CAUTION Posted: Aug 5th 2025. Temporary delay in data updates. We started a migration process that impacts API and data. During the following 2 to 3 weeks, our Packages and APIs may not show the latest data.
Important
This version ofgfwrgives access to Global Fishing Watch API version 3. Starting April 30th, 2024, this is the official API version. For latest API releases, please check our API release notes
A Python package to communicate with Global Fishing Watch APIs was released in April 2025. Check the gfw-api-python-client repository.
The gfwr R package is a simple wrapper for the Global Fishing Watch (GFW) APIs. It provides convenient functions to freely pull GFW data directly into R in tidy formats.
The package currently works with the following APIs:
- Vessels API: vessel search and identity based on AIS self reported data and public registry information
- Events API: encounters, loitering, port visits, AIS-disabling events and fishing events based on AIS data
- Gridded apparent fishing effort (4Wings API): apparent fishing effort based on AIS data
Note: See the Terms of Use page for Global Fishing Watch APIs for information on our API licenses and rate limits.
Installation
You can install the most recent version of gfwr using:
# Check/install remotes
if (!require("remotes"))
install.packages("remotes")
remotes::install_github("GlobalFishingWatch/gfwr",
dependencies = TRUE)gfwr is also in the rOpenSci R-universe, and can be installed like this:
install.packages("gfwr",
repos = c("https://globalfishingwatch.r-universe.dev",
"https://cran.r-project.org"))Once everything is installed, you can load and use gfwr in your scripts with library(gfwr)
Authorization
The use of gfwr requires a GFW API token, which users can request from the GFW API Portal. Save this token to your .Renviron file using usethis::edit_r_environ() and adding a variable named GFW_TOKEN to the file (GFW_TOKEN="PASTE_YOUR_TOKEN_HERE"). Save the .Renviron file and restart the R session to make the edit effective.
gfwr functions are set to use key = gfw_auth() by default so in general you shouldn’t need to refer to the key in your function calls.
If the token configuration was not done properly you will see the following error:
In case you need to specify the key you can use gfw_auth() to save an object
key <- gfw_auth()or fetch the key directly from the .Renviron file
key <- Sys.getenv("GFW_TOKEN")The examples in the package documentation will omit an explicit call to key.
Vessels API
The get_vessel_info() function allows you to get vessel identity details from the Vessels API.
There are two search types: search, and id.
-
searchis performed by using parametersqueryfor basic searches andwherefor advanced searchers using SQL expressions-
querytakes a single identifier that can be the MMSI, IMO, callsign, or shipname as input and identifies all vessels that match. -
wheresearch allows for the use of complex search with logical clauses (AND, OR) and fuzzy matching with terms such as LIKE, using SQL syntax (see examples in the function) -
includesadds information from public registries. Options are “MATCH_CRITERIA”, “OWNERSHIP” and “AUTHORIZATIONS”
-
Basic search by identity markers (search_type = "search")
To get information of a vessel using its MMSI, IMO number, callsign or name, the search can be done directly using the number or the string. For example, to look for a vessel with MMSI = 224224000:
get_vessel_info(query = 224224000,
search_type = "search")
#> 1 total vessels
#> $dataset
#> # A tibble: 1 × 1
#> dataset
#> <chr>
#> 1 public-global-vessel-identity:v3.0
#>
#> $registryInfoTotalRecords
#> # A tibble: 1 × 1
#> registryInfoTotalRecords
#> <int>
#> 1 1
#>
#> $registryInfo
#> # A tibble: 1 × 16
#> index recordId sourceCode ssvid flag shipname nShipname callsign imo
#> <dbl> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 e0c9823749264a… <chr [7]> 2242… ESP AGURTZA… AGURTZAB… EBSJ 8733…
#> # ℹ 7 more variables: transmissionDateFrom <chr>, transmissionDateTo <chr>,
#> # geartypes <chr>, lengthM <int>, tonnageGt <dbl>, vesselInfoReference <chr>,
#> # extraFields <list>
#>
#> $registryOwners
#> # A tibble: 0 × 2
#> # ℹ 2 variables: index <dbl>, <list> <list>
#>
#> $registryPublicAuthorizations
#> # A tibble: 3 × 5
#> index dateFrom dateTo ssvid sourceCode
#> <dbl> <chr> <chr> <chr> <list>
#> 1 1 2019-01-01T00:00:00Z 2019-10-01T00:00:00Z 224224000 <chr [1]>
#> 2 1 2012-01-01T00:00:00Z 2019-01-01T00:00:00Z 224224000 <chr [1]>
#> 3 1 2019-10-15T00:00:00Z 2023-02-01T00:00:00Z 306118000 <chr [1]>
#>
#> $combinedSourcesInfo
#> # A tibble: 2 × 10
#> index vesselId geartypes_name geartypes_source geartypes_yearFrom
#> <dbl> <chr> <chr> <chr> <int>
#> 1 1 3c99c326d-dd2e-175d-… PURSE_SEINE_S… GFW_VESSEL_LIST 2015
#> 2 1 6632c9eb8-8009-abdb-… PURSE_SEINE_S… GFW_VESSEL_LIST 2019
#> # ℹ 5 more variables: geartypes_yearTo <int>, shiptypes_name <chr>,
#> # shiptypes_source <chr>, shiptypes_yearFrom <int>, shiptypes_yearTo <int>
#>
#> $selfReportedInfo
