An indicator of inefficient visualizations: the challenge of transparency during the COVID-19 pandemic in Brazil
The COVID-19 epidemic requires clear and reliable information to guide the population. Visualization is a powerful tool to contribute to the understanding of this data. However, just divulging this resource is not enough to guarantee this understanding. It is important to support users in analyzing this data, making this process easier and more transparent, especially for users with little (or no) literacy. In this work, we define an inefficient graphics indicator, that is, with the potential to be misinterpreted or difficult to understand, according to the basic guidelines of the data visualization area. These guidelines were selected through a literature review, forming a repository of practices that guide good visualization design and provide the indicator assessment items. This proposal can be applied inis necessary because we do not perceive the existence of standards, norms or quality indicators for data visualization that assist in the creation of this artifact efficiently or evaluate the existing ones for improvement, both manually and automatically. This approach can be applied in the most diverse scenarios, being initially in Brazil during the dissemination of COVID-19, analyzing the official visualizations and highlighting the failures of several epidemiological perspectives on the pandemic. Through a study with users where volunteers analyzed the official data and our recommendations, we demonstrated that the indicator is effective in detecting and helping to understand the visualization. We highlight the alert for the need for greater care in the creation of graphics by the government so as not to compromise the understanding of the citizens who use them.
Tangcharoensathien, V., Calleja, N., Nguyen, T., Purnat, T., D’Agostino, M., Garcia-Saiso, S., ... & Ghiga, I. (2020). Framework for Managing the COVID19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation. Journal of medical Internet research, 22(6), e19659.
Munzner, Tamara. Visualization analysis and design. CRC press, 2014.
Unwin, A. Good graphics?. In Handbook of data visualization (pp. 57-78). Springer, Berlin, Heidelberg. 2008.
Tufte, E.R., 2001. The visual display of quantitative information (Vol. 2). Cheshire, CT: Graphics press.
Shneiderman, B. (1996, September). The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings 1996 IEEE symposium on visual languages (pp. 336-343). IEEE.
Saket, B., Moritz, D., Lin, H., Dibia, V., Demiralp, C., & Heer, J. (2018). Beyond heuristics: Learning visualization design. arXiv preprint arXiv:1807.06641.
Mooney, P., & Juhász, L. (2020). Mapping COVID-19: How web-based maps contribute to the infodemic. Dialogues in Human Geography, 2043820620934926.
Few, Stephen; Edge, Perceptual. Data Visualization: Past, Present and Future. IBM Cognos Innovation Center, 2007. Disponível em: [link]. Acesso em 20 de agosto de 2020.
Manovich, L. (2011). What is visualisation?. Visual Studies, 26(1), 36-49.
Dix, A. (2012, January). Introduction to information visualisation. In PROMISE Winter School (pp. 1-27). Springer, Berlin, Heidelberg.
Alhamadi, M. (2020, July). Challenges, Strategies and Adaptations on Interactive Dashboards. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 368-371).
Miller, G.A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97.
Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12, 257-285.
Vessey, Iris, Galletta, Dennis (1991). Cognitive Fit: An Empirical Study of Information Acquisition. Information Systems Research, 2(1), 63-84.
Teets, J. M., Tegarden, D. P., & Russell, R. S. (2010). Using cognitive fit theory to evaluate the effectiveness of information visualizations: An example using quality assurance data. IEEE transactions on visualization and computer graphics, 16(5), 841-853.
Engelke, U., Abdul-Rahman, A., & Chen, M. (2018, October). VISupply: A Supply-Chain Process Model for Visualization Guidelines. In 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) (pp. 1-9). IEEE.
Park, S., & Gil-Garcia, J. R. (2017, June). Understanding transparency and accountability in open government ecosystems: The case of health data 22 https://www.who.int/about/communications/understandable/plain-language visualizations in a state government. In Proceedings of the 18th Annual International Conference on Digital Government Research (pp. 39-47).
