MotionRugs: Visualizing Collective Trends in Space and Time

Abstract

Understanding the movement patterns of collectives, such as flocks of birds or fish swarms, is an interesting open research question. The collectives are driven by mutual objectives or react to individual direction changes and external influence factors and stimuli. The challenge in visualizing collective movement data is to show space and time of hundreds of movements at the same time to enable the detection of spatiotemporal patterns. In this paper, we propose MotionRugs, a novel space efficient technique for visualizing moving groups of entities. Building upon established space-partitioning strategies, our approach reduces the spatial dimensions in each time step to a one-dimensional ordered representation of the individual entities. By design, MotionRugs provides an overlap-free, compact overview of the development of group movements over time and thus, enables analysts to visually identify and explore group-specific temporal patterns. We demonstrate the usefulness of our approach in the field of fish swarm analysis and report on initial feedback of domain experts from the field of collective behavior.

A Comparison of Geographical Propagation Visualizations

“…Animation is a very popular technique in geovisualization for presentation purposes (e.g., [34,44,45]) and it is also used in more data analysis-oriented contexts (e.g., [14,19]). Based on neither small-multiples nor animation, Motion-Rugs [13] can nevertheless be considered as a particular case of juxtaposing location, as it reduces the spatial dimension into sequential one-dimensional slices that compose a simplified representation of a group of entities' movements.…”

Visual Analysis of Spatio‐temporal Phenomena with 1D Projections

“…Popular approaches to achieve such a dimension reduction are space‐filling curves [But71] or orders derived from hierarchical clustering [GG06]. Buchmüller et al [BJC∗18] applied space‐filling curves to create a dense representation of spatio‐temporal movement of multiple objects. In contrast, we focus on the temporal changes in spatial regions, and extend their static overview of the data with interactive means for exploring data instances in detail to support more flexible, in‐depth analyses of visual patterns in their spatial context.…”

“…Recent approaches [BJC∗18; WFG∗18; BSC∗20; ZJW21] have explored the possibility of projecting spatial data into a one‐dimensional space using their position on a space‐filling curve. As a result, locations which are close in 2D or 3D space are also depicted close to each other in 1D representations.…”

Breaking the Curse of Visual Analytics: Accommodating Virtual Reality in the Visualization Pipeline

“…Moreover, there are many visual analytics applications in which no sufficient data basis exists to reconstruct the real world from the data world and adequately display the analysis results in it. However, there is a wide range of domains in which VR could be used to minimize the discrepancy between the data world and the real world, such as in the emerging fields of collective behavior analysis [8], sports analysis [61], and general geo-spatial data analysis [19]. In the following section, we present an example where we deploy our solution approach for a criminal investigation use case.…”