A Declarative Rendering Model for Multiclass Density Maps

• Published in: IEEE transactions on visualization and computer graphics Vol. 25; no. 1; pp. 470 - 480
• Main Authors: Jaemin Jo, Vernier, Frederic, Dragicevic, Pierre, Fekete, Jean-Daniel
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: aggregation; Cartography; Computational modeling; Computer Science; Data management; Data points; Data visualization; declarative specification; Density; density maps; Histograms; Human-Computer Interaction; Image color analysis; multiclass scatterplots; Rendering (computer graphics); Scalability; Semantics; Task analysis; Visualization; visualization grammar; visualization grammar; multiclass scatterplots; aggregation; Scalability; declarative specification; density maps
• ISSN: 1077-2626; 1941-0506; 1941-0506
• DOI: 10.1109/TVCG.2018.2865141

Multiclass maps are scatterplots, multidimensional projections, or thematic geographic maps where data points have a categorical attribute in addition to two quantitative attributes. This categorical attribute is often rendered using shape or color, which does not scale when overplotting occurs. When the number of data points increases, multiclass maps must resort to data aggregation to remain readable. We present multiclass density maps: multiple 2D histograms computed for each of the category values. Multiclass density maps are meant as a building block to improve the expressiveness and scalability of multiclass map visualization. In this article, we first present a short survey of aggregated multiclass maps, mainly from cartography. We then introduce a declarative model-a simple yet expressive JSON grammar associated with visual semantics-that specifies a wide design space of visualizations for multiclass density maps. Our declarative model is expressive and can be efficiently implemented in visualization front-ends such as modern web browsers. Furthermore, it can be reconfigured dynamically to support data exploration tasks without recomputing the raw data. Finally, we demonstrate how our model can be used to reproduce examples from the past and support exploring data at scale.