Assignment 10: Detailed Project Plan
Disaster Risk Reduction and Conflict Mitigation in Programming, Kenya Case Study
1. Summary
In many countries and international organizations, disaster risk reduction and conflict
prevention/conflict resolution/peace building have been handled quite separately, even though both
are related to incidence of humanitarian emergencies and the requirement for response. Kenya faces
multiple hazards: widespread droughts have affected large parts of the country in 2000, 2006 and 2010,
and floods and other natural disasters have been a frequent occurrence in some parts of the country.
Conflict and human-made crises are also common: conflict in pastoral areas is frequently associated
with resource competition during droughts. Pastoral conflict has been exacerbated by proximity to
national boundaries and the flow of small arms from neighboring countries.
Kenya has a well-organized drought management system in place and another government body, the
National Steering Committee on Peace Building and Conflict Management fulfills a similar function with
regard to conflict. The structure of many international organizations working in Kenya (as elsewhere) is
similar: organizations often have units or teams working on conflict resolution or peace building, on
humanitarian response, and on disaster risk reduction—but these groups are often organizationally
separated and working relatively autonomously from each other—not necessarily working towards
common objectives in the same place. Similar language is used to describe similar activities, but the
activities themselves are often not joined up or part of the same strategic plan. The negative
consequences of the interface between conflict and disaster can be worsened by separate
organizational responses to the situation, either blind to the prevailing environment of conflict or the
natural disaster risk.1
Where disaster and conflict intersect, future risks of crisis are increased, mitigating capacities
undermined, and recovery efforts hampered. A self-reinforcing spiral of insecurity can increase
vulnerability and set the scene for a disaster, which can then fuel more conflict, further eroding
household resilience. Given the varied degrees of complexity found in conflict and disaster situations,
integration is not an easy solution, however given their cyclical impact as well as the similarities in root
causes and objectives it is no longer possible to effectively address disaster issues without also
addressing those associated with conflict. Contexts in which conflicts and disasters overlap are daily
realities for affected communities as well as the government, and the local and international
organizations that serve them. The goal of this project would be to analyze the geographical overlap
(likely lagged by several months or years depending on the length of the natural disaster) and therefore
the correlation between incidences of natural disasters and conflict; specifically pastoral conflict, given
its shared root causes with environmental degradation and natural disasters. The aim would be to
influence NGO, government, and other institutional policy towards more integrated DRR programming,
with a focus on both conflict and disaster risk.
Question 1: What is the geospatial relationship between drought, pastoral conflict, and humanitarian
emergencies (famine and the requirement for food aid) in Kenya?
Vulnerabilities are highly dynamic in the context of conflict as well as natural disaster, and this, in turn,
affects exposure to the threats associated with both, and are exacerbated in contexts where the two
1
UNDP (2011) Disaster Conflict Interface Comparative Experiences. United Nations Development Program, Bureau
for Crisis Prevention and Recovery, pg 23
overlap. Interventions that do not account for this complex interplay have the potential to worsen
tensions and increase risk. Furthermore, it is not enough for projects concerned with disaster and
conflict risk reduction to run parallel of each other; instead interventions and analysis need to consider
combined effects. A clearer realization about the cyclical and self-reinforcing nature of conflicts and
natural disasters could lead to programming that would not accidentally exacerbate the risk of one while
trying to reduce the risk of the other in areas prone to both.
Question 2: What are some of the underlying and shared causes of drought and pastoral conflict in
Kenya?
There are several cross-cutting issues that increase both conflict and natural disaster risk. Politicaleconomic factors can lie at the root of conflict, such as limited government investments, high youth
unemployment, and migration. A lack of cooperation over shared resources and poor environmental
management can prolong conflicts and degrade the environment, increasing natural disaster and
conflict risk. A better understanding of shared root causes could translate into more effective
preventive programming that simultaneously addresses conflict and natural disaster risk.
2. Data Sources
Data
Source
Armed Conflict
Location and Events
Data Set - Kenya
Link
Centre for the Study of Civil War (CSCW),
International Peace Research Institute (PRIO),
Oslo
http://www.acleddata.com/
German Technical Cooperation (GTZ). 1996. The
North Kenya GIS Database. United Nations
Environment Programme (UNEP)/GRID-Nairobi.
