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Applying spatial mutual information to AIS data

Published online by Cambridge University Press:  01 October 2021

Bruce A. McArthur  and
Anthony W. Isenor Open the ORCID record for Anthony W. Isenor [Opens in a new window]
Bruce A. McArthur
Affiliation:
Defence R&D Canada – Atlantic Research Centre, Dartmouth, Canada
Anthony W. Isenor*
Affiliation:
Defence R&D Canada – Atlantic Research Centre, Dartmouth, Canada
*
*Corresponding author. E-mail: [email protected]
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Abstract

This paper examines a new interpretation for spatial mutual information based on the mutual information between an attribute value and a spatial random variable. This new interpretation permits the measurement of variations in spatial mutual information over the domain, not only answering the question of whether a spatial dependency exists and the strength of that dependency, but also allowing the identification of where such dependencies exist. Using simulated and real vessel reporting data, the properties of this new interpretation of spatial mutual information are explored. The utility of the technique in detecting spatial boundaries between regions of data having different statistical properties is examined. The technique is shown to successfully identify vessel traffic boundaries, crossing points between traffic lanes, and transitions between regions having differing vessel movement patterns.

Keywords

entropymutual informationautomatic identification systemtraffic pattern
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 1 , January 2022 , pp. 95 - 105
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Copyright
Copyright © Crown Copyright, 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Altieri, L., Cocchi, D. and Roli, G. (2018). A new approach to spatial entropy measures. Environmental and Ecological Statistics, 25, 95–110.CrossRefGoogle Scholar
Altieri, L., Cocchi, D. and Roli, G. (2019). Measuring heterogeneity in urban expansion via spatial entropy. Environmetrics, 30, 25–48.CrossRefGoogle Scholar
Arguedas, V. F., Pallotta, G. and Vespe, M. (2018). Maritime traffic networks: From historical positioning data to unsupervised maritime traffic monitoring. IEEE Transactions on Intelligent Transportation Systems, 19, 722–732.CrossRefGoogle Scholar
Bar-Shalom, Y., Fortmann, T. E. and Cable, P. G. (1990). Tracking and Data Association. Orlando: Acoustical Society of America.CrossRefGoogle Scholar
Blackman, S. S. and Popoli, R. (1999). Design and Analysis of Modern Tracking Systems. Boston: Artech House.Google Scholar
Blasch, E. P., Dorion, E., Valin, P. and Bosse, E. (2010). Ontology Alignment Using Relative Entropy for Semantic Uncertainty Analysis. Proceedings of the IEEE 2010 National Aerospace and Electronics Conference, Dayton, OH, USA.CrossRefGoogle Scholar
Cover, T. M. and Thomas, J. A. (2006) Elements of information theory 2nd edition. In: Edwards, S. (ed.). Information Processing and Management, pp. 400–401. Berlin: John Wiley and Sons.Google Scholar
Creech, J. A. and Ryan, J. F. (2003). AIS: The cornerstone of national security? Journal of Navigation, 56, 31–44.CrossRefGoogle Scholar
DeWeese, M. R. and Meister, M. (1999). How to measure the information gained from one symbol. Network: Computation in Neural Systems, 10, 325–340.CrossRefGoogle ScholarPubMed
Filipiak, D., Strózyna, M., Wecel, K. and Abramowicz, W. (2018). Anomaly Detection in the Maritime Domain: Comparison of Traditional and big Data Approach. Proceedings of the NATO IST-160-RSM Specialists’ Meeting on Big Data and Artificial Intelligence for Military Decision Making, Bordeaux, France.Google Scholar
George, J., Crassidis, J., Singh, T. and Fosbury, A. M. (2011). Anomaly detection using context-aided target tracking. Journal of Advances in Information Fusion, 6, 39–56.Google Scholar
Gilles, S. (1998). Robust description and matching of images. In: Department of Engineering Science, Oxford: Oxford University.Google Scholar
Horn, S., Isenor, A., MacNeil, M. and Turnbull, A. (2015) Matching uncertain identities against sparse knowledge. In: Beierle, C. & Dekhtyar, A. (eds.). International Conference on Scalable Uncertainty Management, pp. 415–420. Quebec City: Lecture Notes in Computer Science.CrossRefGoogle Scholar
Ilachinski, A. (2004). Artificial war: Multiagent-Based Simulation of Combat. Singapore: World Scientific Publishing.CrossRefGoogle Scholar
