Interactive Visual Exploration of Longitudinal Historical Career Mobility Data.

Area of Science:

• Social Science
• Historical Data Analysis
• Quantitative History

Background:

• Increased availability of quantitative historical datasets offers new research avenues.
• The China Government Employee Database-Qing (CGED-Q) comprises career trajectories of over 340,000 Qing dynasty officials (1760-1912).
• Existing statistical methods are insufficient for analyzing complex historical career data due to fine-grained attributes and long time spans.

Purpose of the Study:

• To address the limitations of current statistical approaches for historical career data analysis.
• To introduce CareerLens, an interactive visual analytics system for exploring and understanding historical career mobility.
• To facilitate the study of social mobility and inequality through detailed examination of career trajectories.

Main Methods:

• Development of CareerLens, an interactive visual analytics system.
• Utilizing the CGED-Q dataset, spanning 1760-1912 with over 340,000 government officials.
• Employing a three-level-of-detail analysis: macro (overall mobility), meso (group patterns), and micro (individual relationships).

Main Results:

• CareerLens enables experts to examine career mobility patterns at macro, meso, and micro levels.
• The system aids in understanding overall mobility trends, latent group mobility, and individual social networks.
• Case studies and expert feedback demonstrate the effectiveness and usability of CareerLens for historical career data exploration.

Conclusions:

• CareerLens provides a novel and effective solution for analyzing complex historical career datasets.
• The system enhances the ability of social scientists to study historical social mobility and inequality.
• Interactive visual analytics is crucial for unlocking insights from large-scale historical quantitative data.