class: center, middle, inverse, title-slide # Global Fishing Watch &</br> Google Earth Engine ### Grant R. McDermott | AERE 2017 Workshop</br></br></br></br></br></br> --- ## About me - Environmental economist interested in big data and spatial analysis (among other things). - Incoming faculty at the University of Oregon for AY2017/18. - Currently finishing up a postdoc at UCSB under Chris Costello. - Member of the <a href="http://sfg.msi.ucsb.edu/" target="_blank">Sustainable Fisheries Group</a>. <i class="fa fa-globe"></i> <a href="http://www.grantmcdermott.com" target="_blank">www.grantmcdermott.com</a></br> <i class="fa fa-github"></i> <a href="https://github.com/grantmcdermott" target="_blank">github.com/grantmcdermott</a></br> <i class="fa fa-envelope-square"></i> <a href="mailto:gmcdermott@ucsb.edu" target="_blank">gmcdermott@ucsb.edu</a></br> <i class="fa fa-twitter"></i> <a href="https://twitter.com/grant_mcdermott" target="_blank">@grant_mcdermott</a></br> --- ## Goals for today 1. Introduce you to Global Fishing Watch 2. Introduce you to Google Earth Engine 3. Show you an application that combines 1. and 2. --- count: false ## Goals for today 1. Introduce you to Global Fishing Watch 2. Introduce you to Google Earth Engine 3. Show you an application that combines 1. and 2. Meta goal: Encourage you to use these amazing tools yourself. --- class: inverse, center, middle # Global Fishing Watch --- ## Introduction - Global Fishing Watch (GFW) is a joint initiative between Google, Skytruth and Oceana. - GFW offers unprecented insight into *global* fishing activity. - Includes locations that were previously inaccesible to outside observers. - Includes a suite of tools for accessing and analysing the data. (More on this later.) - Economists are under-represented among the set of current GFW research partners. - We hope that this will change when the data are made public in the next 2-3 months. - AERE workshop participants are getting a head start! ??? - Low-hanging fruit. --- ## Where does GFW get its data? - Ships use AIS (Automatic Identification System) for martime safety. - Broadcast location to navigate and avoid collisions. - Includes info on vessel ID, flag, speed, tonnage, length, destination, etc. - Problems: Location "spoofing". Some fields are incorrectly entered by owner. Raw data contains many mistakes. - Satellite (and terrestrial systems) can receive and record AIS messages too. - AIS is *big* data... - 22 billion messages from 250k industrial fishing vessels over 2012-2016. - 20 million messages being added per day (and growing). ??? - AIS is required on all vessels >300 tons on international voyages. Many countries require smaller vessels to use AIS within their EEZs as well. - A moving vessel broadcasts a position message every 2 to 20 seconds - An anchored vessel every 3 to 6 minutes. - GFW average is 50 satellite positions per vessel per day. --- ## Density of AIS  --- ## Processing and classfying the data - Short version:^[1] Cloud computing and machine learning (convolutional neural network). - Probability that a vessel *is* fishing, `\(P \in [0,1]\)`. - What *type* of fishing is most likely (trawl, long line, purse sein, etc.) - Training data: 240k hours’ worth of AIS data from 624 vessels (569k AIS positions) that have been hand labelled by fisheries experts and/or validated with logbook data. .footnote[ [1] <a href="https://www.youtube.com/embed/uaehB3PJfr0" target="_blank">Lucius Fox</a>: "I just wanted you to know how hard it was." ] ??? - There are actually two CNNs: 1. One to identify vessel characteristics, including length, engine power, and vessel type. (Training data: 73,994 vessels matched to official fleet registries, including about 13,500 fishing vessels.) 2. A second to detect which AIS positions were indicative of fishing activity. (Training data: 240,000 hours’ worth of AIS data, from 624 vessels, with over 569,000 AIS positions labelled by fisheries experts.) --- ## Processing and classfying the data  --- ## GFW dataset - The cleaned GFW dataset contains 70k likely fishing vessels. - AIS-equipped vessels account for 75% of offshore fisheries (>100 nm from land). - Individual vessel tracts. - Where, when and for how long a vessel was fishing. - What type of fishing were they doing. - Other covariates of interest: Flag, tonnage, length, speed, etc. ??? - Small fraction of the world's ~2.5 million motorized fishing vessels... but it contains a majority of active vessels over 24 metres. --- ## Let's explore the data - <a href="http://globalfishingwatch.org/map/?redirect_login=true" target="_blank">Live session</a>. --- count: false ## Let's explore the data - <a href="http://globalfishingwatch.org/map/?redirect_login=true" target="_blank">Live session</a>. - The GFW website is great for (visual) exploration. - It is meant to serve as tool for policy engagement and citizen science. - However, it offers limited scope for in-depth analysis and research. - No API for accessing and downloading the data *en masse*, or for performing statistical analysis. - Enter Google Earth Engine... --- class: inverse, center, middle # Google Earth Engine --- ## Planetary-scale analysis - Google has done the hard work of ingesting (much of) the world's public satellite and remote sensing data. - Landsat, Sentinel, MODIS, NOAA/NCEP/NCAR, etc. - Paired with vast, colocated computational power. - "Bring your code to the data." - In-browser console editor (Javascript) or Python API. - Seemlessly interacts with many other products within the Google ecosystem. - GoogleDrive, Cloud storage, TensorFlow ML library, etc. - I highly recommend applying for the annual GEE User Summit next year. ??? - 200 datasets, 5 million images (4k being added every day), 5 petabytes of data... - Automatic version control plus shareable scripts make for good research practice and reproducible science. --- ## From my inbox last week... <i> > Hey Grant,</br></br> It took me 2 days to download the data[...] So I guess the work is reduced from about 6-10 Months to max 7 days.