Abstract

Real-word phenomena, such as ocean eddies and clouds, tend to split and merge while they are moving around within a space. Their trajectories usually bear one or more branches and are accordingly defined as complex trajectories in this study. The trajectories may show significant spatiotemporal variations in terms of their structures and some of them may be more prominent than the others. The identification of prominent structures in the complex trajectories of such real-world phenomena could better reveal their evolution processes and even shed new light on the driving factors behind them. Methods have been proposed for the extraction of periodic patterns from simple trajectories (i.e., those with linear structure and without any branches) with a focus on mining the related temporal, spatial or semantic information. Unfortunately, it is not appropriate to directly use such methods to examine complex trajectories. This study proposes a novel method to study the periodic patterns of complex trajectories by considering the inherent spatial, temporal and topological information. First, we use a sequence of symbols to represent the various structures of a complex trajectory over its lifespan. We then, on the basis of the PrefixSpan algorithm, propose a periodic pattern mining of structural evolution (PPSE) algorithm and use it to identify the largest and most frequent patterns (LFPs) from the symbol sequence. We also identify potential periodic behaviors. The PPSE method is then used to examine the complex trajectories of the mesoscale eddy in the South China Sea (SCS) from 1993 to 2016. The complex trajectories of ocean eddies in the southeast of Vietnam show are different from other regions in the SCS in terms of their structural evolution processes, as indicated by the LFPs with the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. The LFP in the southeast of Vietnam has the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. Across the SCS, we found seven migration channels. The LFPs of the eddies that migrate through these channels have a temporal cycle of 17–24 years. These channels are also the regions where eddies frequently emerge, as revealed by flow field data.

Highlights

  • The periodic pattern of a trajectory could be defined as cycle of the behaviors of an object that is moving at a regular time interval at specific locations [1]

  • A hierarchical clustering method based on a global similarity measuring algorithm for complex trajectories (GSMCT) proposed by Wang et al [31] was first used to group the complex trajectories of mesoscale eddies in the South China Sea (SCS) from January 1993 to December 2016

  • The largest and most frequent patterns (LFPs) identified in this study provide new insights regarding the evolution of the mesoscale ocean eddies in the SCS

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Summary

Methods

This study used the complex trajectories of mesoscale eddies in the SCS from January 1993 to December 2016. Our research group identified the mesoscale ocean eddies from the sea level anomaly (SLA) data and reconstructed their trajectories [6,20,21]. Our research group used a hybrid detection (HD) method to identify the mesoscale ocean eddies in the SCS [20]. The HD method was developed by integrating the two widely used eddy identification methods: the Okubo–Weiss (OW) [23,24] and the Sea Surface Height (SSH) [25] methods. An eddy exists only if it meets the OW criterion and includes either a local maximum or minimum SLA value

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