Author(s):
Silva, Ricardo Almeida ; Pires, João Moura ; Datia, Nuno ; Santos, Maribel Yasmina ; Martins, Bruno ; Birra, Fernando
Date: 2019
Persistent ID: http://hdl.handle.net/10362/99847
Origin: Repositório Institucional da UNL
Project/scholarship:
info:eu-repo/grantAgreement/FCT/5876/UID%2FCEC%2F04516%2F2013/PT;
Subject(s): Data visualization; Multiple levels of detail; Spatiotemporal patterns; Visual analytics; Software; Media Technology; Hardware and Architecture; Computer Networks and Communications; SDG 3 - Good Health and Well-being; SDG 16 - Peace, Justice and Strong Institutions; Data visualization; Data visualization; Multiple levels of detail; Multiple levels of detail; Spatiotemporal patterns; Spatiotemporal patterns; Visual analytics; Visual analytics; Software; Software; Media Technology; Media Technology; Hardware and Architecture; Hardware and Architecture; Computer Networks and Communications; Computer Networks and Communications; SDG 3 - Good Health and Well-being; SDG 3 - Good Health and Well-being; SDG 16 - Peace, Justice and Strong Institutions; SDG 16 - Peace, Justice and Strong Institutions
Description
UID/CEC/00319/2019 UID/CEC/50021/2019
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.
