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.
Figure 1: Multi-Target Visual Debugging Workflow.(1) A user selects a model from a set of multi-target classifiers to inspect its performance. (2) Through filtering of parl ially correcl results and the visual investigation between the relationships of the n-target, the user can focus on a certain single-target. (3) By inspecting the filtered single-target confusion of multi-classes, a dri ll down onto the unde rlying time series classification data is possible that aims to give meaningful insights into the dass confusions.
A B C Figure 1: ViCCEx -Visual Chemical Contamination Explorer (A) The t-SNE projection enables to identify patterns, trends, and outliers in multivariate time series sensor readings (B) Sampling strategy view to investigating the chemicals measurements taken at each location (C) Metadata panel with filter options and further extracted statistics for each chemical for each sensor location.
ABSTRACTThe goal of the VAST Challenge 2018 Mini Challenge 2 (MC 2) was to unveil the possible causes and effects of environmental pollution in the Boonsong Lekagul Wildlife Preserve. We propose the ViCCEx (Visual Chemical Contamination Explorer) system that enables to interactively explore the sparse multivariate river network sensor reading dataset to identify characteristics, trends, and outliers of the different sensor reading locations over time. The ViCCEx system uses a t-SNE projection to display an overview visualization, a sampling strategy view to highlight the overall sampling strategies of different chemical measurements at each sensor location, and various extracted statistics to highlight the evolution of chemical measurements. The three views are connected via linking and brushing, which enables to explore and identify possible pollution causes and effects in the preserve.
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