Influence of aspect ratio α on the perception of trends and cluster separability for the Sunspot dataset (a, b, c) and the Contraceptive Method Choice (CMC) dataset (d, e). (a) Line chart with the default aspect ratio, and (b) the aspect ratio selected by an existing method (RV), where both methods obscure the trends over the cycles. (c) The aspect ratio selected by our method (imgRV), where detailed cycle oscillations are revealed. (d) Scatter plot of three data clusters with the default aspect ratio, where the visual separation between the two clusters on the left is unclear. (e) The aspect ratio selected by our method shows clearer cluster structures.
Material
Abstract
There are several aspect ratio selection methods for function plots and line charts, but only few can handle general, discrete diagrams such as 2D scatter plots. However, these methods either lack a perceptual foundation or heavily rely on intermediate isoline representations, which depend on choosing the right isovalues and are time-consuming to compute. This paper introduces a general image-based approach for selecting aspect ratios for a wide variety of 2D diagrams, ranging from scatter plots and density function plots to line charts. Our approach is derived from Federer's co-area formula and a line integral representation that enable us to directly construct image-based versions of existing selection methods using density fields. In contrast to previous methods, our approach bypasses isoline computation, so it is faster to compute, while following the perceptual foundation to select aspect ratios. Furthermore, this approach is complemented by an anisotropic kernel density estimation to construct density fields, allowing us to more faithfully characterize data patterns, such as the subgroups in scatterplots or dense regions in time series. We demonstrate the effectiveness of our approach by quantitatively comparing to previous methods and revisiting a prior user study. Finally, we present extensions for ROI banking, multi-scale banking, and the application to image data.
BibTeX
@article{Wang2018ImageBasedAspect,
author = {Y. Wang and Z. Wang and C. Fu and H. Schmauder and O. Deussen and D. Weiskopf},
journal = {IEEE Transactions on Visualization and Computer Graphics},
title = {Image-Based Aspect Ratio Selection},
volume = {to appear},
year = {2018},
url = {http://graphics.uni-konstanz.de/publikationen/Wang2018ImageBasedAspect},
}