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A new geography of civil war: a machine learning approach to measuring the zones of armed conflicts

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  • Kikuta, Kyosuke

Abstract

Where do armed conflicts occur? In applied studies, we may take ad hoc approaches to answer this question. In some regression studies, for instance, a single conflict event can cause an entire province to be classified as a conflict zone. In this paper, I fill this void of knowledge by developing a machine learning method that is less dependent on the areal-unit assumptions and can flexibly estimate conflict zones. I apply the method to a conflict event dataset and create a new dataset of conflict zones. A replication of Daskin and Pringle (2018, Nature 553, 328–332) with the new dataset indicates that the effect of civil war on mammal populations is much smaller than the original estimate.

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  • Kikuta, Kyosuke, 2022. "A new geography of civil war: a machine learning approach to measuring the zones of armed conflicts," Political Science Research and Methods, Cambridge University Press, vol. 10(1), pages 97-115, January.
    • Handle: RePEc:cup:pscirm:v:10:y:2022:i:1:p:97-115_7
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