Interactive Visual Exploration of Longitudinal Historical Career Mobility Data
The increased availability of quantitative historical datasets has provided new research opportunities for multiple disciplines in social science. In this article, we work closely with the constructors of a new dataset, CGED-Q (China Government Employee Database-Qing), that records the career trajec...
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| Veröffentlicht in: | IEEE transactions on visualization and computer graphics 2022-10, Vol.28 (10), p.3441-3455 |
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| container_title | IEEE transactions on visualization and computer graphics |
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| creator | Wang, Yifang Liang, Hongye Shu, Xinhuan Wang, Jiachen Xu, Ke Deng, Zikun Campbell, Cameron Chen, Bijia Wu, Yingcai Qu, Huamin |
| description | The increased availability of quantitative historical datasets has provided new research opportunities for multiple disciplines in social science. In this article, 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. |
| doi_str_mv | 10.1109/TVCG.2021.3067200 |
| format | Article |
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In this article, 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.</description><identifier>ISSN: 1077-2626</identifier><identifier>EISSN: 1941-0506</identifier><identifier>DOI: 10.1109/TVCG.2021.3067200</identifier><identifier>PMID: 33750691</identifier><identifier>CODEN: ITVGEA</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>career mobility ; Careers ; Data visualization ; Datasets ; Digital humanities ; Engineering profession ; Government ; History ; Interactive systems ; quantitative history ; Social groups ; Trajectory ; Visual analytics</subject><ispartof>IEEE transactions on visualization and computer graphics, 2022-10, Vol.28 (10), p.3441-3455</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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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. 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| source | IEEE Explore |
| subjects | career mobility Careers Data visualization Datasets Digital humanities Engineering profession Government History Interactive systems quantitative history Social groups Trajectory Visual analytics |
| title | Interactive Visual Exploration of Longitudinal Historical Career Mobility Data |
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