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Visual Analytics for Characterizing Mobility Aspects of Urban Context

 
: Andrienko, G.; Andrienko, N.; Patterson, F.; Chen, S.; Weibel, R.; Huang, H.; Doulkeridis, C.; Georgiou, H.; Pelekis, N.; Theodoridis, Y.; Nanni, M.; Longhi, L.; Koumparos, A.; Yasar, A.; Kureshi, I.

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Volltext ()

Shi, W.:
Urban Informatics
Cham: Springer Nature, 2021 (The Urban Book Series)
ISBN: 978-981-15-8982-9 (Print)
ISBN: 978-981-15-8983-6 (Online)
ISBN: 978-981-15-8984-3
ISBN: 978-981-15-8985-0
S.727-755
Englisch
Aufsatz in Buch, Elektronische Publikation
Fraunhofer IAIS ()

Abstract
Visual analytics science develops principles and methods for efficient humanâcomputer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement dat a, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.

: http://publica.fraunhofer.de/dokumente/N-637745.html