Visualization comparison of historical figures cohorts

doi:10.11996/JG.j.2095-302X.2023061227

Abstract

Abstract:

In the realm of historical research, conducting cohort comparisons among historical figures holds great significance. It is aspired to extract similarities and differences between cohorts based on the various characteristics of historical figures, thereby gaining a deeper understanding of historical events. However, existing visualization systems predominantly concentrate on exploring single cohorts and their internal differences, neglecting the hierarchical comparisons between cohorts. In order to bridge this gap, a visual analysis method was proposed for comparing cohorts of historical figures. The proposed approach involved the extraction and visualization of the characteristics of historical figure cohorts, leveraging structured historical figure data to facilitate multi-dimensional cohort comparisons. We have developed an interactive visualization system that employed flower-based visual metaphors to encode the diverse features of these cohorts. Historians could interactively explore cohorts and study the correlations, similarities, and differences among various cohorts. Following feedback evaluations by historians and case analyses, the historical figure cohort comparison visualization system has demonstrated its effectiveness as a robust tool for cohort comparison research. Through this system, historians can obtain in-depth insights into historical events.

Key words: visual analysis, historical cohort comparison, knowledge graph, historical figures, digital humanities

CLC Number:

TP391

CHEN Yi-tian, ZHANG Wei, TAN Si-wei, ZHU Rong-chen, WANG Yi-chao, ZHU Min-feng, CHEN Wei. Visualization comparison of historical figures cohorts[J]. Journal of Graphics, 2023, 44(6): 1227-1238.

Figures/Tables 10

Fig. 1 Our analysis workflow consists of four stages ((a) Determining the cohort range; (b) Extracting cohort features; (c) Multi-dimensional cohort comparison; (d) Obtaining insights)

Fig. 2 A network describing the relationship between entities and concepts in the knowledge graph

Fig. 3 Extract a descriptive sequence from the meta path ((a) Eexample of meta path; (b) Examples about Su Shi)

Fig. 4 System overview ((a) The person search view; (b) The cohort analysis view; (c) The cohort comparison view; (d) The description list view)

Fig. 5 Feature analysis ((a) Classifier interpretation; (b) Cohort feature comparison)

Fig. 6 Different representations of the exploratory views in single cohort and cohort comparison ((a) Person timeline in single cohort; (b) Person timeline in cohort comparison; (c) Person trajectory in single cohort; (d) Person trajectory in cohort comparison. Red represents the unique figures of Cohort 1, blue represents the unique figures of Cohort 2, and green represents the overlapping figures of Cohort 1 and 2)

Fig. 7 The exploration process of Neo-Confucianism Cohort and anti-Neo-Confucianism Cohort (Flower 1 is the initial cohort constructed by the figures around Han Tuozhou. Flower 2 and Flower 3 are Han's political opposition cohort and political aid cohort. Flower 4 is a closely related cohort in Han's political opposition cohort. Flower 5 is a cohort that has both supported and opposed Han)

Fig. 8 A part of feature of the initial cohort constructed by the characters around Han Tuozhou (The lower left corner displays the distribution positions of 4 features in the feature analysis view. (1-1) is the feature of political confrontation in Han Tuozhou; (1-2) is the feature of political support in Han Tuozhou; (1-3) is the feature of central institutional departments; (1-4) is the feature of the Central Secretariat)

Fig. 9 Searching for clues of figures who oppose Han Tuozhou politically in three views ((a) Feature analysis view; (b) Person timeline; (c) Interpersonal events)

Fig. 10 Feature comparison between Neo-Confucianism Cohort and anti-Neo-Confucianism Cohort (The comparison of the figure proportion and distribution similarities and differences of the following 4 cohort features: Song Dynasty local official features, Wuzhou Liangzhe dong road features, Jinshi features, and civil servant features)

