DEFINING A METHODOLOGY FOR INTEGRATING SEMANTIC, GEOSPATIAL, AND TEMPORAL TECHNIQUES FOR CONFLICT ANALYSIS

Globally, the absolute number of war deaths has been declining since 1946. And yet, conflict and violence are currently on the rise, with many conflicts today waged between non-state actors such as political militias, criminal, and international terrorist groups. Unresolved regional tensions, a breakdown in the rule of law, absent or co-opted state institutions, illicit economic gain, and the scarcity of resources exacerbated by climate change, have become dominant drivers of conflict (UN. A new era of conflicts, 2022). In the ear of modern technology, data science, machine learning, and AI, the available shall be used to analyze, understand and possibly predict the possibility of conflicts outbreaks in various parts of the world. Moreover, it should provide tools for political scientists to a deeper understanding of political processes and enhance their decision-making processes. This paper focuses on applying data science techniques to process and analyze data in three various data analysis domains: Semantic, Geospatial, and Temporal Analysis. It provides the possible sources of the conflict and other datasets used for the analytics mentioned above. The data is used for research and experimental purposes only. These analytical processes provide the mechanisms to discover the historical data and identify the potential causes of the conflicts.

Automated summary

This paper highlights the significance of using data science techniques to analyze and study the possibility of conflicts. It explores different types of data analysis, such as semantic, geospatial, and temporal analysis, to discover potential causes of conflicts.