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dc.contributor.supervisor Irani, Pourang (Computer Science) en_US
dc.contributor.author Hossain, Mohammad Zahid
dc.date.accessioned 2014-05-26T13:08:44Z
dc.date.available 2014-05-26T13:08:44Z
dc.date.issued 2014-05-26
dc.identifier.uri http://hdl.handle.net/1993/23591
dc.description.abstract Visual analytics of large amounts of spatio-temporal data is challenging due to the overlap and clutter from movements of multiple objects. A common approach for analyzing such data is to consider how groups of items cluster and move together in space and time. However, most methods for showing Spatio-temporal Cluster (STC) properties, concentrate on a few dimensions of the cluster (e.g. the cluster movement direction or cluster density) and many other properties are not represented. Furthermore, while representing multiple attributes of clusters in a single view existing methods fail to preserve the original shape of the cluster or distort the actual spatial covering of the dataset. In this thesis, I propose a simple yet effective visualization, FlockViz, for showing multiple STC data dimensions in a single view by preserving the original cluster shape. To evaluate this method I develop a framework for categorizing the wide range of tasks involved in analyzing STCs. I conclude this work through a controlled user study comparing the performance of FlockViz with alternative visualization techniques that aid with cluster-based analytic tasks. Finally the exploration capability of FlockViz is demonstrated in some real life data sets such as fish movement, caribou movement, eagle migration, and hurricane movement. The results of the user studies and use cases confirm the advantage and novelty of the novel FlockViz design for visual analytic tasks. en_US
dc.subject Data Visualization en_US
dc.subject Theoretical design and implementation of representing data en_US
dc.subject Spatio-temporal data en_US
dc.subject Movement data visualization such as vehicle movement, animal migration etc en_US
dc.subject Cluster visualization en_US
dc.subject How to visualize clusters in Spatio-temporal data en_US
dc.subject Multi-Data analysis en_US
dc.subject Multi-dimensional data visualization to ease analytic tasks en_US
dc.title FlockViz: A Visualization Technique to Facilitate Multi-dimensional Analytics of Spatio-temporal Cluster Data en_US
dc.degree.discipline Computer Science en_US
dc.contributor.examiningcommittee Wang, Yang (Computer Science) Hossain, Ekram (Electrical and Computer Engineering) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2014 en_US


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