#> # A tibble: 2 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 6632c9eb8… 3061… AGURTZA… AGURTZAB… BES PJBL 8733… 418581
#> 2 1 3c99c326d… 2242… AGURTZA… AGURTZAB… ESP EBSJ 8733… 135057
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>Complex searches using where
To do more specific searches (e.g. "imo = '8300949'"), combine different fields ("imo = '8300949' AND ssvid = '214182732'") and do fuzzy matching ("shipname LIKE '%GABU REEFE%' OR imo = '8300949'"), use parameter where instead of query:
get_vessel_info(where = "shipname LIKE '%GABU REEFE%' OR imo = '8300949'",
search_type = "search")
#> 1 total vessels
#> $dataset
#> # A tibble: 1 × 1
#> dataset
#> <chr>
#> 1 public-global-vessel-identity:v3.0
#>
#> $registryInfoTotalRecords
#> # A tibble: 1 × 1
#> registryInfoTotalRecords
#> <int>
#> 1 1
#>
#> $registryInfo
#> # A tibble: 1 × 17
#> index recordId sourceCode ssvid flag shipname nShipname callsign imo
#> <dbl> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 b16ca93ea690fc… <chr [3]> 6290… GMB GABU RE… GABUREEF… C5J278 8300…
#> # ℹ 8 more variables: latestVesselInfo <lgl>, transmissionDateFrom <chr>,
#> # transmissionDateTo <chr>, geartypes <chr>, lengthM <dbl>, tonnageGt <int>,
#> # vesselInfoReference <chr>, extraFields <list>
#>
#> $registryOwners
#> # A tibble: 4 × 7
#> index name flag ssvid sourceCode dateFrom dateTo
#> <dbl> <chr> <chr> <chr> <list> <chr> <chr>
#> 1 1 FISHING CARGO SERVICES PAN 629009266 <chr [2]> 2024-08-07T10:… 2025-…
#> 2 1 FISHING CARGO SERVICES PAN 613590000 <chr [2]> 2022-01-24T09:… 2024-…
#> 3 1 FISHING CARGO SERVICES PAN 214182732 <chr [2]> 2019-02-23T11:… 2022-…
#> 4 1 FISHING CARGO SERVICES PAN 616852000 <chr [2]> 2012-01-08T19:… 2019-…
#>
#> $registryPublicAuthorizations
#> # A tibble: 0 × 2
#> # ℹ 2 variables: index <dbl>, <list> <list>
#>
#> $combinedSourcesInfo
#> # A tibble: 4 × 10
#> index vesselId geartypes_name geartypes_source geartypes_yearFrom
#> <dbl> <chr> <chr> <chr> <int>
#> 1 1 9827ea1ea-a120-f374-… CARRIER GFW_VESSEL_LIST 2024
#> 2 1 0b7047cb5-58c8-6e63-… CARRIER GFW_VESSEL_LIST 2019
#> 3 1 58cf536b1-1fca-dac3-… CARRIER GFW_VESSEL_LIST 2012
#> 4 1 1da8dbc23-3c48-d5ce-… CARRIER GFW_VESSEL_LIST 2022
#> # ℹ 5 more variables: geartypes_yearTo <int>, shiptypes_name <chr>,
#> # shiptypes_source <chr>, shiptypes_yearFrom <int>, shiptypes_yearTo <int>
#>
#> $selfReportedInfo
#> # A tibble: 4 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 9827ea1ea… 6290… GABU RE… GABUREEF… GMB C5J278 8300… 781424
#> 2 1 1da8dbc23… 6135… GABU RE… GABUREEF… CMR TJMC996 8300… 973251
#> 3 1 0b7047cb5… 2141… GABU RE… GABUREEF… MDA ER2732 8300… 642750
#> 4 1 58cf536b1… 6168… GABU RE… GABUREEF… COM D6FJ2 8300… 469834
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>Search by vessel ID
To search by vesselId, use parameter ids and specify search_type = "id".
Note:
vesselIdis an internal ID generated by Global Fishing Watch to connect data accross APIs and involves a combination of vessel and tracking data information. It can be retrieved usingget_vessel_info()and fetching the vector of responses inside$selfReportedInfo$vesselId. See the identity vignette for more information.
Single vessel IDs
get_vessel_info(ids = "8c7304226-6c71-edbe-0b63-c246734b3c01",
search_type = "id")
#> 1 total vessels
#> $dataset
#> # A tibble: 1 × 1
#> dataset
#> <chr>
#> 1 public-global-vessel-identity:v3.0
#>
#> $registryInfoTotalRecords
#> # A tibble: 1 × 1
#> registryInfoTotalRecords
#> <int>
#> 1 5
#>
#> $registryInfo
#> # A tibble: 5 × 17
#> index recordId sourceCode ssvid flag shipname nShipname callsign imo
#> <dbl> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 a8d00ce54b37ad… <chr [4]> 2733… RUS FRIO FO… FRIOFORW… UCRZ 9076…
#> 2 1 a8d00ce54b37ad… <chr [3]> 5111… PLW FRIO FO… FRIOFORW… T8A4891 9076…
#> 3 1 a8d00ce54b37ad… <chr [7]> 2106… CYP FRIO FO… FRIOFORW… 5BWC3 9076…
#> 4 1 a8d00ce54b37ad… <chr [2]> 3413… KNA FRIO FO… FRIOFORW… V4JQ3 9076…
#> 5 1 a8d00ce54b37ad… <chr [3]> 3546… PAN FRIOAEG… FRIOAEGE… 3FGY4 9076…
#> # ℹ 8 more variables: latestVesselInfo <lgl>, transmissionDateFrom <chr>,
#> # transmissionDateTo <chr>, geartypes <chr>, lengthM <int>, tonnageGt <int>,
#> # vesselInfoReference <chr>, extraFields <list>
#>
#> $registryOwners
#> # A tibble: 3 × 7
#> index name flag ssvid sourceCode dateFrom dateTo
#> <dbl> <chr> <chr> <chr> <list> <chr> <chr>
#> 1 1 COLINER RUS 273379740 <chr [2]> 2015-02-27T10:59:43Z 2025-05-31T23:5…
#> 2 1 COLINER CYP 511101495 <chr [1]> 2024-07-04T14:27:04Z 2024-07-24T14:2…
#> 3 1 COLINER CYP 210631000 <chr [1]> 2013-05-15T20:19:43Z 2024-07-04T14:1…
#>
#> $registryPublicAuthorizations
#> # A tibble: 3 × 5
#> index dateFrom dateTo ssvid sourceCode
#> <dbl> <chr> <chr> <chr> <list>
#> 1 1 2023-01-01T00:00:00Z 2024-12-31T00:00:00Z 210631000 <chr [1]>
#> 2 1 2020-01-01T00:00:00Z 2024-12-01T00:00:00Z 210631000 <chr [1]>
#> 3 1 2024-08-09T00:00:00Z 2025-05-01T00:00:00Z 273379740 <chr [1]>
#>
#> $combinedSourcesInfo
#> # A tibble: 8 × 10
