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
N1 - Funding Information:
This research has been sponsored in part by the National 973 Program of China (2015CB352503), Major Program of National Natural Science Foundation of China (61232012), National Natural Science Foundation of China (61422211, 61772456), and National Natural Science Foundation of China (U1609217).
Publisher Copyright:
© 1995-2012 IEEE.
PY - 2018/9/1
Y1 - 2018/9/1
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 social- information 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 social- information of 14 million citizens over 22 days.
KW - Urban data
KW - heterogeneous
KW - spatio-temporal
KW - visual analysis
KW - visual reasoning
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U2 - 10.1109/TVCG.2017.2758362
DO - 10.1109/TVCG.2017.2758362
M3 - Article
C2 - 28976317
AN - SCOPUS:85030776103
VL - 24
SP - 2636
EP - 2648
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
IS - 9
M1 - 8054703
ER -