The global fishing industry operates at a large scale and can have adverse environmental impacts on habitat and species,with an estimated 69% of global fishing stocks estimated to be fully or over exploited. Yet little is known about where and how fishing vessels travel and harvest, particularly in international waters. Using location and trajectory data from Global Fishing Watch, this research applies trajectory clustering and pattern detection and trajectory classification to model the movements of the international fishing industry and understand repeated patterns of behavior. The pattern detection and clustering analysis have implications for understanding what precise areas of the planet are being overfished and how patterns in fishing vessel trajectories can inform international environmental stewardship efforts.
The analysis is divided into three parts:
- Hot-spot clustering to identify overfishing activity
- K-means and DBSCAN clustering of segmented trip trajectories to detect repeated pattern
- Trajectory feature extract and classification modeling to identify trawler vessel behavior
For more details, see final_report.pdf
Data is provided by Global Fishing Watch (https://globalfishingwatch.org).