Paper Info
Title
Visual analytics for spatiotemporal events.
Abstract
Crimes, forest fires, accidents, infectious diseases, or human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, as it can highlight useful patterns or insights and enhance the user’ perception of phenomena. For this reason, modeling phenomena at different LoDs is needed to increase the analytical value of the data, as there is no exclusive LOD at which the data can be analyzed. Current practices work mainly on a single LoD of the phenomena, driven by the analysts’ perception, ignoring that identifying the suitable LoDs is a key issue for pointing relevant patterns. This article presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs do interesting patterns emerge, or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets supported the evaluation and validation of VAST, suggesting LoDs with different interesting spatiotemporal patterns and pointing the type of expected patterns.
Year
DOI
Venue
2019
10.1007/s11042-019-08012-2
Multimedia Tools and Applications
Keywords
Field
DocType
Data visualization, Spatiotemporal patterns, Multiple levels of detail, Visual analytics
Data science,Data visualization,Pattern recognition,Computer science,Visual analytics,Mobile device,Artificial intelligence,Perception
Journal
Volume
Issue
ISSN
78
23
1380-7501
Citations 
PageRank 
References 
1
0.35
0
Authors
6
Name
Order
Citations
PageRank
Ricardo Almeida Silva131.44
João Moura-Pires2227.40
Nuno Datia341.75
Maribel Yasmina Santos414635.41
Bruno Martins544134.58
Fernando Birra6173.80