A Study on Visual Representations for Active Plant Wall Data Analysis
2. System Setup
2.1. Cloud Server
2.2. Local Hardware
2.3. Collected Data
3. Visual Representations
3.1. Line Graph
3.2. Stacked Area Graph
3.3. Horizon Graph
4.1. Apparatus and Viewing Conditions
- General overview of the data: This category considers how fast and easily domain experts think that they perceived an overview of the data present in the visual representation.
- Details in the data: This category concerns the experienced simplicity with which the experts find details in the presented data in the different representations.
- Perception of visual elements: This category considers how the experts perceived the visual elements in the visual representations, such as color schemes used, order and position of variables, as well as the position of the variables labels.
- Complexity of the representations: This category covers how the complexity of the visual representations was perceived by the experts, with regards to visual representation, number of variables, and familiarity with the type of visual representation.
5.1. General Overview of the Data
5.2. Finding Details in the Data
5.3. Perception of Visual Elements
5.4. Complexity of the Representations
7. Limitations and Future Work
- Further evaluations It would be of interest to make evaluations within other, similar, domains in order to investigate the three representations’ efficiency for similar/other tasks. The domain experts in the present study can all be categorized as laypeople when it comes to using visualization as an analysis tool for temporal multivariate data, so increasing the information visualization community’s understanding of how novice users act and respond to this type of visual analysis would certainly be of use.
- Combining representations The Horizon graph is challenging to understand at first. However, after a few minutes, users are able to grasp the notion of overall positive or negative trends. Analyzing details remains, according to presented results, difficult for a substantial time. A possible solution for this would be to overlay a Line graph on top of the colored bands in the Horizon graph. This would likely help users in simultaneously interpreting overview, trends and details more efficiently (see Figure 11).
- Extending representations The Stacked area graph could be extended with smoothing of details in variable not focused on. This could reduce the noise and detail in those variables, resulting in more focus on the interesting variables to better see trends and patterns. However, the selected variable(s) would have a full resolution, displaying all variations over time for detailed analysis, (see Figure 12).
- Storytelling An effective way of explaining complex concepts and engaging the users could be with animation (showing changes over time in the data). It would be of interest to investigate whether storytelling, an ordered sequence of steps each containing words, images, visual representations, video, or any combination thereof, would increase users understanding of the data. This added dimension could produce certain cognitive effects, however, appropriate use of motions might empower users in exploratory data analysis .
- Application Knowledge gained from these, or similar, studies could be implemented as an application, a fully functional dashboard for monitoring of temporal multivariate data. Such dashboard should be implemented with regards to which visual representation that is most suitable to provide (1) overview and (2) details, as well as considering (3) the complexity of the representation and (4) the use of visual elements. In this application, interaction, for example, to select and position variables, would be both necessary to implement and to evaluate.
Conflicts of Interest
- Liu, Y.; Hassan, K.A.; Karlsson, M.; Weister, O.; Gong, S. Active Plant Wall for Green Indoor Climate Based on Cloud and Internet of Things. IEEE Access 2018, 6, 33631–33644. [Google Scholar] [CrossRef]
- Funkhouser, H.G. A Note on a Tenth Century Graph. Osiris 1936, 1, 260–262. [Google Scholar] [CrossRef]
- Harris, R.L. Information Graphics: A Comprehensive Illustrated Reference; Oxford University Press: New York, NY, USA, 2000. [Google Scholar]
- Wattenberg, M. Baby names, visualization, and social data analysis. In Proceedings of the IEEE Symposium on Information Visualization (INFOVIS 2005), Minneapolis, MN, USA, 23–25 October 2005; pp. 1–7. [Google Scholar] [CrossRef]
- Saito, T.; Miyamura, H.N.; Yamamoto, M.; Saito, H.; Hoshiya, Y.; Kaseda, T. Two-tone pseudo coloring: compact visualization for one-dimensional data. In Proceedings of the IEEE Symposium on Information Visualization (INFOVIS 2005), Minneapolis, MN, USA, 23–25 October 2005; pp. 173–180. [Google Scholar] [CrossRef]
- Reijner, H. The Development of the Horizon Graph. 2008. Available online: http://www.stonesc.com/Vis08_Workshop/DVD/Reijner_submission.pdf (accessed on 20 May 2019).
