ThemeRiver: visualizing theme changes over time

Havre, Susan L.; Hetzler, Beth; Nowell, Lucy T.

doi:10.1109/infvis.2000.885098

Cited by 324 publications

(184 citation statements)

References 15 publications

(11 reference statements)

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“…A similar approach has been presented by Krstajic et al [23]. The authors use an approach similar to Themeriver [24], but discretize time (x-axis) and visualize news articles developments, instead of features, over time. The system allows the user to effectively recognize the temporal dynamics of news stories and their relationships.…”

Section: Visualization Approachesmentioning

confidence: 99%

Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction

Stein

Häußler

Jäckle

et al. 2015

IJGI

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With recent advances in sensor technologies, large amounts of movement data have become available in many application areas. A novel, promising application is the data-driven analysis of team sport. Specifically, soccer matches comprise rich, multivariate movement data at high temporal and geospatial resolution. Capturing and analyzing complex movement patterns and interdependencies between the players with respect to various characteristics is challenging. So far, soccer experts manually post-analyze game situations and depict certain patterns with respect to their experience. We propose a visual analysis system for interactive identification of soccer patterns and situations being of interest to the analyst. Our approach builds on a preliminary system, which is enhanced by semantic features defined together with a soccer domain expert. The system includes a range of useful visualizations to show the ranking of features over time and plots the change of game play situations, both helping the analyst to interpret complex game situations. A novel workflow includes improving the analysis process by a learning stage, taking into account user feedback. We evaluate our approach by analyzing real-world soccer matches, illustrate ISPRS Int. J. Geo-Inf. 2015, 4 2160 several use cases and collect additional expert feedback. The resulting findings are discussed with subject matter experts.

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Section: Visualization Approachesmentioning

confidence: 99%

Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction

Stein

Häußler

Jäckle

et al. 2015

IJGI

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“…A very close parallel to conceptual dynamics discussed the visualisation of thematic changes in documents over time based on the river metaphor (Havre et al, 2000). Another example for static distributions of semantic content is (Minghim et al, 2005) for fast content-based visual mapping of document collections.…”

Section: Previous Researchmentioning

confidence: 99%

“…Extensions of these methods have also been developed to analyse gene expressions (Stuart et al, 2003). The ability to capture the temporal changes of a collection is somewhat limited in these methods, whereas a different, two-dimensional visualisation method focuses exclusively on dynamics (Havre et al, 2000). Figure 1 provides some insights to aid the interpretation of the three-dimensional landscape of the document collection described in the next section.…”

Section: Mapping Evolving Semantic Structuresmentioning

confidence: 99%

Demonstrating conceptual dynamics in an evolving text collection

Daranyi

Wittek

2013

J Am Soc Inf Sci Tec

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Based on real world user demands, we demonstrate how animated visualisation of evolving text corpora displays the underlying dynamics of semantic content. To interpret the results, one needs a dynamic theory of word meaning. We suggest that conceptual dynamics as the interaction between kinds of intellectual, emotional etc. content, and language, is key for such a theory. We demonstrate our methodology by two-way seriation which is a popular technique to analyse groups of similar instances and their features, as well as the connections between the groups themselves. The two-way seriated data may be visualised as a two-dimensional heat map or as a three-dimensional landscape where colour codes or height correspond to the values in the matrix. In this paper we focus on two-way seriation of sparse data in the Reuters-21568 test collection. To achieve a meaningful visualisation thereof we introduce a compactly supported convolution kernel similar to filter kernels used in image reconstruction and geostatistics. This filter populates the high-dimensional sparse space with values that interpolate nearby elements, and provides insight into the clustering structure. We also extend two-way seriation to deal with online updates of both the row and column spaces, and, combined with the convolution kernel, demonstrate a three-dimensional visualisation of dynamics.

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“…However, this close combination has not been realized in temporal visualization. There are many temporal graphic representations which could display a temporal pattern of data, such as ThemeRiver (Havre, Hetzler et al 2000), Stacked bar chat (Harris 1999), People garden (Xiong and Donath 1999), MultiCombs (Tominski, Abello et al 2003) and etc. To assist answering the questions, temporal interactive tools should allow one to identify single or multiple points in time, identify points at periodic intervals, or define intervals of certain length in both the linear and the cyclic format.…”

Section: Temporal User Tasks and Temporal Visualizationmentioning

confidence: 99%

Explore Multivariable Spatio-Temporal Data with the Time Wave: Case Study on Meteorological Data

Kraak

2012

Lecture Notes in Geoinformation and Cartography

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ABSTRACT:Traditionally the GIScience community is well able to deal with the locational and attribute component of spatio-temporal data. However, the methods and techniques to deal with the data's temporal component are less developed. This paper introduces a conceptual framework that combines user tasks, available temporal data and visualization theories to discuss temporal visualization. Two limitations of existing method are improved by introducing the time wave environment which is a close combination of temporal graphic representation and temporal interactive tools, and operates in so-called time space. A case study based on meteorological data illustrates the approach.

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ThemeRiver: visualizing theme changes over time

Cited by 324 publications

References 15 publications

Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction

Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction

Demonstrating conceptual dynamics in an evolving text collection

Explore Multivariable Spatio-Temporal Data with the Time Wave: Case Study on Meteorological Data

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