Line-based density plots are used to reduce visual clutter in line charts with a multitude of individual lines. However, these traditional density plots are often perceived ambiguously, which obstructs the user's identification of underlying trends in complex datasets. Thus, we propose a novel image space coloring method for line-based density plots that enhances their interpretability. Our method employs color not only to visually communicate data density but also to highlight similar regions in the plot, allowing users to identify and distinguish trends easily. We achieve this by performing hierarchical clustering based on the lines passing through each region and mapping the identified clusters to the hue circle using circular MDS. Additionally, we propose a heuristic approach to assign each line to the most probable cluster, enabling users to analyze density and individual lines. We motivate our method by conducting a small-scale user study, demonstrating the effectiveness of our method using synthetic and real-world datasets, and providing an interactive online tool for generating colored line-based density plots.

翻译：基于线条的密度图被用于减少包含大量单独线条的折线图中的视觉杂乱。然而，这些传统密度图往往存在感知上的歧义，阻碍了用户在复杂数据集中识别潜在趋势。为此，我们提出了一种新颖的基于线条密度图的图像空间着色方法，以增强其可解释性。我们的方法不仅利用颜色直观传达数据密度，还通过颜色突出图中相似区域，使用户能够轻松识别和区分趋势。我们通过基于穿过每个区域的线条进行层次聚类，并利用圆形MDS将识别出的聚类映射到色环上实现这一目标。此外，我们提出了一种启发式方法，将每条线条分配给最可能的聚类，使用户能够分析密度及单独线条。我们通过开展小规模用户研究来验证该方法，利用合成数据集和真实数据集证明其有效性，并提供一个交互式在线工具以生成着色的基于线条的密度图。