IMSWG. (2011). Canada's Maritime Domain Awareness Strategy. Ottawa: Interdepartmental Marine Security Working Group.Google Scholar
Iphar, C., Napoli, A. and Ray, C. (2015). Detection of False AIS Messages for the Improvement of Maritime Situational Awareness. Proceedings of the Oceans 2015-mts/Ieee Washington.CrossRefGoogle Scholar
Isenor, A. W., St-Hilaire, M.-O., Webb, S. and Mayrand, M. (2016). MSARI: A database for large volume storage and utilisation of maritime data. Journal of Navigation, 70, 276–290.CrossRefGoogle Scholar
Kaluza, P., Kölzsch, A., Gastner, M. T. and Blasius, B. (2010). The complex network of global cargo ship movements. Journal of the Royal Society Interface, 7, 1093–1103.CrossRefGoogle ScholarPubMed
Lapinski, A.-L. S. and Isenor, A. W. (2011). Estimating reception coverage characteristics of AIS. Journal of Navigation, 64, 609–623.CrossRefGoogle Scholar
Lapinski, A.-L. S., Isenor, A. W. and Webb, S. (2016). Simulating surveillance options for the Canadian north. Journal of Navigation, 69, 940–954.CrossRefGoogle Scholar
Leibovici, D. G. (2009). Defining Spatial Entropy From Multivariate Distributions of co-Occurrences. Proceedings of the 9th International Conference on Spatial Information Theory, Berlin, Germany.CrossRefGoogle Scholar
Leibovici, D. G., Claramunt, C., Guyader, D. L. and Brosset, D. (2014). Local and global spatio-temporal entropy indices based on distance-ratios and co-occurrences distributions. International Journal of Geographical Information Science, 28, 1061–1084.CrossRefGoogle Scholar
Li, Y. and Deutsch, C. V. (2010). Mutual Information and Its Application In Spatial Statistics. Edmonton: Centre for Computational Geostatistics.Google Scholar
Liu, M. J., Dobias, P. and Eisler, C. (2015). Fractal patterns in coastal detection on approaches to Canada. Journal of Applied Operational Research, 7, 80–95.Google Scholar
Millar, R. B. and Anderson, M. J. (2004). Remedies for pseudoreplication. Fisheries Research, 70, 397–407.CrossRefGoogle Scholar
Nguyen, D., Vadaine, R., Hajduch, G., Garello, R. and Fablet, R. (2018). A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams. Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics.CrossRefGoogle Scholar
Oikonomopoulos, A., Patras, I. and Pantic, M. (2006). Spatiotemporal salient points for visual recognition of human actions. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36, 710–719.CrossRefGoogle ScholarPubMed
Pallotta, G., Vespe, M. and Bryan, K. (2013). Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy, 15, 2218–2245.CrossRefGoogle Scholar
Pan, J., Jiang, Q. and Shao, Z. (2014). Trajectory clustering by sampling and density. Marine Technology Society Journal, 48, 74–85.CrossRefGoogle Scholar
Rosenfeld, A. and Kak, A. C. (1982). Digital Picture Processing. New York: Academic.Google Scholar
Roy, J. (2008). Anomaly Detection in the Maritime Domain. Proceedings of the SPIE Defence and Security Symposium, Orlando, Florida, USA.CrossRefGoogle Scholar
Scully, B. M., Young, D. L. and Ross, J. E. (2020). Mining marine vessel AIS data to inform coastal structure management. Journal of Waterway, Port, Coastal, and Ocean Engineering, 142, 1–10.Google Scholar
Shannon, C. E. and Weaver, W. (1949). The Mathematical Theory of Communication. Urbana: University of Illinois Press.Google Scholar
Studholme, C., Drapaca, C., Iordanova, B. and Cardenas, V. (2006). Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change. IEEE Transactions on Medical Imaging, 25, 626–639.CrossRefGoogle ScholarPubMed
Vicente-Cera, I., Acevedo-Merion, A., Nebot, E. and Lopez-Ramirez, J. A. (2020). Analyzing cruise ship itineraries patterns and vessels diversity in ports of the european maritime region: A hierarchical clustering approach. Journal of Transport Geography, 85, 1–8.CrossRefGoogle Scholar
Wong, A. K. and Chiu, D. K. (1987). An event-covering method for effective probabilistic inference. Pattern Recognition, 20, 245–255.CrossRefGoogle Scholar
Yan, Z., Xiao, Y., Cheng, L., He, R., Ruan, X., Zhou, X., Li, M. and Bin, R. (2020). Exploring AIS data for intelligent maritime routes extraction. Applied Ocean Research, 101, 102271.CrossRefGoogle Scholar
Yao, Y. Y. (2003) Information-Theoretic measures for knowledge discovery and data mining. In: Karmeshu J., (ed.). Entropy Measures, Maximum Entropy and Emerging Applications, pp. 115–136. Berlin: Springer.CrossRefGoogle Scholar