Interactive selection of multivariate features in large spatiotemporal data

Jingyuan Wang, Robert Sisneros, Jian Huang

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


Selecting meaningful features is central in the analysis of scientific data. Today's multivariate scientific datasets are often large and complex making it difficult to define general features of interest significant to scientific applications. To address this problem, we propose three general, spatiotemporal metrics to quantify the significant properties of data features-concentration, continuity and co-occurrence, named collectively as CO3. We implemented an interactive visualization system to investigate complex multivariate time-varying data from satellite remote sensing with great spatial resolutions, as well as from real-time continental-scale power grid monitoring with great temporal resolutions. The system integrates CO3 metrics with an elegant multi-space user interaction tool to provide various forms of quantitative user feedback. Through these, the system supports an iterative user-driven analysis process. Our findings demonstrate that the CO3 metrics are useful for simplifying the problem space and revealing potential unknown possibilities of scientific discoveries by assisting users to effectively select significant features and groups of features for visualization and analysis. Users can then comprehend the problem better and design future studies using newly discovered scientific hypotheses.

Original languageEnglish (US)
Title of host publicationIEEE Symposium on Pacific Visualization 2013, PacificVis 2013 - Proceedings
Number of pages8
StatePublished - 2013
Event6th IEEE Symposium on Pacific Visualization, PacificVis 2013 - Sydney, NSW, Australia
Duration: Feb 26 2013Mar 1 2013

Publication series

NameIEEE Pacific Visualization Symposium
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773


Other6th IEEE Symposium on Pacific Visualization, PacificVis 2013
CitySydney, NSW


  • Interactive Feature Selection
  • Large Data
  • Metrics
  • Multivariate

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software


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