出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Data mining enables an innovative, largely automatic meta-analysis of the relationship between
political and economic geography analyses of crisis regions. As an example, the two
approaches Global Conflict Risk Index (GCRI) and Fragile States Index (FSI) can be related to
each other. The GCRI is a quantitative conflict risk assessment based on open source data and a
statistical regression method developed by the Joint Research Centre of the European
Commission. The FSI is based on a conflict assessment framework developed by The Fund for
Peace in Washington, DC. In contrast to the quantitative GCRI, the FSI is essentially focused
on qualitative data. Both approaches therefore have closely related objectives, but very different
methodologies and data sources. It is therefore hoped that the two complementary approaches
can be combined to form an even more meaningful meta-analysis, or that contradictions can be
discovered, or that a validation of the approaches can be obtained if there are similarities. We
propose an approach to automatic meta-analysis that makes use of machine learning (data
mining). Such a procedure represents a novel approach in the meta-analysis of conflict risk
analysis.
关键词：Data Science; Deep Learning; Conflict Risk Prediction
