Evaluation and Improvement of Narrative Visualizations
Resumo
In the field of Information Visualization, storytelling techniques help to communicate facts and enhance comprehension. The use of data storytelling best practices can inform the process of creating narrative visualizations and increase the quality of charts used in software applications by improving aspects such as memorability or engagement, for instance, supporting end users in the decision-making process. The main goal of this doctoral research is to develop a method for assessing and improving the quality of narrative visualizations in software products, like scatter plots, line, bar, or pie charts, among others. This has included a case study and a systematic mapping study.
Palavras-chave:
information visualization, data storytelling, narrative visualizations
Referências
Amini, F., Brehmer, M., Bolduan, G., Elmer, C., & Wiederkehr, B. (2018). Evaluating Data-Driven Stories and Storytelling Tools *. In Data-Driven Storytelling (pp. 249–286). https://doi.org/10.1201/9781315281575-11
Battle, L., Duan, P., Miranda, Z., Mukusheva, D., Chang, R., & Stonebraker, M. (2018). Beagle: Automated Extraction and Interpretation of Visualizations from the Web. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3173574
Bertin, J. (1983). Semiology of graphics.
Bertini, E., Tatu, A., & Keim, D. (2011). Quality metrics in high-dimensional data visualization: An overview and systematization. In IEEE Transactions on Visualization and Computer Graphics (Vol. 17, Issue 12, pp. 2203–2212). https://doi.org/10.1109/TVCG.2011.229
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization?: using vision to think. Morgan Kaufmann Publishers.
Carpendale, S. (2008). Evaluating information visualizations. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4950 LNCS, 19–45. https://doi.org/10.1007/978-3-540-70956-5_2
Dimara, E., Bezerianos, A., & Dragicevic, P. (2018). Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support. IEEE Transactions on Visualization and Computer Graphics, 24(1), 749–759. https://doi.org/10.1109/TVCG.2017.2745138
Dimara, E., Zhang, H., Tory, M., & Franconeri, S. (2021). The Unmet Data Visualization Needs of Decision Makers within Organizations. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2021.3074023
Dowding, D., Merrill, J. A., Onorato, N., Barrón, Y., Rosati, R. J., & Russell, D. (2018). The impact of home care nurses’ numeracy and graph literacy on comprehension of visual display information: Implications for dashboard design. Journal of the American Medical Informatics Association, 25(2), 175–182. https://doi.org/10.1093/jamia/ocx042
Gilger, M. (2006). Addressing information display weaknesses for situational awareness. Proceedings - IEEE Military Communications Conference MILCOM. https://doi.org/10.1109/MILCOM.2006.302129
Gorodov, E. Y. E., & Gubarev, V. V. E. (2013). Analytical review of data visualization methods in application to big data. Journal of Electrical and Computer Engineering, 2013. https://doi.org/10.1155/2013/969458
Gotz, D., & Borland, D. (2016). Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization. IEEE Computer Graphics and Applications, 36(3), 90–96. https://doi.org/10.1109/MCG.2016.59
Grainger, S., Mao, F., & Buytaert, W. (2016). Environmental data visualisation for non-scientific contexts: Literature review and design framework. Environmental Modelling and Software, 85, 299–318. https://doi.org/10.1016/j.envsoft.2016.09.004
Gutiérrez, F., Htun, N. N., Schlenz, F., Kasimati, A., & Verbert, K. (2019). A review of visualisations in agricultural decision support systems: An HCI perspective. Computers and Electronics in Agriculture, 163(May), 104844. https://doi.org/10.1016/j.compag.2019.05.053
Haroz, S., & Whitney, D. (2012). How capacity limits of attention influence information visualization effectiveness. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2402–2410. https://doi.org/10.1109/TVCG.2012.233
Henry Riche, N., Hurter, C., Diakopoulos, N., & Carpendale, S. (2018). Data-Driven Storytelling. In Data-Driven Storytelling. https://doi.org/10.1201/9781315281575
Johannesson, P., & Perjons, E. (2014). An introduction to design science. In An Introduction to Design Science (Vol. 9783319106328). Springer International Publishing. https://doi.org/10.1007/978-3-319-10632-8
Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4950 LNCS, 154–175. https://doi.org/10.1007/978-3-540-70956-5_7
Kitchenham, B., & Charters, S. M. (2007). Guidelines for performing systematic literature reviews in software engineering. In Technical report, Ver. 2.3 EBSE Technical Report. EBSE (Vol. 1).
