Paper on CGED-Q visualization in IEEE Transactions on Visualization and Computer Graphics by Bijia Chen, Cameron Campbell and others

Bijia Chen and Cameron Campbell were co-authors on a paper titled “Interactive Visual Exploration of Longitudinal Historical Career Mobility Data” that just appeared in IEEE Transactions on Visualization and Computer Graphics that introduces CareerLens, a visualization platform for exploring the careers of officials and their social networks in the China Government Employee Dataset-Qing. The lead author, Yifang Wang, is a PhD student of Professor Huamin Qu, a colleague in the Department of Computer Science and Engineering at HKUST whose research focuses on visualization and graphics. Other co-authors include Professor Qu and members of his VisLab group.

Here is a link to the paper:

Wang Yifang’s website has a page about the paper that includes a downloadable PDF, for those of you who can’t access it from the IEEE website.

An example of the CareerLens interface

And the abstract…

Abstract: The increased availability of quantitative historical datasets has provided new research opportunities for multiple disciplines in social science. In this paper, we work closely with the constructors of a new dataset, CGED-Q (China Government Employee Database-Qing), that records the career trajectories of over 340,000 government officials in the Qing bureaucracy in China from 1760 to 1912. We use these data to study career mobility from a historical perspective and understand social mobility and inequality. However, existing statistical approaches are inadequate for analyzing career mobility in this historical dataset with its fine-grained attributes and long time span, since they are mostly hypothesis-driven and require substantial effort. We propose CareerLens, an interactive visual analytics system for assisting experts in exploring, understanding, and reasoning from historical career data. With CareerLens, experts examine mobility patterns in three levels-of-detail, namely, the macro-level providing a summary of overall mobility, the meso-level extracting latent group mobility patterns, and the micro-level revealing social relationships of individuals. We demonstrate the effectiveness and usability of CareerLens through two case studies and receive encouraging feedback from follow-up interviews with domain experts