Workflow to detect ship encounters at sea with GIS support
Ana Catarina Martinho Nunes
I
Workflow to detect ship encounters at sea with GIS support
Ana Catarina Martinho Nunes
II
WORKFLOW TO DETECT SHIP ENCOUNTERS AT SEA WITH GIS SUPPORT
Dissertation supervised by Marco Octávio Trindade Painho, PhD NOVA Information Management School
Universidade Nova de Lisboa
Co-supervised by
Paulo Jorge Antunes Nunes, Master Instituto Hidrográfico
Marinha Portuguesa
November 2022
III
DECLARATION OF ORIGINALITY
I declare that the work described in this document is my own and not from someone else. All the assistance I have received from other people is duly acknowledged and all the sources (published or not published) are referenced.
This work has not been previously evaluated or submitted to NOVA Information Management School or elsewhere.
Lisbon, November 23, 2022.
Ana Catarina Martinho Nunes
[Assinatura Digital]
IV
ACKNOWLEDGMENTS
The theme of this dissertation represents a motivation by itself that has facilitated the work, however, this only materialized due to the support of several people and entities.
A special acknowledgement to Professor Marco Painho, the supervisor of this work, for having assisted me in keeping the course, for sharing knowledge, which contributed to enriching the content presented, and for having kept the standard high.
I thank to Commander Paulo Nunes, co-supervisor of this work, for the technical and critical view, as well as for the support in the conception of the model.
To Nelson Marques, from Direção-Geral de Recursos Naturais, Segurança e Serviços Marítimos, I address my deepest gratitude for the authorization to use the AIS data that were indispensable to obtain the results presented.
To Professor Alexandre Neto, I express my gratitude for having helped me to unlock an important step in the algorithm construction, which allowed reaching the desired model.
To Anita Graser, author of the movingpandas library, I thank for clarifying important doubts about it.
I conclude by thanking to my family, who represented a constant pillar in this challenge. To my twin sister, Joana Nunes, a special mention for her patience, support, and dedication in the moments I needed it most.
V
WORKFLOW TO DETECT SHIP ENCOUNTERS AT SEA WITH GIS SUPPORT
ABSTRACT
According to the United Nations, more than 80% of the global trade is currently transported by sea. The Portuguese EEZ has a very extensive area with high maritime traffic, among which illicit activities may occur. This work aims to contribute to the official control of illegal transshipment actions by studying and proposing a new way of detecting encounters between ships.
Ships with specific characteristics use an Automatic Identification System (AIS) on board which transmits a signal via radio frequencies, allowing shore stations to receive static and dynamic data from the ship. Thus, there is an increase in maritime situational awareness and, consequently, in the safety of navigation.
The methodology of this dissertation employs monthly and daily AIS data in the study area, which is located in southern mainland Portugal.
A bibliometric and content analysis was performed in order to assess the state of the art concerning geospatial analysis models of maritime traffic, based on AIS data, and focus on anomalous behaviour detection.
Maritime traffic density maps were created with the support of a GIS (QGIS software), which allowed to characterize the maritime traffic in the study area and, subsequently, to pattern the locations where ship encounters occur. The algorithm to detect ship-to-ship meetings at sea was developed using a rule-based methodology.
After analysis and discussion of results, it was found that the areas where the possibility of ship encounters at sea is greatest are away from the main shipping lanes, but close to areas with fishing vessels.
The study findings and workflow are useful for decision making by the competent authorities for patrolling the maritime areas, focusing on the detection of illegal transhipment actions.
VI
FLUXO DE TRABALHO PARA DETECTAR ENCONTROS DE NAVIOS NO MAR COM APOIO DE SIG
RESUMO
Segundo as Nações Unidas, mais de 80% do comércio global é, atualmente, transportado por via marítima. A ZEE portuguesa tem uma área muito extensa, com tráfego marítimo elevado, entre o qual podem ocorrer atividades ilícitas. Este trabalho pretende contribuir para o controlo oficial de ações de transbordo ilegal, estudando e propondo uma nova forma de deteção de encontros entre navios.
Os navios com determinadas características, utilizam a bordo um Automatic Identification System (AIS) que transmite sinal através de frequências rádio, permitindo que estações em terra recebam dados estáticos e dinâmicos do navio. Deste modo, verifica-se um aumento do conhecimento situacional marítimo e, consequentemente, da segurança da navegação.
Foi realizada uma análise bibliométrica e de conteúdo a fim de avaliar o estado da arte referente a modelos de análise geoespacial do tráfego marítimo, com base em dados AIS, e foco na deteção de comportamentos anómalos.
Na metodologia desta dissertação, são utilizados dados AIS mensais e diários na área de estudo, situada a sul de Portugal Continental.
Foram criados mapas de densidade de tráfego marítimo com o apoio de um SIG (software QGIS), o que permitiu caracterizar o tráfego marítimo na área de estudo e, posteriormente, padronizar os locais onde ocorrem encontros entre navios. O algoritmo para detetar encontros entre navios no mar foi desenvolvido através de uma metodologia baseada em regras.
Após análise e discussão de resultados, constatou-se que as áreas onde a possibilidade de ocorrer encontros de navios no mar é maior, encontram-se afastadas dos corredores principais de navegação, mas próximas de zonas com embarcações de pesca.
Os resultados do estudo e o workflow desenvolvidos são úteis à tomada de decisão pelas autoridades competentes por patrulhar as áreas marítimas, com incidência na deteção de ações de transbordo ilegal.
