Guide: Tracking Illegal Fishing with open data

A guide for open data journalists and human rights analysts on how to follow the trail of potentially illegal fishing and transhipment operations across West Africa (and how to use the same logic for other parts of the world)

West Africa is a hotspot for illegal- and over-fishing. It is also a flourishing piracy zone and stuffed with some of the poorest countries in the world. Last week, our investigation dropped that highlighted the problems of transshipments and European reefer fleets. The term transshipment describes the transferring of fish catch from fishing vessels to larger refrigerated cargo ships (though, there are other types of transshipments, too).

Despite local authorisation programs, the practice is still highly dodgy. It muddies the water for so many sectors. It affects how well we can track fish catch amid the larger food supply chain (it can affect what food ends up on your table). It does encourage trafficking and slave labour.

NGO experts and investigative journalism finds that gaps in AIS transmission, port avoidance, transshipment, and extended time at sea indicate the presence of forced labour

Journalists and investigators often lack sufficient formal data. That’s where open-source intelligence (#OSINT) and alternative data sources, such as from satellites, come in neatly.

Transshipments encourages industrial (over) fishing that leaves mere small quantities of fish for the local fishing sector. With little to play drives fishermen (and fisherwomen) to take up arms in the piracy sector. The region is now the number one global piracy hotspot (while European companies and EU governments refuse to take the blame for any of it).

Nex to motivating piracy, IUU fishing can affect maritime conflict and safety. There is more. Consequences are so far-reaching, it can drive locals without economic perspective in the fishing sector, to migrate to other places, including to Europe. During the pandemic, analysts encountered new thriving levels of numbers of dangerous sea-crossings via the Western Mediterranean sea route.

In short, the knock-on effects caused by Western large scale fishing exploitation in West Africa, are far more drastic than often thought.

Our investigation finds that European reefer operators appear complicit in sending larger-scale vessels to West Africa to help plundering local fish stocks while also jeopardizing maritime safety along the way. If not illegal, it’s drastically damaging. Important to note, the AIS data and data on possible encounters can’t tell us whether the vessels we inspected acted illegally in regards to possible fishing-catch transshipments.

Link to story

This little guide will go over some of the data sources and tools used in tracking the fleets. We’ll explain how to make sense of the data and talk about limits and caveat?

Oceana factsheet PDF on how to spot illegal fishing and human rights abuses

Datasources

One starting point provided a small dataset shared by Trygg Mat Tracking (TMT), an analysis outfit working on illegal fishing activities. The data gave us access to groups of reefer vessels and their names that were previously (prior to 2019) spotted and classified as ‘high risk’ to portray AIS tracking gaps and that fitted patterns of loitering just outside of EEZ in various West African coastal economies (including Angola where the transshipment volume apparently reached concerning levels).

The benefit of having a list of vessels allows to investigate further. We were keen to see what these ships were up to over the past two years. Did their ownership change? Where did they operate. How do these Europen reefer companies market their offering in West Africa?

Global Fishing Watch (GFW) was of instrumental help (their tools, we will discuss shortly).

We also worked with various blacklist providers to check whether any of the reefers reemerged. The motto here of being ‘once a bad apple, always a bad apple”, helped to build some confidence.

Blacklists constitute a horrendous threat to reefer operators. It’s a list of vessels that were previously spotted to have committed illegal activities (authorities heed that list). Some industrial fisher may not, we are told.

The EU maintains one of such blacklists here. TMT also compiles their own. Oceana is another brilliant resource for fishing data and statistics.

Snapshot: Starting point, a list of cargo vessels that we investigated further, a larger share owned by European cargo ship operators

There is a number of tools we used, some listed below:

  • GFW interactive map (with a search function for fishing vessels, overlay maps, and an excellent JSON file uploader function)
  • GFW data downloader (some python Numpy code here, and for R code here on how to use it)
  • GFW Carrier portal (readme here): A great interface that allows to track various fishing cargo vessels and some fishing vessels, and whether there was a no ‘known authorisation’. It has a JSON export function for AIS signal tracks, an amazing open-source resource — far too much underutilized in my view.
  • Vesselfinder: The platform carries some free information that allows to pinpoint vessels, confirm their details (like weight and measurements, which allowed to confirm cargo vessels on satellite images). The connection between reefer and owners and operators as well as flag-state was also important to us. VesselFinder helped in this regard (and offered us limited access to their paid platform to confirm ownership details of reefer vessels)
  • Fleetmon, VesselFinder and MarineTraffic allow to determine the flag state of vessels, some of which helped in the debate on flag of convenience
  • IMO numbers: We used the respective IMO numbers of vessels because these are hard to modify — even if owners, names and flag state and positions changed over time.

