Flow-based scatterplots for sensitivity analysis

Yu Hsuan Chan, Carlos D. Correa, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Scopus citations


Visualization of multi-dimensional data is challenging due to the number of complex correlations that may be present in the data but that are difficult to be visually identified. One of the main causes for this problem is the inherent loss of information that occurs when high-dimensional data is projected into 2D or 3D. Although 2D scatterplots are ubiquitous due to their simplicity and familiarity, there are not a lot of variations on their basic metaphor. In this paper, we present a new way of visualizing multidimensional data using scatterplots. We extend 2D scatterplots using sensitivity coefficients to highlight local variation of one variable with respect to another. When applied to a scatterplot, these sensitivities can be understood as velocities, and the resulting visualization resembles a flow field. We also present a number of operations, based on flow-field analysis, that help users navigate, select and cluster points in an efficient manner. We show the flexibility and generality of this approach using a number of multidimensional data sets across different domains.

Original languageEnglish (US)
Title of host publicationVAST 10 - IEEE Conference on Visual Analytics Science and Technology 2010, Proceedings
Number of pages8
StatePublished - Dec 1 2010
Event1st IEEE Conference on Visual Analytics Science and Technology, VAST 10 - Salt Lake City, UT, United States
Duration: Oct 24 2010Oct 29 2010


Other1st IEEE Conference on Visual Analytics Science and Technology, VAST 10
Country/TerritoryUnited States
CitySalt Lake City, UT


  • Data transformations
  • Model fitting
  • Principal component analysis
  • Uncertainty

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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