Spatio-temporal analyses of the relationship between armed conflict and climate change in Eastern Africa

1. Spatio-temporal analyses of the relationship
between armed conflict and climate change
in Eastern Africa
Riazuddin Kawsar
MScGT Program
Supervisor Co-Supervisor Co-Supervisor Co-Supervisor
Edzer Pebesma Jorge Mateu Pedro Cabral Mário Caetano
IFGI, WWU LSI, UJI ISEGI, UNL ISEGI, UNL

2. Climate and armed conflict
?
• Higher temperature, droughts, poverty and Armed Conflict (Burke et al.
2009, 2010);
• Recent empirical works have been concerned about the link between the
economic shocks and conflict (Miguel et all. 2004; Ciccone 2011);
• Rainfall variations as predictor for changes in GDP growth in sub-Saharan
Africa (SSA);
• SSA is heavily dependent on rain fed agriculture;
• Cross country evidence is found;
Variation in annual average precipitation and temperature has impact on
GDP by means of Agricultural growth but there are disagreement too
(Buhaug 2010)
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3. Conflict and development
?
• 127 civil wars in 73 states with over 16 millions deaths (1994-
1999);
• Enormous human cost but also economic cost: 8% of the world GDP
(Hess 2003);
• Around 58% of these conflicts were in Sub-Saharan Africa;
• One of the main obstacles to development in this continent (i.e.,
Angola, Sudan);
Understanding the determinants is the key for development policy
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4. This study
?
• A step further to understand the relationship between climate change
and armed conflict by taking the analyses to a different scale
– Geographical disaggregation: 55 x 55 km subnational “cells”
– Temporal disaggregation: yearly climate variations
– Rich detailed geo-referenced datasets (1991-2000)
• Various approaches to model the relationship between armed
conflict and climate change
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5. Study area
?
• 1991-2000(data availability)
• About 8,719,926 km2
• Area: 30 % of the Africa
Continent
• number of armed conflict
(AC): 3,289
• region experienced highest
number of AC (31 % )
• Efficient data processing
and analyses
Fig 1 the distribution of the Armed Conflict in Africa
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6. Our contributions
?
• Methodology
– Disaggregated level of analysis in space, space and time;
– Point Process Modelling approach;
– Careful modelling of spatial dependence, spatial and temporal
autocorrelation (using Spatial autoregressive Models)
• Focus on climate within the cell (spatial and temporal fashion)
– Identify cell specific yearly climate variation;
– More closely linked to agriculture then aggregated index;
• New climate indicator
– Weighted Anomaly Soil Water Index (WASWI): captures
variation of precipitation + evaporation + temperature.
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7. Estimation strategies (1/3)
?
• Grid approach (cell and cell/year panel)
• Main dependent variable
– Events: the number of conflicts related episode a cell
experienced during the year (GED, UCDP, Version 1.5)
• Independent Variables
– Soil Water Index (SWI) (TWIN)
– WASWI (dimensionless measure of the relative severity of SWI surplus or deficit)
– Standardized Precipitation Index (SPI) (GPCC)
– Population (GPW, version 3)
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8. Estimation strategies (2/3)
?
• WASWI preparation (Lyon and Barnston 2005)
𝑁
(𝑠𝑤𝑖 𝑖 − 𝑠𝑤𝑖 𝑖 ) 𝑠𝑤𝑖 𝑖
𝑊𝐴𝑆𝑊𝐼 𝑁 =
𝜎𝑖 𝑠𝑤𝑖 𝐴
𝑖=1
• 𝑠𝑤𝑖 𝑖 = is the observed value of SWI for the ith month
• 𝑠𝑤𝑖 𝑖 = represents long term (1991-2000) mean of monthly SWI for the ith
month
• 𝜎 𝑖 = standard deviation of the anomalies of monthly SWI for the ith month
• 𝑠𝑤𝑖 𝐴 = mean annual SWI
𝑠𝑤𝑖 𝑖
• = Weighting factor representing the monthly fraction of annual SWI*
𝑠𝑤𝑖 𝐴
*to reduce large standardized SWI anomalies that might result from small precipitation amounts or higher
temperature and evaporation, occurring near the start or end of dry seasons and to emphasize anomalies during
the heart of rainy seasons. 8

