACLED and GLCNOM datasets to the newly created transnational ethnic group
shape file.
6. Converted GLCNOM raster to polygon.
7. Spatially joined ACLED and GLCNOM polygon datasets to transnational ethnic groups.
8. Summed the number fatalities and conflict events that occurred in a transnational
ethnic groups.
Transnational Ethnic
Fatalities 1997-2013
Conflict Events
Groups Affect by
Conflict
Barundi
21,428
2,994
Banyaruanda
11,050
2,400
Bantu-speaking Pygmy
4,832
681
tribes
Moru-Mangbetu
3,539
319
Acholi
2,153
831
Barega
1,736
174
Bakonjo
1,568
491
Bakomo
1,416
297
Banyoro
660
246
Southern Lwo
76
33
Baganda
44
68
Moru-Mangbetu and Sere- 32
53
9. Summed the number of fatalities and conflict events committed by armed groups within
Mundu-speaking Pygmy
the
different transnational ethnic territories.
tribes
Bari
10Conflict Ethnic Region22
Conflict
Total
Mba
2
Actors
Fatalities 0 Events
1997-2013
Military
9,309
666
Barundi
Forces of
Burundi
(1996-2005)
Hutu Rebels
Military
Forces of
Rwanda
(1994-)
Military
Forces of
Democratic
Republic of
Congo (2001)
ADF-NALU:
Allied
Democratic
ForcesNational
Army for the
Liberation of
6,499
5,492
975
261
Barundi
Bakonjo
2,009
1035
Acholi
1,976
469
Acholi
10. Summed the number fatalities within a given vegetation type
Vegetation
Broadleaved, evergreen tree cover
Closed broadleaved, deciduous closed tree
cover,
Open broadleaved, deciduous tree cover
Regularly flooded tree cover
Mosaic: Tree Cover and other natural
vegetation
Shrub Cover
Herbaceous cover
Sparse herbaceous and sparse shrub cover
Regularly flooded shrub and herbaceous
cover
Cultivated and managed areas
Mosaic: cropland, tree cover and other
natural vegetation
Mosaic: Cropland, shrub and grass cover
water bodies
Artificial surfaces
Fatalities 1997-2013
9,977
2,676
1,377
6,653
1,536
8,680
953
174
2
11,792
2,685
17
1,527
517
Limitations
The largest limitation in this analysis was the accuracy of events from the ACLED dataset. The conflict
data entries within the dataset derived from reports from assorted news agencies, who in turn gather
information from eyewitnesses or other entities, such as government officials or international
organizations. This became a problem when attempting to accurately report events as they occurred;
the fog of war and its aftermath can misrepresent reality completely, making it impossible to give an
precise account of events as they had truly unfolded.
Another limitation this analysis encountered was the precision of information within the GREG dataset.
The dataset portrays an over-simplified grouping of different ethnic groups and fails to delve deeper into
identifying the different tribes that form an ethnic group. For example, within the Banyaruanda ethnic
group there are three different tribes: Hutu, Tutsi and Batwah. Even though they belong to the same
ethnic group, there are enough differences between them to justify their unique classification. In
addition, during the analysis the ethnic and armed conflict data join presented a problem: the join did
not provide a clear indication of the intra-feuding tribes within an ethnic group. I think a large segment
of conflict derives from these factions and need to be considered when analyzing conflict’s
determinates.
Conclusions
This analysis yielded results that partially explain conflict’s prevalence within certain areas of the Great
Lakes region. I found the most significant results came from environment conditions, to which conflict
was most rampant. The isolation of different vegetation environments indicated that a high proportion
of fatalities occurred on cultivated land, which begs the question whether population density
contributes to conflict’s exasperation? With that said, a combination of rural environmental conditions
yielded a higher fatality count than fatalities that ensued on cultivated and urban landscapes. Another
element to conflict this analysis was able to identify, was the predominant groups who initiated violent
acts. Knowing who committed the violent attacks strengthens a contextual frame for conflict’s
preponderance in the region. More than anything, these results incite more questions for further
research. More detailed information and datasets are required to properly pursue a better
understanding of conflict’s complexities and prevalence in the region.