Interactive visual cluster detection in large geospatial datasets based on dynamic density volume visualization

Fei Du, A. Xing Zhu, Feng Qi

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The emerging spatial big data (e.g. detailed spatial trajectories, geo-referenced social media data) provide tremendous opportunities for GIScientists and geographers. However, their large volume also poses challenges to existing spatial data analytical techniques (including visual analytical techniques). This article presents an interactive visual approach to detect clusters from those emerging data sets based on dynamic density volume visualization in a three-dimensional space (two spatial dimensions plus a third temporal or thematic dimension of interest). Cluster can be visually discovered through dynamic adjustment of density to colour/opacity mapping and extracted through flexible selection tools. The approach was tested on a large simulated data-set and a spatial trajectory data-set. The results show that the approach can overcome the visual clotting problem in traditional visualization tools caused by large data volume and facilitate the involvement of domain knowledge in analysis. It can effectively support visual cluster detection in the emerging large geospatial data sets.

Original languageEnglish
Pages (from-to)597-611
Number of pages15
JournalGeocarto International
Volume31
Issue number6
DOIs
StatePublished - 2 Jul 2016

Keywords

  • big data
  • geovisual analytics
  • social media
  • spatial cluster
  • trajectory

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