Relativity and Resolution for High Dimensional Information Visualization with Generalized Association Plots (GAP)

Depicting More Information in Enriched Squarified Treemaps with Layered Glyphs

Information ◽

10.3390/info11020123 ◽

2020 ◽

Vol 11 (2) ◽

pp. 123

Author(s):

Anderson Gregório Marques Soares ◽

Elvis Thermo Carvalho Miranda ◽

Rodrigo Santos do Amor Divino Lima ◽

Carlos Gustavo Resque dos Santos ◽

Bianchi Serique Meiguins

Keyword(s):

Information Visualization ◽

Data Structures ◽

User Study ◽

Relevant Information ◽

Data Representation ◽

High Dimensional ◽

Hierarchical Data ◽

Visual Data ◽

Visual Variable ◽

Data Clusters

The Treemap is one of the most relevant information visualization (InfoVis) techniques to support the analysis of large hierarchical data structures or data clusters. Despite that, Treemap still presents some challenges for data representation, such as the few options for visual data mappings and the inability to represent zero and negative values. Additionally, visualizing high dimensional data requires many hierarchies, which can impair data visualization. Thus, this paper proposes to add layered glyphs to Treemap’s items to mitigate these issues. Layered glyphs are composed of N partially visible layers, and each layer maps one data dimension to a visual variable. Since the area of the upper layers is always smaller than the bottom ones, the layers can be stacked to compose a multidimensional glyph. To validate this proposal, we conducted a user study to compare three scenarios of visual data mappings for Treemaps: only Glyphs (G), Glyphs and Hierarchy (GH), and only Hierarchy (H). Thirty-six volunteers with a background in InfoVis techniques, organized into three groups of twelve (one group per scenario), performed 8 InfoVis tasks using only one of the proposed scenarios. The results point that scenario GH presented the best accuracy while having a task-solving time similar to scenario H, which suggests that representing more data in Treemaps with layered glyphs enriched the Treemap visualization capabilities without impairing the data readability.

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Extending the Attribute Explorer to Support Professional Team-Sport Analysis

Information Visualization ◽

10.1057/palgrave.ivs.9500178 ◽

2008 ◽

Vol 7 (2) ◽

pp. 163-169 ◽

Cited By ~ 4

Author(s):

Pär-Anders Albinsson ◽

Dennis Andersson

Keyword(s):

Information Visualization ◽

Team Sport ◽

Interactive Systems ◽

High Dimensional ◽

Massive Data ◽

Data Sets ◽

Event Data ◽

Football Game ◽

Visualization Techniques ◽

Sport Analysis

Advances in interactive systems and the ability to manage increasing amounts of high-dimensional data provide new opportunities in numerous domains. Information visualization techniques are especially useful in situations where analysts seek patterns and information of interest in massive data sets. In this article, we propose an extension of the original Attribute Explorer (AE) technique by Spence and colleagues to take on the challenges presented in the domain of professional team-sport analysis. We describe the implementation of an extended AE and use football game-event data to highlight the new possibilities.

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