Vega-Lite combines a traditional grammar of graphics, providing visual encoding rules and a composition algebra for layered and multi-view displays, with a novel grammar of interaction, that enables rapid specification of interactive data visualizations.
Abstract:
We present Vega-Lite, a high-level grammar that enables rapid specification of interactive data visualizations. Vega-Lite combines a traditional grammar of graphics, providing visual encoding rules and a composition algebra for layered and multi-view displays, with a novel grammar of interaction. Users specify interactive semantics by composing selections. In Vega-Lite, a selection is an abstraction that defines input event processing, points of interest, and a predicate function for inclusion testing. Selections parameterize visual encodings by serving as input data, defining scale extents, or by driving conditional logic. The Vega-Lite compiler automatically synthesizes requisite data flow and event handling logic, which users can override for further customization. In contrast to existing reactive specifications, Vega-Lite selections decompose an interaction design into concise, enumerable semantic units. We evaluate Vega-Lite through a range of examples, demonstrating succinct specification of both customized interaction methods and common techniques such as panning, zooming, and linked selection.
TL;DR: In this paper , the authors conducted a contextual inquiry study with domain experts using geospatial data in their current work, and found that participants struggled to find and transform GeSpatial data to satisfy spatio-temporal constraints, understand the behavior of geSpatial operators, track geSpatio data provenance, and explore the cartographic design space.
TL;DR: SuperNOVA as discussed by the authors is an open-source interactive tool to help researchers explore existing notebook visual analytics tools and search for related work, including using and manipulating multimodal data in notebooks as well as balancing the degree of visualization-notebook integration.
TL;DR: The Analytical Process Constructor (AnyProc) as mentioned in this paper is a tool chaining platform for data-driven coordination of independent visual analytics (VA) tools that allows mixing and matching of different data exchange strategies over the course of a cross-tool analysis.
TL;DR: This work presents a framework developed on top of SAGE2 platform for cross-device collaborative visual data exploration that provides the users with an environment for visualization compositions that delegate the rendering to the target device, allowing them to augment their large display workspace with portable devices for further exploration territories.
TL;DR: This work shows how representational transparency improves expressiveness and better integrates with developer tools than prior approaches, while offering comparable notational efficiency and retaining powerful declarative components.
TL;DR: The approach is based on graphical perception—the visual decoding of information encoded on graphs—and it includes both theory and experimentation to test the theory, providing a guideline for graph construction.
TL;DR: APT as discussed by the authors is an application-independent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information, based on the view that graphical presentations are sentences of graphical languages.
TL;DR: This work is an unprecedented attempt to synthesize principles of graphic communication with the logic of standard rules applied to writing and topography in an array of more than 1,000 maps and diagrams.
TL;DR: Seven general categories of interaction techniques widely used in Infovis are proposed, organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system.
Q1. What are the contributions in "Vega-lite: a grammar of interactive graphics" ?
The authors present Vega-Lite, a high-level grammar that enables rapid specification of interactive data visualizations. The Vega-Lite compiler automatically synthesizes requisite data flow and event handling logic, which users can override for further customization.
Q2. What are the future works in "Vega-lite: a grammar of interactive graphics" ?
One promising avenue for future work is to develop models and techniques to analogously recommend suitable interaction methods for given visualizations and underlying data types.
Q3. What is the function that applies the selection against the backing datasets?
The filterWith data transform applies the selection against the backing datasets such that only data values that fall within the selection are displayed.
Q4. What are the primary features of a low-level grammar?
Low-level grammars such as Protovis [3], D3 [4], and Vega [22] are useful for explanatory data visualization or as a basis for customized analysis tools, as their primitives offer fine-grained control.
Q5. What is the function that offsets the spatial properties of the backing points?
by): Offsets the spatial properties (or corresponding data fields) of backing points by an amount determined by the coordinates of the sequenced events.
Q6. What is the process of merging components?
Once the necessary components have been built, the compiler performs a bottom-up traversal of the model tree to merge redundant components.
Q7. How does Vega-Lite support expressive interaction methods?
To support expressive interaction methods, the authors first contribute an algebra to compose singleview Vega-Lite specifications into multi-view displays using layer, concatenate, facet and repeat operators.
Q8. What is the function that augments the selection’s event processing?
nearest(): Computes a Voronoi decomposition, and augments the selection’s event processing, such that the data value or visual elementnearest the selection’s triggering event is selected (approximating a Bubble Cursor [11]).
Q9. What is the syntax for creating a composite view?
Their formal definitions are instantiated in a JSON (JavaScript Object Notation) syntax, as shown in Fig. 2.Given multiple unit specifications, composite views can be created using a set of composition operators.
Q10. How can you adapt techniques to a different design?
Specifying common techniques can be time-consuming, requiring tens of lines of JSON, and it is difficult to know how to adapt techniques in pursuit of alternative designs.