TY - JOUR
T1 - A visual analytics approach to understanding spatiotemporal hotspots
AU - MacIejewski, Ross
AU - Rudolph, Stephen
AU - Hafen, Ryan
AU - Abusalah, Ahmad
AU - Yakout, Mohamed
AU - Ouzzani, Mourad
AU - Cleveland, William S.
AU - Grannis, Shaun J.
AU - Ebert, David S.
N1 - 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.
PY - 2010/3
Y1 - 2010/3
N2 - 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.
AB - 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.
KW - Geovisualization
KW - Hypothesis exploration.
KW - Kernel density estimation
KW - Syndromic surveillance
UR - http://www.scopus.com/inward/record.url?scp=76849103505&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=76849103505&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2009.100
DO - 10.1109/TVCG.2009.100
M3 - Article
C2 - 20075482
AN - SCOPUS:76849103505
SN - 1077-2626
VL - 16
SP - 205
EP - 220
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 2
M1 - 5226628
ER -