A correlative analysis process in a visual analytics environment

Abish Malik, Ross Maciejewski, Niklas Elmqvist, Yun Jang, David S. Ebert, Whitney Huang

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

27 Scopus citations

Abstract

Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearson's product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them.

Original languageEnglish (US)
Title of host publicationIEEE Conference on Visual Analytics Science and Technology 2012, VAST 2012 - Proceedings
Pages33-42
Number of pages10
DOIs
StatePublished - Dec 1 2012
Event2012 IEEE Conference on Visual Analytics Science and Technology, VAST 2012 - Seattle, WA, United States
Duration: Oct 14 2012Oct 19 2012

Publication series

NameIEEE Conference on Visual Analytics Science and Technology 2012, VAST 2012 - Proceedings

Other

Other2012 IEEE Conference on Visual Analytics Science and Technology, VAST 2012
CountryUnited States
CitySeattle, WA
Period10/14/1210/19/12

Keywords

  • Visual analytics
  • correlative analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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