References 29

[1]	STONE L. Prosopography[J]. Daedalus, 1971, 100(1): 46-79.
[2]	Harvard University, Academia Sinica, Peking University. China Biographical Database (CBDB)[EB/OL]. (2018-06-01) [2023-03-01]. https://projects.iq.harvard.edu/cbdb.
[3]	ZHANG W, WONG J K, WANG X M, et al. CohortVA: a visual analytic system for interactive exploration of cohorts based on historical data[J]. IEEE Transactions on Visualization and Computer Graphics, 2023, 29(1): 756-766.
[4]	CAO N, LIN Y R, SUN X H, et al. Whisper: tracing the spatiotemporal process of information diffusion in real time[J]. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(12): 2649-2658. DOI URL
[5]	JEFFREYS M, PAPACOSTAS T, BRADLEY J, et al. Prosopography of the Byzantine World (2016)[EB/OL]. [2023-03-01]. https://pbw2016.kdl.kcl.ac.uk/.
[6]	GRAHAM S, RUFFINI G. Network analysis and Greco- Roman prosopography[EB/OL]. [2023-03-01]. https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.601.4636.
[7]	BOL P K. GIS, prosopography and history[J]. Annals of GIS, 2012, 18(1): 3-15. DOI URL
[8]	ZHANG Z Y, GOTZ D, PERER A. Iterative cohort analysis and exploration[J]. Information Visualization, 2015, 14(4): 289-307. DOI URL
[9]	MALIK S, DU F, MONROE M, et al. Cohort comparison of event sequences with balanced integration of visual analytics and statistics[C]// The 20th International Conference on Intelligent User Interfaces. New York: ACM, 2015: 38-49.
[10]	吉璐. 我国长江学者不同学科群体特征对比研究[J]. 山西科技, 2020, 35(5): 9-13.
	JI L. A comparative study on the characteristics of different subject groups of Changjiang scholars[J]. Shanxi Science and Technology, 2020, 35(5): 9-13 (in Chinese).
[11]	RESTREPO-MÉNDEZ M C, LAWLOR D A, HORTA B L, et al. The association of maternal age with birthweight and gestational age: a cross-cohort comparison[J]. Paediatric and Perinatal Epidemiology, 2015, 29(1): 31-40. DOI URL
[12]	张泽宁. 明清正定和松江两府列女群体对比研究[D]. 石家庄: 河北师范大学, 2021.
	ZHANG Z N. A comparative study of the groups of exemplary women between Zhengding and Songjiang district in the Ming and Qing dynasties[D]. Shijiazhuang: Hebei Normal University, 2021 (in Chinese).
[13]	陈晨. 隋末唐初义军群体的历史书写——以《旧唐书》《新唐书》为中心[D]. 延安: 延安大学, 2022.
	CHEN C. Historiography of the insurrectionary army group in late Sui and early Tang dynasty — Based on Jiu Tang Shu and Xin Tang Shu[D]. Yan'an: Yan'an University, 2022 (in Chinese).
[14]	FULDA J, BREHMEL M, MUNZNER T. TimeLineCurator: interactive authoring of visual timelines from unstructured text[J]. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1): 300-309. DOI URL
[15]	BREHMER M, INGRAM S, STRAY J, et al. Overview: the design, adoption, and analysis of a visual document mining tool for investigative journalists[J]. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 2271-2280. DOI URL
[16]	LIU S X, WANG X T, COLLINS C, et al. Bridging text visualization and mining: a task-driven survey[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 25(7): 2482-2504. DOI URL
[17]	ZHANG W, WONG J K, CHEN Y T, et al. ScrollTimes: tracing the provenance of paintings as a window into history[EB/OL]. [2023-03-01]. https://arxiv.org/abs/2306.08834.
[18]	ZHANG W, TAN S W, CHEN S M, et al. Visual reasoning for uncertainty in spatio-temporal events of historical figures[J]. IEEE Transactions on Visualization and Computer Graphics, 2023, 29(6): 3009-3023. DOI URL
[19]	张玮, 谭思危, 刘凯, 等. 宋词研究的新视角: 文本关联与时空可视分析[J]. 计算机辅助设计与图形学学报, 2019, 31(10): 1687-1697.
	ZHANG W, TAN S W, LIU K, et al. A new perspective on the study of literature Song Ci: text correlation and spatio-temporal visual analytics[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(10): 1687-1697 (in Chinese).
[20]	ZHANG W, MA Q, PAN R S, et al. Visual storytelling of Song Ci and the poets in the social-cultural context of Song dynasty[J]. Visual Informatics, 2021, 5(4): 34-40. DOI URL
[21]	位通, 桑宇辰, 史睿. 基于知识重构的年谱时空可视化呈现: 以《朱熹年谱长编》为例[J]. 中国图书馆学报, 2022, 48(2): 62-75.
	WEI T, SANG Y C, SHI R. Spatio-temporal co-occurrence visualization of chronicle based on knowledge reorganization: a case study of Zhu Xi's chronological biography[J]. Journal of Library Science in China, 2022, 48(2): 62-75 (in Chinese).
[22]	杨铭. 基于CBDB的广西历史人物可视化传播设计研究[D]. 桂林: 桂林电子科技大学, 2022.
	YANG M. Research on the visualization and communication design of historical figures in Guangxi based on CBDB[D]. Guilin: Guilin University of Electronic Technology, 2022 (in Chinese).
[23]	AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules in large databases[C]// Proceedings of The International Confernce on Very Large Data Bases. New York: IEEE Press, 1994: 487-499.
[24]	张连文, 郭海鹏. 贝叶斯网引论[M]. 北京: 科学出版社, 2006: 61-62.
	ZHANG L W, GUO H P. Introduction to Bayesian networks[M]. Beijing: Science Press, 2006: 61-62 (in Chinese).
[25]	CHURCH K W, HANKS P. Word association norms, mutual information, and lexicography[J]. Computational Linguistics, 1990, 16(1): 22-29.
[26]	BLONDEL V D, GUILLAUME J L, LAMBIOTTE R, et al. Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008(10): P10008. DOI URL
[27]	SALTON G, BUCKLEY C. Term-weighting approaches in automatic text retrieval[J]. Information Processing & Management, 1988, 24(5): 513-523. DOI URL
[28]	何俊. 庆元党禁的性质与晚宋儒学的派系整合[J]. 中国史研究, 2004(1): 109-117.
	HE J. The nature of party prohibition in Qingyuan and the factional integration of Confucianism in late Song dynasty[J]. Study of Chinese History, 2004(1): 109-117 (in Chinese).
[29]	韩冠群. 从宣押入内到独班奏事: 南宋韩侂胄的专权之路[J]. 北京社会科学, 2016(4): 13-22.
	HAN G Q. The pattern of Han Tuozhou's individual autarchy in Southern Song dynasty[J]. Social Sciences of Beijing, 2016(4): 13-22 (in Chinese).