Users are often interested in exploring ranks over time data to compare the performance or ranking of multiple observations with respect to each other.However,predominant visualization techniques suffer from a high co...Users are often interested in exploring ranks over time data to compare the performance or ranking of multiple observations with respect to each other.However,predominant visualization techniques suffer from a high cognitive load due to visual clutter.We propose Colorslope,a hybrid of Tufte’s slope graph and temporal heatmap,to depict ranks over time in one graph while maintaining an overview and details with scalability.Colorslope combines both canonical visualization methods’complementary benefits:depicting overall trends and enabling the estimation of detailed values.We evaluated the efficacy and effectiveness of Colorslope by comparing it with a standard bump chart and temporal heatmap on various data sizes.We conclude that Colorslope contributes by(1)allowing users to identify extremes of the data and rate of change effectively in a relatively large number of series;(2)allowing the visualization to have better scalability in a larger amount of data(e.g.,30∼50)than a bump chart;and(3)allowing users to gain a better estimate of data values than a heatmap.For a certain size of ranks over time data,Colorslope provides an alternative solution to visualize multiple time series simultaneously that provides both an overview and a certain level of detail.展开更多
基金supported by the Department of Computer Graphics Technology at Purdue University and the Purdue Intelligent Visualization and Interaction Lab(PIVIL).
文摘Users are often interested in exploring ranks over time data to compare the performance or ranking of multiple observations with respect to each other.However,predominant visualization techniques suffer from a high cognitive load due to visual clutter.We propose Colorslope,a hybrid of Tufte’s slope graph and temporal heatmap,to depict ranks over time in one graph while maintaining an overview and details with scalability.Colorslope combines both canonical visualization methods’complementary benefits:depicting overall trends and enabling the estimation of detailed values.We evaluated the efficacy and effectiveness of Colorslope by comparing it with a standard bump chart and temporal heatmap on various data sizes.We conclude that Colorslope contributes by(1)allowing users to identify extremes of the data and rate of change effectively in a relatively large number of series;(2)allowing the visualization to have better scalability in a larger amount of data(e.g.,30∼50)than a bump chart;and(3)allowing users to gain a better estimate of data values than a heatmap.For a certain size of ranks over time data,Colorslope provides an alternative solution to visualize multiple time series simultaneously that provides both an overview and a certain level of detail.
