Evaluating visual channels for multivariate map visualization

Visual differencing, or visual discrimination, is the ability to differentiate between two or more objects in a scene depending on the values of certain attributes. Focusing on multivariate maps visualization, this work examined human’s predictable bias in interpreting visual-spatial information and inference making. Moreover, this study seeks to develop and evaluate new techniques to mitigate the trade-off between proximity and occlusion and to enable analysts to explore multivariate maps. Therefore, we developed a multi-criteria decision-making technique for land suitability using multivariate maps, and we carried out a user study where users are tasked to choose the most suitable piece of land to plant grapes. We designed the user study to evaluate mapping a map’s layers (variables) to visual channels (Transparency, Hue, Saturation and Brightness/Lightness); two color spaces were used Hue Saturation Value (HSV)and Hue Saturation Lightness (HSL). The categorical variables were mapped to the Hue channel and the quantitative/ordinal variables were mapped to either Saturation, Brightness/lightness, or Transparency channels. Our online user study was taken by 85 participants to test the users’ perception of different map visualizations. The statistical analysis of survey responses showed that mapping quantitative layers to the Transparency channel outperformed the other channels, and the use of HSV color space showed a more efficient mapping than HSL, especially for the extreme values in the dataset.