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Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar (3D)

  • Jing He
  • , Haonan Chen*
  • , Lingxiao Li
  • , Yebin Zou
  • *Corresponding author for this work
  • Beijing Key Laboratory of Urban Spatial Information Engineering
  • China University of Mining & Technology, Beijing
  • Capital Normal University
  • Ningxia University

Research output: Contribution to journalArticlepeer-review

Abstract

There are many sources of geographic big data, and most of them come from heterogeneous environments. The data sources obtained in this case contain attribute information of different spatial scales, different time scales and different complexity levels. It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult. In this case, visualizing high-dimensional spatiotemporal trajectory data is extremely challenging. Therefore, i-tStar and its extension i-tStar (3D) proposed, a trajectory behavior feature formoving objects that are integrated into a view with less effort to display and extract spatiotemporal conditions, and evaluate our approach through case studies of an open-pit mine truck dataset. The experimental results show that this method is easier to mine the interaction behavior of multi-attribute trajectory data and the correlation and influence of various indicators of moving objects.

Original languageEnglish
Pages (from-to)211-237
Number of pages27
JournalCMES - Computer Modeling in Engineering and Sciences
Volume135
Issue number1
DOIs
StatePublished - 2023

Keywords

  • Trajectory data
  • multidimensional
  • multivariate
  • technology behavioral characteristics
  • visualization

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