Spatial Reasoning and Data Displays

VanderPlas, Susan; Hofmann, Heike

doi:10.1109/tvcg.2015.2469125

Cited by 25 publications

(23 citation statements)

References 30 publications

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“…Parallel coordinates [80][81][82][83][84][85] and scatterplot [86][87][88] matrix are two of the extensively used spatial mapping techniques. Each axis in parallel coordinates represents a single attribute.…”

Section: Spatial Mappingmentioning

confidence: 99%

A survey of network anomaly visualization

Zhang

Wang

et al. 2017

Sci. China Inf. Sci.

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Network anomaly analysis is an emerging subtopic of network security. Network anomaly refers to the unusual behavior of network devices or suspicious network status. A number of intelligent visual tools are developed to enhance the ability of network security analysts in understanding the original data, ultimately solving network security problems. This paper surveys current progress and trends in network anomaly visualization. By providing an overview of network anomaly data, visualization tasks, and applications, we further elaborate on existing methods to depict various data features of network alerts, anomalous traffic, and attack patterns data. Directions for future studies are outlined at the end of this paper.

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Section: Spatial Mappingmentioning

confidence: 99%

A survey of network anomaly visualization

Zhang

Wang

et al. 2017

Sci. China Inf. Sci.

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“…Wickham et al's line-up protocol -where an analyst must identify a 'real' dataset from a set of decoys constructed under a null hypothesis -is intended to confer statistical credibility to any visual claim of discriminating structure. However, although there is some empirical support for line-up tests as a generalisable classification task [18], there are few examples demonstrating or providing empirical evidence for their use when applied to choropleth maps. The mismatch between the statistical parameters describing a spatial pattern and graphical perception of those parameters is not well understood.…”

mentioning

confidence: 99%

Map LineUps: Effects of spatial structure on graphical inference

Beecham

Dykes

Meulemans

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-Fundamental to the effective use of visualization as an analytic and descriptive tool is the assurance that presenting data visually provides the capability of making inferences from what we see. This paper explores two related approaches to quantifying the confidence we may have in making visual inferences from mapped geospatial data. We adapt Wickham et al.'s 'Visual Line-up' method as a direct analogy with Null Hypothesis Significance Testing (NHST) and propose a new approach for generating more credible spatial null hypotheses. Rather than using as a spatial null hypothesis the unrealistic assumption of complete spatial randomness, we propose spatially autocorrelated simulations as alternative nulls. We conduct a set of crowdsourced experiments (n = 361) to determine the just noticeable difference (JND) between pairs of choropleth maps of geographic units controlling for spatial autocorrelation (Moran's I statistic) and geometric configuration (variance in spatial unit area). Results indicate that people's abilities to perceive differences in spatial autocorrelation vary with baseline autocorrelation structure and the geometric configuration of geographic units. These results allow us, for the first time, to construct a visual equivalent of statistical power for geospatial data. Our JND results add to those provided in recent years by Klippel et al. (2011), Harrison et al. (2014 and Kay & Heer (2015) for correlation visualization. Importantly, they provide an empirical basis for an improved construction of visual line-ups for maps and the development of theory to inform geospatial tests of graphical inference.

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“…For example, Toker et al [TCSC13] and Steichen et al [SCC13] found that perceptual speed, visual working memory , and verbal working memory can influence how people deploy attention to visual elements within grouped bar charts. VanderPlas and Hofmann [VH15] used histograms and dotplots among other charts, and found that spatial ability correlated with performance when identifying which plot was “the most different” in a collection.…”

Section: Discussion Of Findingsmentioning

confidence: 99%

“…The data visualization community has extrapolated the effects of cognitive abilities on the users’ performances and experiences with visualizations from foundational research in psychology. We find literature related to spatial ability [CC97, Che00, VST05, ZK09, FTES13, OPH*15, VH15], perceptual speed [CM08, TCCH12, TCSC13, SCC13, CCH*14a, CCH*14b, SCC14], visual working memory [CM08, DMBM09, APM*11, TCCH12, SCC13, TCSC13, CCH*14b, SCC14], verbal working memory [TCCH12, TCSC13, SCC13, CCH*14a, SCC14], and associative memory [Che00].…”

Section: Cognitive Abilitiesmentioning

confidence: 99%

Survey on Individual Differences in Visualization

Liu

Crouser

Ottley

2020

Computer Graphics Forum

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Developments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people's understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deficiency of having one‐size‐fits‐all visualization interfaces, as well as the significance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence‐based reviews, and spur future inquiry.

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Spatial Reasoning and Data Displays

Cited by 25 publications

References 30 publications

A survey of network anomaly visualization

A survey of network anomaly visualization

Map LineUps: Effects of spatial structure on graphical inference

Survey on Individual Differences in Visualization

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