</br></br> Thanks a ton. This is amazing.</br></br> Best,</br> M </i> ??? - Don't just take my word for it: Here's a recent email that I received after suggesting someone switch to GEE. - It's hard to overstate how valuable that time difference can be. - For a graduate student, that's the difference between finishing you PhD on time and having to find funding (student debt?) for another year. - Or, having the time to write a great job market paper versus to an average one. --- ## Let's explore GEE - <a href="https://code.earthengine.google.com/6267d5e6ae8435a9db3b9b1058089f7c" target="_blank">Live session</a>. --- count: false ## Let's explore GEE - <a href="https://code.earthengine.google.com/6267d5e6ae8435a9db3b9b1058089f7c" target="_blank">Live session</a>. - GEE is an amazing tool for remote sensing data and spatial analysis (in general). - For most researchers, it will also be the primary platform for accessing GFW data. - Caveat: GFW rasters within GEE will lose some information. - E.g. No individual vessel IDs. --- ## Let's take stock before continuing - What can we use GFW data for? 1. Policy effectiveness and spillovers. 2. Interaction between labour/energy/etc. markets and fishing activity. 3. Fisheries supply and demand elasticities relative to broader economic cycles. 4. Validation of fisheries catch and effort data. 5. Continuous data source ready to be paired with natural experiments. 6. Etcetera, etcetera. --- count: false ## Let's take stock before continuing - What can we use GFW data for? 1. **Policy effectiveness and spillovers.** 2. Interaction between labour/energy/etc. markets and fishing activity. 3. Fisheries supply and demand elasticities relative to broader economic cycles. 4. Validation of fisheries catch and effort data. 5. Continuous data source ready to be paired with natural experiments. 6. Etcetera, etcetera. - I'll show you an example application of type 1. --- class: inverse, center, middle # Application: ## "The Blue Paradox" *W.I.P. with Chris Costello, Kyle Meng and Gavin McDonald* --- ## Introduction - Marine Protected Areas (MPAs) and no-take zones (NTZs) are important tools in marine conservation efforts. - However, their efficacy remains in question. - "Paper parks" (e.g. [Edgar at al., 2014](http://www.nature.com/nature/journal/v506/n7487/full/nature13022.html); [Halpern, 2014](http://www.nature.com/nature/journal/v506/n7487/full/nature13053.html)) - Data limitations, questionable modelling? - Most studies ignore incentives and behavourial changes: - Simple before/after (inside/outside) comparisons of abundance. - **Blue paradox:** Delay between MPA announcement and enforcement creates incentive to mine down the stock. ??? - Edgar et al.'s five features: No take, well enforced, old (>10 years), large (>100km2), and isolated by deep water or sand. - Current MPA literature does not account for the fact that the announcement itself may trigger a response in fishing effort. - Blue paradox idea draws inspiration from the Green Paradox ([Sinn, 2012](https://mitpress.mit.edu/books/green-paradox)) and related literature on preemptive habitat destruction under the E.S.A. ([Lueck and Michaels, 2003](http://www.masonlec.org/site/rte_uploads/files/Manne/2014.12.06/LueckMicheal_Class%208.pdf)) --- ## Approach - **Research question:** Does an MPA announcement trigger increased fishing activity? - Focus on the Phoenix Island Protected Area (PIPA). - Identification: Spatial DiD. - Fishing effort per km in neighbouring "treated" (MPA) vs "control" (non-MPA) areas. - NB: Fishers have no incentive to lie about their position pre-enforcement. --- ## Analysis - <a href="https://code.earthengine.google.com/6267d5e6ae8435a9db3b9b1058089f7c" target="_blank">GEE live session</a>. ??? - I'm using a draft version of the GFW raster data that hasn't been made public yet. - Unfortunately, I can't share my full GEE script yet until that's done. --- ## Results (early) <div align="center"> <img src="pics/fishing-normalized.png" height=500> </div> --- ## Conclusions (early) - MPA announcement appears to trigger a preemptive fishing response. - Could even lead to a long-term decline in fish stocks under certain conditions - E.g. Surge in fishing pushes a stock below some min. population threshold. - More work still to be done, but at a minimum suggests that announcement and enforcement gap should be closed. - Granularity of the data should allow us to test different causal hypotheses (e.g. spatial knowledge vs. quasi-property rights). - This kind of analysis is only being made possible now thanks to data innovations like GFW and GEE. --- class: inverse, center, middle # Wrapping up --- ## Wrapping up - GFW provides an unprecedented window into global fishing behaviour. - GEE dramatically lowers the barriers to entry for working with remote sensing and geospatial data. - Provides a way to operationalise many of the ideas that we've been talking about today. - Economists can bring added value to remote sensing data. - Old questions, new insights. - Undoubtedly helps to work with remote sensing experts and non-economists. - Rich possibilities for collaboration. --- class: inverse, center </br></br></br></br></br></br> ## Thank you! ### Questions?