Open Access

ARTICLE

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

by Jing He^1,2, Haonan Chen^3,*, Lingxiao Li⁴, Yebin Zou⁵

1 Institute for Advanced Studies in Humanities and Social Sciences, Beihang University, Beijing, 100083, China
2Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing, 100000, China
3 College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, 100083, China
4 School of Literature, Capital Normal University, Beijing, 100089, China
5 School of Civil and Hydraulic Engineering, Ningxia University, Yinchuan, 750021, China

* Corresponding Author: Haonan Chen. Email:

(This article belongs to the Special Issue: Computational Mechanics Assisted Modern Urban Planning and Infrastructure)

Computer Modeling in Engineering & Sciences 2023, 135(1), 211-237. https://doi.org/10.32604/cmes.2022.020597

Received 02 December 2021; Accepted 13 May 2022; Issue published 29 September 2022

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 for moving 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.

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