State of the Art: Coordinated &amp;amp; Multiple Views in Exploratory Visualization

Roberts, Jonathan C.

doi:10.1109/cmv.2007.20

Cited by 503 publications

(327 citation statements)

References 66 publications

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“…Some features from these systems are present in BirdVis, including map views, colormaps, brushing and coordinated views [37]. However, we had to develop new visual representations for the exploration of dynamic distributions.…”

Section: Related Workmentioning

confidence: 99%

“…The idea is that a user, before exploring a data set, must first choose an environment for the Display Area. An environment is a pre-defined set of coordinated components [37] designed for a set of tasks. The current BirdVis prototype has three environments: Single Species, Single Species with Occurrence Variation, and Multi-Species.…”

Section: Putting It All Together: the Birdvis Toolmentioning

confidence: 99%

“…Practically, this means that maps must be tuned for each species individually.This was done by our collaborators using R scripts-a process that is both tedious and error prone. To support this task, we have adopted a spreadsheetbased layout [37], which allows multiple visualizations (e.g., occurrence maps, predictor importance) to be displayed side-by-side for comparison (see Figure 7). The cells in the spreadsheet can be synchronized in space, time and colormap: changes to one of these view parameters will affect the display of the synchronized cells.…”

Section: Comparing Speciesmentioning

confidence: 99%

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BirdVis: Visualizing and Understanding Bird Populations

Ferreira

Lins

Fink

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Tag cloud lenses in BirdVis are used as a tool to explore and understand relative habitat preferences suggested by a species distribution model over space, time, and across species. Here, we show occurrence maps for the Indigo Bunting. The lenses cover three different regions on three different dates of the breeding season of 2009: May 4, June 22, and August 24, 2009. They show important differences in habitat preferences across these regions as well as how the preferences change over time within a region. The ability to interact with these visualizations, by changing regions and dates, and comparing different bird species, provides an unprecedented tool for scientists to understand bird distribution and consequently obtain insights about the environment and how it changes over time. The supplemental video gives an overview of different features and visualizations provided by BirdVis.Abstract-Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multiscale spatialtemporal patterns. A large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, we have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.

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Section: Related Workmentioning

confidence: 99%

Section: Putting It All Together: the Birdvis Toolmentioning

confidence: 99%

Section: Comparing Speciesmentioning

confidence: 99%

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BirdVis: Visualizing and Understanding Bird Populations

Ferreira

Lins

Fink

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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“…While this is an intriguing data representation idea, each additional data attribute represented adds considerably to the white space in the representation, making this a less-than-compact method for high k dimensions. There is a large body of work focused on multiple coordinated views (MCV) [42], which show different aspects of the data in different views. These multiple views can be explored using interaction techniques such as brushing & linking but, these approaches divide people's attention between multiple views to discover relationships among data entities.…”

Section: Multi-dimensional Data Visualizationmentioning

confidence: 99%

Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

Loorak

Périn

Collins³

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Heterogeneous multi-dimensional data are now sufficiently common that they can be referred to as ubiquitous. The most frequent approach to visualizing these data has been to propose new visualizations for representing these data. These new solutions are often inventive but tend to be unfamiliar. We take a different approach. We explore the possibility of extending well-known and familiar visualizations through including Heterogeneous Embedded Data Attributes (HEDA) in order to make familiar visualizations more powerful. We demonstrate how HEDA is a generic, interactive visualization component that can extend common visualization techniques while respecting the structure of the familiar layout. HEDA is a tabular visualization building block that enables individuals to visually observe, explore, and query their familiar visualizations through manipulation of embedded multivariate data. We describe the design space of HEDA by exploring its application to familiar visualizations in the D3 gallery. We characterize these familiar visualizations by the extent to which HEDA can facilitate data queries based on attribute reordering. Permanent repository link

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“…This interface displays the raw data using an animated 3D model view, as well as the automated analysis output described in section 4. By integrating these views using a multiple-coordinated-views approach [18], our system provides methods to perform interactive analysis across modalities. Shown together in Figure 4, these views allow the user to explore the 3D motion sequence in space and time, in its embedding space, as well as at a clustering level.…”

Section: Interactive Visual Analysis Of Patterns In Motion Datamentioning

confidence: 99%

Combining Automated and Interactive Visual Analysis of Biomechanical Motion Data

Spurlock

Chang²,

Wang

et al. 2010

Advances in Visual Computing

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Abstract. We present a framework for combining automated and interactive visual analysis techniques for use on high-resolution biomechanical data. Analyzing the complex 3D motion of, e.g., pigs chewing or bats flying, can be enhanced by providing investigators with a multi-view interface that allows interaction across multiple modalities and representations. In this paper, we employ nonlinear dimensionality reduction to automatically learn a low-dimensional representation of the data and hierarchical clustering to learn patterns inherent within the motion segments. Our multi-view framework allows investigators to simultaneously view a low-dimensional embedding, motion segment clustering, and 3D visual representation of the data side-by-side. We describe an application to a dataset containing thousands of frames of high-speed, 3D motion data collected over multiple experimental trials.

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State of the Art: Coordinated & Multiple Views in Exploratory Visualization

Cited by 503 publications

References 66 publications

BirdVis: Visualizing and Understanding Bird Populations

BirdVis: Visualizing and Understanding Bird Populations

Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

Combining Automated and Interactive Visual Analysis of Biomechanical Motion Data

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