Advertisement

Journal of Visualization

, Volume 21, Issue 5, pp 885–901 | Cite as

VisUPI: visual analytics for University Personality Inventory data

  • Zhiguang Zhou
  • Xiangjun Zhu
  • Yuhua Liu
  • Qianqian Ren
  • Changbo Wang
  • Tianlong GuEmail author
Regular Paper

Abstract

It is a feasible way to investigate the mental health status of college students through University Personality Inventory (UPI). However, the analysis of original UPI data is a difficult and tedious task, because the traditional software always requires the users to select and reload slices of data by hand. Also, the limited constant functions provided by the software cannot meet the various requirements of domain experts. In this paper, we propose VisUPI, a specialized visualization framework for UPI datasets, aiming at an in-depth analysis of the mental health status of college students. A circular view is firstly designed to layout the questionnaires and visualize the answers of individuals. Then, a decision tree model is employed to classify the investigated students, and a radial hierarchy chart is designed to present the relationship of different groups of students. According to the prior knowledge of UPI, we restructure the network relationship of specified questionnaires, and use the force-directed model to layout the questionnaires, enabling users to better perceive the answers of those students with serious mental health problems. Furthermore, multi-dimensional scaling is used to visualize the dissimilarity between different questionnaires, and the statistical answers are presented through bar charts in detail. Finally, the effectiveness and scalability of VisUPI are demonstrated through case studies with the real-world datasets and the domain-expert interviews.

Graphical Abstract

Keywords

Visual analytics UPI Visual design Mental health Hierarchy visualization 

Notes

Acknowledgements

This work was supported by NFS of China Project Nos. 61303133, U1501252, U1711263, the Natural Science Foundation of Zhejiang Province No. LY18F020024, the National Statistical Scientific Research Project No. 2015LD03 and the First Class Discipline of Zhejiang—A (Zhejiang University of Finance and Economics-Statistics).

Supplementary material

Supplementary material 1 (mp4 8554 KB)

