Skip to main content

Advertisement

SpringerLink
Log in

VisUPI: visual analytics for University Personality Inventory data

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

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

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Balzer M, Deussen O, Lewerentz C (2005) Voronoi treemaps for the visualization of software metrics. In: Softvis, pp 165–172

  • Bederson BB (2001) Photomesa: a zoomable image browser using quantum treemaps and bubblemaps. In: ACM symposium on user interface software and technology, pp 71–80

  • Chen W, Lao T, Xia J, Huang X, Zhu WB, Hu Guan H (2016) Gameflow: narrative visualization of NBA basketball games. IEEE Trans Multimed 18:2247–2256

    Article  Google Scholar 

  • Chen D, Yueping LI, Wang X (2014) Study on the status of medical students’ mental health and influencing factors. Chin J Soc Med 4:254–256

    Google Scholar 

  • 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):2149

    Article  Google Scholar 

  • 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 401–408

  • Liu S, Cui W, Wu Y, Liu M (2014) A survey on information visualization: recent advances and challenges. Vis Comput 30(12):1373–1393

    Article  Google Scholar 

  • 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):2627–2636

    Article  Google Scholar 

  • Neumann P, Carpendale S, Agarawala A (2006) Phyllotrees: phyllotactic patterns for tree layout. In: Eurovis 06 Proceedings of eurographics, pp 59–66

  • 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):1408–1415

    Article  Google Scholar 

  • Quinlan JR (1986) Induction on decision tree. Mach Learn 1(1):81–106

    Google Scholar 

  • Robertson GG (1991) Cone trees: animated 3d visualizations of hierarchical information. In: Sigchi conference on human factors in computing systems, pp 189–194

  • Sacha D, Stoffel A, Stoffel F, Kwon BC (2014) Knowledge generation model for visual analytics. IEEE Trans Vis Comput Graph 20(12):1604–1613

    Article  Google Scholar 

  • 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):946–963

    Article  Google Scholar 

  • Schulz HJ, Hadlak S, Schumann H (2009) Point-based tree representation: a new approach for large hierarchies. In: IEEE pacific visualization symposium pacificvis, pp 81–88

  • 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):1350–1360

    Article  Google Scholar 

  • 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 57–65

  • Su Y (2008) Mental health condition of freshmen by UPI. China J Health Psychol 16:1133–1134

    Google Scholar 

  • 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):2033–2042

    Article  Google Scholar 

  • Van Wijk JJ, Huub VDW (1999) Cushion treemaps: visualization of hierarchical information. In: IEEE symposium on information visualization, p 73

  • 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):1719–1749

    Article  Google Scholar 

  • Wang Z, Ye T, Lu M, Yuan X (2014) Visual exploration of sparse traffic trajectory data. IEEE Trans Vis Comput Graph 20(12):1813

    Article  Google Scholar 

  • 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 PP99:1–1

    Google Scholar 

  • Xia J, Jiang G, Zhang YH, Li R, Chen W (2017) Visual subspace clustering based on dimension relevance. J Vis Lang Comput 41:79–88

    Article  Google Scholar 

  • 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):236–245

    Article  Google Scholar 

  • 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 23:181–190

    Article  Google Scholar 

  • Zhang ML (2007) Effect on university personality inventory(upi)of 1087 college freshman. Progr Mod Biomed 7:1093–1095

    Google Scholar 

  • 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):1843–1852

    Article  Google Scholar 

  • Zhang J, Lanza S, Zhang M, Su B (2015) Structure of the university personality inventory for chinese college students. Psychol Rep 116(3):821

    Article  Google Scholar 

  • 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):181–196

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. Zhejiang University of Finance and Economics, Hangzhou, China

    Zhiguang Zhou, Xiangjun Zhu & Yuhua Liu

  2. Zhejiang University of Water Resources and Electric Power, Hangzhou, China

    Qianqian Ren

  3. East China Normal University, Shanghai, China

    Changbo Wang

  4. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

Authors
  1. Zhiguang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangjun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuhua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qianqian Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Changbo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tianlong Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tianlong Gu.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 8554 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Z., Zhu, X., Liu, Y. et al. VisUPI: visual analytics for University Personality Inventory data. J Vis 21, 885–901 (2018). https://doi.org/10.1007/s12650-018-0499-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-018-0499-x

Keywords

Search

Navigation