#> index vesselId geartypes_name geartypes_source geartypes_yearFrom
#> <dbl> <chr> <chr> <chr> <int>
#> 1 1 0edad163f-f53d-9ddb-… CARRIER GFW_VESSEL_LIST 2024
#> 2 1 8c7304226-6c71-edbe-… CARRIER GFW_VESSEL_LIST 2013
#> 3 1 da1cd7e1b-b8d0-539c-… CARRIER GFW_VESSEL_LIST 2019
#> 4 1 da1cd7e1b-b8d0-539c-… CARRIER GFW_VESSEL_LIST 2019
#> 5 1 da1cd7e1b-b8d0-539c-… CARRIER GFW_VESSEL_LIST 2015
#> 6 1 da1cd7e1b-b8d0-539c-… CARRIER GFW_VESSEL_LIST 2015
#> 7 1 0cb77880e-ee49-2ce4-… CARRIER GFW_VESSEL_LIST 2012
#> 8 1 3c81a942b-bf0a-f476-… CARRIER GFW_VESSEL_LIST 2015
#> # ℹ 5 more variables: geartypes_yearTo <int>, shiptypes_name <chr>,
#> # shiptypes_source <chr>, shiptypes_yearFrom <int>, shiptypes_yearTo <int>
#>
#> $selfReportedInfo
#> # A tibble: 1 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 8c7304226… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 3369802
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>Multiple vessel IDs
To specify more than one vesselId, you can submit a vector:
get_vessel_info(ids = c("8c7304226-6c71-edbe-0b63-c246734b3c01",
"6583c51e3-3626-5638-866a-f47c3bc7ef7c",
"71e7da672-2451-17da-b239-857831602eca"),
search_type = "id")
#> 3 total vessels
#> $dataset
#> # A tibble: 3 × 1
#> dataset
#> <chr>
#> 1 public-global-vessel-identity:v3.0
#> 2 public-global-vessel-identity:v3.0
#> 3 public-global-vessel-identity:v3.0
#>
#> $registryInfoTotalRecords
#> # A tibble: 3 × 1
#> registryInfoTotalRecords
#> <int>
#> 1 2
#> 2 1
#> 3 5
#>
#> $registryInfo
#> # A tibble: 8 × 17
#> index recordId sourceCode ssvid flag shipname nShipname callsign imo
#> <dbl> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 b82d02e5c2c11e… <chr [6]> 4417… KOR ADRIA ADRIA DTBY3 8919…
#> 2 1 b82d02e5c2c11e… <chr [5]> 4417… KOR PREMIER PREMIER DTBY3 8919…
#> 3 2 685862e0626f62… <chr [6]> 5480… PHL JOHNREY… JOHNREYN… DUQA7 8118…
#> 4 3 a8d00ce54b37ad… <chr [4]> 2733… RUS FRIO FO… FRIOFORW… UCRZ 9076…
#> 5 3 a8d00ce54b37ad… <chr [3]> 5111… PLW FRIO FO… FRIOFORW… T8A4891 9076…
#> 6 3 a8d00ce54b37ad… <chr [7]> 2106… CYP FRIO FO… FRIOFORW… 5BWC3 9076…
#> 7 3 a8d00ce54b37ad… <chr [2]> 3413… KNA FRIO FO… FRIOFORW… V4JQ3 9076…
#> 8 3 a8d00ce54b37ad… <chr [3]> 3546… PAN FRIOAEG… FRIOAEGE… 3FGY4 9076…
#> # ℹ 8 more variables: latestVesselInfo <lgl>, transmissionDateFrom <chr>,
#> # transmissionDateTo <chr>, geartypes <chr>, lengthM <dbl>, tonnageGt <dbl>,
#> # vesselInfoReference <chr>, extraFields <list>
#>
#> $registryOwners
#> # A tibble: 3 × 7
#> index name flag ssvid sourceCode dateFrom dateTo
#> <dbl> <chr> <chr> <chr> <list> <chr> <chr>
#> 1 3 COLINER RUS 273379740 <chr [2]> 2015-02-27T10:59:43Z 2025-05-31T23:5…
#> 2 3 COLINER CYP 511101495 <chr [1]> 2024-07-04T14:27:04Z 2024-07-24T14:2…
#> 3 3 COLINER CYP 210631000 <chr [1]> 2013-05-15T20:19:43Z 2024-07-04T14:1…
#>
#> $registryPublicAuthorizations
#> # A tibble: 8 × 5
#> index dateFrom dateTo ssvid sourceCode
#> <dbl> <chr> <chr> <chr> <list>
#> 1 1 2015-10-08T00:00:00Z 2020-07-21T00:00:00Z 441734000 <chr [1]>
#> 2 1 2012-01-01T00:00:00Z 2013-09-19T00:00:00Z 441734000 <chr [1]>
#> 3 1 2013-09-20T00:00:00Z 2025-01-01T00:00:00Z 441734000 <chr [1]>
#> 4 2 2012-01-01T00:00:00Z 2017-10-25T00:00:00Z 548012100 <chr [1]>
#> 5 2 2019-02-10T18:02:49Z 2025-05-01T00:00:00Z 548012100 <chr [1]>
#> 6 3 2023-01-01T00:00:00Z 2024-12-31T00:00:00Z 210631000 <chr [1]>
#> 7 3 2020-01-01T00:00:00Z 2024-12-01T00:00:00Z 210631000 <chr [1]>
#> 8 3 2024-08-09T00:00:00Z 2025-05-01T00:00:00Z 273379740 <chr [1]>
#>
#> $combinedSourcesInfo
#> # A tibble: 12 × 10
#> index vesselId geartypes_name geartypes_source geartypes_yearFrom
#> <dbl> <chr> <chr> <chr> <int>
#> 1 1 aca119c29-95dd-f5c4… TUNA_PURSE_SE… COMBINATION_OF_… 2012
#> 2 1 6583c51e3-3626-5638… TUNA_PURSE_SE… COMBINATION_OF_… 2013
#> 3 2 55889aefb-bef9-224c… TUNA_PURSE_SE… COMBINATION_OF_… 2017
#> 4 2 71e7da672-2451-17da… TUNA_PURSE_SE… COMBINATION_OF_… 2017
#> 5 3 0edad163f-f53d-9ddb… CARRIER GFW_VESSEL_LIST 2024
#> 6 3 8c7304226-6c71-edbe… CARRIER GFW_VESSEL_LIST 2013
#> 7 3 da1cd7e1b-b8d0-539c… CARRIER GFW_VESSEL_LIST 2019
#> 8 3 da1cd7e1b-b8d0-539c… CARRIER GFW_VESSEL_LIST 2019
#> 9 3 da1cd7e1b-b8d0-539c… CARRIER GFW_VESSEL_LIST 2015
#> 10 3 da1cd7e1b-b8d0-539c… CARRIER GFW_VESSEL_LIST 2015
#> 11 3 0cb77880e-ee49-2ce4… CARRIER GFW_VESSEL_LIST 2012
#> 12 3 3c81a942b-bf0a-f476… CARRIER GFW_VESSEL_LIST 2015
#> # ℹ 5 more variables: geartypes_yearTo <int>, shiptypes_name <chr>,
#> # shiptypes_source <chr>, shiptypes_yearFrom <int>, shiptypes_yearTo <int>
#>
#> $selfReportedInfo
#> # A tibble: 3 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 6583c51e3… 4417… ADRIA ADRIA KOR DTBY3 <NA> 360249
#> 2 2 71e7da672… 5480… JOHN RE… JOHNREYN… PHL DUQA-7 8118… 133081
#> 3 3 8c7304226… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 3369802
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>Check the function documentation for examples with the other function arguments and our dedicated vignette for more information about vessel identity markers and the outputs retrieved.