References

  1. Balzer M, Deussen O, Lewerentz C (2005) Voronoi treemaps for the visualization of software metrics. In: Softvis, pp 165–172Google Scholar
  2. Bederson BB (2001) Photomesa: a zoomable image browser using quantum treemaps and bubblemaps. In: ACM symposium on user interface software and technology, pp 71–80Google Scholar
  3. Chen W, Lao T, Xia J, Huang X, Zhu WB, Hu Guan H (2016) Gameflow: narrative visualization of NBA basketball games. IEEE Trans Multimed 18:2247–2256CrossRefGoogle Scholar
  4. Chen D, Yueping LI, Wang X (2014) Study on the status of medical students’ mental health and influencing factors. Chin J Soc Med 4:254–256Google Scholar
  5. Ferreira N, Poco J, Vo HT, Freire J, Silva CT (2013) Visual exploration of big spatio-temporal urban data: a study of New York City taxi trips. IEEE Trans Vis Comput Graph 19(12):2149CrossRefGoogle Scholar
  6. Lamping J, Rao R, Pirolli P (2002) A focus + context technique based on hyperbolic geometry for visualizing large hierarchies. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 401–408Google Scholar
  7. Liu S, Cui W, Wu Y, Liu M (2014) A survey on information visualization: recent advances and challenges. Vis Comput 30(12):1373–1393CrossRefGoogle Scholar
  8. Ma Y, Lin T, Cao Z, Li C, Wang F, Chen W (2016) Mobility viewer: an eulerian approach for studying urban crowd flow. IEEE Trans Intell Transp Syst 17(9):2627–2636CrossRefGoogle Scholar
  9. Neumann P, Carpendale S, Agarawala A (2006) Phyllotrees: phyllotactic patterns for tree layout. In: Eurovis 06 Proceedings of eurographics, pp 59–66Google Scholar
  10. Qu H, Chan WY, Xu A, Chung KL, Lau KH, Guo P (2007) Visual analysis of the air pollution problem in hong kong. IEEE Trans Vis Comput Graph 13(6):1408–1415CrossRefGoogle Scholar
  11. Quinlan JR (1986) Induction on decision tree. Mach Learn 1(1):81–106Google Scholar
  12. Robertson GG (1991) Cone trees: animated 3d visualizations of hierarchical information. In: Sigchi conference on human factors in computing systems, pp 189–194Google Scholar
  13. Sacha D, Stoffel A, Stoffel F, Kwon BC (2014) Knowledge generation model for visual analytics. IEEE Trans Vis Comput Graph 20(12):1604–1613CrossRefGoogle Scholar
  14. Scholz RW, Lu Y (2014) Detection of dynamic activity patterns at a collective level from large-volume trajectory data. Int J Geogr Inf Sci 28(5):946–963CrossRefGoogle Scholar
  15. Schulz HJ, Hadlak S, Schumann H (2009) Point-based tree representation: a new approach for large hierarchies. In: IEEE pacific visualization symposium pacificvis, pp 81–88Google Scholar
  16. Shi R, Yang M, Zhao Y, Zhou F, Huang W, Zhang S (2016) A matrix-based visualization system for network traffic forensics. IEEE Syst J 10(4):1350–1360CrossRefGoogle Scholar
  17. Stasko J, Zhang E (2000) Focus + context display and navigation techniques for enhancingradial, space-filling hierarchy visualizations. In: IEEE symposium on information visualization, 2000. InfoVis 2000, pp 57–65Google Scholar
  18. Su Y (2008) Mental health condition of freshmen by UPI. China J Health Psychol 16:1133–1134Google Scholar
  19. Turkay C, Slingsby A, Hauser H, Wood J (2014) Attribute signatures: dynamic visual summaries for analyzing multivariate geographical data. IEEE Trans Vis Comput Graph 20(12):2033–2042CrossRefGoogle Scholar
  20. Van Wijk JJ, Huub VDW (1999) Cushion treemaps: visualization of hierarchical information. In: IEEE symposium on information visualization, p 73Google Scholar
  21. Von Landesberger T, Kuijper A, Schreck T, Kohlhammer J, Van Wijk JJ, Fekete JD, Fellner DW (2011) Visual analysis of large graphs: state of the art and future research challenges. Comput Graph Forum 30(6):1719–1749CrossRefGoogle Scholar
  22. Wang Z, Ye T, Lu M, Yuan X (2014) Visual exploration of sparse traffic trajectory data. IEEE Trans Vis Comput Graph 20(12):1813CrossRefGoogle Scholar
  23. Wang X, Chou JK, Chen W, Guan H, Chen W, Lao T, Ma KL (2017) A utility-aware visual approach for anonymizing multi-attribute tabular data. IEEE Trans Vis Comput Graph PP99:1–1Google Scholar
  24. Xia J, Jiang G, Zhang YH, Li R, Chen W (2017) Visual subspace clustering based on dimension relevance. J Vis Lang Comput 41:79–88CrossRefGoogle Scholar
  25. Xia J, Ye F, Chen W, Wang Y, Chen W, Ma Y, Tung AKH (2018) Ldsscanner: exploratory analysis of low-dimensional structures in high-dimensional datasets. IEEE Trans Vis Comput Graph 24(1):236–245CrossRefGoogle Scholar
  26. Yang X, Lei S, Daianu M, Tong H, Liu Q, Thompson P (2017) Blockwise human brain network visual comparison using nodetrix representation. IEEE Trans Vis Comput Graph 23:181–190CrossRefGoogle Scholar
  27. Zhang ML (2007) Effect on university personality inventory(upi)of 1087 college freshman. Progr Mod Biomed 7:1093–1095Google Scholar
  28. Zhang J, Yanli E, Ma J, Zhao Y, Xu B, Sun L, Chen J, Yuan X (2014) Visual analysis of public utility service problems in a metropolis. IEEE Trans Vis Comput Graph 20(12):1843–1852CrossRefGoogle Scholar
  29. Zhang J, Lanza S, Zhang M, Su B (2015) Structure of the university personality inventory for chinese college students. Psychol Rep 116(3):821CrossRefGoogle Scholar
  30. Zhao Y, Liang X, Fan X, Wang Y, Yang M, Zhou F (2014) Mvsec: multi-perspective and deductive visual analytics on heterogeneous network security data. J Vis 17(3):181–196CrossRefGoogle Scholar
  31. Zhou F, Huang W, Zhao Y, Shi Y, Liang X, Fan X (2015) Entvis: a visual analytic tool for entropy-based network traffic anomaly detection. IEEE Comput Graph Appl 35(6):42–50.  https://doi.org/10.1109/MCG.2015.97 CrossRefGoogle Scholar

Copyright information

© The Visualization Society of Japan 2018

Authors and Affiliations

  • Zhiguang Zhou
    • 1
  • Xiangjun Zhu
    • 1
  • Yuhua Liu
    • 1
  • Qianqian Ren
    • 2
  • Changbo Wang
    • 3
  • Tianlong Gu
    • 4
    Email author
  1. 1.Zhejiang University of Finance and EconomicsHangzhouChina
  2. 2.Zhejiang University of Water Resources and Electric PowerHangzhouChina
  3. 3.East China Normal UniversityShanghaiChina
  4. 4.Guilin University of Electronic TechnologyGuilinChina

Personalised recommendations