乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:16:45Z","timestamp":1740122205611,"version":"3.37.3"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007601","name":"horizon 2020","doi-asserted-by":"publisher","award":["MASTER project 777695"],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Geoinformatica"],"published-print":{"date-parts":[[2022,10]]},"abstract":"Abstract<\/jats:title>In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch reports (i.e., the quantity and type of fish caught) of the main fishing market of the area. We present all the phases of the database creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. We implement the database by using MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, we perform various analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management. Furthermore, we investigate the use of machine learning methods for predicting the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation in order to drive specific policy design. A variety of prediction methods, taking as input the data in the database and environmental factors such as sea temperature, waves height and Clorophill-a, are put at work in order to assess their prediction ability in this field. To the best of our knowledge, our work represents the first attempt to integrate fishing ships trajectories derived from AIS data, environmental data and catch data for spatio-temporal prediction of CPUE \u2013 a challenging task.<\/jats:p>","DOI":"10.1007\/s10707-022-00463-4","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T23:03:42Z","timestamp":1648681422000},"page":"551-579","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["From multiple aspect trajectories to predictive analysis: a case study on fishing vessels in the Northern Adriatic sea"],"prefix":"10.1007","volume":"26","author":[{"given":"Bruno","family":"Brandoli","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0295-8787","authenticated-orcid":false,"given":"Alessandra","family":"Raffaet\u00e0","sequence":"additional","affiliation":[]},{"given":"Marta","family":"Simeoni","sequence":"additional","affiliation":[]},{"given":"Pedram","family":"Adibi","sequence":"additional","affiliation":[]},{"given":"Fateha Khanam","family":"Bappee","sequence":"additional","affiliation":[]},{"given":"Fabio","family":"Pranovi","sequence":"additional","affiliation":[]},{"given":"Giulia","family":"Rovinelli","sequence":"additional","affiliation":[]},{"given":"Elisabetta","family":"Russo","sequence":"additional","affiliation":[]},{"given":"Claudio","family":"Silvestri","sequence":"additional","affiliation":[]},{"given":"Amilcar","family":"Soares","sequence":"additional","affiliation":[]},{"given":"Stan","family":"Matwin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"463_CR1","doi-asserted-by":"crossref","unstructured":"Adibi P, Pranovi F, Raffaet\u00e0 A, Russo E, Silvestri C, Simeoni M, Soares A, Matwin S (2019) Predicting fishing effort and catch using semantic trajectories and machine learning. In: Multiple-aspect analysis of semantic trajectories - first international workshop, MASTER 2019, Lecture notes in computer science, vol 11889. Springer, pp 83\u201399","DOI":"10.1007\/978-3-030-38081-6_7"},{"issue":"3","key":"463_CR2","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","volume":"46","author":"N Altman","year":"1992","unstructured":"Altman N (1992) An introduction to kernel and nearest-neighbor nonparametric regression. American Statistician 46(3):175\u2013185. https:\/\/doi.org\/10.1080\/00031305.1992.10475879","journal-title":"American Statistician"},{"issue":"4","key":"463_CR3","first-page":"349","volume":"16","author":"K Amiri","year":"2018","unstructured":"Amiri K, Shabanipour N, Eagderi S (2018) Forecasting the catch of kilka species (Clupeonella spp.) using time series SARIMA models in the southern caspian sea. Caspian Journal of Environmental Sciences 16(4):349\u2013358","journal-title":"Caspian Journal of Environmental Sciences"},{"issue":"2","key":"463_CR4","doi-asserted-by":"publisher","first-page":"653","DOI":"10.31018\/jans.v9i2.1252","volume":"9","author":"A Anuja","year":"2017","unstructured":"Anuja A, Yadav VK, Bharti VS, Kumar NR (2017) Trends in marine fish production in Tamil Nadu using regression and autoregressive integrated moving average (ARIMA) model. Journal of Applied and Natural Science 9(2):653\u2013657. https:\/\/doi.org\/10.31018\/jans.v9i2.1252","journal-title":"Journal