VII
KEYWORDS
Automatic Identification System Illegal transshipment
Trajectories Ship encounters
Geographic Information System
PALAVRAS-CHAVE
Automatic Identification System Transbordo ilegal
Trajetórias
Encontros de navios
Sistema de Informação Geográfica
VIII
LIST OF ABBREVIATIONS AND ACRONYMS
AIS Automatic Identification System
DGRM Direção-Geral de Recursos Naturais, Segurança e Serviços Marítimos
DL Decreto-Lei
EEZ Exclusive Economic Zone
EMSA European Maritime Safety Agency
EU European Union
GIS Geographic Information System IMO International Maritime Organization MMSI Maritime Mobile Satellite Identity NATO North Atlantic Treaty Organization
NM Nautical Mile
PJ Polícia Judiciária
SQL Structured Query Language SSI Sistema de Segurança Interna TSS Traffic Separation Schemes
UNCTAD United Nations Conference on Trade and Development USA United States of America
IX
INDEX OF THE TEXT
1. INTRODUCTION ... 1
1.1. Background ... 2
1.2. Objectives ... 4
1.3. Structure ... 5
2. LITERATURE REVIEW ... 5
2.1. Bibliometric analysis ... 5
2.1.1. Dataset preparation ... 6
2.1.2. Bibliometric analysis summary ... 7
2.1.3. Bibliometric analysis: Social Structure ... 8
2.1.4. Bibliometric analysis: Intellectual Structure ... 10
2.2. Content analysis ... 14
3. METHODS ... 16
3.1. Data ... 17
3.2. Study area ... 17
3.3. Exploratory data analysis ... 19
3.4. Construction of trajectories ... 19
3.5. Traffic Density map ... 20
3.6. Ship encounters model ... 21
4. RESULTS ANALYSIS AND DISCUSSION ... 23
4.1. Exploratory data analysis ... 23
4.2. Construction of trajectories ... 25
4.3. Traffic Density map ... 26
4.4. Ship encounters model ... 29
5. CONCLUSIONS ... 37
6. BIBLIOGRAPHIC REFERENCES ... 39
ANNEXES ... 43
A - CONTENT ANALYSIS METHODOLOGIES ... 43
B – CODE FOR CONSTRUCTING SHIP TRAJECTORIES ... 45
C – CODE FOR SHIP ENCOUNTERS MODEL ... 46
X
INDEX OF TABLES
Table 1 – Statistical summary of the final document set (source: Bibliometrix) ... 7 Table 2 – Most relevant publication sources of the final document set (source: Bibliometrix) ... 11 Table 3 – AIS type codes ... 25 Table 4 – Types of ships detected in encounters ... 36 Table A - 1 – Top-10 manuscripts per citations ... 44
XI
INDEX OF FIGURES
Figure 1 – Portuguese EEZ. ... 1
Figure 2 – Information exchange between AIS stations (NATO, 2021) ... 3
Figure 3 – Potential transshipment events (encounters) by Miller et al., 2018 ... 4
Figure 4 – Dataset preparation workflow (software: Bizagi Modeler) ... 6
Figure 5 – The most relevant authors (source: Bibliometrix) ... 8
Figure 6 – Production over time by the most relevant authors (source: Bibliometrix) ... 9
Figure 7 – Country scientific production and network (source: Bibliometrix) ... 9
Figure 8 – Country collaboration map (source: Bibliometrix) ... 10
Figure 9 – Annual Scientific Production (source: Bibliometrix) ... 11
Figure 10 – Word cloud (source: Bibliometrix) ... 12
Figure 11 – Thematic Evolution (source: Bibliometrix)... 12
Figure 12 – Word Dynamics (source: Bibliometrix) ... 13
Figure 13 – Most global cited documents (source: Bibliometrix) ... 14
Figure 14 – Main methodology workflow (software: Bizagi Modeler) ... 16
Figure 15 – Study area ... 18
Figure 16 – Daily AIS data in the study area ... 18
Figure 17 – Monthly AIS data in the study area ... 19
Figure 18 – Density map workflow (software: Bizagi Modeler) ... 20
Figure 19 – Density map model ... 21
Figure 20 – Ship encounters model (software: Bizagi Modeler) ... 22
Figure 21 – Speed vessel histogram ... 23
Figure 22 – Speed vessel histogram without outliers ... 23
Figure 23 – Vessel type histogram ... 24
Figure 24 – Trajectories classified by vessel speed ... 25
Figure 25 – Trajectories classified by vessel type ... 26
Figure 26 – Ships density maps: a) total, b) merchant, c) fishing, d) passenger ... 28
Figure 27 – Density map of total ships with TSS. ... 29
Figure 28 – Ship encounters detections (Jupyter Notebook) ... 30
Figure 29 – Ship encounters detections (QGIS) ... 30
Figure 30 – Tracks of detected ships ... 31
Figure 31 – Tracks of detected ships (first pair) ... 32
Figure 32 – Tracks of detected ships (second pair) ... 32
Figure 33 – Density map of total ships with the detected encounters ... 33
Figure 34 – Density map of merchant vessels with the detected encounters ... 34
Figure 35 – Density map of fishing vessels with the detected encounters ... 35
Figure 36 – Density map of passenger ships with the detected encounters ... 36
1
1. INTRODUCTION
According to the most updated statistics from United Nations, the sea is the main route for global market goods, currently shipping transport more than 80% of world trade (United Nations Conference on Trade and Development (UNCTAD), 2021; International Maritime Organization (IMO), 2020). Inside Maritime areas under Portuguese jurisdiction, the maritime traffic is especially high.
The Portuguese Exclusive Economic Zone (EEZ) extends over 1 727 408 km2 (Timonet and Abecasis, 2020) and this immense area is one of the largest in Europe and the twentieth largest in the world. National jurisdiction over the huge area shown in the map (Figure 1) is synonymous of protect, patrol and control several activities on maritime domain. At the same time over the same space licit and illicit activities could coexists. Among transits, recreational or fishing activities, illicit activities may occur (Pereira, 2018), such as drug smuggling, human trafficking, illegal fishing, pollution from illegal discharges, among others (United Nations, 2019). This work intends to offer a contribution to the official control activities by studying and proposing a new way of detecting illegal transshipments.
Figure 1 – Portuguese EEZ.
In Portugal, maritime transport has been reported to be the most commonly used way to traffic large quantities of certain narcotics, namely cocaine and cannabis. In the year 2020, drug arrested by sea included 82.7% of all cocaine and 70.5% of all cannabis arrested in Portugal (Polícia
2 Judiciária (PJ), 2020). Drug trafficking is one of the focuses of organized crime, as it is a country with a geographical position that favours the transit of hashish and cocaine as a result of the existing connections with Brazil and Latin America. As part of the organisation of this criminal structure, there may exist support cells on national territory to introduce drugs into the European space. Additionally, maritime transport was also identified as a route of human trafficking (Sistema de Segurança Interna (SSI), 2020).
Those illicit activities are potentially carried out through transshipment, which consists of an encounter between two ships at sea in order to transfer between them any kind of load (Miller et al., 2018).
This study is focused on finding the most effective methodology to detect encounters between ships that are hypothetically performing illegal transhipments and, consequently, identify more suited navigation patterns for these activities.
