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So far David Kroodsma has created 19 blog entries.

Match-making at sea: how to find fishing fleets

Staring at the Global Fishing Watch map, your eye is inevitably drawn to patterns of vessels that move together. These fishing patterns are most evident in the world’s longline fleets, which you can sometimes see moving north and south, as temperatures change, following migrations of fish. These fishing vessels are moving in “fleets” with [...]

By | 2019-03-03T11:33:57+00:00 February 28th, 2019|Data and technology|

A who’s who for the oceans: building a global database of fishing vessels

Jaeyoon Park, Data Scientist Our groundbreaking online map tracks the movements of commercial fishing vessels all over the world. As part of our ambition to reveal and analyse the fishing activity responsible for the majority of the world’s marine catch, we’re constantly working to improve the quality of the data behind the [...]

By | 2018-11-08T07:39:25+00:00 November 8th, 2018|Data and technology, Map|

Half the Ocean? A Response to the University of Washington’s Blog

On the pages of Science Magazine (comment and response), Twitter, the University of Washington's Sustainable Fisheries blog, and The Atlantic, my co-authors and I have engaged in a healthy debate with University of Washington researchers about how to measure the global footprint of fisheries. This exchange has helped raise awareness of different ways to measure, understand, [...]

By | 2018-10-16T12:55:47+00:00 September 11th, 2018|Data blog, News & Views|

Measuring the Footprint of Fisheries

Earlier this year we published in Science the first global assessment of fishing activity using AIS data, providing an unprecedented view into the activity of tens of thousands of large, industrial fishing vessels. We’ve made these fishing effort data publicly available, which is now leading a slew of advances in our understanding of fishing [...]

By | 2018-08-23T19:02:12+00:00 August 23rd, 2018|Data and technology, Data blog, News & Views|

Accessing GFW Data in BigQuery Using R

One of the ways we have released our data is through Google’s BigQuery. BigQuery allows one to easily aggregate the data or select only the region or time period of interest. We have posted some example queries here. One our colleagues, Juan Mayorga of UCSB and National Geographic Pristine Seas, recently wrote a tutorial on [...]

By | 2018-06-25T05:01:15+00:00 June 15th, 2018|Data blog|

Over Half the Ocean is Fished

The recent publication of Global Fishing Watch’s paper, Tracking the global footprint of fishing, in the journal, Science, has attracted considerable media attention and commentary. Two common headlines used in media reports are that “over half the ocean is fished” and that the spatial extent of fishing is “four times that of agriculture.” In responses to [...]

By | 2019-04-12T16:59:07+00:00 March 15th, 2018|Data blog|

Our Data in Earth Engine

Today, with our publication in Science, we are releasing fishing effort data for 2012 to 2016. One of the ways we are releasing it is through Google’s Earth Engine. There is a bit of a steep learning curve on Earth Engine – you have to be able to code in JavaScript or Python. But once [...]

By | 2019-04-12T16:44:39+00:00 February 22nd, 2018|Data blog|

Our Data in BigQuery

Today, with our publication in Science, we are releasing fishing effort data for 2012 to 2016. One of the ways we are releasing it is through Google’s BigQuery. If you have not used BigQuery, vist here and click on try it free to get started. You can query up to one terabyte per month for no charge, which [...]

By | 2018-06-25T05:00:26+00:00 February 22nd, 2018|Data blog|

The Dynamics of the Global Fishing Fleet – Interactive

Our research paper, “Tracking the global footprint of fisheries," was published today in Science. A key finding of the study is that fishing is remarkably non-seasonal at a global scale. What matters far more than any natural annual cycle, it turns out, are cultural and political factors: fishers in North America and Europe don't [...]

By | 2019-06-10T12:53:50+00:00 February 22nd, 2018|Research and analysis|

Our Dataset — Now Available

Today is an exciting day for the Global Fishing Watch Research program. Working with six partner institutions, we have published the first ever global analysis of fishing effort using Automated Identification System (AIS) data. Our paper, "Tracking the global footprint of fisheries," is in the February 23rd edition of Science. Along with the publication of this [...]

By | 2018-02-22T14:22:48+00:00 February 22nd, 2018|Research and analysis|

AIS Coverage – Data Coming Soon

Along with the recent release of our fishing effort data, we are also releasing information on how good AIS coverage is for different parts of the world. That is, in some parts of the ocean, an AIS signal sent by a vessel is very likely to be in our database, and in others, it is less [...]

By | 2019-04-12T11:33:53+00:00 February 21st, 2018|Data blog|

Global Anchorage Database

Today Global Fishing Watch releases the first version of our global database of anchorages. This database contains over 100,000 locations where AIS transmitting vessels congregated and includes large industrial ports, smaller fishing harbors, and individual docks and piers. For more information regarding the development of this dataset, ways in which you can contribute to [...]

By | 2019-04-12T12:04:52+00:00 December 3rd, 2017|Data blog|

Temporal Footprint of Transshipment

Based on a lengthly review of refrigerated cargo vessels, we have just updated our transshipment data. You can read about the update here or download the slightly updated report and data here. The overall story of transshipments, as Nate notes in this post, is unchanged. This is a very rich dataset, giving the time and duration of thousands [...]

By | 2019-04-12T17:16:57+00:00 August 18th, 2017|Data blog|

Fishing localization using the vessel-scoring library

We published a logistic regression model for fishing localization a while a go as a python library built on top of scikit-learn. In this blog-post I’ll give you a quick introduction to how it can be used. The data we’re going to use is an AIS track exported from Google’s BigQuery, containing the columns timestamp (seconds since epoch), course (degrees) [...]

By | 2019-04-12T11:53:58+00:00 June 2nd, 2017|Data blog|
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