Liu, XiaotongHu, YifanNorth, StephenShen, Han‐WeiChen, Min and Benes, Bedrich2018-04-052018-04-0520181467-8659https://doi.org/10.1111/cgf.12526https://diglib.eg.org:443/handle/10.1111/cgf12526Displaying 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.information visualizationsmall multiplesmulti‐dimensional scalingI.3.3 [Computer Graphics]: Picture/Image Generation‐Display algorithms10.1111/cgf.125267-18