Evaluation of trend localization with multi-variate visualizations

Fig. 1 . Multi-variate visualization techniques we evaluated in our study, from left to right: Brush Strokes, Data-Driven Spots, Oriented Slivers, Color Blending, and Dimensional Stacking. These images depict a tri-county area in central Ohio. The encoded information is generated from a synthetic data set generated for the purposes of the study. See Section 3 for a explanation of the encoding. Abstract-Multi-valued data sets are increasingly common, with the number of dimensions growing. A number of multi-variate visualization techniques have been presented to display such data. However, evaluating the utility of such techniques for general data sets remains difficult. Thus most techniques are studied on only one data set. Another criticism that could be levied against previous evaluations of multi-variate visualizations is that the task doesn't require the presence of multiple variables. At the same time, the taxonomy of tasks that users may perform visually is extensive. We designed a task, trend localization, that required comparison of multiple data values in a multi-variate visualization. We then conducted a user study with this task, evaluating five multivariate visualization techniques from the literature (Brush Strokes, Data-Driven Spots, Oriented Slivers, Color Blending, Dimensional Stacking) and juxtaposed grayscale maps. We report the results and discuss the implications for both the techniques and the task

Digital representation of park use and visual analysis of visitor activities

Urban public parks can serve an important function by contributing to urban citizens' quality of life. At the same time, they can be the location of processes of displacement and exclusion. Despite this ambiguous role, little is known about actual park use patterns. To learn more about park use in three parks in Zurich, Switzerland, extensive data on visitor activities was collected using a new method based on direct recording via a portable GIS solution. Then, the data was analyzed using qualitative and quantitative methods. This paper examines whether geographic visualization of these data can help domain experts like landscape designers and park managers to assess park use. To maximize accessibility, the visualizations are made available through a web-interface of a common, off-the-shelf GIS. The technical limitations imposed by this choice are critically assessed, before the available visualization techniques are evaluated in respect to the needs and tasks of practitioners with limited knowledge on spatial analysis and GIS. Key criteria are each technique's level of abstraction and graphical complexity. The utility and suitability of the visualization techniques is characterized for the distinct phases of exploration, analysis and synthesis. The findings suggest that for a target user group of practitioners, a combination of dot maps showing the raw data and surface maps showing derived density values for several attributes serves the purpose of knowledge generation best

DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co-correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explainability in the field of visual analytics

Leaping Into Memories: Space-Time Deep Feature Synthesis

The success of deep learning models has led to their adaptation and adoption by prominent video understanding methods. The majority of these approaches encode features in a joint space-time modality for which the inner workings and learned representations are difficult to visually interpret. We propose LEArned Preconscious Synthesis (LEAPS), an architecture-agnostic method for synthesizing videos from the internal spatiotemporal representations of models. Using a stimulus video and a target class, we prime a fixed space-time model and iteratively optimize a video initialized with random noise. We incorporate additional regularizers to improve the feature diversity of the synthesized videos as well as the cross-frame temporal coherence of motions. We quantitatively and qualitatively evaluate the applicability of LEAPS by inverting a range of spatiotemporal convolutional and attention-based architectures trained on Kinetics-400, which to the best of our knowledge has not been previously accomplished

Abstract visualization of large-scale time-varying data

The explosion of large-scale time-varying datasets has created critical challenges for scientists to study and digest. One core problem for visualization is to develop effective approaches that can be used to study various data features and temporal relationships among large-scale time-varying datasets. In this dissertation, we first present two abstract visualization approaches to visualizing and analyzing time-varying datasets. The first approach visualizes time-varying datasets with succinct lines to represent temporal relationships of the datasets. A time line visualizes time steps as points and temporal sequence as a line. They are generated by sampling the distributions of virtual words across time to study temporal features. The key idea of time line is to encode various data properties with virtual words. We apply virtual words to characterize feature points and use their distribution statistics to measure temporal relationships. The second approach is ensemble visualization, which provides a highly abstract platform for visualizing an ensemble of datasets. Both approaches can be used for exploration, analysis, and demonstration purposes. The second component of this dissertation is an animated visualization approach to study dramatic temporal changes. Animation has been widely used to show trends, dynamic features and transitions in scientific simulations, while animated visualization is new. We present an automatic animation generation approach that simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. We also extend the concept of animated visualization to non-traditional time-varying datasets--network protocols--for visualizing key information in abstract sequences. We have evaluated the effectiveness of our animated visualization with a formal user study and demonstrated the advantages of animated visualization for studying time-varying datasets

NIH-NSF Visualization Research Challenges Report

Engineering and Applied Science