1,280 research outputs found
High-dimensional glyph-based visualization and interactive techniques.
The advancement of modern technology and scientific measurements has led to datasets growing in both size and complexity, exposing the need for more efficient and effective ways of visualizing and analysing data. Despite the amount of progress in visualization methods, high-dimensional data still poses a number of significant challenges in terms of the technical ability of realising such a mapping, and how accurate they are actually interpreted. The different data sources and characteristics which arise from a wide range of scientific domains as well as specific design requirements constantly create new special challenges for visualization research. This thesis presents several contributions to the field of glyph-based visualization. Glyphs are parametrised objects which encode one or more data values to its appearance (also referred to as visual channels) such as their size, colour, shape, and position. They have been widely used to convey information visually, and are especially well suited for displaying complex, multi-faceted datasets. Its major strength is the ability to depict patterns of data in the context of a spatial relationship, where multi-dimensional trends can often be perceived more easily. Our research is set in the broad scope of multi-dimensional visualization, addressing several aspects of glyph-based techniques, including visual design, perception, placement, interaction, and applications. In particular, this thesis presents a comprehensive study on one interaction technique, namely sorting, for supporting various analytical tasks. We have outlined the concepts of glyph- based sorting, identified a set of design criteria for sorting interactions, designed and prototyped a user interface for sorting multivariate glyphs, developed a visual analytics technique to support sorting, conducted an empirical study on perceptual orderability of visual channels used in glyph design, and applied glyph-based sorting to event visualization in sports applications. The content of this thesis is organised into two parts. Part I provides an overview of the basic concepts of glyph-based visualization, before describing the state-of-the-art in this field. We then present a collection of novel glyph-based approaches to address challenges created from real-world applications. These are detailed in Part II. Our first approach involves designing glyphs to depict the composition of multiple error-sensitivity fields. This work addresses the problem of single camera positioning, using both 2D and 3D methods to support camera configuration based on various constraints in the context of a real-world environment. Our second approach present glyphs to visualize actions and events "at a glance". We discuss the relative merits of using metaphoric glyphs in comparison to other types of glyph designs to the particular problem of real-time sports analysis. As a result of this research, we delivered a visualization software, MatchPad, on a tablet computer. It successfully helped coaching staff and team analysts to examine actions and events in detail whilst maintaining a clear overview of the match, and assisted in their decision making during the matches. Abstract shortened by ProQuest
Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species
Invasive species are a major cause of ecological damage and commercial
losses. A current problem spreading in North America and Europe is the vinegar
fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and
healthy fruits and is therefore of concern to fruit growers, such as vintners.
Consequently, large amounts of data about infestations have been collected in
recent years. However, there is a lack of interactive methods to investigate
this data. We employ ensemble-based classification to predict areas susceptible
to infestation by D. suzukii and bring them into a spatio-temporal context
using maps and glyph-based visualizations. Following the information-seeking
mantra, we provide a visual analysis system Drosophigator for spatio-temporal
event prediction, enabling the investigation of the spread dynamics of invasive
species. We demonstrate the usefulness of this approach in two use cases
Understanding Hidden Memories of Recurrent Neural Networks
Recurrent neural networks (RNNs) have been successfully applied to various
natural language processing (NLP) tasks and achieved better results than
conventional methods. However, the lack of understanding of the mechanisms
behind their effectiveness limits further improvements on their architectures.
In this paper, we present a visual analytics method for understanding and
comparing RNN models for NLP tasks. We propose a technique to explain the
function of individual hidden state units based on their expected response to
input texts. We then co-cluster hidden state units and words based on the
expected response and visualize co-clustering results as memory chips and word
clouds to provide more structured knowledge on RNNs' hidden states. We also
propose a glyph-based sequence visualization based on aggregate information to
analyze the behavior of an RNN's hidden state at the sentence-level. The
usability and effectiveness of our method are demonstrated through case studies
and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2017
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