dc.contributor.author | Rodríguez, J.P. | |
dc.contributor.author | Irigoien, X. | |
dc.contributor.author | Duarte, C.M. | |
dc.contributor.author | Eguíluz, V.M. | |
dc.date.accessioned | 2024-05-17T11:35:20Z | |
dc.date.available | 2024-05-17T11:35:20Z | |
dc.date.issued | 2024-12-01 | |
dc.identifier.citation | EPJ Data Science: 13 (1): 23 (2024) | es_ES |
dc.identifier.uri | http://hdl.handle.net/10810/68016 | |
dc.description | © The Author(s) 2024. | es_ES |
dc.description.abstract | Automated positioning devices can generate large datasets with information on the movement of humans, animals and objects, revealing patterns of movement, hot spots and overlaps among others. However, in the case of Automated Information Systems (AIS), attached to vessels, observed strange behaviors in the tracking datasets may come from intentional manipulation of the electronic devices. Thus, the analysis of anomalies can provide valuable information on suspicious behavior. Here, we analyze anomalies of fishing vessel trajectories obtained with the Automatic Identification System. The map of silent anomalies, those that occur when positioning data are absent for more than 24 hours, shows that they are most likely to occur closer to land, with 87.1% of anomalies observed within 100 km of the coast. This behavior suggests the potential of identifying silence anomalies as a proxy for illegal activities. With the increasing availability of high-resolution positioning of vessels and the development of powerful statistical analytical tools, we provide hints on the automatic detection of illegal activities that may help optimize the management of fishing resources. © The Author(s) 2024. | es_ES |
dc.description.sponsorship | J.P.R. received support from Juan de la Cierva Formación program (Ref. FJC2019-040622-I) funded by MCIN/AEI/10.13039/501100011033, the Spanish Research Agency MCIN/AEI/10.13039/501100011033 via project MISLAND (PID2020-114324GB-C22), and the Vicenç Mut program from Govern de les Illes Balears. This research is supported by María de Maeztu Excellence Unit 2023-2027 Refs. CEX2021-001201-M and CEX2021-001164-M, funded by MCIN/AEI/10.13039/501100011033. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | EPJ Data Science | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIN/FJC2019-040622-I | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIN/PID2020-114324GB-C22 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIN/CEX2021-001164-M | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIN/CEX2021-001201-M | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | * |
dc.subject | Automatic Identification System (AIS) | es_ES |
dc.subject | Exclusive Economic Zones (EEZ) | es_ES |
dc.subject | Fishing vessels | es_ES |
dc.subject | Marine Protected Areas (MPA) | es_ES |
dc.subject | Marine Protected Areas (MPA) | es_ES |
dc.title | Identification of suspicious behavior through anomalies in the tracking data of fishing vessels | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | Atribución-NoComercial-CompartirIgual 3.0 España | * |
dc.relation.publisherversion | https://dx.doi.org/10.1140/epjds/s13688-024-00459-0 | es_ES |
dc.identifier.doi | 10.1140/epjds/s13688-024-00459-0 | |