Using topological analysis to support event-guided exploration in urban data

Harish Doraiswamy, Nivan Ferreira, Theodoros Damoulas, Juliana Freire, Claudio T. Silva

Research output: Contribution to journalArticlepeer-review


The explosion in the volume of data about urban environments has opened up opportunities to inform both policy and administration and thereby help governments improve the lives of their citizens, increase the efficiency of public services, and reduce the environmental harms of development. However, cities are complex systems and exploring the data they generate is challenging. The interaction between the various components in a city creates complex dynamics where interesting facts occur at multiple scales, requiring users to inspect a large number of data slices over time and space. Manual exploration of these slices is ineffective, time consuming, and in many cases impractical. In this paper, we propose a technique that supports event-guided exploration of large, spatiooral urban data. We model the data as time-varying scalar functions and use computational topology to automatically identify events in different data slices. To handle a potentially large number of events, we develop an algorithm to group and index them, thus allowing users to interactively explore and query event patterns on the fly. A visual exploration interface helps guide users towards data slices that display interesting events and trends. We demonstrate the effectiveness of our technique on two different data sets from New York City (NYC): data about taxi trips and subway service. We also report on the feedback we received from analysts at different NYC agencies.

Original languageEnglish (US)
Article number6876004
Pages (from-to)2634-2643
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number12
StatePublished - Dec 31 2014


  • Computational topology
  • event detection
  • spatiooral index
  • urban data
  • visual exploration

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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