9. Estimation strategies (3/3)
?
Fig 2 space-time plot of Soil Water Index (SWI)
Fig 3 space-time plot of WASWI Fig 4 average of yearly WASWI (1991-2000)
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10. Point process modelling (1/3)
(with aggregated data)
?
Fig 5 area−interaction process
(adopted from Baddeley 2010)
Fig 6 Point wise critical envelopes for inhomogeneous version of the L-
function in Inhomogeneous area-interaction process
Table 1 Fitted coefficients for trend formula using non-stationary area-interaction process
(Intercept) Population WASWI SPI
-16.30236 0.00044 -0.16475 0.87916
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11. Point process modelling (2/3)
(for disaggregated data)
?
Fig 7 year 1999 (with covariates Fig 8 year 1999 (with covariates Fig 9 year 1999 (with covariates
of the year 1999) of the year 1998) of the year 1997)
Fig 10 year 1997 (with Fig 11 year 1997 (with Fig 12 year 1997 (with
covariates of the year 1997) covariates of the year 1996) covariates of the year 1995)
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12. Point process modelling (3/3)
(with disaggregated data)
?
Fig 13 Standardized fitted coefficients for Inhomogeneous cluster point process model
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13. Space-time point process modelling (1/2)
(with disaggregated data)
?
Fig 14 Image of spatial Fig 15 3-30 𝐾 𝑆𝑇 𝑢, 𝑣 − 𝜋𝑢2 𝑣 with various distance and time sequence for the
intensity based on kernel case events without covariates
Fig 16 Image of spatial Fig 17 𝐾 𝑆𝑇 𝑢, 𝑣 − 𝜋𝑢2 𝑣 with small u (up to 440 km) and v (up to 20
intensity based on covariates days) for the case events with covariates 13

14. Space-time point process modelling (2/2)
(with disaggregated data)
?
Fig 18 Superimposed events (red) with simulated Fig 19 Superimposed events (red) with simulated
(black) realization of point process using kernel (black) realization of point process using covariates
>.001 >.001
>.005 >.005
<.01 <.01
Fig 20 P-value for 𝐾 𝑆𝑇 𝑢, 𝑣 − 𝜋𝑢2 𝑣 for the Fig 21 P-value for 𝐾 𝑆𝑇 𝑢, 𝑣 − 𝜋𝑢2 𝑣 for the
case events without covariates from 100 case events with covariates from 100
simulation simulation
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15. Spatial cross sectional models
(with aggregated data )
?
Table 2 Spatial autoregressive model output for the aggregated data (year 1991 to 2000)
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16. Impacts in spatial durbin model
?
j1 j2 j3
Spillover Effect
Lesage and Pace (2007) j8 i j4
Feedback Effect
j7 j6 j5
Table 3 impacts in SDM output for the aggregated data for the year 1991 to 2000
Direct impact: fitted coefficient + additional feedback impact
Indirect impact: spillover impact (neighboring impact)
Total impact: Direct impact + indirect impact
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17. Spatio-temporal SAR models
?
Fig 21 Neighbors addressed for of temporally lagged SAR model (left), model 2 (middle) and model 3 (right).
This figure is adopted from Espindola, Pebesma, et al. (2011)
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18. Spatio-temporal SAR models
?
Table 4 Spatio-temporal autoregressive model (SAR) output from the disaggregated data for the year 1991 to 2000
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19. Findings
• Local level negative relationship between conflict and climate change
– change in WASWI impact change in AC by -0.1981 or -0.1657
– Conflicts are clustered up to 200 km
• Spatio-temporal spillover
– Conflict in the own cell associated to a (0.3651) increase in the
probability of conflict of the following year
• Climate change indicator: long term WASWI measured at the cell level are
strong local conflict predictor
So…
Climate measures of a particular year don’t have significant effect on armed
conflict outbreak of the following year but Climate Change (long term
measure) has significant effect on Armed Conflict outbreak
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20. Conclusion
• Sub national disaggregated studies may provide more support for the
resource conflict nexus. Further study for the whole continent is
recommended
• Our findings also gave an indication of acceptance of such hypothesis, that
in future the conflict situation is going to be worse due to climate change
(temperature increase, decrease in precipitation, results less water in soil
can trigger more conflict outbreak)
• Incising the temporal resolution (seasonal level) we can get a more clear
picture
• Separating the spatial and temporal factors can take us another step forward
in understanding the determinants which is the key for development policy.
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21. Linking climate change and armed conflict,
we can answer tomorrow’s World Peace
today
Thank you
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