Over the state of art phase several methodologies have been found for investigation of navigation patterns in order to know the maritime situation and to predict certain behaviours. Due to the increase of satellites and ground stations, which allow receiving and transmitting the Automatic Identification System (AIS) signal, the amount of data associated with these has increased significantly, which is very useful for detecting navigation patterns and searching for anomalies (Pallotta et al., 2013).
1.1. Background
The “International Convention for the Safety of Life at Sea” (SOLAS) of 1974, IMO revised in 2015 is the main regulation with instructions for the use of AIS on board (IMO, 2015). It was reiterated the mandatory use of AIS, established in 2004, by ships with more than 300 gross tonnage displacement in international traffic, or 500 gross tonnage and carry cargo in non-international traffic, or by all ships that carry passengers (Silveira et al., 2013). In addition, the European Union (EU) determined in 2014 that all fishing vessels longer than 15 meters must use AIS (McCauley et al., 2016). The AIS equipment allows the automatic exchange of information between stations (between ships, coastal stations, and communications satellite (North Atlantic Treaty Organization (NATO), 2021), as shown in Figure 2) using Very High Frequency (VHF) radio frequencies. Data carried out by AIS signals is encoded according to standardized protocols.
There are specific programs to decode and interpret AIS messages, depending on their content.
(Silveira et al., 2013).
3 Figure 2 – Information exchange between AIS stations (NATO, 2021)
Initially built on to prevent collisions between ships, AIS is currently used to improve “safety of life at sea; the safety and efficiency of navigation; and the protection of the marine environment”
(IMO, 2015). Through the link between the identity of the ships and their respective positions, ships of interest can be tracked. This kind of information is very useful in search and rescue actions and for increasing knowledge about the maritime situation, through a quick and concise exchange of information. In this sense, AIS has allowed a significant improvement in maritime situational awareness (IMO, 2015). With the increase in merchant shipping and the respective use of AIS this kind of datasets push the limits of big data analysis, information from AIS increases systematically each year. However, in order to transform the obtained data into geospatial information, it is necessary to perform a spatio-temporal analysis using various processes in an effective and systematic way (Zhu, 2011).
This type of analysis makes it possible to measure maritime traffic densities, seasonal changes in navigation and characterize navigation patterns by type of ship, among other aspects. In addition to patterns, the detection of behaviour that is out of the ordinary is integrated into maritime situational knowledge using rule-based or anomaly detection methods. In the rules-based method, certain criteria and rules are used, such as, for example, filtering the speed practiced by ships or identifying the presence in prohibited areas. On the other hand, the detection of anomalies focuses on the deviation from the expected patterns, defined in a learning phase of an unsupervised approach, identifying unusual behaviours, which may differ from each other (Pallotta et al., 2013). The behaviours that this work expects to identify are the encounters between ships. These may represent illegal transshipment events to exchange cargo, supplies, or personnel (Miller et al., 2018).
Vessel tracking based on AIS systems is one of the few tools available to detect these transshipment events. Larger ships, such as those carrying cargo, do not usually turn off the AIS for safety reasons. However, some smaller vessels may do it illegally or send incorrect information through the AIS, which makes it difficult to detect them. Even so, despite these constraints, the
4 use of AIS and its tracking represents a very useful tool for trying to detect encounters between vessels and for other purposes such as maintaining sustainability in fishing (Miller et al., 2018).
The transshipment actions normally take place far away from coast on open seas. Illegal transshipment has been detected among fisheries fleets, mainly for fish transfer and non- authorized ship to shore fish disembark. This kind of practices saves fuel for fleets owners and increases the efficiency of transporting fish to markets. However, the introduction of illegal fish into the market affects seafood control and transparency, which severely undermines the sustainability of ocean fisheries. Additionally, transshipment has also been linked to human trafficking that can be used as slaves while the ship remains at sea (Miller et al., 2018).
Figure 3 shows "Potential transshipment events (encounters)", whose data were obtained between 2012 and December 2017, and recorded in the study by Miller et al. (2018). These encounters occurred between transshipment capable vessels and fishing vessels.
Figure 3 – Potential transshipment events (encounters) by Miller et al., 2018
It can be seen that Portuguese waters have few events classified as potential transshipment encounters. This can be justified by the efficiency of maritime surveillance in this country (Pinto, 2017). However, as real-time tools to detect these ship encounters are scarce, it is expected that many will go undetected. Despite this reference, the workflow defined in the present work can be applied to any area on the high seas, regardless of jurisdiction, which could become very useful in the zones of the previous map with higher concentrations of events.
1.2. Objectives
In an attempt to mitigate the illegal activities that occur among maritime traffic in Portuguese waters, the main objective of this work is to find mechanisms to detect ship encounters at sea, which could represent potential illegal transshipment actions.
5 As specific objectives, the following were defined:
Perform a bibliometric and content analysis in order to assess the state-of-the-art in geospatial analysis models of maritime traffic based on AIS data, highlighting the detection of anomalous behaviours;
Develop an algorithm that integrates the construction of trajectories;
Using the constructed trajectories, create maritime traffic density maps in the study area, with the support of a Geographic Information System (GIS), in order to evaluate the traffic differences and visualize where the main types of ships predominate;
Develop an algorithm to detect ship encounters in the high seas;
1.3. Structure
The dissertation is organized into the following main chapters:
Literature review – where the state of the art on the subject of maritime traffic analysis methodologies based on AIS data is presented through a bibliometric analysis (section 2.1) and content analysis (section 2.2).
Methods – include the presentation of the data used (section 3.1) and the study area (section 3.2) where the model is developed. Here the main tools used in the development of the work are described, as well as the adopted problem-solving strategy (sections 3.3, 3.4, 3.5 and 3.6).
Results analysis and discussion – in this chapter the developed density maps are presented (section 4.3) and analyzed, as well as the developed algorithm model (section 4.4), including its outputs.
Conclusions - findings will be reached on the possibility of using a new algorithm to detect illegal transshipment activities at sea. It will be summarized the presented workflow for detecting ship encounters at sea, the obstacles that appeared and some suggestions for future work.
2. LITERATURE REVIEW
The literature review in this dissertation was divided into two main parts: it starts with a bibliometric analysis, carried out using scientific mapping tools, in order to assess the state-of-the-art on patterns and anomalous behaviours detection from AIS data; and a content analysis, where the methodologies used by other authors are analysed, according to the results of the bibliometric analysis.