Let’s go through an example to illustrate how to investigate these kinds of vessels and how the approach can be transferred to other parts of the world.

To illustrate the point on frequent encounters, the Ocean Fresh (IMO: 8301175) comes to mind. Between 2012 and November 2020, the Norvegian flagged reefer portrayed a conspicuous volume of encounters of unknown authorisation off the coast of Mauritania. It ‘fished’ there under an Irish agreement in Mauritanian waters, sources from 2017 said:

Possible transhipments of Norvegian-flagged Ocean Fresh

The Ocean Fresh took in pelagic fish from smaller Mauritanian flagged purse seiners and was processing them at sea. How damaging the lack of transparency is shows Greenpeace in a report from 2017, where it says that it was “impossible to get information on the legality of this activity and that the case was reported to Mauritanian, Norwegian, and European authorities”.

Import geoJSON files from GFW’s carrier vessel platform into geospatial data analysis platform QGIS

We can take this data into QGIS, by downloading the JSON files, though without the encounter/connector points (which is still a bit annoying — we hope this is coming as an export feature. However, you can find all the geodata in the data dump files on the site, too).

We can also export the path elements as KML files from QGIS and import them into Google Earth Pro, which is neat if we want to have a reliable satellite interface. By working with QGIS and Google Earth Pro (which you see below), which made it easier to analyse discontinued AIS signal tracks.

With the AIS data, possible encounters with fishing vessels and their IMO numbers at hand, we can now compare fishing volume on GFW’s interactive map (this is usually the critical point where you might spot irregularities that could lead to a scoop, but it takes time, so don't get frustrated).

Picking a fishing vessel off the coast of Guinea-Bissau

The investigative article we published had limited space. The point the investigation didn't make clearly enough (as it was not the main focus) was that piracy attack locations and AIS tracking signal gaps don't line up.

It’s of some importance because cargo vessel operators told us, despite operating legally out of Europe, they did turn off their AIS signals because of the piracy threat. But judging by the locations published by IMB (that located recent attacks), the gaps don't line up. In short, it provides some evidence that some reefer ships who went ‘dark’ jeopardising maritime safety without adequate excuse (though, even if it did, they are required to keep their AIS signal on).

One ‘analogue’ technique to compare the piracy attacks and AIS signal gaps is to use the overlay function in Google Earth Pro. We use one map by IMB Piracy Reporting Centre and import it into Google Earth to compare it with AIS signal tracks left by cargo reefer Veracruz (the vessels was decommissioned last year)

Finally, we can check various open and commercial satellite providers to confirm some of the encounters at-sea. The problems we often encountered included that confirmed AIS tracks, showing a rendezvous, often took place during a cloudy day, or at night, or days for which open-source data were not available on platforms like Sentinel Hub and Sentinel 2 images.

When we did have access to higher-resolution satellite, encounters took often place so far out at sea, that images didn’t cover the area.

An alternative to combat the frustration of satellite images is to use RF scanning satellite data. As a mean to investigate fishing crimes from a journalism perspective, I am currently looking for ways and partners to develop this area further. There is also nightlight images of fishing vessels we used for other, similar investigations.

The investigation can be read here

The caveats of the data is that it’s hard to say whether many of the transhipment encounters were sheer illegal. The AIS data simply doesn't carry that kind of intelligence on its own. It has to be combined with other data sources. In order to know for sure, it’s important to work with the local authorities of the respective EEZs.

Finally, it was important to us not to make any false claims in the piece. So we checked thoroughly with the various data providers and certification bodies like Friend Of The Sea. I thanks all these sources who contributed to the results. I hope we could show that the tools, code and data we used for this investigation can to some extend be useful to replicate it for other parts of the world. Please do reach out if you have questions or want to collaborate.

Investigative journalist with a technical edge, interested in open source investigations, satellite imgs, R, python, AI, data journalism and injustice

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