Nairobi: UNEP/GRID
Nairobi
http://www.wri.org/publication/c
ontent/9291
District Boundaries
(2001)
National Road
Network
Major towns
Ethnic Boundaries
Precipitation (1995 –
2011)
UN funded Second Administrative Level
Boundaries (SALB)
International Livestock Research Institute
http://www.wri.org/publication/c
ontent/9291
http://www.ilri.org/gis/
International Livestock Research Institute
International Livestock Research Institute
FEWS NET - Africa RFE Images
Average annual
rainfall in Kenya
(1950-1990)
Kenya drought years
and humanitarian
emergencies
World Resource Institute
http://www.ilri.org/gis/
http://www.ilri.org/gis/
http://earlywarning.usgs.gov/few
s/africa/web/imgbulks4.php?img
type=rf&spextent=a
http://www.wri.org/publication/c
ontent/9291
http://www.worldclim.org/
(1997 – 2010)
Distance to water
points (boreholes,
etc) in northern
Kenya (1996)
Kenya National Disaster Management Report
1.
http://www.kecosce.org/do
wnloads/DRAFT_DISASTER_
MANAGEMENT_POLICY.pdf
3. Data Analysis
The main analysis I will be utilizing will be the construction of a conflict vulnerability index. However
prior to the index I will first do an analysis to show the correlation between conflict and drought.
Using the armed conflict data I have extracted conflict based on ethnic clashes, raids, and clashes over
pasture or water to create a variable only looking at ‘traditional conflict,’ mainly for the purpose of
extracting election conflict which has different causal variables. For each year for which I have conflict
data I have classified the year as either a drought or non-drought year based on Kenya’s annual disaster
report. To show the correlation between drought and conflict I created a graph with ‘year’ on the x axis
and incident of traditional conflict on the y axis. Furthermore, to create a spatial representation of this I
broke up the conflict variable into conflict in the drought years and non-drought years and used kernel
density analysis with the raster calculator to spatially show how density of conflict increases in drought
years (subtracted non-drought conflict density from drought conflict density similar to the in-class
Uganda conflict exercise).
Once the correlation between drought and conflict has been established I will construct an index of
vulnerability using several variables in combination with incidence of drought and conflict to identify
areas prone to conflict. This will be composed of 6 variables and 5 categories based on the literature:
1. Population density
a. Distance to road
b. Distance to major town
2. Distance to water (boreholes, spring, rivers)
3. Distance to ethnic boundary
4. Distance to past conflict
5. Areas prone to drought
To calculate the five distance variables (road, major town, water, ethnic boundary, incidence of conflict)
I will be using the spatial analysis Euclidian distance tool and then reclassifying. The classes will be the
same for each variable and based on the ‘distance to water’ variable which was already classified as:
1: within 5 kilometers
2: 5-10 kilometers
3: 10-15 kilometers
4: 15-20 kilometers
5: greater than 20 kilometers
The greater the proximity the more vulnerable the area is to conflict.
To calculate the drought component of the vulnerability index I will use the precipitation data. I have 36
individual observations per year for the relevant years to my study: 1997-2010. A drought is defined as
75% or less of the average rainfall. I also have average rainfall data for Kenya for the past 50 years. I will
aggregate the precipitation data on a yearly basis, multiply it by 10 (only 36 observations per year, 360
days a year), divide it by the 50 year average annual data in order to get a fraction. I will then reclassify
to create a binomial variable for each 1 square kilometer for which the data has spatial resolution
(1=drought, 0=no drought). Areas that are affected by drought more consistently or even consecutively
appear to have even greater incidences of conflict; therefore, I will aggregate the yearly binomial values
and based on the final result reclassify them into 5 groups as with the distance variables above: from
most to least vulnerable.
The six reclassified variables will then be added together using the sum raster tool (and perhaps again
reclassified using the reclassify tool) to create an overall vulnerability to conflict index.
4. Final Products
The final products will consist of the following:
1. Line graph ‘year versus incident of traditional conflict’, identifying drought years and points of
humanitarian disaster.
2. Kernel density map showing the increase in conflict during drought years (by standard deviation)
I will also show 4 distance maps based on the above ‘1 to 5 vulnerability’ classification:
1. Population density map using a combination of distance to road and distance to major town.
2. Distance to water map
3. Distance to ethnic boundary map
4. Distance to incidence of conflict (all conflict, not just traditional) map
5. Drought vulnerability, the reclassified aggregated binomial drought/precipitation variable
The final map will be the overall ‘Vulnerability to Conflict’ Map based on the summation of the above 5
maps reclassified to show different levels of vulnerability.