2018
DOI: 10.1109/tvcg.2017.2743998
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Structuring Visualization Mock-Ups at the Graphical Level by Dividing the Display Space

Abstract: Mock-ups are rapid, low fidelity prototypes, that are used in many design-related fields to generate and share ideas. While their creation is supported by many mature methods and tools, surprisingly few are suited for the needs of information visualization. In this article, we introduce a novel approach to creating visualizations mock-ups, based on a dialogue between graphic design and parametric toolkit explorations. Our approach consists in iteratively subdividing the display space, while progressively infor… Show more

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Cited by 9 publications
(4 citation statements)
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References 43 publications
(43 reference statements)
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“…Spatial Data Structures are the basis for Space Partitioning [10] and Bounding Volumes [11]. Space Partitioning is a method of space sub-partitioning into convex regions.…”
Section: Methodsmentioning
confidence: 99%
“…Spatial Data Structures are the basis for Space Partitioning [10] and Bounding Volumes [11]. Space Partitioning is a method of space sub-partitioning into convex regions.…”
Section: Methodsmentioning
confidence: 99%
“…A number of authors [21,33,50,56] have pointed out that such systems impose a bottom-up workflow, where design is driven by data rather than by graphics, layout, and aesthetics.…”
Section: Visualization Grammars and Toolkitsmentioning
confidence: 99%
“…Years later, Bret Victor [49] demonstrates an interactive data-driven system for creating custom data visualizations through a loop-based workflow that resembles programming. Research systems that combine graphics-driven visualization authoring with programming workflows (either textual or visual) include iVoLVER [26], Hanpuku [10], and d3-gridding [50].…”
Section: Design-oriented Visualization Authoring Toolsmentioning
confidence: 99%
“…Spatial Data Structures are the basis for Space Partitioning [17] and Bounding Volumes [18]. Space Partitioning is a method of space sub-partitioning into convex regions.…”
Section: Introductionmentioning
confidence: 99%