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<p>Identifying and understanding the causes of ethnic conflicts in low- and middle-income countries (LMICs) is challenging. Research has yet to predict ethnic conflict from a spatial and ethnographic approach. To address this important knowledge gap, I ask: <i>How can we predict ethnic conflict in a data-scarce LMIC such as Kyrgyzstan?</i> To answer this question, I used the Climate-Migration-Conflict Nexus framework to identify factors influencing ethnic conflict in Kyrgyzstan. I explored the use of spatial analysis and a multi-criteria evaluation model specifically using the analytic hierarchy process, to incorporate the various types of human-environmental datasets into a series of intermediary maps that are overlaid to create a final map of regions that are the most susceptible to experience ethnic conflict. Overall, this study shows that the complexity of ethnic conflict scenarios within LMICs may be better understood from a qualitative and quantitative approach with the final result identifying the region (oblast) of Batken as the most susceptible to experience ethnic conflict.</p>

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Spatial dynamics of forecasting ethnic conflict in Kyrgyzstan

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Chelsea L. Cervantes De Blois

摘要

Identifying and understanding the causes of ethnic conflicts in low- and middle-income countries (LMICs) is challenging. Research has yet to predict ethnic conflict from a spatial and ethnographic approach. To address this important knowledge gap, I ask: How can we predict ethnic conflict in a data-scarce LMIC such as Kyrgyzstan? To answer this question, I used the Climate-Migration-Conflict Nexus framework to identify factors influencing ethnic conflict in Kyrgyzstan. I explored the use of spatial analysis and a multi-criteria evaluation model specifically using the analytic hierarchy process, to incorporate the various types of human-environmental datasets into a series of intermediary maps that are overlaid to create a final map of regions that are the most susceptible to experience ethnic conflict. Overall, this study shows that the complexity of ethnic conflict scenarios within LMICs may be better understood from a qualitative and quantitative approach with the final result identifying the region (oblast) of Batken as the most susceptible to experience ethnic conflict.