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Comparing interactive and automated mapping systems for supporting fisheries enforcement activities—a case study on vessel monitoring systems (VMS)

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Abstract

The conservation of wild fisheries resources in the face of an ever-increasing world demand for seafood requires the use of a number of management tools, including no-take zones, and gear, species, and temporal restrictions. One way of enforcing some of these regulations is through the use of Vessel Monitoring System (VMS) data that provides enforcement officers with the position of fishing vessels in the management area. The increasing volume of movement data collected using VMS calls for new methods that could help analysts extract useful knowledge from these large data sets. Various approaches have been proposed for visualizing and exploring movement data and detecting patterns within these data, but those approaches have generally not been tested in a real-world context or compared together, making their actual usability and utility unclear. This paper describes, compares, and assesses three such approaches in the context of fisheries enforcement: an existing system used for fisheries enforcement operations in Canada (VUE), a novel Hybrid Spatio-temporal Filtering (HSF) system developed by the authors, and an automated Behavioural Change Point Analysis (BCPA) system. A field trial was conducted with experienced fisheries enforcement officers to compare and contrast the benefits and drawbacks of the three approaches. While all three presented advantages and disadvantages, the interactivity of VUE and HSF were identified as desirable features, as they provide analysts with more control over the data, while allowing flexible data exploration. BCPA, while providing an automated approach to the data analysis, was pointed out as being too much of a “black box”, causing unease among the experts who require a level of transparency similar to that of legally admissible evidence. In the end, the experts suggested that the best approach would be to merge the analytical power of their existing VUE system with the exploratory power of the HSF system. This study provides insight into the value of using interactive mapping and filtering approaches in support of data analysis in the context of fisheries enforcement.

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References

  • Andrienko N, Andrienko G (2007) Designing visual analytics methods for massive collections of movement data. Cartographica 42:117–138

    Article  Google Scholar 

  • Andrienko N, Andrienko G (2008) Supporting visual exploration of massive movement data. In: Proceedings of the working conference on advanced visual interfaces. ACM, Napoli, pp 474–475

    Chapter  Google Scholar 

  • Andrienko G, Andrienko N, Fischer R, Mues V, Schuck A (2006) Reactions to geovisualization: an experience from a European project. Int J Geogr Inf Sci 20:1149–1171

    Article  Google Scholar 

  • Andrienko G, Andrienko N, Jankowski P, Keim DA, Kraak M-J, MacEachren AM, Wrobel S (2007a) Geovisual analytics for spatial decision support: setting the research agenda. Int J Geogr Inf Sci 21:839–857

    Article  Google Scholar 

  • Andrienko G, Andrienko N, Wrobel S (2007b) Visual analytics tools for analysis of movement data. ACM SIGKDD Explor Newsl 9:38–46

    Article  Google Scholar 

  • Andrienko G, Andrienko N, Dykes J, Fabrikant SI, Wachowicz M (2008) Geovisualization of dynamics, movement and change: key issues and developing approaches in visualization research. Inf Vis 7:173–180

    Article  Google Scholar 

  • Andrienko G, Andrienko N, Heurich M (2011a) An event-based conceptual model for context-aware movement analysis. Int J Geogr Inf Sci 25:1347–1370

    Article  Google Scholar 

  • Andrienko G, Andrienko N, Hurter C, Rinzivillo S, Wrobel S (2011b) From movement tracks through events to places: extracting and characterizing significant places from mobility data. In: IEEE S Vis Anal. IEEE, Providence, pp 159–168

    Google Scholar 

  • Bertrand S, Bertrand A, Guevara-Carrasco R, Gerlotto F (2007) Scale-invariant movements of fishermen: the same foraging strategy as natural predators. Ecol Appl 17:331–337

    Article  Google Scholar 

  • Chang S-K, Liu K-Y, Song Y-H (2010) Distant water fisheries development and vessel monitoring system implementation in Taiwan—history and driving forces. Mar Policy 34:541–548

    Article  Google Scholar 

  • Chen Z, Shen H, Zhou X (2011) Discovering popular routes from trajectories. In: Proceedings of the 27th International Conference on Data Engineering. Hannover, Germany, pp 900–911

  • Demšar U, Virrantaus K (2010) Space–time density of trajectories: exploring spatio-temporal patterns in movement data. Int J Geogr Inf Sci 24:1527–1542