Kosara, R., & MacKinlay, J. (2013). Storytelling: The next step for visualization. Computer, 46(5), 44–50. https://doi.org/10.1109/MC.2013.36
Lam, H., Bertini, E., Isenberg, P., Plaisant, C., Carpendale Empirical, S., & Lam Enrico Bertini Petra Isenberg Catherine Plaisant Sheelagh Carpendale, H. (2012). Empirical Studies in Information Visualization: Seven Scenarios. Institute of Electrical and Electronics Engineers, 18(9), 1520–1536. https://doi.org/10.1109/TVCG.2011.279ï
Lee, S., Kim, S. H., & Kwon, B. C. (2017). VLAT: Development of a Visualization Literacy Assessment Test. IEEE Transactions on Visualization and Computer Graphics, 23(1), 551–560. https://doi.org/10.1109/TVCG.2016.2598920
Lezcano Airaldi, A., Diaz-Pace, J. A., & Irrazábal, E. (2021). Data-driven Storytelling to Support Decision Making in Crisis Settings: A Case Study. JUCS - Journal of Universal Computer Science 27(10): 1046-1068, 27(10), 1046–1068. https://doi.org/10.3897/JUCS.66714
Munzner, T., & Maguire, E. (Graphic artist). (2014). Visualization analysis & design. In Cs.Ubc.Ca. A K Peters/CRC Press.
Nussbaumer Knaflic, C. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley. [link].
Ojo, A., & Heravi, B. (2017). Patterns in Award Winning Data Storytelling. https://doi.org/10.1080/21670811.2017.1403291, 6(6), 693–718.
Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64, 1–18. https://doi.org/10.1016/J.INFSOF.2015.03.007
Plaisant, C. (2004). The challenge of information visualization evaluation. Proceedings of the Workshop on Advanced Visual Interfaces AVI, 109–116. https://doi.org/10.1145/989863.989880
Post, F. H., Nielson, G. M., & Bonneau, G.-P. (2003). Data visualization: the state of the art. Kluwer Academic.
Qu, Z., & Hullman, J. (n.d.). Evaluating Visualization Sets: Trade-offs Between Local Effectiveness and Global Consistency. https://doi.org/10.1145/1235
Rhyne, T.-M., Lee, B., Riche, N. H., Isenberg, P., & Carpendale, S. (2015). More Than Telling a Story: Transforming Data into Visually Shared Stories. www.gapminder.org/
Riche, N. H., Hurter, C., Diakopoulos, N., & Carpendale, S. (2018). Data-driven storytelling. A K Peters/CRC Press.
Runeson, P., & Höst, M. (2009). Guidelines for conducting and reporting case study research in software engineering. Empirical Software Engineering, 14(2), 131–164. https://doi.org/10.1007/s10664-008-9102-8
Saraiya, P., North, C., & Duca, K. (2005). An insight-based methodology for evaluating bioinformatics visualizations. IEEE Transactions on Visualization and Computer Graphics, 11(4), 443–456. https://doi.org/10.1109/TVCG.2005.53
Sarikaya, A., Correll, M., Bartram, L., Tory, M., & Fisher, D. (2019). What do we talk about when we talk about dashboards? IEEE Transactions on Visualization and Computer Graphics, 25(1), 682–692. https://doi.org/10.1109/TVCG.2018.2864903
Tory, M., & Möller, T. (2005). Evaluating visualizations: Do expert reviews work? IEEE Computer Graphics and Applications, 25(5), 8–11. https://doi.org/10.1109/MCG.2005.102
Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press. https://www.edwardtufte.com/tufte/books_vdqi
Wall, E., Agnihotri, M., Matzen, L., Divis, K., Haass, M., Endert, A., & Stasko, J. (2019). A heuristic approach to value-driven evaluation of visualizations. IEEE Transactions on Visualization and Computer Graphics, 25(1), 491–500. https://doi.org/10.1109/TVCG.2018.2865146
Ware, C. (2020). Information visualization?: perception for design (4th ed.). Morgan Kaufmann.