Events API
The get_event() function allows you to get data on specific vessel activities from the Events API. Event types include apparent fishing events, potential transshipment events (two-vessel encounters and loitering by refrigerated carrier vessels), port visits, and AIS-disabling events (“gaps”). Find more information about events in our caveat documentation.
Events in a given time range
You can get events in a given date range. By not specifying vessels, the response will return results for all vessels.
get_event(event_type = "ENCOUNTER",
start_date = "2020-01-01",
end_date = "2020-01-02")
#> [1] "Downloading 288 events from GFW"
#> # A tibble: 288 × 17
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2019-12-31 17:50:00 2020-01-01 16:10:00 b8e1833490587… encounter 68.7 50.3
#> 2 2020-01-01 01:30:00 2020-01-01 08:00:00 70e89e159d487… encounter 35.7 126.
#> 3 2020-01-01 00:00:00 2020-01-01 03:20:00 4498204f090d5… encounter 24.6 119.
#> 4 2020-01-01 17:30:00 2020-01-01 20:30:00 ba82c55975741… encounter 38.5 121.
#> 5 2020-01-01 19:50:00 2020-01-01 23:10:00 2b86ea68f197c… encounter 25.2 120.
#> 6 2020-01-01 21:40:00 2020-01-02 22:20:00 73851e37ae331… encounter -17.3 -79.6
#> 7 2020-01-01 00:00:00 2020-01-01 02:30:00 be45e3a9d4ec6… encounter 5.97 156.
#> 8 2020-01-01 00:00:00 2020-01-01 23:50:00 0ed5fa5331429… encounter 44.5 136.
#> 9 2020-01-01 13:00:00 2020-01-01 21:30:00 465f111158482… encounter 38.5 121.
#> 10 2020-01-01 04:20:00 2020-01-01 08:30:00 3ad47684e3477… encounter 2.00 168.
#> # ℹ 278 more rows
#> # ℹ 11 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vesselId <chr>, vessel_name <chr>, vessel_ssvid <chr>, vessel_flag <chr>,
#> # vessel_type <chr>, vessel_publicAuthorizations <list>,
#> # vessel_nextPort <list>, event_info <list>Note: We do not recommend trying too large downloads, such as all encounters for all vessels over a long period of time. This will possibly return time out (524) errors. Our API team is working on another API specific for large downloads in the future.
Events in a specific area
You can provide a polygon in sf format or the region code (such as an EEZ code) to filter the raster. Check the function documentation for more information about parameters region and region_source
# fishing events in user shapefile
test_polygon <- sf::st_bbox(c(xmin = -70,
xmax = -40,
ymin = -10,
ymax = 5),
crs = 4326) |>
sf::st_as_sfc() |>
sf::st_as_sf()
get_event(event_type = "FISHING",
start_date = "2020-10-01",
end_date = "2020-10-31",
region = test_polygon,
region_source = "USER_SHAPEFILE")
#> [1] "Downloading 59 events from GFW"
#> # A tibble: 59 × 17
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2020-10-20 03:23:35 2020-10-20 16:14:26 97790a3a15dc5f… fishing 4.89 -51.8
#> 2 2020-10-01 23:29:31 2020-10-03 03:11:17 4d538f3b37e2ae… fishing 4.74 -51.5
#> 3 2020-10-03 05:50:14 2020-10-05 03:35:27 b68b49e7e3655c… fishing 4.73 -51.5
#> 4 2020-10-30 12:22:15 2020-10-30 13:51:56 8524e3c9d9c25f… fishing 3.79 -50.1
#> 5 2020-10-01 12:54:31 2020-10-01 21:26:31 083f87bff85928… fishing 4.75 -51.6
#> 6 2020-10-05 08:50:27 2020-10-06 17:35:21 c75671db2488b8… fishing 4.71 -51.5
#> 7 2020-10-26 14:08:01 2020-10-26 18:20:07 f3f5f186fc457f… fishing 3.59 -49.9
#> 8 2020-10-03 21:08:06 2020-10-04 03:31:11 3ce13bbe2752ec… fishing 4.75 -51.6
#> 9 2020-10-21 12:16:04 2020-10-21 21:10:32 7b723d79331bab… fishing 4.71 -51.5
#> 10 2020-10-03 05:29:59 2020-10-03 20:13:15 4795fa87f11dc8… fishing 4.75 -51.5
#> # ℹ 49 more rows
#> # ℹ 11 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vesselId <chr>, vessel_name <chr>, vessel_ssvid <chr>, vessel_flag <chr>,
#> # vessel_type <chr>, vessel_publicAuthorizations <list>,
#> # vessel_nextPort <lgl>, event_info <list>Events for specific vessels
To extract events for specific vessels, the Events API needs vesselId as input, so you always need to use get_vessel_info() first to extract vesselId from $selfReportedInfo in the response.