Santos, V., Camara, P., Bernardini, F., Viterbo, J., & Jorge, D. A framework for constructing open data map visualizations. In Proceedings of the XIV Brazilian Symposium on Information Systems (pp. 1-7), 2018.
Chen, M., Grinstein, G., Johnson, C. R., Kennedy, J., & Tory, M. (2017). Pathways for theoretical advances in visualization. IEEE computer graphics and applications, 37(4), 103-112.
Cappelli, C. Uma Abordagem para Transparência em Processos Organizacionais Utilizando Aspectos. Tese de Doutorado, PUC-Rio, Rio de Janeiro, Brasil, 2009.
Boscarioli, C.; Araujo, R. M.; Maciel, R. S. P., 2017. I GranDSI-BR – Grand Research Challenges in Information Systems in Brazil 2016-2026. Special Committee on Information Systems (CE-SI). Brazilian Computer Society (SBC).
Meireles, A. I., dos Santos, R. P., & Cappelli, C. Construindo um Questionário para Avaliar Transparência em Portais de Ecossistemas de Software. In Anais do VIII Workshop sobre Aspectos da Interação Humano-Computador para a Web Social (pp. 25-35). SBC, 2017.
Amorim, R. P. C., & de Menezes, C. S. (2016, May). Metodologia de Avaliação de Portais da Transparência Municipais. In Anais do XII Simpósio Brasileiro de Sistemas de Informação (pp. 017-024). SBC.
Dresch, A. (2013). Design science e design science research como artefatos metodológicos para engenharia de produção.
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). “A Design Science Research Methodology for Information Systems Research”, J. Manag. Inf. Syst., vol. 24, no 8, p. 45–78.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1-26.
Lee, N., & Rojas, E. M. (2009). Developing effective visual representations to monitor project performance. In Construction Research Congress 2009: Building a Sustainable Future (pp. 826-835).
Isett, K. R., & Hicks, D. M. (2018). Providing public servants what they need: Revealing the “unseen” through data visualization. Public Administration Review, 78(3), 479-485.
Senay, H., & Ignatius, E. (1990). Rules and principles of scientific data visualization. Institute for Information Science and Technology, Department of Electrical Engineering and Computer Science, School of Engineering and Applied Science, George Washington University.
Kelleher, C., & Wagener, T. (2011). Ten guidelines for effective data visualization in scientific publications. Environmental Modelling & Software, 26(6), 822-827.
Rheingans, P. L. (2000, May). Task-based color scale design. In 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making (Vol. 3905, pp. 35-43). International Society for Optics and Photonics.
Siirtola, H. (2019, July). The cost of pie charts. In 2019 23rd International Conference Information Visualisation (IV) (pp. 151-156). IEEE.
Grainger, S., Mao, F., & Buytaert, W. (2016). Environmental data visualisation for non-scientific contexts: Literature review and design framework. Environmental Modelling & Software, 85, 299-318.
Kopp, T., Riekert, M., & Utz, S. (2018). When cognitive fit outweighs cognitive load: Redundant data labels in charts increase accuracy and speed of information extraction. Computers in Human Behavior, 86, 367-376.
Gramazio, C. C., Schloss, K. B., & Laidlaw, D. H. (2014). The relation between visualization size, grouping, and user performance. IEEE transactions on visualization and computer graphics, 20(12), 1953-1962.
Wilke, C. O. (2019). Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media.
Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons.
Rees, D., & Laramee, R. S. (2019, February). A survey of information visualization books. In Computer Graphics Forum (Vol. 38, No. 1, pp. 610- 646).
Segel, E., & Heer, J. (2010). Narrative visualization: Telling stories with data. IEEE transactions on visualization and computer graphics, 16(6), 1139-1148.
Barcellos, R. Avaliação da Qualidade e Interpretabilidade de Visualizações de Dados. (Masters dissertation, Universidade Federal Fluminense - UFF) Niterói, 2017. 87 p. In portuguese.
Zhu, Y. Measuring effective data visualization. In International Symposium on Visual Computing (pp. 652-661). Springer. Berlin, 2007.