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Fisheries data analysis, big data visualization, and applications based on the next generation oceanic information system

The “yellow card” sanction warning from the European Commission (EUC) has brought attention to the Taiwan government in fighting illegal, unreported, and unregulated (IUU) fishing. Unless further actions are taken, the EUC will issue a “red card” and Taiwan will suffer a loss of 13 million euros yearly for not being able to export marine products. Besides passing new laws, the Fisheries Agency has funded the development of a next-generation oceanic information system (DeepSea 9) to help them monitor, control, and surveil overseas vessels. Although operational, this system is only being used for administrative purposes only. This project will use the available infrastructure and initiate scientific research on the data available.

The DeepSea 9 system uses the vessel monitoring system (VMS) system to track vessels, but this is only for overseas fisheries. The Fisheries Agency of Taiwan has another system, namely the voyage data recorder (VDR) system for offshore and coastal fisheries. The information these systems hold is voluminous, including more than 58 billion rows of GPS data, fishing logbooks, transshipments and landing records, and administrative information. We will attempt to interface these two systems together and use their data in conjunction with scientific research.

The initial design of these systems includes storing the data and provide an efficient way to retrieve voyages of vessels. Preliminary studies show that with the voyages data available in DeepSea 9 and the VDR systems, we can aggregate the information to identify roles of fishing ports, i.e., busy ports, unused ports, and sanctuary ports which serves as a hideout for severe weather condition. Moreover, DeepSea 9 is constructed based on the underlying hardware and algorithms used in the preliminary designs.

In the following years, besides attempting to interface 2 systems into one, we will try to automate the detection of fishing hotspots by using the data in the systems. We propose a quantization algorithm to filter out uninteresting spots, i.e., the fishing vessel is traveling or resting. We will attempt to improve the speed of the algorithm to meet the huge computational demands. Following the results, we will try to classify the fishing activities of vessels. We will collect logbooks and auction records and correlate them to the database. This will assist us in understanding the distribution of fish schools on the ocean and help us estimate the yearly catch. More importantly, we will design visualization systems using 3D WebGL, which is capable of handling more than a million data entities at once, to help people understand what the underlying data means.

Project funded by the Ministry of Science and Technology, Executive Yuan, Taiwan under grant MOST 107-2221-E-019 -037 -MY3.

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