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Informatics 2014, 1(1), 100-125; doi:10.3390/informatics1010100

Analyzing Spatiotemporal Anomalies through Interactive Visualization

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.
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)
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Abstract

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.
Keywords: visualization, spatiotemporal data analysis, anomaly detection visualization, spatiotemporal data analysis, anomaly detection
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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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