Advertisement

Journal of Coastal Conservation

, Volume 17, Issue 1, pp 105–119 | Cite as

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

  • René A. Enguehard
  • Rodolphe Devillers
  • Orland Hoeber
Article

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.

Keywords

Fisheries enforcement Vessel monitoring system VMS Movement analysis Visualization Geovisual analytics 

Notes

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.

References

  1. Andrienko N, Andrienko G (2007) Designing visual analytics methods for massive collections of movement data. Cartographica 42:117–138CrossRefGoogle Scholar
  2. 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 474–475CrossRefGoogle Scholar
  3. 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 20:1149–1171CrossRefGoogle Scholar
  4. 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 21:839–857CrossRefGoogle Scholar
  5. Andrienko G, Andrienko N, Wrobel S (2007b) Visual analytics tools for analysis of movement data. ACM SIGKDD Explor Newsl 9:38–46CrossRefGoogle Scholar
  6. 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 7:173–180CrossRefGoogle Scholar
  7. Andrienko G, Andrienko N, Heurich M (2011a) An event-based conceptual model for context-aware movement analysis. Int J Geogr Inf Sci 25:1347–1370CrossRefGoogle Scholar
  8. 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 159–168Google Scholar
  9. Bertrand S, Bertrand A, Guevara-Carrasco R, Gerlotto F (2007) Scale-invariant movements of fishermen: the same foraging strategy as natural predators. Ecol Appl 17:331–337CrossRefGoogle Scholar
  10. 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 34:541–548CrossRefGoogle Scholar
  11. 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 900–911Google Scholar
  12. Demšar U, Virrantaus K (2010) Space–time density of trajectories: exploring spatio-temporal patterns in movement data. Int J Geogr Inf Sci 24:1527–1542CrossRefGoogle Scholar
  13. 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 62:611–622CrossRefGoogle Scholar
  14. Dodge S, Weibel R, Lautenschütz A-K (2008) Towards a taxonomy of movement patterns. Inf Vis 7:240–252CrossRefGoogle Scholar
  15. Eagle N, Pentland AS (2009) Eigenbehaviors: identifying structure in routine. Behav Ecol Sociobiol 63:1057–1066CrossRefGoogle Scholar
  16. 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 1–2Google Scholar
  17. 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
  18. Gottfried B (2011) Interpreting motion events of pairs of moving objects. GeoInformatica 15:247–271CrossRefGoogle Scholar
  19. Gurarie E, Andrews RD, Laidre KL (2009) A novel method for identifying behavioural changes in animal movement data. Ecol Lett 12:395–408CrossRefGoogle Scholar
  20. Hintzen NT, Piet GJ, Brunel T (2010) Improved estimation of trawling tracks using cubic Hermite spline interpolation of position registration data. Fish Res 101:108–115CrossRefGoogle Scholar
  21. 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 115–116:31–43CrossRefGoogle Scholar
  22. 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 28:1450–1464CrossRefGoogle Scholar
  23. Jennings S, Lee J (2012) Defining fishing grounds with vessel monitoring system data. ICES J Mar Sci 69:51–63CrossRefGoogle Scholar
  24. Jern M, Åström T, Johansson S (2008) GeoAnalytics tools applied to large geospatial datasets. In: IEEE Infor Vis. IEEE, Columbus, pp 362–372Google Scholar
  25. 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 1–8Google Scholar
  26. Kim, R, Hogan, P (2011) World Wind JAVA SDK http://worldwind.arc.nasa.gov/java/. Accessed 08 December 2011
  27. 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 8:185–203CrossRefGoogle Scholar
  28. 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 756–763Google Scholar
  29. 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 67:1260–1271CrossRefGoogle Scholar
  30. Lundblad P, Jern M, Forsell C (2008) Voyage analysis applied to geovisual analytics. In: IEEE Infor Vis. IEEE, Columbus, pp 381–388Google Scholar
  31. 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 80:854–865CrossRefGoogle Scholar
  32. 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 64:248–255CrossRefGoogle Scholar
  33. Molenaar EJ, Tsamenyi M (2000) Satellite-based vessel monitoring systems for fisheries management: international legal aspects. Int J Mar Coast Law 15:65–110Google Scholar
  34. 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 100:248–254CrossRefGoogle Scholar
  35. Murawski SA, Wigley SE, Fogarty MJ, Rago PJ, Mountain DG (2005) Effort distribution and catch patterns adjacent to temperate MPAs. ICES J Mar Sci 62:1150–1167Google Scholar
  36. Nams VO (2005) Using animal movement paths to measure response to spatial scale. Oecologia 143:179–88CrossRefGoogle Scholar
  37. Ou P, Wang H (2009) Prediction of stock market index movement by ten data mining techniques. Mod Appl Sci 3:28–42Google Scholar
  38. Raymond B, Hosie G (2009) Network-based exploration and visualisation of ecological data. Ecol Model 220:673–683CrossRefGoogle Scholar
  39. 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 114–119Google Scholar
  40. Rodighiero D (2010) Guidelines to visualize vessels in a geographic information system. In: IEEE Infor Vis. IEEE, Salt Lake City, pp 455–459Google Scholar
  41. 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 68:687–695CrossRefGoogle Scholar
  42. 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 1–9CrossRefGoogle Scholar
  43. 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 1–7CrossRefGoogle Scholar
  44. Thomas J, Cook K (2005) Illuminating the path: research and development agenda for visual analytics. IEEE Computer Society, Los AlamitosGoogle Scholar
  45. 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 1–8Google Scholar
  46. Tufte ER (2001) The visual display of quantitative information, 2nd edn. Graphics Press, CheshireGoogle Scholar
  47. Ware C (2004) Information visualization: perception for design, 2nd edn. Morgan Kaufmann, San FranciscoGoogle Scholar
  48. Willems N, van de Wetering H, van Wijk JJ (2009) Visualization of vessel movements. Comput Graph Forum 28:959–966CrossRefGoogle Scholar
  49. With KA (1994) Using fractal analysis to assess how species perceive landscape structure. Landscape Ecol 9:25–36CrossRefGoogle Scholar
  50. Witt MJ, Godley BJ (2007) A step towards seascape scale conservation: using vessel monitoring systems (VMS) to map fishing activity. PLoS One 2:e1111CrossRefGoogle Scholar
  51. Zhao J, Forer P, Harvey AS (2008) Activities, ringmaps and geovisualization of large human movement fields. Inf Vis 7:198–209CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • René A. Enguehard
    • 1
  • Rodolphe Devillers
    • 1
    • 2
  • Orland Hoeber
    • 3
    • 4
  1. 1.Department of GeographyMemorial University of NewfoundlandSt. John’sCanada
  2. 2.ARC Centre of Excellence for Coral Reefs StudiesJames Cook UniversityTownsvilleAustralia
  3. 3.Department of Computer ScienceUniversity of ReginaReginaCanada
  4. 4.Department of Computer ScienceMemorial University of NewfoundlandSt. John’sCanada

Personalised recommendations