Evaluating Multi-attributes on Cause and Effect Relationship Visualization

Juhee Bae, Elio Ventocilla, Maria Riveiro, Tove Helldin, Göran Falkman

Abstract

This paper presents findings about visual representations of cause and effect relationship’s direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.

References

  1. Alimadadi Jani, S. (2013). Propagation of change and visualization of causality in dependency structures [Master thesis]. Simon Fraser University. British Columbia, Canada.
  2. Bisantz, A., Cao, D., Jenkins, M., Pennathur, M., P.and Farry, Roth, E., Potter, S., & J., P. (2011). Comparing uncertainty visualizations for a dynamic decision-making task. In Journal of Cognitive Engineering and Decision Making 5(3) (pp. 277-293).
  3. Bonneau, G.-P., Hege, H.-C., Johnson, C. R., Oliveira, M. M., Potter, K., Rheingans, P., & Schultz, T. (2014). Overview and state-of-the-art of uncertainty visualization. In C. D. Hansen, M. Chen, C. R. Johnson, A. E. Kaufman, & H. Hagen (Eds.), Scientific visualization: Uncertainty, multifield, biomedical, and scalable visualization (pp. 3-27). London: Springer London.
  4. Chen, M., Trefethen, A., Bañares Alcántara, R., Jirotka, M., Coecke, B., Ertl, T., & Schmidt, A. (2011). From data analysis and visualization to causality discovery. In Computer, 44(10) (pp. 84-87).
  5. Elmqvist, N., & Tsigas, P. (2003). Growing squares: Animated visualization of causal relations. In Proceedings of the 2003 ACM Symposium on Software Visualization (pp. 17-26).
  6. Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129-1164.
  7. Ghoniem, M., Fekete, J.-D., & Castagliola, P. (2004). A comparison of the readability of graphs using nodelink and matrix-based representations. In Proc. of the 4th IEEE Symposium on Information Visualization (INFOVIS'04) (p. 17-24).
  8. Guo, H., Huang, J., & Laidlaw, D. (2015). Representing uncertainty in graph edges: An evaluation of paired visual variables. In IEEE Transactions on Visualization and Computer Graphics, 21(10) (pp. 1173-1186).
  9. Holten, D., Isenberg, P., Van Wijk, J., & Fekete, J. (2011). An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs. In In 2011 IEEE Pacific Visualization Symposium (pp. 195-202).
  10. Holten, D., & van Wijk, J. (2009). A user study on visualizing directed edges in graphs. In In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2299-2308).
  11. Kadaba, N. R., Irani, P. P., & Leboe, J. (2007). Visualizing causal semantics using animations. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1254-1261.
  12. Kubíc?ek, P., & Šašinka, C?. (2011). Thematic uncertainty visualization usability-comparison of basic methods. Annals of GIS, 17(4), 253-263.
  13. MacEachren, A. M., Robinson, A., Hopper, S., Gardner, S., Murray, R., Gahegan, M., & Hetzler, E. (2005). Visualizing geospatial information uncertainty: What we know and what we need to know. Cartography and Geographic Information Science, 32(3), 139-160.
  14. MacEachren, A. M., Roth, R. E., O'Brien, J., Li, B., Swingley, D., & Gahegan, M. (2012). Visual semiotics & uncertainty visualization: An empirical study. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2496-2505.
  15. Pane, J. F., Corbett, A. T., & John, B. E. (1996). Assessing dynamics in computer-based instruction. In Proc. of the SIGCHI Conference on Human Factors in Computing Systems (pp. 197-204).
  16. Potter, K., Rosen, P., & Johnson, C. R. (2012). From quantification to visualization: A taxonomy of uncertainty visualization approaches. In Uncertainty Quantification in Scientific Computing(pp. 226-249). Springer.
  17. Sanyal, J., Zhang, S., Bhattacharya, G., Amburn, P., & Moorhead, R. (2009). A user study to compare four uncertainty visualization methods for 1D and 2D datasets. IEEE Trans. on Visualization and Computer Graphics, 15, 1209-1218.
  18. Scholz, R. W., & Lu, Y. (2014). Uncertainty in geographic data on bivariate maps: An examination of visualization preference and decision making. ISPRS International Journal of Geo-Information, 3(4), 1180-1197.
  19. Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: can it facilitate? International Journal of Human-Computer Studies, 57(4), 247-262.
  20. Wang, J., & Mueller, K. (2016). The visual causality analyst: An interactive interface for causal reasoning. In IEEE Transactions on Visualization and Computer Graphics, 22(1) (pp. 230-239).
  21. Zuk, T., & Carpendale, S. (2006). Theoretical analysis of uncertainty visualizations. In Proceedings of the SPIE-VDA (Vol. 6060).
Download


Paper Citation


in Harvard Style

Bae J., Ventocilla E., Riveiro M., Helldin T. and Falkman G. (2017). Evaluating Multi-attributes on Cause and Effect Relationship Visualization . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017) ISBN 978-989-758-228-8, pages 64-74. DOI: 10.5220/0006102300640074


in Bibtex Style

@conference{ivapp17,
author={Juhee Bae and Elio Ventocilla and Maria Riveiro and Tove Helldin and Göran Falkman},
title={Evaluating Multi-attributes on Cause and Effect Relationship Visualization},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)},
year={2017},
pages={64-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006102300640074},
isbn={978-989-758-228-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)
TI - Evaluating Multi-attributes on Cause and Effect Relationship Visualization
SN - 978-989-758-228-8
AU - Bae J.
AU - Ventocilla E.
AU - Riveiro M.
AU - Helldin T.
AU - Falkman G.
PY - 2017
SP - 64
EP - 74
DO - 10.5220/0006102300640074