2.1. Bibliometric analysis
As established, with the purpose of determining the state-of-the-art on the subject, a bibliometric analysis was undertaken, using as research mapping tools the VOSviewer (Van Eck & Waltman,
6 2010) and R-Studio software, where the open-source bibliometrix package (Aria & Cuccurullo, 2017) is applied. It is a quantitative method, where certain indicators are obtained about the documents analyzed, such as number of citations, co-citations of authors or keywords. The main results are visualized in the form of networks or graphs (Gil et al., 2020).
2.1.1. Dataset preparation
The compilation of the workflow performed to prepare the set of documents to be analysed was based on the methodology presented by Gil et al. (2020), and is summarized in Figure 4.
Figure 4 – Dataset preparation workflow (software: Bizagi Modeler)
The beginning of the bibliographic research process was carried out in the SCOPUS documentation database through the following query:
ALL ( ( pattern* OR rout* OR track* OR trajector* OR traffic OR transshipment OR transhipment OR "illegal" OR crim* OR meeting* OR encounter* ) AND ( mssis OR
"Automatic Information System" OR "SAT-AIS" OR "S-AIS" OR "T-AIS" ) AND ( ship* OR vessel* ) AND ( "maritime" OR "sea" ) ).
After carrying out this research, whose elaboration of the query tried to minimize the dispersion of themes, 192 results were obtained. Subsequently, these documents were submitted to a filtering and validation process, focusing on the title, abstract and keywords, which resulted in 79 documents. During the filtering process, documents that studied trajectories of ship cranes, technological specifications of AIS antennas, and detection of ship patterns through remote sensing were excluded.
Other documents considered relevant that were found in the bibliographic references of the validated set were selected and added, from which a final sample of 102 documents was obtained.
The final dataset was then processed using VOSviewer and R-Studio software to perform the bibliometric analysis.
7 2.1.2. Bibliometric analysis summary
The bibliometric analysis was divided into two components: social, where information about authors, countries and institutions is obtained; and intellectual, where data on scientific production, citations and keywords are presented.
From the processing of the final dataset of 102 documents, the first results present a statistical summary shown in Table 1. It is possible to verify that the documents come from 74 different sources and that the oldest article was published in 2008, while the most recent it is already from the present year of 2022. Regarding the types of documents, scientific articles and conference papers were preferred. It is also noted that at least 301 authors produce scientific work on this matter, with a collaboration index of 3.09. This index indicates the average of co-authors who participate only in documents with multiple authors (Gil et al., 2020).
Description Results
Main information about data
Timespan 2008:2022
Sources (journals, books, etc) 74
Documents 102
Average years from publication 4.77
Average citations per documents 30.11
Average citations per year per doc 3.753
References 3880
Document types
Article 49
Book chapter 2
Conference paper 45
Review 6
Document contents Keywords plus (id) 820
Author's keywords (de) 335
Authors
Authors 301
Author appearances 383
Authors of single-authored documents 7 Authors of multi-authored documents 294
Authors collaboration
Single-authored documents 7
Documents per author 0.339
Authors per document 2.95
Co-authors per documents 3.75
Collaboration index 3.09
Table 1 – Statistical summary of the final document set (source: Bibliometrix)
8 2.1.3. Bibliometric analysis:Social Structure
In this section some results of the bibliometric analysis regarding the social structure are presented. Within the social structure itself, the bibliometric analysis will be divided between authors and countries of publication.
Among the set of 102 documents, Figure 5 presents the 10 most relevant authors, that is, those who wrote more articles on the subject under study. It can be observed the total number of articles they wrote and the fractionalized frequency (defined by the equation (1)), related to the contribution of each author (Gil et al., 2020).
( ) = 1
ℎ (ℎ)
!"#
(1)
Where AUj correspond to the set of documents co-authored by the author j and h is each document from AUj (Aria & Cuccurullo, 2017).
Figure 5 – The most relevant authors (source: Bibliometrix)
According to the previous figure, Ângelo Teixeira from Instituto Superior Técnico (Portugal), Konstantinos Tserpes from Harokopio University of Athens (Greece), and Michele Vespe from European Commission were the most productive authors in this set of documents. As for the fractionalized frequency, the highest values correspond to A. Teixeira and Giuliana Pallotta.
Regarding the production of articles carried out by these authors, Figure 6 shows their distribution over time.
0 1 2 3 4 5 6
Number of articles
Author
Articles
Articles Fractionalized
9 Figure 6 – Production over time by the most relevant authors (source: Bibliometrix)
It can be seen that, among the most relevant authors, A. Teixeira is the one that published over a longer period, while K. Tserpes and Ioannis Kontopoulos were the ones that published the most in a single year (2020). Additionally, it can be noted that M. Vespe was the author who, among this group, started publishing on the subject in 2011, while A. Teixeira and Hao Rong were the last to publish until the present date, in 2022.
Regarding the scientific contribution per country, the graph in Figure 7 shows the number of articles published by the top-10 countries with the highest scientific production on the topic under study, as well as the number of articles that come from the collaboration of these countries with others.
Figure 7 – Country scientific production and network (source: Bibliometrix) 96
42
27 22 20 19 17 16 16 13
28
13
6 8 11 8 6 5 4 3
0 20 40 60 80 100
Number of articles
Country
Country scientific production
Country network
10 It can be observed that it was in China where more articles on the subject were published, as well as where greater collaboration with other countries was recorded. Italy follows in both indicators, with less than half as many publications and collaborations as China.
Additionally, Figure 8 shows the map with the collaboration between the countries. The most expressive connections represent the country pairs that had the most collaboration, namely the China and United States of America (USA), the Canada and Poland, and the Italy and USA.
Figure 8 – Country collaboration map (source: Bibliometrix)
2.1.4. Bibliometric analysis:Intellectual Structure
In this section some results of the bibliometric analysis regarding the intellectual structure are presented, which will be divided between production, sources, keywords and co-citations.
Through Figure 9, it can be seen that scientific production on the subject has increased over time.
11 Figure 9 – Annual Scientific Production (source: Bibliometrix)
The sources of publications also represent an important indicator, as it provides information on which ones should be further explored in search of articles on the same topic. In this sense, Table 2 presents the top-5 sources with the highest number of publications referring to the set of documents under analysis.
Most relevant publication sources Number of articles IEEE transactions on intelligent transportation systems 6
Journal of navigation 6
Ocean Engineering 6
Reliability engineering and system safety 5
18th international conference on information fusion, fusion 2015 2 Table 2 – Most relevant publication sources of the final document set (source: Bibliometrix)
It can be observed that the most relevant sources of published documents on the topic under study were the “IEEE transactions on intelligent transportation systems”, “Journal of navigation”
and “Ocean Engineering” journals.