of Applied and Natural Science"},{"issue":"1","key":"463_CR5","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1111\/tgis.12011","volume":"18","author":"V Bogorny","year":"2014","unstructured":"Bogorny V, Renso C, de Aquino AR, de Lucca Siqueira F, Alvares LO (2014) Constant-a conceptual data model for semantic trajectories of moving objects. Transactions in GIS 18(1):66\u201388","journal-title":"Transactions in GIS"},{"issue":"2","key":"463_CR6","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging Predictors. Machine Learning 24(2):123\u2013140","journal-title":"Machine Learning"},{"key":"463_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Machine Learning 45:5\u201332","journal-title":"Machine Learning"},{"key":"463_CR8","doi-asserted-by":"crossref","unstructured":"Br\u00fcggemann S, Bereta K, Xiao G, Koubarakis M (2016) Ontology-based data access for maritime security. In: European semantic web conference. Springer, pp 741\u2013757","DOI":"10.1007\/978-3-319-34129-3_45"},{"key":"463_CR9","unstructured":"Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, Niculae V, Prettenhofer P, Gramfort A, Grobler J, Layton R, VanderPlas J, Joly A, Holt B, Varoquaux G (2013) API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: languages for data mining and machine learning, pp 108\u2013122"},{"key":"463_CR10","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1162\/089976602760128081","volume":"148","author":"CC Chang","year":"2002","unstructured":"Chang CC, Lin CJ (2002) Training nu-support vector regression: theory and algorithms. Neural Computation 148:1959\u20131977","journal-title":"Neural Computation"},{"key":"463_CR11","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD \u201916, p 785-794. Association for computing machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"463_CR12","unstructured":"Claramunt C, Ray C, Camossi E, Jousselme A, Hadzagic M, Andrienko GL, Andrienko NV, Theodoridis Y, Vouros GA, Salmon L (2017) Maritime data integration and analysis: recent progress and research challenges. In: Proceedings of the 20th international conference on extending database technology, pp 192\u2013197"},{"key":"463_CR13","unstructured":"Dividino R, Soares A, Matwin S, Isenor AW, Webb S, Brousseau M (2018) Semantic integration of real-time heterogeneous data streams for ocean-related decision making. In: Big data and artificial intelligence for military decision making. STO"},{"issue":"2","key":"463_CR14","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.fishres.2007.06.004","volume":"86","author":"J Estrada","year":"2007","unstructured":"Estrada J, Silva C, Y\u00e1\u00f1ez E, Rodriguez N, Pulido-Calvo I (2007) Monthly catch forecasting of anchovy Engraulis ringens in the north area of Chile: Non-linear univariate approach. Fisheries Research 86(2):188\u2013200","journal-title":"Fisheries Research"},{"key":"463_CR15","doi-asserted-by":"crossref","unstructured":"Etemad M, Etemad Z, Soares A, Bogorny V, Matwin S, Torgo L (2020) Wise sliding window segmentation: A classification-aided approach for trajectory segmentation. In: Canadian conference on artificial intelligence. Springer, pp 208\u2013219","DOI":"10.1007\/978-3-030-47358-7_20"},{"issue":"7","key":"463_CR16","doi-asserted-by":"publisher","first-page":"3891","DOI":"10.1111\/gcb.15081","volume":"26","author":"JA Fernandes","year":"2020","unstructured":"Fernandes JA, Rutterford L, Simpson SD, Butensch\u00f6n M, Fr\u00f6licher TL, Yool A, Cheung WWL, Grant A (2020) Can we project changes in fish abundance and distribution in response to climate? Global Change Biology 26(7):3891\u20133905","journal-title":"Global Change Biology"},{"key":"463_CR17","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.marpol.2017.12.013","volume":"94","author":"C Ferr\u00e0","year":"2018","unstructured":"Ferr\u00e0 C, Tassetti AN, Grati F, Pellini G, Polidori P, Scarcella G, Fabi G (2018) Mapping change in bottom trawling activity in the Mediterranean Sea through AIS data. Marine Policy 94:275\u2013281","journal-title":"Marine Policy"},{"issue":"1","key":"463_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Machine Learning 63(1):3\u201342. https:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Machine Learning"},{"key":"463_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10994-017-5642-8","volume":"106","author":"HM Gomes","year":"2017","unstructured":"Gomes HM, Bifet A, Read J, Barddal