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

## TL;DR

This paper evaluates four compact visualization techniques for time series data on maps, comparing their effectiveness across various tasks and highlighting the strengths and weaknesses of each method.

## Contribution

It introduces two variations of collapsed horizon graphs and provides a comprehensive quantitative comparison of four visualization techniques for spatial time series data.

## Key findings

- Compact boxplots perform well across tasks.
- Horizon graphs are best for maximum value detection.
- Collapsed horizon graphs are advantageous for tasks needing higher horizontal resolution.

## Abstract

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.