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Uncertainty in Data Visualizations: A neuroscience experiment on uncertainty visualizations
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 1.35 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
Recent times have seen a huge increase in data driven decision making. With the emergence of Big
Data and Business Intelligence, user reports contain more data than ever before creating challenges in
visualizing uncertainty in data. We present a comparison study where the traditional uncertainty
visualizations error bars and violin plots compare to Hypothetical Outcome Plots. Hypothetical
Outcome Plots is a visualization technique drawing animated samples from a distribution, visualizing
uncertainty throughout the distribution of samples. Using neuroscience practices, we have conducted
an eye-tracking experiment tracing the comparison between Hypothetical Outcome Plots and
traditional uncertainty visualizations. We test Hypothetical Outcome Plots in different transitions
across multiple visualization designs. The results show that static visualizations were easier for the
participants to use as decision aid. While HOP had a lower total score, HOP worked better when
visualized in a bar chart state and with bigger transitions between each draw. Moreover, eye-tracking
metrics exhibit the difference in difficulty between the visualizations, indicating that participants
familiarity with the visualization highly affected their ability to make the best decision.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
Palavras-chave
Data Visualization Uncertainty Hypothetical Outcome Plots Neuroscience Eye tracking SDG 4 - Quality education SDG 9 - Industry, innovation and infrastructure