Willett, W., Heer, J., Hellerstein, J. M., & Agrawala, M. (2011). CommentSpace: Structured support for collaborative visual analysis. Conference on Human Factors in Computing Systems - Proceedings, 3131–3140. https://doi.org/10.1145/1978942.1979407
Yin, R. K. (2017). Case Study Research and Design. In Sage Publications (6th ed.). SAGE Publications, Inc.
Battle, L., Duan, P., Miranda, Z., Mukusheva, D., Chang, R., & Stonebraker, M. (2018). Beagle: Automated Extraction and Interpretation of Visualizations from the Web. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3173574
Bertin, J. (1983). Semiology of graphics.
Bertini, E., Tatu, A., & Keim, D. (2011). Quality metrics in high-dimensional data visualization: An overview and systematization. In IEEE Transactions on Visualization and Computer Graphics (Vol. 17, Issue 12, pp. 2203–2212). https://doi.org/10.1109/TVCG.2011.229
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization?: using vision to think. Morgan Kaufmann Publishers.
Carpendale, S. (2008). Evaluating information visualizations. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4950 LNCS, 19–45. https://doi.org/10.1007/978-3-540-70956-5_2
Dimara, E., Bezerianos, A., & Dragicevic, P. (2018). Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support. IEEE Transactions on Visualization and Computer Graphics, 24(1), 749–759. https://doi.org/10.1109/TVCG.2017.2745138
Dimara, E., Zhang, H., Tory, M., & Franconeri, S. (2021). The Unmet Data Visualization Needs of Decision Makers within Organizations. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2021.3074023
Dowding, D., Merrill, J. A., Onorato, N., Barrón, Y., Rosati, R. J., & Russell, D. (2018). The impact of home care nurses’ numeracy and graph literacy on comprehension of visual display information: Implications for dashboard design. Journal of the American Medical Informatics Association, 25(2), 175–182. https://doi.org/10.1093/jamia/ocx042
Gilger, M. (2006). Addressing information display weaknesses for situational awareness. Proceedings - IEEE Military Communications Conference MILCOM. https://doi.org/10.1109/MILCOM.2006.302129
Gorodov, E. Y. E., & Gubarev, V. V. E. (2013). Analytical review of data visualization methods in application to big data. Journal of Electrical and Computer Engineering, 2013. https://doi.org/10.1155/2013/969458
Gotz, D., & Borland, D. (2016). Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization. IEEE Computer Graphics and Applications, 36(3), 90–96. https://doi.org/10.1109/MCG.2016.59
Grainger, S., Mao, F., & Buytaert, W. (2016). Environmental data visualisation for non-scientific contexts: Literature review and design framework. Environmental Modelling and Software, 85, 299–318. https://doi.org/10.1016/j.envsoft.2016.09.004
Gutiérrez, F., Htun, N. N., Schlenz, F., Kasimati, A., & Verbert, K. (2019). A review of visualisations in agricultural decision support systems: An HCI perspective. Computers and Electronics in Agriculture, 163(May), 104844. https://doi.org/10.1016/j.compag.2019.05.053
Haroz, S., & Whitney, D. (2012). How capacity limits of attention influence information visualization effectiveness. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2402–2410. https://doi.org/10.1109/TVCG.2012.233
Henry Riche, N., Hurter, C., Diakopoulos, N., & Carpendale, S. (2018). Data-Driven Storytelling. In Data-Driven Storytelling. https://doi.org/10.1201/9781315281575
Johannesson, P., & Perjons, E. (2014). An introduction to design science. In An Introduction to Design Science (Vol. 9783319106328). Springer International Publishing. https://doi.org/10.1007/978-3-319-10632-8
Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4950 LNCS, 154–175. https://doi.org/10.1007/978-3-540-70956-5_7
Kitchenham, B., & Charters, S. M. (2007). Guidelines for performing systematic literature reviews in software engineering. In Technical report, Ver. 2.3 EBSE Technical Report. EBSE (Vol. 1).
