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Spatio-temporal analyses of the relationship between armed conflict and climate change in the eastern Africa
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Autores
Resumo(s)
Despite recent methodological improvements and higher data availability, the
Climate Change (CC) and Armed Conflict (AC) studies are suffering from poor data
and inappropriate research designs (e.g., Incompatibilities of scale). This study fills
the gaps by taking the climate conflict analyses into a different scale (e.g., 55 km x
55 km sub-national cell/year) and uses high resolution Geo-referenced data sets. This
study presents the results from 10 years (1991-2000) of observations and a rigorous
modelling methodology to understand the effects of climate change on the conflict
occurrence in the Eastern Africa. The main objective of the study is to identify and
understand the conflict dynamics, verify the pattern of conflict distribution, possible
interaction between the conflict sites and the influence of climatic covariates of
conflict outbreak. We have found that if the climate related anomaly increases, the
probability of armed conflict outbreak also increases significantly. To identify the
effect of climate change on armed conflict we have modeled the relationship between
them, using different kinds of point process models and Spatial Autoregressive
(SAR) Lag models for both spatial and spatio-temporal cases. In modelling, we have
introduced one new climate indicator, termed as Weighted Anomaly Soil Water
Index (WASWI), which is a dimensionless measure of the relative severity of soil
water containment indicating in the form of surplus or deficit. In all the models the
coefficients of WASWI were found negative and to be significant, predicting armed
conflict at 0.05 level of significance for the whole period. The conflicts were found
to be clustered up to 200 kilometers and the local level negative relationship between
conflict and climate suggests that change in WASWI impacts changes in AC by -
0.1981 or -0.1657. We have also found that the conflict in the own cell associated to
a ( app. 0.7) increase in the probability of conflict occurances in the neighbouring
cell and also to a (app. 0.6) increase of the following years (spatio-temporal). So,
climate change indicators are a vital predictor of armed conflict and provides a
proper predictive framework for conflict expectation. This study also provides a
sound methodological framework for climate conflict research which encompasses
two big approaches, point process modelling and lattice approach with careful
modelling of spatial dependence, spatial and sptio-temporal autocorrelation, etc.
Descrição
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
Palavras-chave
Armed Conflict Climate Change Spatial Point Pattern analysis Spatial distribution pattern Spatio-temporal modelling Spatial Autoregressive model Climate conflict relationship