A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics
- Is it possible to identify local anomalies using one or a combination of features given a port of origin and a port of destination?
- Is it possible to make sense of the interpolation and the uncertainty it may cause when determining anomalies?
2.1. Automatic Identification System (AIS)
2.2. Anomaly Detection
2.2.1. Types of Anomalies
2.2.2. Anomaly Detection by Vessel Type
2.3. Spatial Region, Trajectory, and Subtrajectory
2.4. Global and Local Anomaly Detection
2.5. Visual Analytics
3. Related Work
3.1. Automated Anomaly Detection of Vessel Trajectories
3.2. Visual Anomaly Detection of Trajectories in Maritime Traffic
- The tool should support the identification of trips that may have anomalous behavior.
- The tool should support the identification of local anomalies.
- The tool should improve the user’s understanding of where interpolation has happened in a trajectory and its impact, if any, on anomalies.
- The tool should support some explanation of the cause of the anomaly.
4.2. Framework Overview
4.3. Tool Rationale
4.3.1. Why Use Spatial Regions?
4.3.2. Why Use Mean and Scores?
4.3.3. Why Show a Map?
4.3.4. Why Show Scores in a Table-Like Visualization?
4.4. Trip Outlier Scoring Tool (TOST)
4.4.1. Data Interpolation
4.4.2. Subtrajectory Attributes
4.4.3. Subtrajectory Score
5. A Use Case
6. Tests with Users
6.1. Experimental Setup
6.3. User Test Questions
- How many trips are outliers?Our idea with this question was to validate whether the participants could identify which trips were outliers. They would have to filter the data either by brushing or typing directly into the filter component. Since asking for several IDs can be time-consuming and prone to errors, we asked for the number of trips that were outliers.
- What is the identifier of the trip with the highest score?In this question, we tried to see if the participants understood both how to sort trips and the ranking concept to find the trajectory that was the most anomalous in a given scenario.
- Which spatial regions have more outliers than others?The purpose of this question was to check whether the participants could use the score distribution plots to identify and visualize spatial regions with a higher number of anomalies.
- In which spatial regions did trip X have an outlier behavior?In this question, we wanted to see if the participants understood the score concept and how to visualize it, either by hovering over a row and seeing the score at the bottom of the table or by looking at the axis at the top of the table.
- How much interpolation do you think there is in this scenario?Ideally, we would like to see the data set with few interpolations. Based on this information, and without using any type of sorting, how much interpolation do you think there is in this data set? This question tries to assess if using color to interpret the interpolation gives an overall idea of the amount of interpolation used in the data set.
- How many trips have, on average, above 50% interpolation?This question tries to verify if the participant understood how the interpolation concept is displayed and if they are able to find the number of trajectories for which there is not enough information to label it as anomalous.
- For a given trip in a scenario, choose the most appropriate option.In this question, we put together the concepts of score, interpolation, and trajectory together. The user then had to choose one of the following options:
- It is not an outlier; it has a good score and good interpolation.
- It is an outlier; it has a bad score and bad interpolation.
- I can’t say, there is too much interpolation, or the interpolation seems incorrect.
7. Conclusions and Limitations
Data Availability Statement
Conflicts of Interest
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|1||(1) How many trips are outliers?||0||100%||100%|
|(2) What is the Id of the trip with the highest score?||542||80%||100%|
|(3) Which spatial regions have more outliers than others?||None||50%||50%|
|2||(4) How many trips are outliers?||10||70%||100%|
|(5) What is the Id of the trip with the highest score?||270||90%||100%|
|(6) Which spatial regions have more outliers than others?||3;4;5;6;7;8||30%||50%|
|3||(7) How many trips are outliers?||25||60%||100%|
|(8) What is the Id of the trip with the highest score?||2276||90%||100%|
|4||(10) In which spatial regions the trip 1006 had an outlier behaviour?||4||70%||100%|
|(11) In which spatial regions the trip 1059 had an outlier behaviour?||6||80%||100%|
|(12) In which spatial regions the trip 1079 had an outlier behaviour?||9||80%||100%|
|5||(13) How much interpolation do you think there is in this dataset||-||-||-|
|(14) How many trips have, on average, above 50% interpolation?||14||80%||100%|
|6||(15) How much interpolation do you think there is in this dataset||-||-||-|
|(16) How many trips have, on average, above 50% interpolation?||21||70%||100%|
|7||(17) How much interpolation do you think there is in this dataset||-||-||-|
|(18) How many trips have, on average, above 50% interpolation?||32||70%||100%|
|8||(19) Given the trip 2276 choose the most appropriate option||It is an outlier||20%||100%|
|(20) Given the trip 1963 choose the most appropriate option||It is not an outlier||100%||100%|
|(21) Given the trip 3062 choose the most appropriate option||I can’t say||80%||100%|
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Abreu, F.H.O.; Soares, A.; Paulovich, F.V.; Matwin, S. A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics. ISPRS Int. J. Geo-Inf. 2021, 10, 412. https://doi.org/10.3390/ijgi10060412
Abreu FHO, Soares A, Paulovich FV, Matwin S. A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics. ISPRS International Journal of Geo-Information. 2021; 10(6):412. https://doi.org/10.3390/ijgi10060412Chicago/Turabian Style
Abreu, Fernando H. O., Amilcar Soares, Fernando V. Paulovich, and Stan Matwin. 2021. "A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics" ISPRS International Journal of Geo-Information 10, no. 6: 412. https://doi.org/10.3390/ijgi10060412