Scattering Points in Parallel Coordinates

Yuan, Xiaoru; Guo, Peihong; He, Xiao; Hong, Zhou; Qu, Huamin

doi:10.1109/tvcg.2009.179

Cited by 110 publications

(9 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also enable rapid detection of nonlinear regression between two data sets by drawing points along a curve in space. 16 Because of these benefits and because points can both be preattentively distinguished from lines and take up only a small fraction of the display space of lines, we find that augmenting a PC display by superimposing a matrix of SP can improve the overall perceptual salience of relationships in the same-sized display region.…”

Section: Scatter Plots Overlay (Spo)mentioning

confidence: 85%

“…extended this and developed a method using multidimensional scaling and animation to smoothly transition from a PC display to the combined display of parallel coordinates and scatter plots in the same graph. 16 Their technique converts two or more dimensions of parallel coordinate plots into scattered points intersecting with the parallel coordinate lines. In the case of two dimensions, these plots are similar to the plots used in SPO but differ due to an additional transform to map them to PC lines.…”

Section: Scatter Plots Overlay (Spo)mentioning

confidence: 99%

See 1 more Smart Citation

Increasing the perceptual salience of relationships in parallel coordinate plots

Harter

Alabi

et al. 2012

Visualization and Data Analysis 2012

View full text Add to dashboard Cite

We present three extensions to parallel coordinates that increase the perceptual salience of relationships between axes in multivariate data sets: (1) luminance modulation maintains the ability to preattentively detect patterns in the presence of overplotting, (2) adding a one-vs.-all variable display highlights relationships between one variable and all others, and (3) adding a scatter plot within the parallel-coordinates display preattentively highlights clusters and spatial layouts without strongly interfering with the parallel-coordinates display. These techniques can be combined with one another and with existing extensions to parallel coordinates, and two of them generalize beyond cases with known-important axes. We applied these techniques to two real-world data sets (relativistic heavy-ion collision hydrodynamics and weather observations with statistical principal component analysis) as well as the popular car data set. We present relationships discovered in the data sets using these methods.

show abstract

Section: Scatter Plots Overlay (Spo)mentioning

confidence: 85%

Section: Scatter Plots Overlay (Spo)mentioning

confidence: 99%

Increasing the perceptual salience of relationships in parallel coordinate plots

Harter

Alabi

et al. 2012

Visualization and Data Analysis 2012

View full text Add to dashboard Cite

show abstract

“…To improve the performance of correlation analysis, several hybrid methods can combine parallel coordinates with other visual mappings. For example, small scatter plots can be placed on top of parallel coordinates [91] or embedded between adjacent axes of parallel coordinates [92]. Claessen et al [93] designed FLINAPlots, which aggregates scatter plots and histograms onto parallel To improve the performance of correlation analysis, several hybrid methods can combine parallel coordinates with other visual mappings.…”

Section: Diversity Of Researchmentioning

confidence: 99%

“…Claessen et al [93] designed FLINAPlots, which aggregates scatter plots and histograms onto parallel To improve the performance of correlation analysis, several hybrid methods can combine parallel coordinates with other visual mappings. For example, small scatter plots can be placed on top of parallel coordinates [91] or embedded between adjacent axes of parallel coordinates [92]. Claessen et al [93] designed FLINAPlots, which aggregates scatter plots and histograms onto parallel coordinates and enables analysis with various visualizations from multiple perspectives (see Figure 36a).…”

Section: Diversity Of Researchmentioning

confidence: 99%

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

Chen

et al. 2019

IJGI

View full text Add to dashboard Cite

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...

show abstract

“…Besides researched challenges such as axis re-ordering, one axis merely corresponds to one attribute making it a difficult task to compare different attribute combinations with each other. Recent work proposes to augment PCPs with Scatterplots [56]. Scatterplots are a prominent means to make subspaces visually accessible and can enhance the analysis of subspace patterns.…”

Section: Introductionmentioning

confidence: 99%