At a Glance: Pixel Approximate Entropy as a Measure of Line Chart Complexity

Ryan, Gabriel; Mosca, Ab; Chang, Remco; Wu, Eugene

doi:10.1109/tvcg.2018.2865264

Cited by 31 publications

(24 citation statements)

References 64 publications

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“…While the presented algorithms were initially developed for clinical research, they later spread to diverse fields such as neuroengineering [ 35 ], visual pattern recognition [ 36 ], neuroinformatics [ 37 ], ecology [ 38 ], psychiatry [ 39 ], electronics [ 40 ], voice recognition [ 41 ] or finance [ 42 ]. As their application is increasing lately, in this paper we have presented a clear path for understanding the logic behind ApEn and SampEn to help researchers understand their foundations and correct application.…”

Section: Discussionmentioning

confidence: 99%

Approximate Entropy and Sample Entropy: A Comprehensive Tutorial

Delgado-Bonal

Marshak

2019

Entropy

419

252

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Approximate Entropy and Sample Entropy are two algorithms for determining the regularity of series of data based on the existence of patterns. Despite their similarities, the theoretical ideas behind those techniques are different but usually ignored. This paper aims to be a complete guideline of the theory and application of the algorithms, intended to explain their characteristics in detail to researchers from different fields. While initially developed for physiological applications, both algorithms have been used in other fields such as medicine, telecommunications, economics or Earth sciences. In this paper, we explain the theoretical aspects involving Information Theory and Chaos Theory, provide simple source codes for their computation, and illustrate the techniques with a step by step example of how to use the algorithms properly. This paper is not intended to be an exhaustive review of all previous applications of the algorithms but rather a comprehensive tutorial where no previous knowledge is required to understand the methodology.

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Section: Discussionmentioning

confidence: 99%

Approximate Entropy and Sample Entropy: A Comprehensive Tutorial

Delgado-Bonal

Marshak

2019

Entropy

419

252

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“…This enables us to optimize overall visibility and reduce visual clutter by displaying curves with more variability in the back and less fluctuating curves in front. However, many other properties such as visual complexity, as described by Ryan et al [RMCW18], geometric features derived from families of curves by Konyha et al [KLM * 12], or statistical features could be used instead.…”

Section: Feature Encodingmentioning

confidence: 99%

Line Weaver: Importance‐Driven Order Enhanced Rendering of Dense Line Charts

Trautner

Brückner

2021

Computer Graphics Forum

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Line charts are an effective and widely used technique for visualizing series of ordered two‐dimensional data points. The relationship between consecutive points is indicated by connecting line segments, revealing potential trends or clusters in the underlying data. However, when dealing with an increasing number of lines, the render order substantially influences the resulting visualization. Rendering transparent lines can help but unfortunately the blending order is currently either ignored or naively used, for example, assuming it is implicitly given by the order in which the data was saved in a file. Due to the non‐commutativity of classic alpha blending, this results in contradicting visualizations of the same underlying data set, so‐called “hallucinators”. In this paper, we therefore present line weaver, a novel visualization technique for dense line charts. Using an importance function, we developed an approach that correctly considers the blending order independently of the render order and without any prior sorting of the data. We allow for importance functions which are either explicitly given or implicitly derived from the geometric properties of the data if no external data is available. The importance can then be applied globally to entire lines, or locally per pixel which simultaneously supports various types of user interaction. Finally, we discuss the potential of our contribution based on different synthetic and real‐world data sets where classic or naive approaches would fail.

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“…Concurrently considering multiple factors and their conflicts and interactions would likely be difficult for designers, such that an automated model might be important for weighting their complexities. We were inspired by the recent modeling work [20,31,40,43,75] and appealed to psychophysical laws [31,52,69,82], entropy [15,68,70,71], perceptual proxies [39,58], serial-position and ordering effects [36], visual memory (e.g., [3,10,54]), neighborhood effects [87], and distractors [77]. We are capable of providing (very) preliminary recommendations given the inputs (see Fig.…”

Section: Implications and Future Directionsmentioning

confidence: 99%

Rethinking the ranks of visual channels

McColeman¹,

Yang²,

Brady³

et al. 2021

Preprint

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Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is surprisingly little existing work that tests this assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values.To simulate the information extracted from a glance at a visualization, we instead asked participants to immediately reproduce a set of values from memory after they were shown the visualization. These values could be shown in a bar graph (position (bar)), line graph (position (line)), heat map (luminance), bubble chart (area), misaligned bar graph (length), or `wind map' (angle). With a Bayesian multilevel modeling approach, we show how the rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision, and error measures. The ranking did not hold, even for reproductions of only 2 marks, and the new probabilistic ranking was highly inconsistent for reproductions of different numbers of marks. Other factors besides channel choice had an order of magnitude more influence on performance, such as the number of values in the series (e.g., more marks led to larger errors), or the value of each mark (e.g., small values were systematically overestimated). Every visual channel was worse for displays with 8 marks than 4, consistent with established limits on visual memory. These results point to the need for a body of empirical studies that move beyond two-value ratio judgments as a baseline for reliably ranking the quality of a visual channel, including testing new tasks (detection of trends or motifs), timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands)

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At a Glance: Pixel Approximate Entropy as a Measure of Line Chart Complexity

Cited by 31 publications

References 64 publications

Approximate Entropy and Sample Entropy: A Comprehensive Tutorial

Approximate Entropy and Sample Entropy: A Comprehensive Tutorial

Line Weaver: Importance‐Driven Order Enhanced Rendering of Dense Line Charts

Rethinking the ranks of visual channels

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