4D Time Density of Trajectories: Discovering Spatiotemporal Patterns in Movement Data

Yebin Zou,Yijin Chen,Gehu Pang,Jing He,Kaixuan Zhang

doi:10.3390/ijgi7060212

Abstract

Modern positioning and sensor technology enable the acquisition of movement positions and attributes on an unprecedented scale. Therefore, a large amount of trajectory data can be used to analyze various movement phenomena. In cartography, a common way to visualize and explore trajectory data is to use the 3D cube (e.g., space-time cube), where trajectories are presented as a tilted 3D polyline. As larger movement datasets become available, this type of display can easily become confusing and illegible. In addition, movement datasets are often unprecedentedly massive, high-dimensional, and complex (e.g., implicit spatial and temporal relations and interactions), making it challenging to explore and analyze the spatiotemporal movement patterns in space. In this paper, we propose 4D time density as a visualization method for identifying and analyzing spatiotemporal movement patterns in large trajectory datasets. The movement range of the objects is regarded as a 3D geographical space, into which the fourth dimension, 4D time density, is incorporated. The 4D time density is derived by modeling the movement path and velocity separately. We present a time density algorithm, and demonstrate it on the simulated trajectory and a real dataset representing the movement data of aircrafts in the Hong Kong International and the Macau International Airports. Finally, we consider wider applications and further developments of time density.

Highlights

Recent ubiquity and widespread use of modern positioning and context-aware devices have enabled the acquisition of movement positions and attributes of almost any type of moving object, and have produced large amounts of trajectories data [1]
Another concept associated with the home range is the utilization distribution [8], which is a probability surface on the 2D region that represents the possibility of finding animals in a particular area [1,9,10]
We use the dynamic properties of the spatial trajectories to derive the 4D time density, and visually analyze and explore the movement patterns of the moving objects in 3D geographical space

Summary

Introduction

Recent ubiquity and widespread use of modern positioning and context-aware devices have enabled the acquisition of movement positions and attributes of almost any type of moving object, and have produced large amounts of trajectories data [1] These data are usually collected as a series of trajectories; that is, when an object moves in the basic 3D geographic space of our physical world, the movement of each object can be presented as a tilted 3D polyline in space [2]. The decreased size and widespread use of animal tracking labels allow animal ecologists to collect large amounts of trajectory data describing animal movements These data are usually composed of trajectories in space and time [1,5], which are commonly analyzed and visualized using the methods for home range/utilization distribution estimation [6]. These two concepts often focus on the spatial distribution of the measured positions only in 2D space and ignore the time series of the measurements

Methods

Results

Discussion

Conclusion