- Javed, W.; McDonnel, B.; Elmqvist, N. Graphical Perception of Multiple Time Series. IEEE Trans. Vis. Comput. Graph. 2010, 16, 927–934. [Google Scholar] [CrossRef] [PubMed]
- Thudt, A.; Walny, J.; Perin, C.; Rajabiyazdi, F.; MacDonald, L.; Vardeleon, D.; Greenberg, S.; Carpendale, S. Assessing the Readability of Stacked Graphs. In Proceedings of the Graphics Interface Conference (GI), Victoria, BC, Canada, 1–3 June 2016. [Google Scholar] [CrossRef]
- Byron, L.; Wattenberg, M. Stacked Graphs –Geometry & Aesthetics. IEEE Trans. Vis. Comput. Graph. 2008, 14, 1245–1252. [Google Scholar] [CrossRef] [PubMed]
- Heer, J.; Kong, N.; Agrawala, M. Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, 4–9 April 2009; pp. 1303–1312. [Google Scholar] [CrossRef]
- Jabbari, A.; Blanch, R.; Dupuy-Chessa, S. Beyond Horizon Graphs: Space Efficient Time Series Visualization with Composite Visual Mapping. In Proceedings of the 30eme Conférence Francophone Sur L’interaction Homme-Machine, Brest, France, 23–26 October 2018; pp. 73–82. [Google Scholar] [CrossRef]
- Cheng, X.; Cook, D.; Hofmann, H. Enabling interactivity on displays of multivariate time series and longitudinal data. J. Comput. Graph. Stat. 2016, 25, 1057–1076. [Google Scholar] [CrossRef]
- Riehmann, P.; Reibert, J.; Opolka, J.; Froehlich, B. Touch the Time: Touch-Centered Interaction Paradigms for Time-Oriented Data. In EuroVis 2018—Short Papers; Johansson, J., Sadlo, F., Schreck, T., Eds.; The Eurographics Association: Brno, Czech Republic, 2018. [Google Scholar] [CrossRef]
- Rind, A.; Lammarsch, T.; Aigner, W.; Alsallakh, B.; Miksch, S. TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data. IEEE Trans. Vis. Comput. Graph. 2013, 19, 2247–2256. [Google Scholar] [CrossRef] [PubMed]
- Janetzko, H.; Sacha, D.; Stein, M.; Schreck, T.; Keim, D.A.; Deussen, O. Feature-driven visual analytics of soccer data. In Proceedings of the 2014 IEEE Conference on Visual Analytics Science and Technology (VAST), Paris, France, 9–14 November 2014; pp. 13–22. [Google Scholar] [CrossRef]
- Cook, D.J. Making Sense of Sensor Data. IEEE Pervasive Comput. 2007, 6, 105–108. [Google Scholar] [CrossRef]
- Aigner, W.; Miksch, S.; Schumann, H.; Tominski, C. Visualization of Time-Oriented Data, 1st ed.; Springer Publishing Company, Incorporated: London, UK, 2011. [Google Scholar] [CrossRef]
- Tufte, E. The Visual Display of Quantitative Informations, 2nd ed.; Graphics Press: Cheshire, CT, USA, 2001. [Google Scholar]
- Federico, P.; Hoffmann, S.; Rind, A.; Aigner, W.; Miksch, S. Qualizon Graphs: Space-efficient Time-series Visualization with Qualitative Abstractions. In Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces, Como, Italy, 27–29 May 2014; pp. 273–280. [Google Scholar] [CrossRef]
- Voloshyn, D. Modeling Time-Series of Stock Price by Stochastic Context-Free Grammars. In Theoretical and Applied Aspects of Cybernetics, Proceedings of the 3rd International Scientific Conference of Students and Young Scientists—Kyiv: Bukrek—336p; CiteSeer: Kyiv, Ukraine, 2013; p. 69. ISBN 978-966-399-538-0. [Google Scholar]
- Jabbari, A. Multiple Visual Mapping for Visualization of Large Time-Series. In Proceedings of the Actes des Rencontres Doctorales de la 28ième Conférence Francophone sur l’Interaction Homme-Machine, Fribourg, Switzerland, 25 October 2016; p. 29. [Google Scholar]
