1 research outputs found
Looking beyond the horizon: Evaluation of four compact visualization techniques for time series in a spatial context
Visualizing time series in a dense spatial context such as a geographical map
is a challenging task, which requires careful balance between the amount of
depicted data and perceptual precision. Horizon graphs are a well-known
technique for compactly representing time series data. They provide fine
details while simultaneously giving an overview of the data where extrema are
emphasized. Horizon graphs compress the vertical resolution of the individual
line graphs, but they do not affect the horizontal resolution. We present two
variations of a new visualization technique called collapsed horizon graphs
which extend the idea of horizon graphs to two dimensions. Our main
contribution is a quantitative evaluation that experimentally compares four
visualization techniques with high visual information resolution (compact
boxplots, horizon graphs, collapsed horizon graphs, and braided collapsed
horizon graphs). The experiment investigates the performance of these
techniques across tasks addressing both individual graphs as well as groups of
adjacent graphs. Compact boxplots consistently provide good results for all
tasks, horizon graphs excel, for instance, in maximum tasks but underperform in
trend detection. Collapsed horizon graphs shine in certain tasks in which an
increased horizontal resolution is beneficial. Moreover, our results indicate
that the visual complexity of the techniques highly affects users' confidence
and perceived task difficulty.Comment: 12 pages, 12 figure