Single vessel events
vessel_info <- get_vessel_info(query = 224224000)
#> 1 total vessels
vessel_info$selfReportedInfo
#> # A tibble: 2 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 6632c9eb8… 3061… AGURTZA… AGURTZAB… BES PJBL 8733… 418581
#> 2 1 3c99c326d… 2242… AGURTZA… AGURTZAB… ESP EBSJ 8733… 135057
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>The results show this vessel’s story is grouped in two vesselIds.
To get a list of port visits for that vessel, you can use a single vesselId of your interest:
id <- vessel_info$selfReportedInfo$vesselId
id
#> [1] "6632c9eb8-8009-abdb-baf9-b67d65f20510"
#> [2] "3c99c326d-dd2e-175d-626f-a3c488a4342b"
get_event(event_type = "PORT_VISIT",
vessels = id[1],
confidences = 4
)
#> [1] "Downloading 25 events from GFW"
#> # A tibble: 25 × 16
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2020-08-11 11:43:45 2020-08-11 19:34:16 c6042c5da685f… port_vis… 14.7 -17.4
#> 2 2021-06-17 13:49:26 2021-06-21 17:10:23 8abe85865ca20… port_vis… 5.20 -4.05
#> 3 2021-11-11 18:41:10 2021-11-20 18:43:26 af0cb5d7ee288… port_vis… 5.20 -4.04
#> 4 2020-01-11 11:18:49 2020-01-15 11:54:49 889beb4fc4bfb… port_vis… 5.23 -4.02
#> 5 2020-04-01 05:55:58 2020-04-05 15:03:18 953e1cf8246db… port_vis… 5.23 -4.02
#> 6 2020-06-10 13:51:11 2020-06-13 13:51:28 8f14e93f2e157… port_vis… 5.23 -3.97
#> 7 2019-11-15 14:15:11 2019-11-19 07:49:20 bbeed3f884a6f… port_vis… 5.20 -4.02
#> 8 2020-07-06 06:45:06 2020-07-12 09:13:39 6845cffacfe25… port_vis… 5.20 -4.02
#> 9 2020-09-26 16:17:55 2020-10-01 18:59:15 80f2a666bf03b… port_vis… 5.20 -4.02
#> 10 2020-08-19 09:44:55 2020-08-19 18:39:59 724b8c1b2fb6d… port_vis… 16.9 -25.0
#> # ℹ 15 more rows
#> # ℹ 10 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vesselId <chr>, vessel_name <chr>, vessel_ssvid <chr>, vessel_flag <chr>,
#> # vessel_type <chr>, vessel_nextPort <lgl>, event_info <list>But to get the whole event history, it’s better to use the whole vector of vesselId for that vessel. Notice how the following request provides more results than the previous one:
get_event(event_type = "PORT_VISIT",
vessels = id, #using the whole vector of vesselIds
confidences = 4
)
#> [1] "Downloading 74 events from GFW"
#> # A tibble: 74 × 16
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2018-02-05 10:13:21 2018-03-18 05:06:54 d68f0f1720a44… port_vis… 14.7 -17.4
#> 2 2018-04-20 09:48:47 2018-04-21 14:26:57 9b2a5156168df… port_vis… 5.22 -4.02
#> 3 2020-08-11 11:43:45 2020-08-11 19:34:16 c6042c5da685f… port_vis… 14.7 -17.4
#> 4 2018-06-24 06:19:15 2018-06-29 10:14:59 4a2248c5a04ac… port_vis… 5.21 -4.02
#> 5 2016-10-04 17:15:47 2016-10-09 21:02:28 2d376318fda23… port_vis… 5.23 -4.02
#> 6 2021-06-17 13:49:26 2021-06-21 17:10:23 8abe85865ca20… port_vis… 5.20 -4.05
#> 7 2018-08-11 06:32:24 2018-08-14 11:09:41 4de1a24bd0d04… port_vis… 5.23 -3.97
#> 8 2015-11-04 05:22:13 2015-11-07 10:46:28 b7250410f8c7a… port_vis… 5.20 -4.04
#> 9 2021-11-11 18:41:10 2021-11-20 18:43:26 af0cb5d7ee288… port_vis… 5.20 -4.04
#> 10 2015-12-29 14:52:13 2016-01-03 16:38:59 ecd93cc08b521… port_vis… 5.29 -4.01
#> # ℹ 64 more rows
#> # ℹ 10 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vesselId <chr>, vessel_name <chr>, vessel_ssvid <chr>, vessel_flag <chr>,
#> # vessel_type <chr>, vessel_nextPort <lgl>, event_info <list>Note: Try narrowing your search using
start_dateandend_dateif the request is too large and returns a time out error (524)
When a date range is provided to get_event() using both start_date and end_date, any event overlapping that range will be returned, including events that start prior to start_date or end after end_date. If just start_date or end_date are provided, results will include all events that end after start_date or begin prior to end_date, respectively.
Note:
Because encounter events are events between two vessels, a single event will be represented twice in the data, once for each vessel. To capture this information and link the related data rows, theidfield for encounter events includes an additional suffix (1 or 2) separated by a period. Thevesselfield will also contain different information specific to each vessel.
Events for multiple vessels
As another example, let’s combine the Vessels and Events APIs to get fishing events for a list of USA-flagged trawlers:
# Download the list of USA trawlers
usa_trawlers <- get_vessel_info(
where = "flag='USA' AND geartypes='TRAWLERS'",
search_type = "search",
quiet = TRUE
)
# Set quiet = TRUE if you want the output to return silentlyThis list returns 6498 vesselIds belonging to 4175 vessels.
usa_trawlers$selfReportedInfo
#> # A tibble: 6,674 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 d32af732… 3680… SUPERMA… SUPERMAN… USA WDJ8890 <NA> 267397
#> 2 1 5446e7cd… 3680… SUPERMA… SUPERMAN… USA <NA> <NA> 6184
#> 3 2 47b94476… 3668… ORION ORION USA <NA> <NA> 23007
#> 4 3 9f555214… 3680… PELICAN PELICAN USA WDJ8253 <NA> 415618
#> 5 3 15cea26f… 3680… PELICAN PELICAN USA <NA> <NA> 407
#> 6 4 0dddd2a8… 3673… SHAMROCK SHAMROCK USA WDD8722 <NA> 2720
#> 7 4 695b254f… 3673… SHAMROCK SHAMROCK USA <NA> <NA> 477
#> 8 4 ac994bda… 3673… <NA> <NA> USA WDD8722 <NA> 3179
#> 9 5 bc29946f… 3667… ALEX ALEX USA WDA2216 <NA> 840887
#> 10 5 0e4052b1… 3667… <NA> <NA> USA WDA2216 <NA> 8403
#> # ℹ 6,664 more rows
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>To fetch events for this list of vessels, we will use the vesselId column and send it to the vessels parameter in get_event() function.
