CorrelatedMultiples: Spatially Coherent Small Multiples With Constrained Multi‐Dimensional Scaling
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Date
2018
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© 2018 The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Displaying small multiples is a popular method for visually summarizing and comparing multiple facets of a complex data set. If the correlations between the data are not considered when displaying the multiples, searching and comparing specific items become more difficult since a sequential scan of the display is often required. To address this issue, we introduce CorrelatedMultiples, a spatially coherent visualization based on small multiples, where the items are placed so that the distances reflect their dissimilarities. We propose a constrained multi‐dimensional scaling (CMDS) solver that preserves spatial proximity while forcing the items to remain within a fixed region. We evaluate the effectiveness of our approach by comparing CMDS with other competing methods through a controlled user study and a quantitative study, and demonstrate the usefulness of CorrelatedMultiples for visual search and comparison in three real‐world case studies.
Description
@article{10.1111:cgf.12526,
journal = {Computer Graphics Forum},
title = {{CorrelatedMultiples: Spatially Coherent Small Multiples With Constrained Multi‐Dimensional Scaling}},
author = {Liu, Xiaotong and Hu, Yifan and North, Stephen and Shen, Han‐Wei},
year = {2018},
publisher = {© 2018 The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.12526}
}