    Article  Google Scholar 

  • Deng R, Dichmont C, Milton D, Haywood M, Vance D, Hall N, Die D (2005) Can vessel monitoring system data also be used to study trawling intensity and population depletion? The example of Australia’s northern prawn fishery. Can J Fish Aquat Sci 62:611–622

    Article  Google Scholar 

  • Dodge S, Weibel R, Lautenschütz A-K (2008) Towards a taxonomy of movement patterns. Inf Vis 7:240–252

    Article  Google Scholar 

  • Eagle N, Pentland AS (2009) Eigenbehaviors: identifying structure in routine. Behav Ecol Sociobiol 63:1057–1066

    Article  Google Scholar 

  • Enguehard RA, Devillers R, Hoeber O (2011) Geovisualization of fishing vessel movement patterns using hybrid fractal / velocity signatures. In: Proceedings of the 2011 International GeoViz Workshop. Hamburg, Germany, pp 1–2

  • Enguehard RA, Hoeber O, Devillers R (2012) Interactive exploration of movement data: a case study of geovisual analytics for fishing vessel analysis. Inf Vis. doi:10.1177/1473871612456121

  • Gottfried B (2011) Interpreting motion events of pairs of moving objects. GeoInformatica 15:247–271

    Article  Google Scholar 

  • Gurarie E, Andrews RD, Laidre KL (2009) A novel method for identifying behavioural changes in animal movement data. Ecol Lett 12:395–408

    Article  Google Scholar 

  • Hintzen NT, Piet GJ, Brunel T (2010) Improved estimation of trawling tracks using cubic Hermite spline interpolation of position registration data. Fish Res 101:108–115

    Article  Google Scholar 

  • Hintzen NT, Bastardie F, Beare D, Piet GJ, Ulrich C, Deporte N, Egekvist J, Degel H (2012) VMStools: open-source software for the processing, analysis and visualisation of fisheries logbook and VMS data. Fish Res 115–116:31–43

    Article  Google Scholar 

  • Hu W, Xiao X, Fu Z, Xie D, Tan T, Maybank S (2006) A system for learning statistical motion patterns. IEEE Trans Pattern Anal Mach Intell 28:1450–1464

    Article  Google Scholar 

  • Jennings S, Lee J (2012) Defining fishing grounds with vessel monitoring system data. ICES J Mar Sci 69:51–63

    Article  Google Scholar 

  • Jern M, Åström T, Johansson S (2008) GeoAnalytics tools applied to large geospatial datasets. In: IEEE Infor Vis. IEEE, Columbus, pp 362–372

    Google Scholar 

  • Johansson S, Jern M (2007) GeoAnalytics visual inquiry and filtering tools in parallel coordinates plots. In: Proceedings of the 15th annual ACM international symposium on advances in geographic information systems. ACM, Seattle, pp 1–8

    Google Scholar 

  • Kim, R, Hogan, P (2011) World Wind JAVA SDK http://worldwind.arc.nasa.gov/java/. Accessed 08 December 2011

  • Kwan M-P (2000) Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set. Transport Res C-Emer 8:185–203

    Article  Google Scholar 

  • Laxhammar R, Falkman G, Sviestins E (2009) Anomaly detection in sea traffic—a comparison of the Gaussian mixture model and the kernel density estimator. In: Proceedings of the 12th international conference on information fusion. IEEE, Seattle, pp 756–763

    Google Scholar 

  • Lee J, South AB, Jennings S (2010) Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. ICES J Mar Sci 67:1260–1271

    Article  Google Scholar 

  • Lundblad P, Jern M, Forsell C (2008) Voyage analysis applied to geovisual analytics. In: IEEE Infor Vis. IEEE, Columbus, pp 381–388

    Google Scholar 

  • Mårell A, Ball JP, Hofgaard A (2002) Foraging and movement paths of female reindeer: insights from fractal analysis, correlated random walks, and Lévy flights. Can J Zoolog 80:854–865

    Article  Google Scholar 

  • 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 J Mar Sci 64:248–255

    Article  Google Scholar 

  • Molenaar EJ, Tsamenyi M (2000) Satellite-based vessel monitoring systems for fisheries management: international legal aspects. Int J Mar Coast Law 15:65–110

