Skip to content

GlobalFishingWatch/gfwr

BranchesTags

Repository files navigation

gfwr: Access data from Global Fishing Watch APIs

DOI Project Status: Active - The project has reached a stable, usable state and is being actively developed. Licence :registry status badge

Important
This is a major release of the package, with several breaking changes in function names and two new endpoints: AIS vessel presence and SAR vessel detections. Please take a look at the changelog. Several improvements are on the way in subsequent minor releases, please log any feedback in the issues.

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:

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)

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:

Error in `httr2::req_perform()`:
! HTTP 401 Unauthorized.

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 gfw_vessel_info() function allows you to get vessel identity details from the Vessels API.

There are two search types: search, and id.

  • search is performed by using parameters query for basic searches and where for advanced searchers using SQL expressions
    • query takes a single identifier that can be the MMSI, IMO, callsign, or shipname as input and identifies all vessels that match.
    • where search 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)
    • includes adds 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:

gfw_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 × 17
#>   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 8 more variables: latestVesselInfo <lgl>, 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 6632c9eb8-8009-abdb-… PURSE_SEINE_S… GFW_VESSEL_LIST                2019
#> 2     1 3c99c326d-dd2e-175d-… PURSE_SEINE_S… 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: 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:

gfw_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 1da8dbc23-3c48-d5ce-… CARRIER        GFW_VESSEL_LIST                2022
#> 2     1 58cf536b1-1fca-dac3-… CARRIER        GFW_VESSEL_LIST                2012
#> 3     1 0b7047cb5-58c8-6e63-… CARRIER        GFW_VESSEL_LIST                2019
#> 4     1 9827ea1ea-a120-f374-… CARRIER        GFW_VESSEL_LIST                2024
#> # 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…         1131941
#> 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: vesselId is 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 using gfw_vessel_info() and fetching the vector of responses inside $selfReportedInfo$vesselId. See the identity vignette for more information.

Single vessel IDs

gfw_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 2025-10-31T14:5…
#> 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 2025-11-01T00:00:00Z 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 0cb77880e-ee49-2ce4-… CARRIER        GFW_VESSEL_LIST                2012
#> 3     1 8c7304226-6c71-edbe-… CARRIER        GFW_VESSEL_LIST                2013
#> 4     1 da1cd7e1b-b8d0-539c-… CARRIER        GFW_VESSEL_LIST                2015
#> 5     1 da1cd7e1b-b8d0-539c-… CARRIER        GFW_VESSEL_LIST                2015
#> 6     1 da1cd7e1b-b8d0-539c-… CARRIER        GFW_VESSEL_LIST                2019
#> 7     1 da1cd7e1b-b8d0-539c-… CARRIER        GFW_VESSEL_LIST                2019
#> 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:

gfw_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                        1
#> 2                        2
#> 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 685862e0626f62… <chr [6]>  5480… PHL   JOHNREY… JOHNREYN… DUQA7    8118…
#> 2     2 b82d02e5c2c11e… <chr [6]>  4417… KOR   ADRIA    ADRIA     DTBY3    8919…
#> 3     2 b82d02e5c2c11e… <chr [5]>  4417… KOR   PREMIER  PREMIER   DTBY3    8919…
#> 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 2025-10-31T14:5…
#> 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 2012-01-01T00:00:00Z 2017-10-25T00:00:00Z 548012100 <chr [1]> 
#> 2     1 2019-02-10T18:02:49Z 2025-11-01T00:00:00Z 548012100 <chr [1]> 
#> 3     2 2015-10-08T00:00:00Z 2020-07-21T00:00:00Z 441734000 <chr [1]> 
#> 4     2 2012-01-01T00:00:00Z 2013-09-19T00:00:00Z 441734000 <chr [1]> 
#> 5     2 2013-09-20T00:00:00Z 2025-01-01T00:00:00Z 441734000 <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 2025-11-01T00:00:00Z 273379740 <chr [1]> 
#> 
#> $combinedSourcesInfo
#> # A tibble: 12 × 10
#>    index vesselId             geartypes_name geartypes_source geartypes_yearFrom
#>    <dbl> <chr>                <chr>          <chr>                         <int>
#>  1     1 71e7da672-2451-17da… TUNA_PURSE_SE… COMBINATION_OF_…               2017
#>  2     1 55889aefb-bef9-224c… TUNA_PURSE_SE… COMBINATION_OF_…               2017
#>  3     2 aca119c29-95dd-f5c4… TUNA_PURSE_SE… COMBINATION_OF_…               2012
#>  4     2 6583c51e3-3626-5638… TUNA_PURSE_SE… COMBINATION_OF_…               2013
#>  5     3 0edad163f-f53d-9ddb… CARRIER        GFW_VESSEL_LIST                2024
#>  6     3 0cb77880e-ee49-2ce4… CARRIER        GFW_VESSEL_LIST                2012
#>  7     3 8c7304226-6c71-edbe… CARRIER        GFW_VESSEL_LIST                2013
#>  8     3 da1cd7e1b-b8d0-539c… CARRIER        GFW_VESSEL_LIST                2015
#>  9     3 da1cd7e1b-b8d0-539c… CARRIER        GFW_VESSEL_LIST                2015
#> 10     3 da1cd7e1b-b8d0-539c… CARRIER        GFW_VESSEL_LIST                2019
#> 11     3 da1cd7e1b-b8d0-539c… CARRIER        GFW_VESSEL_LIST                2019
#> 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 71e7da672… 5480… JOHN RE… JOHNREYN… PHL   DUQA-7   8118…          133081
#> 2     2 6583c51e3… 4417… ADRIA    ADRIA     KOR   DTBY3    <NA>           360249
#> 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 gfw_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.

