Force-Directed Timelines: Visualizing & Exploring Temporal Patterns

Abstract

Visualization has shown to be a valuable tool in the analysis of large and complex temporal datasets, aided by the emergence of new models such as Time Curves, which distorts timelines to position time points based on their similarity with each other, reflecting changes in the data over time. In this paper, we further explore time-series functionally and aesthetically by presenting an interactive and parameter-based implementation of the Time Curves model, complemented with the addition of supporting visualizations and data analysis methods. In our implementation we introduce Time Paths, a force-directed layout that can dynamically transform the original model to not only smoothen the transitions between time points, but also reduce visual noise in favor of portraying overall patterns. The proposed addition of visual elements to the model includes temporal glyphs and a supporting timeline graph which help discover and better understand temporal patterns across complex datasets. Through interactive exploration, we demonstrate how these methods can be used to analyze and identify the main agents at the source of significant instances in three biological datasets. These methods are presented within CroP, a data visualization tool with coordinated multiple views aimed at the analysis of biological datasets.