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Visualizing time series in a dense spatial context such as a geographical mapis a challenging task, which requires careful balance between the amount ofdepicted data and perceptual precision. Horizon graphs are a well-knowntechnique for compactly representing time series data. They provide finedetails while simultaneously giving an overview of the data where extrema areemphasized. Horizon graphs compress the vertical resolution of the individualline graphs, but they do not affect the horizontal resolution. We present twovariations of a new visualization technique called collapsed horizon graphswhich extend the idea of horizon graphs to two dimensions. Our maincontribution is a quantitative evaluation that experimentally compares fourvisualization techniques with high visual information resolution (compactboxplots, horizon graphs, collapsed horizon graphs, and braided collapsedhorizon graphs). The experiment investigates the performance of thesetechniques across tasks addressing both individual graphs as well as groups ofadjacent graphs. Compact boxplots consistently provide good results for alltasks, horizon graphs excel, for instance, in maximum tasks but underperform intrend detection. Collapsed horizon graphs shine in certain tasks in which anincreased horizontal resolution is beneficial. Moreover, our results indicatethat the visual complexity of the techniques highly affects users' confidenceand perceived task difficulty.

Looking beyond the horizon: Evaluation of four compact visualization techniques for time series in a spatial context