Unknown
In:
[Paper] In: 17. International Symposium on Spatial and Temporal Databases, SSTD 2021, 23.-25.08.2021, Online ; pp. 126-129 .
Publication Date:
2022-01-25
Description:
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.
Type:
Conference or Workshop Item
,
NonPeerReviewed
Format:
text
DOI:
10.1145/3469830.3470896
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