    Google Scholar 

  • Mullowney DR, Dawe EG (2009) Development of performance indices for the Newfoundland and Labrador snow crab (Chionoecetes opilio) fishery using data from a vessel monitoring system. Fish Res 100:248–254

    Article  Google Scholar 

  • Murawski SA, Wigley SE, Fogarty MJ, Rago PJ, Mountain DG (2005) Effort distribution and catch patterns adjacent to temperate MPAs. ICES J Mar Sci 62:1150–1167

    Google Scholar 

  • Nams VO (2005) Using animal movement paths to measure response to spatial scale. Oecologia 143:179–88

    Article  Google Scholar 

  • Ou P, Wang H (2009) Prediction of stock market index movement by ten data mining techniques. Mod Appl Sci 3:28–42

    Google Scholar 

  • Raymond B, Hosie G (2009) Network-based exploration and visualisation of ecological data. Ecol Model 220:673–683

    Article  Google Scholar 

  • Rocha JAMR, Times VC, Oliveira G, Alvares LO, Bogorny V (2010) DB-SMoT: a direction-based spatio-temporal clustering method. In: Proceedings of the 5th IEEE international conference intelligent systems. IEEE, London, pp 114–119

    Google Scholar 

  • Rodighiero D (2010) Guidelines to visualize vessels in a geographic information system. In: IEEE Infor Vis. IEEE, Salt Lake City, pp 455–459

    Google Scholar 

  • Saitoh S-I, Mugo R, Radiarta IN, Asaga S, Takahashi F, Hirawake T, Ishikawa Y, Awaji T, In T, Shima S (2011) Some operational uses of satellite remote sensing and marine GIS for sustainable fisheries and aquaculture. ICES J Mar Sci 68:687–695

    Article  Google Scholar 

  • Schwehr KD, McGillivary PA (2007) Marine ship automatic identification system (AIS) for enhanced coastal security capabilities: an oil spill tracking application. In: Proceedings of the 2007 Oceans Conference. IEEE, Vancouver, pp 1–9

    Chapter  Google Scholar 

  • Shneiderman B, Plaisant C (2006) Strategies for evaluating information visualization tools. In: Proceedings of the 2006 AVI workshop on beyond time and errors novel evaluation methods for information visualization. ACM, Venice, pp 1–7

    Chapter  Google Scholar 

  • Thomas J, Cook K (2005) Illuminating the path: research and development agenda for visual analytics. IEEE Computer Society, Los Alamitos

    Google Scholar 

  • Tomaszewski BM, Robinson AC, Weaver C, Stryker M, MacEachren AM (2007) Geovisual analytics and crisis management. In: Proceedings of the 4th international information systems for crisis response and management (ISCRAM) conference. Delft, Netherlands, pp 1–8

  • Tufte ER (2001) The visual display of quantitative information, 2nd edn. Graphics Press, Cheshire

    Google Scholar 

  • Ware C (2004) Information visualization: perception for design, 2nd edn. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Willems N, van de Wetering H, van Wijk JJ (2009) Visualization of vessel movements. Comput Graph Forum 28:959–966

    Article  Google Scholar 

  • With KA (1994) Using fractal analysis to assess how species perceive landscape structure. Landscape Ecol 9:25–36

    Article  Google Scholar 

  • Witt MJ, Godley BJ (2007) A step towards seascape scale conservation: using vessel monitoring systems (VMS) to map fishing activity. PLoS One 2:e1111

    Article  Google Scholar 

  • Zhao J, Forer P, Harvey AS (2008) Activities, ringmaps and geovisualization of large human movement fields. Inf Vis 7:198–209

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for the funding of this project through the Strategic Projects Grant STPGP 365189–08, and the Canadian Foundation for Innovation (CFI) and Memorial University of Newfoundland for providing the laboratory infrastructure. We also would like to thank Fisheries and Oceans Canada, and especially Jerry Black and Trevor Fradsham and their groups, for their collaboration and having provided access to the data, as well as the fisheries enforcement officers who participated in this study.

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Correspondence to René A. Enguehard.

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Enguehard, R.A., Devillers, R. & Hoeber, O. Comparing interactive and automated mapping systems for supporting fisheries enforcement activities—a case study on vessel monitoring systems (VMS). J Coast Conserv 17, 105–119 (2013). https://doi.org/10.1007/s11852-012-0222-3

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  • DOI: https://doi.org/10.1007/s11852-012-0222-3

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