For clarity, we should try to send groups of vesselIds that belong to the same vessels. For this, we can check the index column in the $selfReportedInfo dataset.
Note:
get_event()can receive severalvesselIdsat a time but will fail when the character length of the whole request is too long (~100,000 characters). This means it will fail with error HTTP 422: Unprocessable entity when too manyvesselIdsare requested, this value can be around 2,800vesselIdsdepending on the other parameters of the search.
For this example, we will send the vesselIds corresponding to the first twenty vessels in the response:
each_USA_trawler <- usa_trawlers$selfReportedInfo[, c("index", "vesselId")]
# how many vessels correspond to the first twenty vessels.
(twenty_usa_trawlers <- each_USA_trawler %>% filter(index <= 20))
#> # A tibble: 51 × 2
#> index vesselId
#> <dbl> <chr>
#> 1 1 d32af7320-0748-9a63-abd7-48ad721e63b8
#> 2 1 5446e7cd1-1f75-4672-d859-01211df72fba
#> 3 2 47b944765-5819-b2ab-8c2e-cfc82bd2e82c
#> 4 3 9f5552145-50ed-92f4-4514-5177b1a6511d
#> 5 3 15cea26f5-57ad-acac-4cbf-b45cefb7ab04
#> 6 4 0dddd2a83-3626-24f1-0fe6-3c4d45bbb409
#> 7 4 695b254f7-7e6c-ff50-dc63-55139d9e0101
#> 8 4 ac994bdab-b59c-9fcc-659e-40179e5dddfb
#> 9 5 bc29946f2-2b0b-9613-054a-cd59327226d9
#> 10 5 0e4052b12-2e16-969f-d3e3-2a266be74255
#> # ℹ 41 more rowsThere are 51 vesselIds corresponding to those 20 vessels.
Let’s pass the vector of vesselIds to Events API. Now get the list of fishing events for these trawlers in January, 2020:
fishing_events <- get_event(event_type = "FISHING",
vessels = twenty_usa_trawlers$vesselId,
start_date = "2020-01-01",
end_date = "2020-02-01")
#> [1] "Downloading 63 events from GFW"
fishing_events
#> # A tibble: 63 × 17
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2019-12-31 01:38:26 2020-01-02 05:06:56 b630325a08b11… fishing 41.4 -68.7
#> 2 2020-01-05 03:57:26 2020-01-05 05:24:51 0a79fe4183554… fishing 44.4 -124.
#> 3 2020-01-25 09:38:35 2020-01-25 10:34:01 6b4c28449e7fe… fishing 40.1 -72.7
#> 4 2020-01-18 06:54:55 2020-01-18 07:29:56 5ff9724477b70… fishing 41.4 -69.3
#> 5 2020-01-13 15:19:52 2020-01-13 17:47:54 c5409b543781f… fishing 41.5 -69.3
#> 6 2020-01-16 15:07:04 2020-01-16 17:16:20 db046d9ebb664… fishing 41.4 -69.3
#> 7 2020-01-17 18:15:20 2020-01-17 23:14:40 4fe0db533d5a6… fishing 41.5 -70.2
#> 8 2020-01-17 09:10:03 2020-01-17 17:57:00 e5d2760a2cd5e… fishing 41.5 -70.1
#> 9 2020-01-05 15:00:22 2020-01-06 00:35:34 873cf3ee8755c… fishing 41.4 -68.7
#> 10 2020-01-15 21:35:14 2020-01-16 11:01:12 2c309e235a0d1… fishing 41.4 -68.7
#> # ℹ 53 more rows
#> # ℹ 11 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vesselId <chr>, vessel_name <chr>, vessel_ssvid <chr>, vessel_flag <chr>,
#> # vessel_type <chr>, vessel_publicAuthorizations <list>,
#> # vessel_nextPort <lgl>, event_info <list>The columns starting by vessel hold the vessel-related information for each event: vesselId, vessel_name, ssvid (MMSI), flag, vessel type and public authorizations.
fishing_events %>%
dplyr::select(starts_with("vessel"))
#> # A tibble: 63 × 7
#> vesselId vessel_name vessel_ssvid vessel_flag vessel_type
#> <chr> <chr> <chr> <chr> <chr>
#> 1 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 2 0203066e4-4f99-c97a-7b94-0e… PROSPECTOR 367428110 USA fishing
#> 3 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 4 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 5 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 6 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 7 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 8 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 9 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 10 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> # ℹ 53 more rows
#> # ℹ 2 more variables: vessel_publicAuthorizations <list>, vessel_nextPort <lgl>When no events are available, the get_event() function returns nothing.
get_event(event_type = "FISHING",
vessels = twenty_usa_trawlers$vesselId[2],
start_date = "2020-01-01",
end_date = "2020-01-01"
)
#> [1] "Your request returned zero results"
#> NULLApparent fishing effort API
The get_raster() function gets a raster from the 4Wings API and converts the response to a data frame. In order to use it, you should specify:
- The spatial resolution, which can be
LOW(0.1 degree) orHIGH(0.01 degree) - The temporal resolution, which can be
HOURLY,DAILY,MONTHLY,YEARLYorENTIRE. - The variable to group by:
FLAG,GEARTYPE,FLAGANDGEARTYPE,MMSIorVESSEL_ID - The date range
note: this must be 366 days or less - The region polygon in
sfformat or the region code (such as an EEZ code) to filter the raster - The source for the specified region. Currently,
EEZ,MPA,RFMOorUSER_SHAPEFILE(forsfshapefiles).
