Big Data Conflict Forecasting: Operationalizing the Data Science Team

Authored by:
Dr Diane Zorrie, Mihhail Berezovski
Published on 6/15/2021
Out of stock; click here to propose a re-print funding line

To demonstrate how data-enabled intelligence and planning could be employed through a data science team, this occasional paper explores the practicality of using big data analytical techniques to identify local conflict patterns with operational-level consequences. This project offers the beginning of a modeling project for predictive analysis on the correlation between essential services and the incidence of attack in an active wartime environment. By creating data layers from existing information on essential services and comparing those data points with instances of attack, this research ultimately seeks to provide better models to forecast patterns of conflict in different sociopolitical contexts.