Pyramid-based Scatterplots Sampling for Progressive and Streaming Data Visualization

Xin Chen, Jian Zhang, Chi-Wing Fu, Jean-Daniel Fekete, Yunhai Wang

View presentation: 2021-10-28T16:15:00Z GMT-0600 Change your timezone on the schedule page
2021-10-28T16:15:00Z
Exemplar figure, described by caption below
Different sampling methods for presenting the “New York City TLC Trip Record” data with 2M data points, which are partitioned into chunks, each of 100k data points. (a) The opaque scatterplot is overlaid on the New York map and rendered as (b) a transparent density map, where some major features are highlighted. (c,d,e) The top and bottom rows show the results of different sampling methods in the 9th and 10th frames, respectively, where each result has around 1K points sampled from the original data chunk. It shows our method can preserve relative density and outliers while maintaining temporal coherence.
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Direct link to video on YouTube: https://youtu.be/_dj2w4nAqfs

Abstract

We present a pyramid-based scatterplot sampling technique to avoid overplotting and enable progressive and streaming visualization of large data. Our technique is based on a multiresolution pyramid-based decomposition of the underlying density map and makes use of the density values in the pyramid to guide the sampling at each scale for preserving the relative data densities and outliers. We show that our technique is competitive in quality with state-of-the-art methods and runs faster by about an order of magnitude. Also, we have adapted it to deliver progressive and streaming data visualization by processing the data in chunks and updating the scatterplot areas with visible changes in the density map. A quantitative evaluation shows that our approach generates stable and faithful progressive samples that are comparable to the state-of-the-art method in preserving relative densities and superior to it in keeping outliers and stability when switching frames. We present two case studies that demonstrate the effectiveness of our approach for exploring large data.