VAUD: A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data

Chen, W; Huang, Z; Wu, F; Zhu, M; Guan, H; Maciejewski, R

VAUD: A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data

Chen, W Huang, Z Wu, F Zhu, M

Guan, H Maciejewski, R

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Visualization and Computer Graphics, 2018, 24 (9), pp. 2636 - 2648
Issue Date:: 2018-09-01

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Field	Value	Language
dc.contributor.author	Chen, W	en_US
dc.contributor.author	Huang, Z	en_US
dc.contributor.author	Wu, F	en_US
dc.contributor.author	Zhu, M https://orcid.org/0000-0002-6711-3099	en_US
dc.contributor.author	Guan, H	en_US
dc.contributor.author	Maciejewski, R	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	IEEE Transactions on Visualization and Computer Graphics, 2018, 24 (9), pp. 2636 - 2648	en_US
dc.identifier.issn	1077-2626	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131982
dc.description.abstract	© 1995-2012 IEEE. Urban data is massive, heterogeneous, and spatio-temporal, posing a substantial challenge for visualization and analysis. In this paper, we design and implement a novel visual analytics approach, Visual Analyzer for Urban Data (VAUD), that supports the visualization, querying, and exploration of urban data. Our approach allows for cross-domain correlation from multiple data sources by leveraging spatial-temporal and social inter-connectedness features. Through our approach, the analyst is able to select, filter, aggregate across multiple data sources and extract information that would be hidden to a single data subset. To illustrate the effectiveness of our approach, we provide case studies on a real urban dataset that contains the cyber-, physical-, and social- information of 14 million citizens over 22 days.	en_US
dc.relation.ispartof	IEEE Transactions on Visualization and Computer Graphics	en_US
dc.relation.isbasedon	10.1109/TVCG.2017.2758362	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	VAUD: A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	24	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

© 1995-2012 IEEE. Urban data is massive, heterogeneous, and spatio-temporal, posing a substantial challenge for visualization and analysis. In this paper, we design and implement a novel visual analytics approach, Visual Analyzer for Urban Data (VAUD), that supports the visualization, querying, and exploration of urban data. Our approach allows for cross-domain correlation from multiple data sources by leveraging spatial-temporal and social inter-connectedness features. Through our approach, the analyst is able to select, filter, aggregate across multiple data sources and extract information that would be hidden to a single data subset. To illustrate the effectiveness of our approach, we provide case studies on a real urban dataset that contains the cyber-, physical-, and social- information of 14 million citizens over 22 days.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/131982