Looking beyond the horizon: Evaluation of four compact visualization techniques for time series in a spatial context
This work addresses the challenge of visualizing dense time series data on maps for users in fields like data analysis or geography, but it is incremental as it builds on existing horizon graph methods.
The study evaluated four compact visualization techniques for time series in spatial contexts, finding that compact boxplots performed consistently well across tasks, while horizon graphs excelled in maximum tasks but underperformed in trend detection, and collapsed horizon graphs were beneficial where increased horizontal resolution was needed.
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.