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Given a scatterplot with tens of thousands of points or even more, a naturalquestion is which sampling method should be used to create a small but "good"scatterplot for a better abstraction. We present the results of a user studythat investigates the influence of different sampling strategies on multi-classscatterplots. The main goal of this study is to understand the capability ofsampling methods in preserving the density, outliers, and overall shape of ascatterplot. To this end, we comprehensively review the literature and selectseven typical sampling strategies as well as eight representative datasets. Wethen design four experiments to understand the performance of differentstrategies in maintaining: 1) region density; 2) class density; 3) outliers;and 4) overall shape in the sampling results. The results show that: 1) randomsampling is preferred for preserving region density; 2) blue noise sampling andrandom sampling have comparable performance with the three multi-class samplingstrategies in preserving class density; 3) outlier biased density basedsampling, recursive subdivision based sampling, and blue noise sampling performthe best in keeping outliers; and 4) blue noise sampling outperforms the othersin maintaining the overall shape of a scatterplot.

Evaluation of Sampling Methods for Scatterplots