- Tominski, C.; Schumann, H.; Andrienko, G.; Andrienko, N. Stacking-Based Visualization of Trajectory Attribute Data. IEEE Trans. Vis. Comput. Graph. 2012, 18, 2565–2574. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Gogolou, A.; Tsandilas, T.; Palpanas, T.; Bezerianos, A. Comparing Similarity Perception in Time Series Visualizations. IEEE Trans. Vis. Comput. Graph. 2019, 25, 523–533. [Google Scholar] [CrossRef] [PubMed]
- Perin, C.; Vernier, F.; Fekete, J.D. Interactive Horizon Graphs: Improving the Compact Visualization of Multiple Time Series. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris, France, 27 April–2 May 2013; pp. 3217–3226. [Google Scholar] [CrossRef]
- Kosara, R.; Hauser, H.; Gresh, D.L. An Interaction View on Information Visualization. In Eurographics 2003—STARs; Eurographics Association: Granada, Spain, 2003. [Google Scholar] [CrossRef]
- Blascheck, T.; Besançon, L.; Bezerianos, A.; Lee, B.; Isenberg, P. Glanceable Visualization: Studies of Data Comparison Performance on Smartwatches. IEEE Trans. Vis. Comput. Graph. 2019, 25, 630–640. [Google Scholar] [CrossRef] [PubMed]
- Deber, J.; Jota, R.; Forlines, C.; Wigdor, D. How Much Faster is Fast Enough?: User Perception of Latency & Latency Improvements in Direct and Indirect Touch. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea, 18–23 April 2015; pp. 1827–1836. [Google Scholar] [CrossRef]
- Knoedel, S.; Hachet, M. Multi-touch RST in 2D and 3D spaces: Studying the impact of directness on user performance. In Proceedings of the 2011 IEEE Symposium on 3D User Interfaces (3DUI), Singapore, 19–20 March 2011; pp. 75–78. [Google Scholar] [CrossRef]
- Wang, X.; Besançon, L.; Ammi, M.; Isenberg, T. Augmenting Tactile 3D Data Navigation With Pressure Sensing. Comput. Graph. Forum 2019, 38. Available online: https://hal.archives-ouvertes.fr/hal-02091999/ (accessed on 20 May 2019).
- Tory, M.; Moller, T. Evaluating visualizations: do expert reviews work? IEEE Comput. Graph. Appl. 2005, 25, 8–11. [Google Scholar] [CrossRef] [PubMed]
- Lewis, C.; Rieman, J. Task-Centered User Interface Design. A Practical Introduction. 1993. Available online: http://hcibib.org/tcuid/tcuid.pdf (accessed on 20 May 2019).
- Carpendale, S. Evaluating Information Visualizations. In Information Visualization: Human-Centered Issues and Perspectives; Kerren, A., Stasko, J.T., Fekete, J.D., North, C., Eds.; Springer: Berlin/Heidelberg, Germany, 2008; pp. 19–45. [Google Scholar] [CrossRef]
- Anderson, C. Presenting and evaluating qualitative research. Am. J. Pharm. Educ. 2010, 74, 141. [Google Scholar] [CrossRef] [PubMed]
- Caine, K. Local Standards for Sample Size at CHI. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, San Jose, CA, USA, 7–12 May 2016; pp. 981–992. [Google Scholar] [CrossRef]
- Costabile, M.F.; Fogli, D.; Letondal, C.; Mussio, P.; Piccinno, A. Domain-Expert Users and their Needs of Software Development. In Proceedings of the HCI 2003 End User Development Session, Crète, Greece, 22–27 June 2003. [Google Scholar]
- Nielsen, J.; Molich, R. Heuristic Evaluation of User Interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Seattle, WA, USA, 1–5 April 1990; pp. 249–256. [Google Scholar] [CrossRef]
- Wongsuphasawat, K.; Shneiderman, B. Finding comparable temporal categorical records: A similarity measure with an interactive visualization. In Proceedings of the 2009 IEEE Symposium on Visual Analytics Science and Technology, Atlantic City, NJ, USA, 12–13 October 2009; pp. 27–34. [Google Scholar] [CrossRef]
