Using Animation to Alleviate Overdraw in Multiclass Scatterplot Matrices

Chen, Helen; Engle, Sophie; Joshi, Alark; Ragan, Eric D.; Yuksel, Beste F.; Harrison, Lane

doi:10.1145/3173574.3173991

Cited by 31 publications

(23 citation statements)

References 49 publications

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“…Chen et al [12] have recently introduced animation to alleviate overdraw in multiclass scatterplot matrices (SPLOMs). They animate the drawing order of the points to help the user see every point at any position by watching it for a sufficient time.…”

Section: Nonaggregate Methodsmentioning

confidence: 99%

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A Declarative Rendering Model for Multiclass Density Maps

Vernier

Dragicevic

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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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.

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Section: Nonaggregate Methodsmentioning

confidence: 99%

“…However, as we discussed before, multiclass density maps do not need to be cartographic. For example, they can include the combinations of multiple scatterplots showing abstract data [12].…”

Section: Examples From Cartographymentioning

confidence: 99%

A Declarative Rendering Model for Multiclass Density Maps

Vernier

Dragicevic

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…The improvements were relative to scatterplots where density was not explicitly encoded (implicitly encoded as position); plus, density is necessarily correlated to position, which makes motion-position a double encoding for density. Similarly, animated scatterplot matrices that encoded density with flickering were found superior to conventional ones in density judgement tasks [5].…”

Section: Animated Scatterplotsmentioning

confidence: 95%

Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

Veras

Collins

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Figure 1: Feature distributions in a typical direction stimuli. Values are sampled from independent distributions. The target point (marked in red) receives an outlying value in the relevant dimension (either direction or speed). Depending on the condition, the target can have mean or maximum (salient) values in the irrelevant dimensions. For instance, in the condition +color, the target has salient color. The scatterplot in the bottom left is the first frame; the arrows in the bottom right represent the displacement between the initial and final frames, which is animated.

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“…However, Multiclass SPLOMs also have problems with over-plotting, and most existing techniques to counter this problem only focus on individual scatter plots with a single class. An animated SPLOM by Chen et al [127] that uses a flicker-based animation exhibits better performance in identifying dense areas and is even more easily interpreted and more powerful than static scatter matrices. Figure 48b shows this animated approach on several typical multiclass and multivariable datasets of different scales (accessed from Figure 48a shows all the pairwise scatter plots of attributes on a single view, with multiple scatter plots in a matrix format [126], as the result of dimensionality reduction.…”

Section: Bubble Chartsmentioning

confidence: 99%

“…SPLOM visualization examples: (a) exploration of a seven-dimensional car dataset[126]; and (b) system interface of the flicker-based animated SPLOM[127].…”

mentioning

confidence: 99%

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Chen

et al. 2019

IJGI

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Trajectory big data have significant applications in many areas, such as traffic management, urban planning and military reconnaissance. Traditional visualization methods, which are represented by contour maps, shading maps and hypsometric maps, are mainly based on the spatiotemporal information of trajectories, which can macroscopically study the spatiotemporal conditions of the entire trajectory set and microscopically analyze the individual movement of each trajectory; such methods are widely used in screen display and flat mapping. With the improvement of trajectory data quality, these data can generally describe information in the spatial and temporal dimensions and involve many other attributes (e.g., speed, orientation, and elevation) with large data amounts and high dimensions. Additionally, these data have relatively complicated internal relationships and regularities, whose analysis could cause many troubles; the traditional approaches can no longer fully meet the requirements of visualizing trajectory data and mining hidden information. Therefore, diverse visualization methods that present the value of massive trajectory information are currently a hot research topic. This paper summarizes the research status of trajectory data-visualization techniques in recent years and extracts common contemporary trajectory data-visualization methods to comprehensively cognize and understand the fundamental characteristics and diverse achievements of trajectory-data visualization.First, we should fully understand the conceptual distinction between "multidimensional" and "multivariate". Generally, "multidimensional" is used to describe the dimensionality of independent variables in physical space and concentrates on expressing spatial and temporal concepts, while "multivariate" is used for attribute-count description that indicates the attribute amount involved in the data. In fact, trajectory data are typically enormous, high-dimensional, spatiotemporal vector attribute data. We are interested not only in comprehending dimensional information for spatiotemporal-element depictions, but also in the visualization research with attribute-feature expression. Starting in Section 2, we present relevant visualization techniques and methods that function with multisource trajectory data. The chosen examples are for illustration and the list is not exhaustive-we only focus on small but representative choices. Universal Multivariate VisualizationThe trajectory visualization reflects the changing patterns of time-varying geolocations and corresponding attribute information, including basic-dimensional information and the attached thematic variables of space-time objects. However, these variables usually do not exist independently but are combined with each other, and the trajectory data of different sources, scales, or types are plotted in a hybrid manner. We separately list visualization techniques that integrate multiple attributes. As a powerful and portable carrier that encodes abundant information in methods of visual cha...

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Using Animation to Alleviate Overdraw in Multiclass Scatterplot Matrices

Cited by 31 publications

References 49 publications

A Declarative Rendering Model for Multiclass Density Maps

A Declarative Rendering Model for Multiclass Density Maps

Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

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