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Open AccessArticle

Analyzing Spatiotemporal Anomalies through Interactive Visualization

by 1, 1,*, 2 and 3
1
Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA
2
State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China
3
IBM Research, Beijing, 100193, China
*
Author to whom correspondence should be addressed.
Informatics 2014, 1(1), 100-125; https://doi.org/10.3390/informatics1010100
Received: 24 February 2014 / Revised: 23 April 2014 / Accepted: 21 May 2014 / Published: 3 June 2014
(This article belongs to the Special Issue Interactive Visualizations: Design, Technologies, and Applications)
As we move into the big data era, data grows not just in size, but also in complexity, containing a rich set of attributes, including location and time information, such as data from mobile devices (e.g., smart phones), natural disasters (e.g., earthquake and hurricane), epidemic spread, etc. We are motivated by the rising challenge and build a visualization tool for exploring generic spatiotemporal data, i.e., records containing time location information and numeric attribute values. Since the values often evolve over time and across geographic regions, we are particularly interested in detecting and analyzing the anomalous changes over time/space. Our analytic tool is based on geographic information system and is combined with spatiotemporal data mining algorithms, as well as various data visualization techniques, such as anomaly grids and anomaly bars superimposed on the map. We study how effective the tool may guide users to find potential anomalies through demonstrating and evaluating over publicly available spatiotemporal datasets. The tool for spatiotemporal anomaly analysis and visualization is useful in many domains, such as security investigation and monitoring, situation awareness, etc. View Full-Text
Keywords: visualization, spatiotemporal data analysis, anomaly detection visualization, spatiotemporal data analysis, anomaly detection
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Zhang, T.; Liao, Q.; Shi, L.; Dong, W. Analyzing Spatiotemporal Anomalies through Interactive Visualization. Informatics 2014, 1, 100-125.

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