Kosara, R., & MacKinlay, J. (2013). Storytelling: The next step for visualization. Computer, 46(5), 44–50. https://doi.org/10.1109/MC.2013.36
Lam, H., Bertini, E., Isenberg, P., Plaisant, C., Carpendale Empirical, S., & Lam Enrico Bertini Petra Isenberg Catherine Plaisant Sheelagh Carpendale, H. (2012). Empirical Studies in Information Visualization: Seven Scenarios. Institute of Electrical and Electronics Engineers, 18(9), 1520–1536. https://doi.org/10.1109/TVCG.2011.279ï
Lee, S., Kim, S. H., & Kwon, B. C. (2017). VLAT: Development of a Visualization Literacy Assessment Test. IEEE Transactions on Visualization and Computer Graphics, 23(1), 551–560. https://doi.org/10.1109/TVCG.2016.2598920
Lezcano Airaldi, A., Diaz-Pace, J. A., & Irrazábal, E. (2021). Data-driven Storytelling to Support Decision Making in Crisis Settings: A Case Study. JUCS - Journal of Universal Computer Science 27(10): 1046-1068, 27(10), 1046–1068. https://doi.org/10.3897/JUCS.66714
Munzner, T., & Maguire, E. (Graphic artist). (2014). Visualization analysis & design. In Cs.Ubc.Ca. A K Peters/CRC Press.
Nussbaumer Knaflic, C. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley. [link].
Ojo, A., & Heravi, B. (2017). Patterns in Award Winning Data Storytelling. https://doi.org/10.1080/21670811.2017.1403291, 6(6), 693–718.
Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64, 1–18. https://doi.org/10.1016/J.INFSOF.2015.03.007
Plaisant, C. (2004). The challenge of information visualization evaluation. Proceedings of the Workshop on Advanced Visual Interfaces AVI, 109–116. https://doi.org/10.1145/989863.989880
Post, F. H., Nielson, G. M., & Bonneau, G.-P. (2003). Data visualization: the state of the art. Kluwer Academic.
Qu, Z., & Hullman, J. (n.d.). Evaluating Visualization Sets: Trade-offs Between Local Effectiveness and Global Consistency. https://doi.org/10.1145/1235
Rhyne, T.-M., Lee, B., Riche, N. H., Isenberg, P., & Carpendale, S. (2015). More Than Telling a Story: Transforming Data into Visually Shared Stories. www.gapminder.org/
Riche, N. H., Hurter, C., Diakopoulos, N., & Carpendale, S. (2018). Data-driven storytelling. A K Peters/CRC Press.
Runeson, P., & Höst, M. (2009). Guidelines for conducting and reporting case study research in software engineering. Empirical Software Engineering, 14(2), 131–164. https://doi.org/10.1007/s10664-008-9102-8
Saraiya, P., North, C., & Duca, K. (2005). An insight-based methodology for evaluating bioinformatics visualizations. IEEE Transactions on Visualization and Computer Graphics, 11(4), 443–456. https://doi.org/10.1109/TVCG.2005.53
Sarikaya, A., Correll, M., Bartram, L., Tory, M., & Fisher, D. (2019). What do we talk about when we talk about dashboards? IEEE Transactions on Visualization and Computer Graphics, 25(1), 682–692. https://doi.org/10.1109/TVCG.2018.2864903
Tory, M., & Möller, T. (2005). Evaluating visualizations: Do expert reviews work? IEEE Computer Graphics and Applications, 25(5), 8–11. https://doi.org/10.1109/MCG.2005.102
Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press. https://www.edwardtufte.com/tufte/books_vdqi
Wall, E., Agnihotri, M., Matzen, L., Divis, K., Haass, M., Endert, A., & Stasko, J. (2019). A heuristic approach to value-driven evaluation of visualizations. IEEE Transactions on Visualization and Computer Graphics, 25(1), 491–500. https://doi.org/10.1109/TVCG.2018.2865146
Ware, C. (2020). Information visualization?: perception for design (4th ed.). Morgan Kaufmann.
Willett, W., Heer, J., Hellerstein, J. M., & Agrawala, M. (2011). CommentSpace: Structured support for collaborative visual analysis. Conference on Human Factors in Computing Systems - Proceedings, 3131–3140. https://doi.org/10.1145/1978942.1979407
Yin, R. K. (2017). Case Study Research and Design. In Sage Publications (6th ed.). SAGE Publications, Inc.
Publicado
13/06/2022
Como Citar
LEZCANO AIRALDI, Andrea.
Evaluation and Improvement of Narrative Visualizations. In: CONGRESSO IBERO-AMERICANO EM ENGENHARIA DE SOFTWARE (CIBSE), 25. , 2022, Córdoba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 368-375.
DOI: https://doi.org/10.5753/cibse.2022.20986.
