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Where have all the larvae gone? Towards Fast Main Pathway Identification from Geospatial Trajectories

Published: 23 August 2021 Publication History

Abstract

The distribution of passively drifting particles within highly turbulent flows is a classic problem in marine sciences. The use of trajectory clustering on huge amounts of simulated marine trajectory data to identify main pathways of drifting particles has not been widely investigated from a data science perspective yet. In this paper, we propose a fast and computationally light method to efficiently identify main pathways in large amounts of trajectory data. It aims at overcoming some of the issues of probabilistic maps and existing trajectory clustering approaches. Our approach is evaluated against simulated larvae dispersion data based on a real-world model that have been produced as part of work in the marine science domain.

References

[1]
Pedram Adibi, Fabio Pranovi, Alessandra Raffaetà, Elisabetta Russo, Claudio Silvestri, Marta Simeoni, Amilcar Soares, and Stan Matwin. 2020. Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning. In Lecture Notes in Computer Science. Springer International Publishing, 83–99. https://doi.org/10.1007/978-3-030-38081-6_7
[2]
Isaac Brodsky. 2018. H3: Uber’s hexagonal hierarchical spatial index.
[3]
Zaiben Chen, Heng Tao Shen, and Xiaofang Zhou. 2011. Discovering popular routes from trajectories. In 2011 IEEE 27th International Conference on Data Engineering. IEEE, 900–911. https://doi.org/10.1109/ICDE.2011.5767890
[4]
Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (Portland, Oregon) (KDD’96). AAAI Press, 226–231.
[5]
Fosca Giannotti, Mirco Nanni, Fabio Pinelli, and Dino Pedreschi. 2007. Trajectory pattern mining. In KDD 2007, Pavel Berkhin (Ed.). ACM Press, New York, New York, USA, 330. https://doi.org/10.1145/1281192.1281230
[6]
Iraklis Varlamis, K. Tserpes, M. Etemad, Amílcar Soares Júnior, and S. Matwin. 2019. A Network Abstraction of Multi-vessel Trajectory Data for Detecting Anomalies. undefined (2019).
[7]
Jiawei Han Jae-gil Lee. 2007. Trajectory Clustering: A Partition-and-Group Framework.
[8]
Xiaoming Liu, Luxi Dong, Chunlin Shang, and Xiangda Wei. 2020. An Improved High-Density Sub Trajectory Clustering Algorithm. IEEE Access 8(2020), 46041–46054. https://doi.org/10.1109/ACCESS.2020.2974059
[9]
Alex Polcyn. 2016. traclus_impl. https://github.com/apolcyn/traclus_impl.
[10]
Willi Rath, Christina Schmidt, and Siren Rühs. 2021. Mediterranean Sea Trajectory Data Examples. https://doi.org/10.5281/zenodo.4650317
[11]
Vojtěch Uher, Petr Gajdoš, Václav Snášel, Yu-Chi Lai, and Michal Radecký. 2019. Hierarchical Hexagonal Clustering and Indexing. https://doi.org/10.3390/sym11060731
[12]
Erik van Sebille, Stephen M Griffies, Ryan Abernathey, Thomas P Adams, Pavel Berloff, Arne Biastoch, Bruno Blanke, Eric P Chassignet, Yu Cheng, Colin J Cotter, 2017. Lagrangian ocean analysis: fundamentals and practices. Ocean Modelling (2017).
[13]
Xuantong Wang, Jing Li, and Tong Zhang. [n.d.]. A Machine-Learning Model for Zonal Ship Flow Prediction Using AIS Data: A Case Study in the South Atlantic States Region. https://doi.org/10.3390/jmse7120463
[14]
Yu Zheng. 2015. Trajectory Data Mining. ACM Transactions on Intelligent Systems and Technology 6, 3 (2015), 1–41. https://doi.org/10.1145/2743025

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  • (2023)Computing marine plankton connectivity under thermal constraintsFrontiers in Marine Science10.3389/fmars.2023.106605010Online publication date: 25-Jan-2023

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          cover image ACM Other conferences
          SSTD '21: Proceedings of the 17th International Symposium on Spatial and Temporal Databases
          August 2021
          173 pages
          ISBN:9781450384254
          DOI:10.1145/3469830
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          Publication History

          Published: 23 August 2021

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          Author Tags

          1. pathway identification
          2. trajectory clustering
          3. transition network mining

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          • Helmholtz School for Marine Data Science (MarDATA)

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          • (2023)Computing marine plankton connectivity under thermal constraintsFrontiers in Marine Science10.3389/fmars.2023.106605010Online publication date: 25-Jan-2023

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