JP, Enembreck F, Pfahringer B, Holmes G, Abdessalem T (2017) Adaptive random forests for evolving data stream classification. Machine Learning 106:1\u201327. https:\/\/doi.org\/10.1007\/s10994-017-5642-8","journal-title":"Machine Learning"},{"key":"463_CR20","doi-asserted-by":"crossref","unstructured":"Jie X, Chaozhong W, Zhijun C, Xiaoxuan C (2017) A novel estimation algorithm for interpolating ship motion. In: 2017 4th International conference on transportation information and safety (ICTIS). IEEE, pp 557\u2013562","DOI":"10.1109\/ICTIS.2017.8047821"},{"key":"463_CR21","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) LightGBM: A highly efficient gradient boosting decision tree. In: Proceedings of the 31st international conference on neural information processing systems, NIPS\u201917. Curran Associates Inc., Red Hook, NY, USA, pp 3149\u20133157"},{"issue":"7","key":"463_CR22","first-page":"10","volume":"5","author":"O Kehinde","year":"2018","unstructured":"Kehinde O, Joseph G (2018) Time series modelling for forecasting artisanal fish production in Nigeria. Int J Adv Multidiscip Res 5(7):10\u201317","journal-title":"Int J Adv Multidiscip Res"},{"issue":"2","key":"463_CR23","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1504\/IJBDI.2020.107375","volume":"7","author":"I Kontopoulos","year":"2020","unstructured":"Kontopoulos I, Chatzikokolakis K, Zissis D, Tserpes K, Spiliopoulos G (2020) Real-time maritime anomaly detection: detecting intentional AIS switch-off. Int J Big Data Intell 7(2):85\u201396","journal-title":"Int J Big Data Intell"},{"issue":"4","key":"463_CR24","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1080\/13658816.2020.1792914","volume":"35","author":"I Kontopoulos","year":"2021","unstructured":"Kontopoulos I, Varlamis I, Tserpes K (2021) A distributed framework for extracting maritime traffic patterns. International Journal of Geographical Information Science 35(4):767\u2013792","journal-title":"International Journal of Geographical Information Science"},{"key":"463_CR25","doi-asserted-by":"crossref","unstructured":"Le Guyader D, Ray C, Brosset D (2018) Identifying small-scale fishing zones in France using AIS data. In: Advances in shipping data analysis and modeling: tracking and mapping maritime flows in the age of big data, chap 15. Taylor & Francis","DOI":"10.4324\/9781315271446-15"},{"key":"463_CR26","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1051\/alr\/2017038","volume":"30","author":"D Le Guyader","year":"2014","unstructured":"Le Guyader D, Ray C, Gourmelon F, Brosset D (2014) Defining high-resolution dredge fishing grounds with automatic identification system (AIS) data. Aquat Living Resour 30:39. https:\/\/doi.org\/10.1051\/alr\/2017038","journal-title":"Aquat Living Resour"},{"key":"463_CR27","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.fishres.2016.06.021","volume":"183","author":"I Maina","year":"2016","unstructured":"Maina I, Kavadas S, Somarakis S, Tserpes G, Stratis G (2016) A methodological approach to identify fishing grounds: A case study on Greek trawlers. Fisheries Research 183:326\u2013339","journal-title":"Fisheries Research"},{"issue":"1","key":"463_CR28","doi-asserted-by":"publisher","first-page":"140","DOI":"10.17582\/journal.pjar\/2020\/33.1.140.145","volume":"33","author":"Q Mehmood","year":"2020","unstructured":"Mehmood Q, Sial M, Sharif S, Hussain A, Riaz M, Shaheen N (2020) Forecasting the fisheries production in Pakistan for the year 2017\u20132026, using Box-Jenkin\u2019s methodology. Pakistan Journal of Agricultural Research 33(1):140\u2013145","journal-title":"Pakistan Journal of Agricultural Research"},{"issue":"4","key":"463_CR29","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1111\/tgis.12526","volume":"23","author":"RDS Mello","year":"2019","unstructured":"Mello RDS, Bogorny V, Alvares LO, Santana LHZ, Ferrero CA, Frozza AA, Schreiner GA, Renso C (2019) MASTER: A multiple aspect view on trajectories. Transactions in GIS 23(4):805\u2013822","journal-title":"Transactions in GIS"},{"issue":"2","key":"463_CR30","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1093\/icesjms\/fsl026","volume":"64","author":"CM Mills","year":"2006","unstructured":"Mills CM, Townsend SE, Jennings S, Eastwood PD, Houghton CA (2006) Estimating high resolution trawl fishing effort from satellite-based vessel monitoring system data. ICES Journal of Marine Science 64(2):248\u2013255","journal-title":"ICES