Visual attributes, such as size, shape, or colour, play a crucial role in the interpretation of data visualizations and can be used to represent the data at different scales. On 2D displays, human viewers then perceive the differences between scales based on such visual attributes. While it can be challenging for human to interpret data in dense visualizations in 2D, the additional depth dimension offered by 3D could make some aspects of data more visible.
To analyze the potential impact of depth perception on dense data visualization, we developed a novel visualization method and visualized dense COVID-19 time-series data of four European countries (France, Germany, United Kingdom, and Turkey). In our novel visualization, we aimed to increase the visibility of individual data points to ease visual perception and visualized daily total cases in the depth dimension.
We conducted a user study with 20 participants where we compared a conventional 2D visualization with the proposed novel 3D visualization method for COVID-19 data.
The results show that the novel 3D visualization method facilitated understanding of the complexity of COVID-19 case data and decreased misinterpretations by 40%. Overall, 13 out of 20 participants preferred to see the COVID-19 data with the proposed method. Participants’ comments on the novel visualization method revealed that the increased visibility of individual data points decreases the cognitive load of the participants, which might explain the outcome.
The results of our work identify that the depth dimension offered by 3D visualizations can assist users in understanding dense data.