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 languageEnglish (US)
JournalIEEE Transactions on Visualization and Computer Graphics
StateAccepted/In press - Sep 29 2017


  • 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

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