Author(s): Kawsar, Riazuddin
Date: 2013
Persistent ID: http://hdl.handle.net/10362/9189
Origin: Repositório Institucional da UNL
Subject(s): Armed Conflict; Climate Change; Spatial Point Pattern analysis; Spatial distribution pattern; Spatio-temporal modelling; Spatial Autoregressive model; Climate conflict relationship
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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.
