2018/11/07 by Gabriel Ryan, Ab Mosca, Abigail Mosca +6 · 3 citations
Computer Science · Economics, Econometrics and Finance · Mathematics · Neuroscience · Physics and Astronomy · #Artificial intelligence #Chart #Complex Systems and Time Series Analysis #Computer science #Data mining #Entropy (arrow of time) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Judgement #Mathematics #Measure (data warehouse) #Neural dynamics and brain function #Statistical Mechanics and Entropy #Statistics #Visualization #cs.HC
paper · pdf · doi:10.48550/arxiv.1811.03180
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/11/07 · openalex publication_date 2018/11/07 · arxiv updated 2018/11/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.