A Method Framework for Exploratory Analysis of Open Source Combat Losses Data

Combat loss data serves as a critical foundation for evaluating combat effectiveness, optimizing equipment support, and revealing tactical patterns in modern military research. The rise of Open-Source Intelligence (OSINT) has made available diverse, multi-source heterogeneous equipment loss data on the internet. However, existing analytical methods often focus on singular models, lacking multi-dimensional correlation mining capabilities. This limitation creates a disconnect between raw data and actionable tactical insights. To address this gap, this paper proposes a four-phase exploratory analysis framework for OSINT-derived battle damage data: Data Processing, Data Validation, Correlation Analysis, and Pattern Extracting. This framework enables the mapping of raw data onto battle loss patterns, ultimately revealing equipment employment patterns and core determinants in modern warfare.