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) |
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Journal | IEEE Transactions on Visualization and Computer Graphics |
DOIs | |
State | Accepted/In press - Sep 29 2017 |
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