The bibliometrix package allows the production of a word cloud based on the most frequently used keywords in the documents under analysis (Ahmi, 2022). According to the word cloud in Figure 10, the most frequent keywords among the analysed documents are “anomaly detection”,
“ships”, “automation”, “trajectories” and “data mining”. These represent the most relevant terms for anyone researching this topic.
0 5 10 15 20 25
Number of articles
Year
12 Figure 10 – Word cloud (source: Bibliometrix)
In addition, this package also applies a methodology based on that presented by Cobo et al.
(2011), which consists of a Thematic Evolution Analysis, based on a co-word network analysis and clustering (Aria & Cuccurullo, 2022). By the thematic evolution presented in Figure 11, it is possible to deduce that concepts related to awareness, assessment and monitoring evolved to other more practical and technological ones, such as automatic, machine learning, and trajectory construction.
Figure 11 – Thematic Evolution (source: Bibliometrix)
13 Figure 12 presents the dynamics over time of the 10 most used keywords in the set of documents under analysis. The occurrences of all expressions increased from 2008 to 2022, however, the most recently used are "automatic Identification system", "automation", "ships", "trajectories" and
"data mining".
Figure 12 – Word Dynamics (source: Bibliometrix)
To complete the bibliometric analysis, there was an attempt to identify which documents on this theme are most cited among the scientific community. Figure 13 shows the top-10 of these documents, where it can be noted that the article "A survey of vision-based trajectory learning and analysis for surveillance" by Morris and Trivedi (2008) was the most frequently cited, with a total of 375 citations. Looking at the date, it can be deduced that it was one of the first documents to address this theme and, thus, it has been used as a basis for several subsequent investigations. Supporting this assumption, in this top-10, there are 3 more articles that were published in 2008. Considering the relevance of these documents for anyone intending to research on this topic, the summary of the methodology applied in each of the top-10 is presented in Table A - 1 from Annex A.
0 5 10 15 20 25 30 35 40 45
Cumulative occurrences
Year
Automatic identification system Automation
Ships Trajectories Data mining Anomaly detection Artificial intelligence Clustering algorithms Maritime traffic
Waterway transportation
14 Figure 13 – Most global cited documents (source: Bibliometrix)
2.2. Content analysis
The content analysis is an important phase of this work, from which it is intended to acquire knowledge about the topic under study but also to avoid the development of methodologies to obtain certain solutions that already exist. In addition, this approach allows the focus and the narrowing of the topic under study (Gil et al., 2020).
As an area increasingly explored in recent years, there are several methodologies for obtaining patterns in maritime traffic based on AIS data. Based on the content of the analysed references, it was found that, regardless of the techniques applied, most focus on 4 main steps (Machado et al., 2019):
1) Acquisition and pre-processing of AIS data;
2) Spatio-temporal data analysis, according to static and dynamic information;
87 93
94 110
122
198 223
341 343
375
0 50 100 150 200 250 300 350 400
Global citations
Morris & Trivedi, 2008, A survey of vision-based trajectory learning and analysis for surveillance.
Pallotta et al., 2013, Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction.
Lee et al., 2008, Trajectory Outlier Detection: A Partition-and-Detect Framework.
Ristic et al., 2008, Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction.
Silveira et al., 2013, Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal.
Natale et al., 2015, Mapping fishing effort through AIS data.
De souza et al., 2016, Improving fishing pattern detection from satellite AIS using data mining and machine learning.
Laxhammar, 2008, Anomaly detection for sea surveillance.
Tu et al., 2018, Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey from Data to Methodology.
Lane et al., 2010, Maritime anomaly detection and threat assessment.
15 3) Construction of trajectories, according to the identification of each ship;
4) Patterns and outliers detection.
AIS data acquisition can be derived from various sources, but usually originates from decoding AIS messages or from databases with historical records. These historical AIS datasets are increasingly large and complex (Ristic et al., 2008). Each object is related to a specific ship, identified by Maritime Mobile Satellite Identity (MMSI), and contains static and dynamic information relative to a given instant and geographical position (Pallotta et al., 2013).
The static information is related to the ship identification, which includes the type, call sign, name, IMO number, size, among others. As for the dynamic information, it refers to the behaviour of the ship at that instant which, in addition to the position, includes the course over ground, speed over ground and other data that may be updated in the subsequent AIS messages (Pallotta et al., 2013). Due to the amount of AIS data, being classified as big data, these should be handled through automatic processing techniques (Pallotta et al., 2013).
The trajectory construction process can be done through several algorithms; however, its basis should be similar, consisting in the aggregation of the historical vector information (position and velocity) of each ship (Pallotta et al., 2013).
After the construction of the trajectories, some adopt machine learning techniques such as the unsupervised clustering method to group them into main routes and then to obtain their waypoints, i.e., relevant points in a route that indicate significant changes of direction for a large number of ships (Morris & Trivedi, 2008; Pallotta et al., 2013). In this type of methodology, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can be highlighted, which is frequently used to form clusters based on the points density (Ester et al., 1996).
The creation of clusters allows the identification of navigation patterns (Laxhammar, 2008), and outliers are susceptible to analysis in order to identify anomalous ship behaviour (Morris & Trivedi, 2008). The DBSCAN method allows the prior removal of noise and outliers that may interfere with anomaly detection (Pallotta et al., 2013).
A technique applied for anomaly detection is the use of thresholds related to, for example, traffic density or probability distributions. These thresholds separate ships that follow standard trajectories from those that perform anomalous behaviours (Laxhammar, 2008; Ristic et al., 2008). The main types of ship anomalies to be detected are related to position, speed or time (Tu et al., 2018). The challenge is often in adjusting this threshold, which will make the model more or less sensitive (Laxhammar, 2008). However, despite being simple, this type of model has the limitation of analysing the data momentarily, without considering the history of that ship or the interaction with other ships. Consequently, it cannot detect anomalous (and illicit) activities involving more than one ship and occurring over several moments in time (Laxhammar, 2008), such as piracy attacks or illegal transshipment.
Based on the patterns acquired from the AIS data, statistical techniques can be applied to identify specific outliers. An example of this is presented by Silveira et al. (2013), in which a statistical method is developed to obtain the collision risk off the Portuguese coast.
