Visualizing Dynamic Data with Maps

Mashima, Daisuke; Kobourov, Stephen G.; Hu, Yifan

doi:10.1109/tvcg.2011.288

Cited by 52 publications

(22 citation statements)

References 28 publications

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“…The problem of effectively conveying change over time using a map-based visualization was studied by Harrower [54]. More recently, Mashima et al [68] use the GMap framework [57] to visualize dynamic graphs with the geographic map metaphor; see Fig. 5.…”

Section: Alternative Representationsmentioning

confidence: 99%

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Lecture Notes in Computer Science

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Abstract. Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, its the structure of the dynamic graph that evolves as vertices and edges are added or removed from the graph. In a multivariate scenario, however, attributes play an important role and can also evolve over time. In this chapter, we characterize and survey methods for visualizing temporal multivariate networks. We also explore future applications and directions for this emerging area in the fields of information visualization and graph drawing.

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Section: Alternative Representationsmentioning

confidence: 99%

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Lecture Notes in Computer Science

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“…There are many contexts in which dissimilarities and the statistical values vary over time, for instance stock markets, or in which individual appear and disappear over time, for instance most used words related to a trending topic in Twitter. Thus, modeling dynamical SBMs which can handle these temporal changes seems to be also a very interesting problem, [13,15,41]. …”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Visualizing proportions and dissimilarities by Space-filling maps: A Large Neighborhood Search approach

Carrizosa

Guerrero

Morales

2017

Computers & Operations Research

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In this paper we address the problem of visualizing a set of individuals, which have attached a statistical value given as a proportion, and a dissimilarity measure. Each individual is represented as a region within the unit square, in such a way that the area of the regions represent the proportions and the distances between them represent the dissimilarities. To enhance the interpretability of the representation, the regions are required to satisfy two properties. First, they must form a partition of the unit square, namely, the portions in which it is divided must cover its area without overlapping. Second, the portions must be made of a connected union of rectangles which verify the so-called box-connectivity constraints, yielding a visualization map called Space-filling Box-connected Map (SBM). The construction of an SBM is formally stated as a mathematical optimization problem, which is solved heuristically by using the Large Neighborhood Search technique. The methodology proposed in this paper is applied to three real-world datasets: the first one concerning financial markets in Europe and Asia, the second one about the letters in the English alphabet, and finally the provinces of The Netherlands as a geographical application.

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“…These map-style visualisations are based on a spatial metaphor whereby similar documents (or themes) are clustered together and placed physically nearer to each other. They have been used for a range of applications, including providing thematic clustering of search results [1] and document collections [8], helping software developers make sense of large repositories of source code [17], allowing users to explore music libraries where "islands" on the map represent different genres or styles of music [26], and visualising user traffic on the Internet radio station last.fm [22].…”

Section: Spatial Metaphors For Information Visualisationmentioning

confidence: 99%

Exploring Large Digital Library Collections Using a Map-Based Visualisation

Hall

Clough

2013

Research and Advanced Technology for Digital Libraries

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Abstract. In this paper we describe a novel approach for exploring large document collections using a map-based visualisation. We use hierarchically structured semantic concepts that are attached to the documents to create a visualisation of the semantic space that resembles a Google Map. The approach is novel in that we exploit the hierarchical structure to enable the approach to scale to large document collections and to create a map where the higher levels of spatial abstraction have semantic meaning. An informal evaluation is carried out to gather subjective feedback from users. Overall results are positive with users finding the visualisation enticing and easy to use.

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Visualizing Dynamic Data with Maps

Cited by 52 publications

References 28 publications

Temporal Multivariate Networks

Temporal Multivariate Networks

Visualizing proportions and dissimilarities by Space-filling maps: A Large Neighborhood Search approach

Exploring Large Digital Library Collections Using a Map-Based Visualisation

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