A visual analytics approach to understanding spatiotemporal hotspots

Ross Maciejewski, Stephen Rudolph, Ryan Hafen, Ahmad Abusalah, Mohamed Yakout, Mourad Ouzzani, William S. Cleveland, Shaun J. Grannis, David S. Ebert

Research output: Contribution to journalArticle

69 Citations (Scopus)

Abstract

As data sources become larger and more complex, the ability to effectively explore and analyze patterns among varying sources becomes a critical bottleneck in analytic reasoning. Incoming data contain multiple variables, high signal-to-noise ratio, and a degree of uncertainty, all of which hinder exploration, hypothesis generation/exploration, and decision making. To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data aberrations or hotspots. In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal data sets. Our system allows users to search for hotspots in both space and time, combining linked views and interactive filtering to provide users with contextual information about their data and allow the user to develop and explore their hypotheses. Statistical data models and alert detection algorithms are provided to help draw user attention to critical areas. Demographic filtering can then be further applied as hypotheses generated become fine tuned. This paper demonstrates the use of such tools on multiple geospatiotemporal data sets.

Original languageEnglish (US)
Article number5226628
Pages (from-to)205-220
Number of pages16
JournalIEEE Transactions on Visualization and Computer Graphics
Volume16
Issue number2
DOIs
StatePublished - Mar 2010
Externally publishedYes

Fingerprint

Aberrations
Data structures
Signal to noise ratio
Decision making
Uncertainty

Keywords

  • Geovisualization
  • Hypothesis exploration.
  • Kernel density estimation
  • Syndromic surveillance

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Maciejewski, R., Rudolph, S., Hafen, R., Abusalah, A., Yakout, M., Ouzzani, M., ... Ebert, D. S. (2010). A visual analytics approach to understanding spatiotemporal hotspots. IEEE Transactions on Visualization and Computer Graphics, 16(2), 205-220. [5226628]. https://doi.org/10.1109/TVCG.2009.100

A visual analytics approach to understanding spatiotemporal hotspots. / Maciejewski, Ross; Rudolph, Stephen; Hafen, Ryan; Abusalah, Ahmad; Yakout, Mohamed; Ouzzani, Mourad; Cleveland, William S.; Grannis, Shaun J.; Ebert, David S.

In: IEEE Transactions on Visualization and Computer Graphics, Vol. 16, No. 2, 5226628, 03.2010, p. 205-220.

Research output: Contribution to journalArticle

Maciejewski, R, Rudolph, S, Hafen, R, Abusalah, A, Yakout, M, Ouzzani, M, Cleveland, WS, Grannis, SJ & Ebert, DS 2010, 'A visual analytics approach to understanding spatiotemporal hotspots', IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 2, 5226628, pp. 205-220. https://doi.org/10.1109/TVCG.2009.100
Maciejewski, Ross ; Rudolph, Stephen ; Hafen, Ryan ; Abusalah, Ahmad ; Yakout, Mohamed ; Ouzzani, Mourad ; Cleveland, William S. ; Grannis, Shaun J. ; Ebert, David S. / A visual analytics approach to understanding spatiotemporal hotspots. In: IEEE Transactions on Visualization and Computer Graphics. 2010 ; Vol. 16, No. 2. pp. 205-220.
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