16 Among the most common anomaly detection methods, besides the statistical and machine learning ones, neural networks are also frequently used (Tu et al., 2018).
In the case of the identification of anomalies that may represent encounters between ships, the trajectories previously built are used to calculate "the distance between any two ships as a function of time". In this way, the closest point of approach between a pair of ships is obtained considering their positions and the instant of time recorded (Lane et al., 2010).
3. METHODS
In order to encourage the development of this theme and the accessible replication of the results that will be presented, open source tools were used.
The applied methodology, as shown in Figure 14, started with the collection of the data for the study area and with its preprocessing, where an exploratory analysis was carried out. Then, the trajectories of the ships that crossed the study area, in a period of 24 hours, were constructed using the MovingPandas library (Graser, 2019) in python.
Subsequently, maritime traffic density maps were created with the support of a GIS, applicable to all ships and to their main types, in order to characterize navigation in the study area. The GIS software used to accomplish this task and to complement the data processing and visualization of results was QGIS version 3.16 (QGIS.org, 2021).
This type of maritime traffic density map may have other applications, such as port management, planning, optimising the effort of patrol vessels, maritime transportation safety (Wang et al., 2019), etc.
Figure 14 – Main methodology workflow (software: Bizagi Modeler)
Finally, and as the main goal, it was applied a rule-based methodology to develop an algorithm to detect behaviours in maritime traffic that appear to be encounters between ships in the study area. For this, python programming and Structured Query Language (SQL) were used through the Jupiter Notebook (a web computing environment), provided by the Anaconda platform.
The results of the developed algorithm have to be analyzed in detail because its purpose is to detect hypothetical encounters of ships that carry out illegal transshipment, according to the rules defined for this model. Additionally, these results were overlaid on the density maps created
17 previously in order to identify the navigation patterns where the probability of detecting encounters between ships is greater.
3.1. Data
The raw data used are from AIS and were provided by Direção-Geral de Recursos Naturais, Segurança e Serviços Marítimos (DGRM), specifically for this work. These AIS data come from satellites (SAT-AIS) and from vessel traffic services (T-AIS). To protect the confidentiality of ships, the identity elements contained in the AIS data are hidden.
The data were divided into two sets, limited to the study area:
The first includes data from an entire month (May 2021), which corresponds to 16 119 903 points, in order to produce more solid density maps and ensure the reliability of the characterization of navigation in the study area;
The second includes data from a 24-hour period (May 18, 2021), which corresponds to 543 804 points, used to build the algorithm for detecting encounters between ships, so that it can be run in a timely manner to identify actions in near real time.
All data were referenced in CRS WGS 84 (EPSG 4326).
3.2. Study area
The study area will be centred on the Southern Portugal Maritime Zone and is shown in Figure 15.
These encounters are more important on the high seas, as along the coast or in port areas, they may be meaningless (Lane et al., 2010), so the study area was adjusted accordingly.
18 Figure 15 – Study area
Additionally, daily and monthly AIS data in the study area are presented in Figure 16 and Figure 17, respectively.
Figure 16 – Daily AIS data in the study area
19 Figure 17 – Monthly AIS data in the study area
3.3. Exploratory data analysis
An exploratory data analysis of the AIS data was performed, using python language, which allowed to eliminate outliers and understand the content of the data. To perform these tasks, the GeoPandas library (Jordahl et al., 2020) and plot visualization were used through the Jupyter notebook (Kluyver et al., 2016), which helped to determine the constitution of the data and detect some outliers.
This process started with the shapefile import of the daily AIS data and its subsequent conversion into GeoDataFrame format through the GeoPandas library.
In the exploratory data analysis, data with MMSI equal to 0 were excluded, as this MMSI number appears by default on ships whose AIS does not have the correct value. In trajectory construction, this would be a problem as it would join points from different ships.
Histograms were constructed to better understand the distribution and frequency of data in terms of speed and ship types in the study area. Negative speed values were excluded, as well as very high values that may be errors or that don't fit in an encounter situation with another ship.
3.4. Construction of trajectories
By definition, a trajectory can be described as a “sequence of points with two-dimensional coordinates” and can be defined by the equation (2), where ($, ) is the position of the ship in two-dimensional cartesian space at the moment (Mehri et al., 2021.).
20
% = [ ($', ') , ($), )), … , ($+, +)] (2) For this work, as mentioned before, the MovingPandas python library (Graser, 2019) was selected to perform this task after pre-processing the AIS data. In this process, each trajectory is composed of a series of geometries ordered in time. MovingPandas uses functions and classes of trajectories based on the GeoPandas library which, in turn, is an extension of the Pandas library, and allows the use of other data types and geometric operations, as well as the storage of data in GeoDataFrames (Graser, 2019).
The algorithm that was developed to build the trajectories, with support from the MovingPandas library, was also built on the Jupyter notebook and the Matplotlib python library was used for quality control and results analysis.
3.5. Traffic Density map
The Figure 18 shows the workflow to be applied in the construction of density maps, which was based on the methodology used by the European Maritime Safety Agency (EMSA), 2019.
Figure 18 – Density map workflow1 (software: Bizagi Modeler)
The software used was QGIS and a volume of monthly AIS data was used in this trajectory dataset. It was considered that data from one month was significant to characterize the navigation pattern in the study area. A model was developed in the QGIS model designer tool that, when executed, performs the four tasks listed in the Figure 18.
The model developed in QGIS to build the density maps is shown in Figure 19. The input called
"trajectories" is the output of the trajectory construction algorithm mentioned in the previous subchapter. As stated in the data description, to create the density maps, these trajectories were built with data from a whole month.
1NM =Nautical Mile
21 Figure 19 – Density map model
The model presented in Figure 19 consists of performing four main tasks:
Create a grid at the study area extent, where each cell has the dimension of 1 nautical mile (NM) by 1 NM;
Cut the grid by the exact boundaries of the study area;
Count the number of trajectories crossing each grid cell;
Rank the previous count and assign colours to make it noticeable which areas have the highest traffic density.
3.6. Ship encounters model
Figure 20 presents the part of the methodology considered to be the most relevant of the work, as it is in this step that encounters of ships at sea will be detected, with potential behaviour to carry out illicit transshipment of material or personnel. The determination of the criteria was based on the methodology presented by Miller et al. (2018). In that particular study, AIS data were used to detect transshipment between fishing vessels. Thus, the criteria used in this new algorithm to define what can be considered a rendezvous situation are presented in Figure 20.