- Isenberg, T.; Isenberg, P.; Chen, J.; Sedlmair, M.; Möller, T. A Systematic Review on the Practice of Evaluating Visualization. IEEE Trans. Vis. Comput. Graph. 2013, 19, 2818–2827. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Lundstrom, C.; Rydell, T.; Forsell, C.; Persson, A.; Ynnerman, A. Multi-Touch Table System for Medical Visualization: Application to Orthopedic Surgery Planning. IEEE Trans. Vis. Comput. Graph. 2011, 17, 1775–1784. [Google Scholar] [CrossRef] [PubMed]
- Sousa, M.; Mendes, D.; Paulo, S.; Matela, N.; Jorge, J.; Lopes, D.S. VRRRRoom: Virtual Reality for Radiologists in the Reading Room. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, CO, USA, 6–11 May 2017; pp. 4057–4062. [Google Scholar] [CrossRef]
- Isenberg, P.; Zuk, T.; Collins, C.; Carpendale, S. Grounded Evaluation of Information Visualizations. In Proceedings of the 2008 Workshop on BEyond Time and Errors: Novel evaLuation Methods for Information Visualization, Florence, Italy, 5 April 2008; pp. 6:1–6:8. [Google Scholar] [CrossRef]
- Besançon, L.; Issartel, P.; Ammi, M.; Isenberg, T. Hybrid Tactile/Tangible Interaction for 3D Data Exploration. IEEE Trans. Vis. Comput. Graph. 2017, 23, 881–890. [Google Scholar] [CrossRef] [PubMed]
- Besançon, L.; Semmo, A.; Biau, D.J.; Frachet, B.; Pineau, V.; Sariali, E.H.; Taouachi, R.; Isenberg, T.; Dragicevic, P. Reducing Affective Responses to Surgical Images through Color Manipulation and Stylization. In Proceedings of the Joint Symposium on Computational Aesthetics, Sketch-Based Interfaces and Modeling, and Non-Photorealistic Animation and Rendering, Victoria, BC, Canada, 17–19 August 2018; pp. 4:1–4:13. [Google Scholar] [CrossRef]
- Dimara, E.; Bezerianos, A.; Dragicevic, P. Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support. IEEE Trans. Vis. Comput. Graph. 2018, 24, 749–759. [Google Scholar] [CrossRef] [PubMed]
- Forrin, N.D.; MacLeod, C.M. This time it’s personal: The memory benefit of hearing oneself. Memory 2018, 26, 574–579. [Google Scholar] [CrossRef] [PubMed]
- Tweedie, L. Characterizing Interactive Externalizations. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, Atlanta, GA, USA, 22–27 March 1997; pp. 375–382. [Google Scholar] [CrossRef]
- Spence, R. Information Visualization: Design for Interaction, 2nd ed.; Prentice-Hall, Inc.: Upper Saddle River, NJ, USA, 2007; ISBN 0132065509. [Google Scholar]
- Nakakoji, K.; Takashima, A.; Yamamoto, Y. Cognitive Effects of Animated Visualization in Exploratory Visual Data Analysis. In Proceedings of the Fifth International Conference on Information Visualisation(IV), London, UK, 25–27 July 2001; p. 0077. [Google Scholar] [CrossRef]
|Stacked area graph|
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Akram Hassan, K.; Liu, Y.; Besançon, L.; Johansson, J.; Rönnberg, N. A Study on Visual Representations for Active Plant Wall Data Analysis. Data 2019, 4, 74. https://doi.org/10.3390/data4020074
Akram Hassan K, Liu Y, Besançon L, Johansson J, Rönnberg N. A Study on Visual Representations for Active Plant Wall Data Analysis. Data. 2019; 4(2):74. https://doi.org/10.3390/data4020074Chicago/Turabian Style
Akram Hassan, Kahin, Yu Liu, Lonni Besançon, Jimmy Johansson, and Niklas Rönnberg. 2019. "A Study on Visual Representations for Active Plant Wall Data Analysis" Data 4, no. 2: 74. https://doi.org/10.3390/data4020074