User-defined shapefile
You can load an sf shapefile with your area of interest and fetch apparent fishing effort for this area using region_source = "USER_SHAPEFILE" and region = [YOUR_SHAPE]. We added a sample shapefile inside gfwr to show how "USER_SHAPEFILE" works:
data("test_shape")
test_shape
#> Simple feature collection with 1 feature and 0 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 56.74815 ymin: 0 xmax: 70 ymax: 21.79799
#> Geodetic CRS: WGS 84
#> geometry
#> 1 MULTIPOLYGON (((70 15.20471...
get_raster(
spatial_resolution = "LOW",
temporal_resolution = "YEARLY",
group_by = "FLAG",
start_date = "2021-01-01",
end_date = "2021-02-01",
region_source = "USER_SHAPEFILE",
region = test_shape
)
#> # A tibble: 2,686 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 15.1 60.4 2021 CHN 2 15.6
#> 2 15.2 61.4 2021 CHN 3 13.3
#> 3 15.7 63.7 2021 CHN 2 14.6
#> 4 15.6 62.7 2021 CHN 1 11.7
#> 5 4.6 65.7 2021 TWN 1 1.82
#> 6 5.8 67.2 2021 TWN 1 10.2
#> 7 15.4 60.6 2021 CHN 1 7.81
#> 8 12.5 62.2 2021 CHN 1 5.78
#> 9 6 68.8 2021 <NA> 1 5.46
#> 10 12.2 60.5 2021 CHN 1 12.6
#> # ℹ 2,676 more rowsApparent fishing effort in preloaded EEZ, RFMOs and MPAs
EEZ
If you want raster data from a particular EEZ, you can use the get_region_id() function to get the EEZ id, and enter that code in the region_name argument of get_raster() instead of the region shapefile (with region_source = "EEZ"):
# use EEZ function to get EEZ code of Cote d'Ivoire
code_eez <- get_region_id(region_name = "CIV", region_source = "EEZ")
get_raster(spatial_resolution = "LOW",
temporal_resolution = "YEARLY",
group_by = "FLAG",
start_date = "2021-01-01",
end_date = "2021-10-01",
region = code_eez$id,
region_source = "EEZ")
#> # A tibble: 595 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 1.7 -5.6 2021 BLZ 1 0.39
#> 2 4 -3.6 2021 BES 1 2.99
#> 3 3.1 -4 2021 FRA 1 5.1
#> 4 4.4 -3.7 2021 BLZ 1 0.72
#> 5 4.7 -5.8 2021 CHN 1 3.62
#> 6 1.7 -5.9 2021 ESP 1 0.35
#> 7 3.8 -5.4 2021 BLZ 1 1.43
#> 8 2.5 -5.4 2021 FRA 1 7.45
#> 9 4.4 -3.7 2021 BES 2 6.12
#> 10 4 -4.2 2021 ESP 1 3.1
#> # ℹ 585 more rowsYou could search for just one word in the name of the EEZ and then decide which one you want:
(get_region_id(region_name = "France", region_source = "EEZ"))
#> # A tibble: 3 × 5
#> iso3 label id GEONAME POL_TYPE
#> <chr> <chr> <dbl> <chr> <chr>
#> 1 <NA> Joint regime area: Spain / France 48966 Joint regime area: Spa… Joint r…
#> 2 <NA> Joint regime area: France / Italy 48976 Joint regime area: Fra… Joint r…
#> 3 FRA France 5677 French Exclusive Econo… 200NMFrom the results above, let’s say we’re interested in the French Exclusive Economic Zone, 5677
get_raster(spatial_resolution = "LOW",
temporal_resolution = "YEARLY",
group_by = "FLAG",
start_date = "2021-01-01",
end_date = "2021-10-01",
region = 5677,
region_source = "EEZ"
)
#> # A tibble: 5,618 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 49.8 -0.6 2021 NLD 7 16.5
#> 2 45.2 -2 2021 BEL 4 59.3
#> 3 42.6 3.4 2021 FRA 6 37.8
#> 4 45.1 -3.4 2021 ESP 20 92
#> 5 51.2 2.1 2021 BEL 3 22.0
#> 6 48.7 -4.2 2021 FRA 1 1.01
#> 7 49.9 -1.7 2021 BEL 1 1.71
#> 8 46.2 -3.9 2021 FRA 7 45.4
#> 9 44.8 -1.8 2021 ESP 9 51.0
#> 10 47.1 -2.6 2021 FRA 14 174.
#> # ℹ 5,608 more rowsMarine Protected Areas (MPAs)
A similar approach can be used to search for a specific Marine Protected Area, in this case the Phoenix Island Protected Area (PIPA)
# use region id function to get MPA code of Phoenix Island Protected Area
code_mpa <- get_region_id(region_name = "Phoenix",
region_source = "MPA")
code_mpa
#> # A tibble: 2 × 2
#> id label
#> <chr> <chr>
#> 1 309888 Phoenix Islands Protected Area - Protected Area
#> 2 555512002 Phoenix Islands Protected Area - World Heritage Site (natural or mi…
get_raster(spatial_resolution = "LOW",
temporal_resolution = "YEARLY",
group_by = "FLAG",
start_date = "2015-01-01",
end_date = "2015-06-01",
region = code_mpa$id[1],
region_source = "MPA")
#> # A tibble: 38 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 -3.6 -176. 2015 KOR 1 1.98
#> 2 -2.9 -176. 2015 FSM 1 2.77
#> 3 -2.6 -176. 2015 KOR 1 13.9
#> 4 -3 -176. 2015 FSM 1 2.16
#> 5 -3.6 -176. 2015 KIR 1 6.07
#> 6 -2.8 -176. 2015 KOR 1 10.4
#> 7 -3.5 -176. 2015 KOR 1 3.11
#> 8 -1 -170. 2015 KOR 1 2.39
#> 9 -4.1 -176. 2015 KOR 1 2.67
#> 10 -4.2 -176. 2015 KOR 1 0.05
#> # ℹ 28 more rowsRegional Fisheries Management Organizations (RFMOs)
It is also possible to filter rasters to regional fisheries management organizations (RFMO) like "ICCAT", "IATTC", "IOTC", "CCSBT" and "WCPFC".