22 Figure 20 – Ship encounters model (software: Bizagi Modeler)
A distance of 300 yards2 between ships was selected as this is a suspect value for ships that are meeting, as ships that are in normal transit maintain a greater distance from others for safety reasons, at least between 1000 yards to 2 nautical miles (Hsu, 2014).
It was considered that speed of 5 knots or less could indicate anomalous behaviour by a vessel, namely purposeful slowing down to be able to transship illegally, as usual transit speeds range between 5 and 15 knots (Xiao et al., 2015).
In areas nearshore there are many ships that come close to each other because they are in maneuvers around the ports, either leaving or mooring. The criterion of a minimum distance from the coast of 5 nautical miles is introduced to avoid that those proximities, for a certain time period, are considered to be meetings between ships (Lane et al., 2010) to undertake illegal transshipment.
Finally, to prevent the mistake of selecting random crossings between ships, only those that maintain the previous criteria during one hour are considered relevant. This period of time allows ships to perform illegal transshipment.
Annex C presents the code used to build this model to detect ship encounters at sea.
In the development of this algorithm, only 1-day AIS data were used, since the aim will be to run it to detect encounters between ships in near real time, in order to represent an effective support in the identification and monitoring of these situations.
The model for ship encounter detection at sea was built according to the rules-based methodology. During the development of the algorithm, the constraint arose that it was not feasible to perform all the intended geospatial analysis tasks with the python libraries, namely GeoPandas and MovingPandas. It was then necessary to perform queries on a database in PostgreSQL (PostgreSQL Global Development Group, 2021), which is an object-relational database type. PostGIS (PostGIS, n.d.) is an extension applicable to the PostgreSQL database.
2 274.32 meters
23 It provides support for geographic objects, allowing SQL queries to be made concerning their location (PostGIS, n.d.). To support the creation and management of this database, the open software pgAdmin 4 was used as an interface. Nevertheless, it was possible to integrate postgreSQL queries into the python algorithm, in Jupyter Notebook, through functions that connect to the database and convert GeoDataFrames into postgreSQL tables or vice versa.
These functions used belong to the GeoPandas library and are called GeoDataFrame.from_postgis and GeoDataFrame.to_postgis. While the first one creates a GeoDataFrame from a SQL query that implies a geometry column, the second one loads a GeoDataFrame into a PostgreSQL database (Jordahl et al., 2020).
4. RESULTS ANALYSIS AND DISCUSSION
4.1.Exploratory data analysis
After importing the shapefile of the daily AIS data, through the Jupyter notebook, it was converted into a GeoDataFrame through the GeoPandas library. Subsequently, the data with MMSI equal to 0 were excluded, in order to avoid an incorrect construction of trajectories by joining points from different ships that transmit their identification incorrectly. In this task, 274 records were discarded.
Then histograms were visualized with certain AIS fields, namely speed practiced (Figure 21) and vessel typology (Figure 23).
Figure 21 – Speed vessel histogram
It was found that the velocity field had outliers, so all values below 0 knots and above 30 knots were excluded, resulting in the distribution of Figure 22. In this task, 22 records were discarded.
Figure 22 – Speed vessel histogram without outliers
24 Regarding the highest peaks, should be highlighted the interval between 10 and 15 knots that can correspond to ships in transit, and the interval between 0 and 5 knots which may correspond to vessels that are fishing or have had to reduce speed for some particular reason. This interval will be used as a criterion in the algorithm for detecting encounters between ships, as illegal transshipment activities are more likely to occur at lower speeds.
Figure 23 – Vessel type histogram
It can be seen that the most frequent type of ship is 70, which corresponds to merchant vessels.
The second most frequent type is 30, which corresponds to fishing vessels (MarineCadastre.gov, 2018). Table 3 presents the identification of all types of ships contained in this AIS dataset.
Vessel type Description
0 Not Available, default 1-19 Reserved for future use
30 Fishing
31 Towing
35 Military operations
36 Sailing
37 Pleasure craft
52 Tug
60 Passenger, all ships of this type 69 Passenger, no additional information 70 Cargo, all ships of this type
71 Cargo, hazardous category A 72 Cargo, hazardous category B 79 Cargo, no additional information 80 Tanker, all ships of this type 81 Tanker, hazardous category A 82 Tanker, hazardous category B 83 Tanker, hazardous category C 84 Tanker, hazardous category D
25 89 Tanker, no additional information
90 Other Type, all ships of this type 99 Other Type, no additional information
Table 3 – AIS type codes
These exploratory data analysis procedures were repeated for the shapefile of the monthly AIS data.
4.2.
Construction of trajectories
As stated in the methodology chapter, the ship trajectories were built by applying the MovingPandas python library to the AIS data, after its pre-processing. The algorithm is described in Annex B.
The trajectories can be visualized after they have been plotted by the Matplotlib python library. In Figure 24 the trajectories are classified according to the vessel speed and in Figure 25 the trajectories are classified by vessel type.
Figure 24 – Trajectories classified by vessel speed
As seen in the exploratory analysis, the intervals between 0 and 5 knots and between 10 and 15 knots are also highlighted in the trajectories (Figure 24). However, in this case, it is already possible to perform a spatial correlation. Thus, it is confirmed that the vessels between 0 and 5 knots are closer to land and with irregular trajectories, which may indicate that they are fishing vessels or that they are performing other activities than transit. On the other hand, the ships between 10 and 15 knots are concentrated in corridors, which suggests that they are in transit, both to the North and to the South.
26 Figure 25 – Trajectories classified by vessel type
Regarding Figure 25, which displays the trajectories classified by ship type, the Table 3 allows a more complete reading of the legend and an understanding of which ship types were identified in the study area. It can be seen that the majority that are circulating in the corridors are type 70 – cargo ship –, which makes sense because they are large and operate in the main ports, without stops along the way. Next to these are narrower corridors where there are mostly type 80 – tanker – probably with hazardous cargo as they are in separated corridors. Additionally, it can be seen that type 30 – fishing vessels – are close to land, corresponding to some vessels between 0 and 5 knots with irregular behaviour.
4.3.
Traffic Density map
The application of the model developed in QGIS to create density maps originated several results, particularly those shown in Figure 26. These consist of monthly density maps for all ships and for the main ship types identified, namely merchant, fishing and passenger ships.