Journal of Marine Science"},{"issue":"2","key":"463_CR31","doi-asserted-by":"publisher","first-page":"225","DOI":"10.17576\/jsm-2015-4402-09","volume":"44","author":"S Nurdin","year":"2015","unstructured":"Nurdin S, Ahmad Mustapha M, Lihan T, Abd Ghaffar M (2015) Determination of potential fishing grounds of Rastrelliger kanagurta using satellite remote sensing and GIS technique. Sains Malaysiana 44(2):225\u2013232","journal-title":"Sains Malaysiana"},{"key":"463_CR32","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.fishres.2015.05.008","volume":"170","author":"J Paige Eveson","year":"2015","unstructured":"Paige Eveson J, Hobday A, Hartog J, Spillman C, Rough K (2015) Seasonal forecasting of tuna habitat in the Great Australian Bight. Fisheries Research 170:39\u201349","journal-title":"Fisheries Research"},{"issue":"4","key":"463_CR33","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1145\/2501654.2501656","volume":"45","author":"C Parent","year":"2013","unstructured":"Parent C, Spaccapietra S, Renso C, Andrienko G, Andrienko N, Bogorny V, Damiani ML, Gkoulalas-Divanis A, Macedo J, Pelekis N et al (2013) Semantic trajectories modeling and analysis. ACM Computing Surveys (CSUR) 45(4):42","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"463_CR34","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12:2825\u20132830","journal-title":"Journal of Machine Learning Research"},{"key":"463_CR35","doi-asserted-by":"crossref","unstructured":"Petry LM, Soares A, Bogorny V, Brandoli B, Matwin S (2020) Challenges in vessel behavior and anomaly detection: from classical machine learning to deep learning. In: Advances in artificial intelligence - 33rd canadian conference on artificial intelligence, Lecture notes in computer science, vol 12109. Springer, pp 401\u2013407","DOI":"10.1007\/978-3-030-47358-7_41"},{"key":"463_CR36","unstructured":"Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) CatBoost: unbiased boosting with categorical features. In: Proceedings of the 32nd international conference on neural information processing systems, NIPS\u201918. Curran Associates Inc., Red Hook, NY, USA, p 6639\u20136649"},{"key":"463_CR37","unstructured":"Raman RK, Das BK (2019) Forecasting shrimp and fish catch in chilika lake over time series analysis. Time Series Analysis - Data, Methods, and Applications"},{"issue":"6","key":"463_CR38","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s40009-017-0581-2","volume":"40","author":"RK Raman","year":"2017","unstructured":"Raman RK, Sathianandan TV, Sharma AP, Mohanty BP (2017) Modelling and forecasting marine fish production in odisha using seasonal ARIMA model. National Academy Science Letters 40(6):393\u2013397. https:\/\/doi.org\/10.1007\/s40009-017-0581-2","journal-title":"National Academy Science Letters"},{"key":"463_CR39","unstructured":"Rovinelli G, Matwin S, Pranovi F, Russo E, Silvestri C, Simeoni M, Raffaet\u00e0 A (2021) Multiple aspect trajectories: a case study on fishing vessels in the Northern Adriatic sea. In: BMDA 2021: 4th International workshop on big mobility data analytics, CEUR workshop proceedings, vol 2841. CEUR-WS.org"},{"issue":"6088","key":"463_CR40","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536. https:\/\/doi.org\/10.1038\/323533a0","journal-title":"Nature"},{"key":"463_CR41","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1016\/j.future.2018.07.007","volume":"110","author":"GM Santipantakis","year":"2020","unstructured":"Santipantakis GM, Glenis A, Patroumpas K, Vlachou A, Doulkeridis C, Vouros GA, Pelekis N, Theodoridis Y (2020) Spartan: Semantic integration of big spatio-temporal data from streaming and archival sources. Future Generation Computer Systems 110:540\u2013555. https:\/\/doi.org\/10.1016\/j.future.2018.07.007","journal-title":"Future Generation Computer Systems"},{"issue":"3","key":"463_CR42","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1093\/icesjms\/fsx230","volume":"75","author":"JL Shepperson","year":"2018","unstructured":"Shepperson JL, Hintzen NT, Szostek CL, Bell E, Murray LG, Kaiser MJ (2018) A comparison of VMS and AIS data: the effect of data coverage and vessel position recording frequency on estimates of fishing footprints. ICES Journal of Marine Science 75(3):988\u2013998","journal-title":"ICES Journal of Marine Science"},{"key":"463_CR43","doi-asserted-by":"crossref","unstructured":"Soares A, Dividino R, Abreu F, Brousseau M, Isenor AW, Webb