Visual analytics for spatiotemporal events

• Published in: Multimedia tools and applications Vol. 78; no. 23; pp. 32805 - 32847
• Main Authors: Silva, Ricardo Almeida, Pires, João Moura, Datia, Nuno, Santos, Maribel Yasmina, Martins, Bruno, Birra, Fernando
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2019; Springer Nature B.V
• Subjects: Accidents; Analytics; Computer Communication Networks; Computer Science; Data analysis; Data Structures and Information Theory; Datasets; Electronic devices; Forest fires; Geographical locations; Infectious diseases; Mathematical analysis; Multimedia; Multimedia Information Systems; Murders & murder attempts; Perception; Perceptions; Spacetime; Special Purpose and Application-Based Systems; Visualization; Multiple levels of detail; Visual analytics; Spatiotemporal patterns; Data visualization
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-08012-2

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.