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dc.contributor.authorPolitikos, Dimitrios V.
dc.contributor.authorKleftogiannis, Dimitrios A.
dc.contributor.authorTsiaras, Kostas
dc.contributor.authorRose, Kenneth A.
dc.date.accessioned2021-02-21T11:22:24Z
dc.date.available2021-02-21T11:22:24Z
dc.date.issued2021-02-15
dc.date.submitted2020-03-31
dc.identifier.citationPolitikos, D. V., Kleftogiannis, D., Tsiaras, K., & Rose, K. A. (2021). MovCLUfish  : A data mining tool for discovering fish movement patterns from individual-based models. Limnology and Oceanography: Methods. doi:10.1002/lom3.10421
dc.identifier.issn1541-5856
dc.identifier.issn1541-5856
dc.identifier.doi10.1002/lom3.10421
dc.identifier.urihttp://hdl.handle.net/10754/667524
dc.description.abstractSpatially explicit individual-based models (IBMs) are useful tools for simulating the movement of discrete fish individuals within dynamic and heterogeneous environments. However, processing the IBM outputs is complicated because fish individuals are continuously adjusting their behavior in response to changing environmental conditions. Here, we present a new analysis tool, called MovCLUfish, that uses data mining to identify patterns from the trajectories of the individuals generated from IBMs. MovCLUfish is configured to identify features of fish behavior related to occupation (area of fish presence), dynamics of aggregation (how fish individuals are distributed within the area of presence), and mobility (how fish move between subregions). MovCLUfish receives as input the fish locations (longitude, latitude) at fixed times during a specific time period and performs spatial clustering on consecutive timestamps, considering them as moving objects. Fish locations are grouped into clusters whose features (centroid, shape, size, density) are used to provide further information about the spatial distributions. The clusters are analyzed using three built-in pattern mining methods: tracking moving centroids (TMC), aggregating moving clusters (AMC), and tracking fish mobility (TFM). TMC detects shifts in the distribution of fish over time, AMC visualizes the way fish aggregations change geographically over time, and TFM provides quantitative information on the patterns of exchange and connectivity of individuals among regions within the domain. We describe the workflow of MovCLUfish and illustrate its applicability using output from an IBM model configured for anchovy in the Eastern Mediterranean Sea. Further avenues for improvement and expansion of MovCLUfish are discussed.
dc.description.sponsorshipThe research of Dimitris Kleftogiannis was funded by the KAUST Base Research Funds to professor Panos Kalnis. We thank Nikolaos Zarokanelos for his useful comments. We express our appreciation to the two reviewers for the careful reading of the manuscript and their thoughtful comments.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/10.1002/lom3.10421
dc.rightsArchived with thanks to Limnology and Oceanography: Methods
dc.titleMovCLUfish : A data mining tool for discovering fish movement patterns from individual-based models
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalLimnology and Oceanography: Methods
dc.rights.embargodate2022-02-15
dc.eprint.versionPost-print
dc.contributor.institutionInstitute of Marine Biological Resources and Inland, Hellenic Center for Marine Research (HCMR) Argyroupoli Greece
dc.contributor.institutionInstitute of Oceanography, Hellenic Centre for Marine Research (HCMR) Anavyssos Greece
dc.contributor.institutionUniversity of Maryland Center for Environmental Science, Horn Point Laboratory Cambridge Maryland USA
kaust.personKleftogiannis, Dimitrios A.
dc.date.accepted2021-01-28
kaust.acknowledged.supportUnitKAUST Base Research Funds
dc.date.published-online2021-02-15
dc.date.published-print2021-04


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