Xue Yumeng, Paetzold Patrick, Kehlbeck Rebecca, Chen Bin, Kwan Kin Chung, Wang Yunhai, Deussen Oliver
IEEE Trans Vis Comput Graph. 2024 Jan;30(1):825-835. doi: 10.1109/TVCG.2023.3327149. Epub 2023 Dec 25.
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.
基于线条的密度图用于减少包含众多单独线条的折线图中的视觉混乱。然而，这些传统的密度图常常被模糊地感知，这阻碍了用户识别复杂数据集中的潜在趋势。因此，我们提出了一种用于基于线条的密度图的新颖图像空间着色方法，以增强其可解释性。我们的方法不仅使用颜色在视觉上传达数据密度，还突出显示图中的相似区域，从而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执行层次聚类，并使用圆形多维尺度缩放将识别出的聚类映射到色相环来实现这一点。此外，我们提出了一种启发式方法，将每条线分配到最可能的聚类中，使用户能够分析密度和单独的线条。我们通过进行小规模用户研究来推动我们的方法，使用合成数据集和真实世界数据集证明我们方法的有效性，并提供一个交互式在线工具来生成基于线条的彩色密度图。