27 a)
b)
28 c)
d)
Figure 26 – Ships density maps: a) total, b) merchant, c) fishing, d) passenger
It can be seen that density maps are useful for identifying differences in maritime traffic. Through the map a) it is verified that there are well-defined corridors. In order to understand whether these zones of higher density coincide with the Traffic Separation Schemes (TSS) defined by Decreto- Lei (DL) 198/2006, a shapefile with these polygons was overlaid on the density map, which can be seen in Figure 27. These official corridors identify geographic areas where navigation must pass, in the north-south and south-north direction, as well as specific corridors for ships carrying dangerous cargo, which are further west, next to each main corridor.
29 Figure 27 – Density map of total ships with TSS.
Thus, it is confirmed that the highest traffic areas correspond to the TSS, which reveals the importance of these density maps in the assessment of maritime traffic. With this correlation, it can be confirmed that cargo ships and tankers (with hazardous cargo) that were in transit (between 10 and 15 knots) and were identified in the corridors of the trajectories plots (Figure 25 e Figure 24, Respectively), were navigating in the respective TSS corridors.
4.4.
Ship encounters model
Using the rule-based methodology, the model for ship encounter detection at sea was built, as presented in Annex C. The model development included queries to a postgreSQL database which was created using the interface of the open source software pgAdmin 4. Those queries were integrated into the python algorithm through Jupyter Notebook.
The model tasks that led to the results under analysis began by selecting all ships in the study area that were more than 5 nautical miles from the coast and that had speeds equal to or less than 5 knots. Additionally, they selected ships that were within 300 yards of another ship, and that maintained all of these conditions for at least 1 hour.
Figure 28 (from Jupyter Notebook) and Figure 29 (from QGIS) present the model results for detecting ship encounters at sea, applied to trajectories constructed with daily AIS data.
30 Figure 28 – Ship encounters detections (Jupyter Notebook)
While Jupyter Notebook allows to observe the results instantaneously when running the code, QGIS allows to analyse the attribute table and to understand which ships are involved in the detected encounters. Figure 29 allows visualizing which are the ships identified in the encounter situations under analysis. Their identification was omitted for security reasons, but it can be seen that each ship has been identified twice.
Figure 29 – Ship encounters detections (QGIS)
Based on the analysis of the previous figure, it appears that four ships are in the conditions specified by the algorithm, despite being marked with red circles four situations classified as encounters. It is noticeable that each pair of detected ships has two events considered as encounters.
31 In order to understand the behaviour of the ships identified in the encounters, an analysis of their total daily track within the study area was carried out.
Figure 30 – Tracks of detected ships
From Figure 30 it can be seen that QGIS allowed visualising the tracks of the 4 ships involved in the encounters, over a whole day. The encounters under analysis were found to be located along the north-eastern edge of the study area, not far from the coast (between 7 and 11,3 NM from the coast). In order to better visualize the tracks of the ships involved, Figure 31 and Figure 32 show, on a larger scale, the pairs of ships that have been identified. The first pair is the one that was identified further west and the second pair is the one that was further east. In both cases, it can be seen that the tracks are similar in each pair for a significant period of the route. More specifically, the timings and courses shown in the figures demonstrate that the ship pairs were in close proximity for an equivalent period of time and moving in the same direction. Additionally, they show similar reduced speeds. Such values may indicate transhipment actions.
32 Figure 31 – Tracks of detected ships (first pair)
Figure 32 – Tracks of detected ships (second pair) Ship B
11:03 – 11h30 Course 220
Speed 2.7
Ship A 11:00 – 11h22
Course 219 Speed 2.9
Ship C 07:58 – 08h03
Course 073 Speed 2.3
Ship D 07:54 – 08h02
Course 076 Speed 2.0
33 In a practical use, in which illegal transshipment activities would be under investigation, these ships would be subject to a more detailed investigation based on the results presented in Figure 31 and Figure 32. A technique that can significantly help to understand if there are potential illegal actions includes the integration of satellite images, especially optical and high-resolution ones.
Depending on the temporal resolution of the images, there may be records of the proximity of ships, in which possible exchanges of people or goods can be seen.
Additionally, the detected encounters were overlaid on the density maps, in order to identify the characteristics of maritime traffic in these areas. Thus, within the investigation and monitoring of illegal activities, there may be a focus on certain areas according to the traffic density where these situations are more likely to occur. In this way, the maritime surveillance effort can be better targeted by the competent authorities.
Figure 33 shows the overlapping of the encounters with the total density map, in which it appears that they occurred in areas with moderate traffic. The meetings were all located in yellow areas, away from the heavy traffic areas, symbolised in red.
Figure 33 – Density map of total ships with the detected encounters
Figure 34 shows the overlapping of the encounters with the density map of merchant vessels, in which it can be seen that they occurred in areas with reduced traffic. The meetings were located in blue areas, away from areas with moderate or high merchant ship traffic, symbolised in yellow or red respectively.
34 Figure 34 – Density map of merchant vessels with the detected encounters
Figure 35 shows the overlapping of the encounters with the density map of fishing vessels, in which it can be seen that they occurred in moderate and high traffic areas. The encounters were located in yellow or orange areas, which correspond to areas of moderate or high traffic density of fishing vessels. In parallel, they are far from the areas with the lowest density of this type of vessel, which are blue or green areas.
35 Figure 35 – Density map of fishing vessels with the detected encounters
Figure 36 shows the overlapping of the encounters with the density map of passenger ships, in which it can be seen that they occurred in areas with reduced traffic. All the encounters were detected in blue areas, where the lowest density of this type of vessel is recorded.
36 Figure 36 – Density map of passenger ships with the detected encounters
Based on the analysis of the previous images, it can be assumed that the probability of these encounters occurring is higher in areas where fishing vessels are present. On the other hand, in TSS areas, with greater maritime traffic, mainly from merchant ships, this probability is lower.
To understand this correlation, Table 4 shows the types of ships that were detected, as well as their nationality and length.
Ship number Type Flag Length
Ship A Fishing vessel Portuguese 15 m
Ship B Fishing vessel Portuguese 15 m
Ship C Fishing vessel Portuguese 25 m
Ship D Fishing vessel Portuguese 26 m
Table 4 – Types of ships detected in encounters
It can be observed that all the vessels detected are Portuguese fishing vessels, which may be an indication that they are engaged in fishing activity, like other vessels in the same area. However, the detected pairs may have also carried out an illegal transshipment of fish or other goods. It is verified that the length of the ships is similar within each detected pair, that is, between ships A and B, and between ships C and D. This similarity can also facilitate transshipment activities.
