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 journalArticlepeer-review

106 Scopus citations


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
Issue number2
StatePublished - Mar 2010

Bibliographical note

Funding Information:
The authors would like to thank the Purdue University Student Health Center, the Indiana State Department of Health, and the Police Department of West Lafayette, Indiana, for providing the data. This work has been funded by the US Department of Homeland Security Regional Visualization and Analytics Center (RVAC) Center of Excellence and the US National Science Foundation (NSF) under Grants 0811954, 0328984, and 0121288.


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


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