S, Matwin S (2019) CRISIS: Integrating AIS and ocean data streams using semantic web standards for event detection. In: International conference on military communications and information systems","DOI":"10.1109\/ICMCIS.2019.8842749"},{"issue":"18","key":"463_CR44","doi-asserted-by":"publisher","first-page":"3691","DOI":"10.1080\/0143116031000117029","volume":"24","author":"H Solanki","year":"2003","unstructured":"Solanki H, Dwivedi R, Nayak S, Somvanshi V, Gulati D, Pattnayak S (2003) Fishery forecast using OCM chlorophyll concentration and AVHRR SST: validation results off Gujarat coast. India. International Journal of Remote Sensing 24(18):3691\u20133699","journal-title":"India. International Journal of Remote Sensing"},{"issue":"7","key":"463_CR45","doi-asserted-by":"publisher","first-page":"e0158248","DOI":"10.1371\/journal.pone.0158248","volume":"11","author":"EN de Souza","year":"2016","unstructured":"de Souza EN, Boerder K, Matwin S, Worm B (2016) Improving fishing pattern detection from satellite AIS using data mining and machine learning. PloS One 11(7):e0158248","journal-title":"PloS One"},{"key":"463_CR46","doi-asserted-by":"publisher","first-page":"542","DOI":"10.3389\/fmars.2021.643318","volume":"8","author":"E Torres-Irineo","year":"2021","unstructured":"Torres-Irineo E, Salas S, Eu\u00e1n-\u00c1vila JI, Palomo LE, Quijano Qui\u00f1ones DR, Coronado E, Joo R (2021) Spatio-temporal determination of small-scale vessels fishing grounds using a vessel monitoring system in the southeastern gulf of mexico. Frontiers in Marine Science 8:542","journal-title":"Frontiers in Marine Science"},{"key":"463_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"VN Vapnik","year":"1995","unstructured":"Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag, New York Inc"},{"key":"463_CR48","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1080\/17445647.2016.1195299","volume":"12","author":"M Vespe","year":"2016","unstructured":"Vespe M, Gibin M, Alessandrini A, Natale F, Mazzarella F, Osio GC (2016) Mapping EU fishing activities using ship tracking data. Journal of Maps 12:520\u2013525","journal-title":"Journal of Maps"},{"key":"463_CR49","doi-asserted-by":"publisher","unstructured":"Wen Y, Zhang Y, Huang L, Zhou C, Xiao C, Zhang F, Peng X, Zhan W, Sui Z (2019) Semantic modelling of ship behavior in harbor based on ontology and dynamic bayesian network. ISPRS International Journal of Geo-Information 8(3) . https:\/\/doi.org\/10.3390\/ijgi8030107","DOI":"10.3390\/ijgi8030107"},{"issue":"3","key":"463_CR50","doi-asserted-by":"publisher","first-page":"415","DOI":"10.31018\/jans.v12i3.2353","volume":"12","author":"AK Yadav","year":"2020","unstructured":"Yadav AK, Das KK, Das P, Raman RK, Kumar J, Das BK (2020) Growth trends and forecasting of fish production in Assam, India using ARIMA model. Journal of Applied and Natural Science 12(3):415\u2013421","journal-title":"Journal of Applied and Natural Science"},{"issue":"1","key":"463_CR51","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s11852-020-00796-y","volume":"25","author":"SM Yusop","year":"2021","unstructured":"Yusop SM (2021) Determination of spatio-temporal distribution of rastrelliger kanagurta using modelling techniques for optimal fishing. Journal of Coastal Conservation 25(1):15\u201315","journal-title":"Journal of Coastal Conservation"},{"key":"463_CR52","doi-asserted-by":"crossref","unstructured":"Zim\u00e1nyi E, Sakr M, Lesuisse A (2020) MobilityDB: A Mobility Database Based on PostgreSQL and PostGIS. ACM Trans Database Syst 45(4):1\u201342","DOI":"10.1145\/3406534"}],"container-title":["GeoInformatica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-022-00463-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10707-022-00463-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-022-00463-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T12:44:08Z","timestamp":1700397848000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10707-022-00463-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,31]]},"references-count":52,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["463"],"URL":"https:\/\/doi.org\/10.1007\/s10707-022-00463-4","relation":{},"ISSN":["1384-6175","1573-7624"],"issn-type":[{"type":"print","value":"1384-6175"},{"type":"electronic","value":"1573-7624"}],"subject":[],"published":{"date-parts":[[2022,3,31]]},"assertion":[{"value":"6 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}