get_raster(spatial_resolution = "LOW",
temporal_resolution = "DAILY",
group_by = "FLAG",
start_date = "2021-01-01",
end_date = "2021-01-04",
region = "ICCAT",
region_source = "RFMO")
#> # A tibble: 16,878 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <date> <chr> <dbl> <dbl>
#> 1 38.9 26.8 2021-01-02 TUR 2 4.35
#> 2 59 0.1 2021-01-03 GBR 2 13.9
#> 3 59.9 25.1 2021-01-03 FIN 2 3
#> 4 59.9 -2.8 2021-01-03 GBR 1 1.11
#> 5 47.6 -3 2021-01-02 FRA 1 2.41
#> 6 51.4 -8.9 2021-01-02 IRL 1 0.79
#> 7 58.9 10.6 2021-01-03 SWE 1 0.58
#> 8 38.7 26.7 2021-01-03 TUR 1 13.4
#> 9 15.7 -29.7 2021-01-03 JPN 1 1.09
#> 10 11.8 -16.8 2021-01-03 KOR 1 0.88
#> # ℹ 16,868 more rowsNote: For a complete list of MPAs, RFMOs and EEZ, check the function
get_regions()
When your API request times out
For API performance reasons, the get_raster() function restricts individual queries to a single year of data. However, even with this restriction, it is possible for API request to time out before it completes. When this occurs, the initial get_raster() call will return an HTTP 524 error, and subsequent API requests using any gfwr get_ function will return an HTTP 429 error until the original request completes:
Error in
httr2::req_perform(): ! HTTP 429 Too Many Requests. • Your application token is not currently enabled to perform more than one concurrent report. If you need to generate more than one report concurrently, contact us at apis@globalfishingwatch.org
Although no data was received, the request is still being processed by the APIs and will become available when it completes. To account for this, gfwr includes the get_last_report() function, which lets users request the results of their last API request with get_raster().
The get_last_report() function will tell you if the APIs are still processing your request and will download the results if the request has finished successfully. You will receive an error message if the request finished but resulted in an error or if it’s been >30 minutes since the last report was generated using get_raster(). For more information, see the Get last report generated endpoint documentation on the Global Fishing Watch API page.
Reverse region id search
The get_region_id() function also works in reverse. If a region id is passed as a numeric to the function as the region_name, the corresponding region label or iso3 code can be returned. This is especially useful when events are returned with regions.
Using the same example with twenty trawlers fishing events, fishing_events, you can see the eez information is returned as the numeric code in the "eez" column.
fishing_events <- get_event(event_type = "FISHING",
vessels = twenty_usa_trawlers$vesselId,
start_date = "2020-01-01",
end_date = "2020-02-01") %>%
# extract EEZ id code
dplyr::mutate(eez = as.character(
purrr::map(purrr::map(regions, purrr::pluck, "eez"),
paste0, collapse = ","))) %>%
dplyr::select(eez, eventId, eventType, start, end, lat, lon)
#> [1] "Downloading 63 events from GFW"
fishing_events
#> # A tibble: 63 × 7
#> eez eventId eventType start end lat lon
#> <chr> <chr> <chr> <dttm> <dttm> <dbl> <dbl>
#> 1 8456 b630325… fishing 2019-12-31 01:38:26 2020-01-02 05:06:56 41.4 -68.7
#> 2 8456 0a79fe4… fishing 2020-01-05 03:57:26 2020-01-05 05:24:51 44.4 -124.
#> 3 8456 6b4c284… fishing 2020-01-25 09:38:35 2020-01-25 10:34:01 40.1 -72.7
#> 4 8456 5ff9724… fishing 2020-01-18 06:54:55 2020-01-18 07:29:56 41.4 -69.3
#> 5 8456 c5409b5… fishing 2020-01-13 15:19:52 2020-01-13 17:47:54 41.5 -69.3
#> 6 8456 db046d9… fishing 2020-01-16 15:07:04 2020-01-16 17:16:20 41.4 -69.3
#> 7 8456 4fe0db5… fishing 2020-01-17 18:15:20 2020-01-17 23:14:40 41.5 -70.2
#> 8 8456 e5d2760… fishing 2020-01-17 09:10:03 2020-01-17 17:57:00 41.5 -70.1
#> 9 8456 873cf3e… fishing 2020-01-05 15:00:22 2020-01-06 00:35:34 41.4 -68.7
#> 10 8456 2c309e2… fishing 2020-01-15 21:35:14 2020-01-16 11:01:12 41.4 -68.7
#> # ℹ 53 more rowsWe can apply get_region_id() to the numeric vector to extract the labels:
fishing_events %>%
mutate(eez_name = purrr::map_df(as.numeric(fishing_events$eez),
~get_region_id(region_name = .x,
region_source = "EEZ"))$label) %>%
dplyr::relocate(eez, eez_name)
#> # A tibble: 63 × 8
#> eez eez_name eventId eventType start end
#> <chr> <chr> <chr> <chr> <dttm> <dttm>
#> 1 8456 United States b63032… fishing 2019-12-31 01:38:26 2020-01-02 05:06:56
#> 2 8456 United States 0a79fe… fishing 2020-01-05 03:57:26 2020-01-05 05:24:51
#> 3 8456 United States 6b4c28… fishing 2020-01-25 09:38:35 2020-01-25 10:34:01
#> 4 8456 United States 5ff972… fishing 2020-01-18 06:54:55 2020-01-18 07:29:56
#> 5 8456 United States c5409b… fishing 2020-01-13 15:19:52 2020-01-13 17:47:54
#> 6 8456 United States db046d… fishing 2020-01-16 15:07:04 2020-01-16 17:16:20
#> 7 8456 United States 4fe0db… fishing 2020-01-17 18:15:20 2020-01-17 23:14:40
#> 8 8456 United States e5d276… fishing 2020-01-17 09:10:03 2020-01-17 17:57:00
#> 9 8456 United States 873cf3… fishing 2020-01-05 15:00:22 2020-01-06 00:35:34
#> 10 8456 United States 2c309e… fishing 2020-01-15 21:35:14 2020-01-16 11:01:12
#> # ℹ 53 more rows
#> # ℹ 2 more variables: lat <dbl>, lon <dbl>