Interactive Visual Exploration of Longitudinal Historical Career Mobility Data

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.

FlexEvent: going beyond Case‐Centric Exploration and Analysis of Multivariate Event Sequences

“…Event sequences are typically visualized using timelines [GGJ * 21, CvW17,LWD * 16,WGW * 20,CXR17,YM22,HPK * 21], hierarchical visualizations [GGJ * 21, WGGP * 11, WG12, GS14, LWD * 16, JGC * 20, YM22], matrix visualizations [GGJ * 21, YM22], bar chart visualizations [GGJ * 21, MSD * 16], and Sankey flow diagrams [DBZSD20,KPS15,HLI * 15,GGJ * 21,MXC * 19,WMH * 21, YM22, WLS * 21]. Often, case metadata is displayed next to the main visualization serving as a filter [KPS15, GS14, JGC * 20, MXC * 19,WLS * 21]. Two well‐known examples of hierarchical visualization are DecisionFlow [GS14] and LifeFlow [WGGP * 11], one of the first to use flow visualizations for sequential event data.…”

“…Others use different levels of granularity [CPYQ18, MSM * 21] potentially combined with aggregation/simplification ( e.g. , frequent pattern mining [CXR17,LWD * 16,MXC * 19, PW14, WGW * 20, WLG * 21, WLS * 21]). Similarly, in this work, we also provide users with two levels of granularity in combination with frequent sequential pattern mining to deal with long sequences.…”

AirLens: Multi‐Level Visual Exploration of Air Quality Evolution in Urban Agglomerations

“…CoreFlow [LKD∗17] could automatically extract the branching patterns in event sequences and summarize the event sequences as a tree structure. For visualization of event sequences, flow‐based visualization is an effective visual representation of large‐scale sequence data, with the Sankey‐based structure [WLS∗21,WLG∗21] or tree‐based structure [LKD∗17]. Matrix‐based and list‐based visualizations were also used to provide a scalable overview of event sequences [ZLD∗15] and compare different event sequences [WGW∗20].…”

Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

“…It lacks a macro summary of careers, which is essential to understand the aggregated behavioral patterns in social science. Visualization studies use career data as a scenario in the event sequence analysis, including similarity analysis [18,20,32,60] and visual sequence summarization [24,25]. CV3 [20] is a system for comparing multiple resumes that assist recruiters in finding suitable candidates.…”