Issue No. 02 - March/April (2010 vol. 16)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.100
Ross Maciejewski , Purdue University, West Lafayette
Stephen Rudolph , Purdue University Regional Visualization and Analytics Center, Scottsdale
Ryan Hafen , Purdue University, West Lafayette
Ahmad M. Abusalah , Purdue University, Saint Paul
Mohamed Yakout , Purdue University, West Lafayette
Mourad Ouzzani , Purdue University, West Lafayette
William S. Cleveland , Purdue Univeristy, West Lafayette
Shaun J. Grannis , Regenstrief Institute, Inc. and Indiana University School of Medicine, Indianapolis
David S. Ebert , Purdue University, West Lafayette
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.
Geovisualization, kernel density estimation, syndromic surveillance, hypothesis exploration.
A. M. Abusalah et al., "A Visual Analytics Approach to Understanding Spatiotemporal Hotspots," in IEEE Transactions on Visualization & Computer Graphics, vol. 16, no. , pp. 205-220, 2009.