9 Scopus citations


Spatial datasets, such as tweets in a geographic area, often exhibit different distribution patterns at multiple levels of scale, such as live updates about events occurring in very specific locations on the map. Navigating in such multi-scale data-rich spaces is often inefficient, requires users to choose between overview or detail information, and does not support identifying spatial patterns at varying scales. In this paper, we propose TopoGroups, a novel context-preserving technique that aggregates spatial data into hierarchical clusters to improve exploration and navigation at multiple spatial scales. The technique uses a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates. Our user study explores multiple visual encoding strategies for To-poGroups including color, transparency, shading, and shapes in order to convey the hierarchical and statistical information of the geographical aggregates at different scales.

Original languageEnglish (US)
Title of host publicationCHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publicationExplore, Innovate, Inspire
PublisherAssociation for Computing Machinery
Number of pages12
ISBN (Electronic)9781450346559
StatePublished - May 2 2017
Event2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 - Denver, United States
Duration: May 6 2017May 11 2017

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


Conference2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
Country/TerritoryUnited States


  • Context preservation
  • Geospatial visualization
  • Multi-scale analysis
  • Social media

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design


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