At a Glance: Pixel Approximate Entropy as a Measure of Line Chart Complexity
| Autor: | Remco Chang, Abigail Mosca, Eugene Wu, Gabriel Ryan |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
animal structures Line chart Pixel genetic structures Computer science Entropy (statistical thermodynamics) Computer Science - Human-Computer Interaction computer.software_genre Computer Graphics and Computer-Aided Design Approximate entropy Human-Computer Interaction (cs.HC) Chart Signal Processing Entropy (information theory) Computer Vision and Pattern Recognition Data mining Time series Entropy (energy dispersal) computer Entropy (arrow of time) Software Entropy (order and disorder) |
| Zdroj: | IEEE transactions on visualization and computer graphics. |
| ISSN: | 1941-0506 |
| Popis: | When inspecting information visualizations under time critical settings, such as emergency response or monitoring the heart rate in a surgery room, the user only has a small amount of time to view the visualization “at a glance”. In these settings, it is important to provide a quantitative measure of the visualization to understand whether or not the visualization is too “complex” to accurately judge at a glance. This paper proposes Pixel Approximate Entropy (PAE), which adapts the approximate entropy statistical measure commonly used to quantify regularity and unpredictability in time-series data, as a measure of visual complexity for line charts. We show that PAE is correlated with user-perceived chart complexity, and that increased chart PAE correlates with reduced judgement accuracy. ‘We also find that the correlation between PAE values and participants’ judgment increases when the user has less time to examine the line charts. |
| Databáze: | OpenAIRE |
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