  Line attributes such as width and dashing are commonly used to encode
information. However, many questions on the perception of line attributes
remain, such as how many levels of attribute variation can be distinguished or
which line attributes are the preferred choices for which tasks. We conducted
three studies to develop guidelines for using stylized lines to encode scalar
data. In our first study, participants drew stylized lines to encode
uncertainty information. Uncertainty is usually visualized alongside other
data. Therefore, alternative visual channels are important for the
visualization of uncertainty. Additionally, uncertainty -- e.g., in weather
forecasts -- is a familiar topic to most people. Thus, we picked it for our
visualization scenarios in study 1. We used the results of our study to
determine the most common line attributes for drawing uncertainty: Dashing,
luminance, wave amplitude, and width. While those line attributes were
especially common for drawing uncertainty, they are also commonly used in other
areas. In studies 2 and 3, we investigated the discriminability of the line
attributes determined in study 1. Studies 2 and 3 did not require specific
application areas; thus, their results apply to visualizing any scalar data in
line attributes. We evaluated the just-noticeable differences (JND) and derived
recommendations for perceptually distinct line levels. We found that
participants could discriminate considerably more levels for the line attribute
width than for wave amplitude, dashing, or luminance.
