Abstract
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 socialinformation of 14 million citizens over 22 days.
| Original language | English (US) |
|---|---|
| Journal | IEEE Transactions on Visualization and Computer Graphics |
| DOIs | |
| State | Accepted/In press - Sep 29 2017 |
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Keywords
- Cognition
- Data visualization
- Heterogeneous
- Public transportation
- Spatio-temporal
- Trajectory
- Urban areas
- Urban data
- Visual Analysis
- Visual analytics
- Visual Reasoning
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Computer Graphics and Computer-Aided Design
Cite this
VAUD : A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data. / Chen, Wei; Huang, Zhaosong; Wu, Feiran; Zhu, Minfeng; Guan, Huihua; Maciejewski, Ross.
In: IEEE Transactions on Visualization and Computer Graphics, 29.09.2017.Research output: Contribution to journal › Article
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TY - JOUR
T1 - VAUD
T2 - A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data
AU - Chen, Wei
AU - Huang, Zhaosong
AU - Wu, Feiran
AU - Zhu, Minfeng
AU - Guan, Huihua
AU - Maciejewski, Ross
PY - 2017/9/29
Y1 - 2017/9/29
N2 - 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 socialinformation of 14 million citizens over 22 days.
AB - 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 socialinformation of 14 million citizens over 22 days.
KW - Cognition
KW - Data visualization
KW - Heterogeneous
KW - Public transportation
KW - Spatio-temporal
KW - Trajectory
KW - Urban areas
KW - Urban data
KW - Visual Analysis
KW - Visual analytics
KW - Visual Reasoning
UR - http://www.scopus.com/inward/record.url?scp=85030776103&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030776103&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2017.2758362
DO - 10.1109/TVCG.2017.2758362
M3 - Article
C2 - 28976317
AN - SCOPUS:85030776103
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
ER -