gfw_event(event_type = "ENCOUNTER",
          start_date = "2020-01-01",
          end_date = "2020-01-02")
#> [1] "Downloading 280 events from GFW"
#> # A tibble: 280 × 17
#>    start               end                 eventId       eventType    lat    lon
#>    <dttm>              <dttm>              <chr>         <chr>      <dbl>  <dbl>
#>  1 2019-12-31 16:00:00 2020-01-01 08:50:00 fe5c55975661… encounter  -3.44 -147. 
#>  2 2019-12-17 14:10:00 2020-01-02 04:10:00 2cacbbe4a0ca… encounter  67.5    15.5
#>  3 2020-01-01 19:30:00 2020-01-01 23:50:00 2879e4d9ffee… encounter  30.6   123. 
#>  4 2020-01-01 09:50:00 2020-01-01 20:10:00 33d3283d01e2… encounter -61.5    34.8
#>  5 2020-01-01 17:30:00 2020-01-01 20:30:00 ba82c5597574… encounter  38.5   121. 
#>  6 2020-01-01 19:50:00 2020-01-01 23:10:00 2b86ea68f197… encounter  25.2   120. 
#>  7 2020-01-01 06:30:00 2020-01-01 20:00:00 2568331d0c9c… encounter  30.0   123. 
#>  8 2020-01-01 07:00:00 2020-01-01 21:00:00 9d79c8e60caa… encounter -61.5    34.8
#>  9 2020-01-01 01:20:00 2020-01-01 10:20:00 26a13082cf66… encounter  21.5   113. 
#> 10 2020-01-01 18:00:00 2020-01-01 21:20:00 871c84bcefd9… encounter  38.5   121. 
#> # ℹ 270 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()
gfw_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 2020-10-01 12:54:31 2020-10-01 21:26:31 083f87bff859… fishing    4.75   -51.6
#>  2 2020-10-05 08:50:27 2020-10-06 17:35:21 c75671db2488… fishing    4.71   -51.5
#>  3 2020-10-01 23:29:31 2020-10-03 03:11:17 4d538f3b37e2… fishing    4.74   -51.5
#>  4 2020-10-03 05:50:14 2020-10-05 03:35:27 b68b49e7e365… fishing    4.73   -51.5
#>  5 2020-10-30 12:22:15 2020-10-30 13:51:56 8524e3c9d9c2… fishing    3.79   -50.1
#>  6 2020-10-21 23:23:14 2020-10-22 18:05:07 b4600d56f238… fishing    4.69   -51.5
#>  7 2020-10-01 12:09:55 2020-10-02 22:03:36 d8ed35e85829… fishing    4.73   -51.5
#>  8 2020-10-06 00:20:33 2020-10-06 02:05:18 86b9ba806295… fishing   -0.0721 -47.8
#>  9 2020-10-26 13:14:51 2020-10-26 16:19:22 7ede29f87412… fishing    0.136  -47.8
#> 10 2020-10-26 03:33:23 2020-10-26 06:35:25 690af38fe1e5… fishing    0.116  -47.9
#> # 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 gfw_vessel_info() first to extract vesselId from $selfReportedInfo in the response.

Single vessel events

vessel_info <- gfw_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"

gfw_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 2021-11-11 18:41:10 2021-11-20 18:43:26 af0cb5d7ee288… port_vis…  5.20  -4.04
#>  2 2019-11-15 14:15:11 2019-11-19 07:49:20 bbeed3f884a6f… port_vis…  5.20  -4.02
#>  3 2021-10-17 09:52:51 2021-10-17 16:06:40 d133e151d9edd… port_vis… 14.6  -17.4 
#>  4 2019-12-06 11:02:09 2019-12-11 10:20:04 bcd967c59306c… port_vis…  5.20  -4.04
#>  5 2020-06-10 13:51:11 2020-06-13 13:51:28 8f14e93f2e157… port_vis…  5.23  -3.97
#>  6 2020-06-20 12:33:45 2020-06-20 19:43:10 a8f5401a3bbec… port_vis… 14.6  -17.4 
#>  7 2020-03-05 13:28:59 2020-03-10 02:26:24 0d845cb306614… port_vis…  5.20  -4.02
#>  8 2020-11-01 14:17:48 2020-11-06 12:25:53 f39043169c3c4… port_vis…  5.20  -4.02
#>  9 2020-01-11 11:18:49 2020-01-15 11:54:49 889beb4fc4bfb… port_vis…  5.23  -4.02
#> 10 2020-07-06 06:45:06 2020-07-12 09:13:39 6845cffacfe25… port_vis…  5.20  -4.02
#> # 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:

gfw_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 2021-08-01 12:58:44 2021-08-16 16:00:15 a26f4940e189cc… port_vis…  5.20 -4.02
#>  2 2019-02-10 10:25:52 2019-02-20 13:06:20 b03ab6d307b88e… port_vis…  5.20 -4.02
#>  3 2018-09-19 06:32:12 2018-09-22 11:02:34 04989af5b8533a… port_vis…  5.23 -3.97
#>  4 2019-06-07 09:01:12 2019-06-18 23:23:02 567f5179cc1d5b… port_vis…  5.23 -4.02
#>  5 2019-03-23 02:17:24 2019-03-28 19:47:18 92281e785f29e2… port_vis…  5.23 -3.97
#>  6 2021-11-11 18:41:10 2021-11-20 18:43:26 af0cb5d7ee2883… port_vis…  5.20 -4.04
#>  7 2018-11-06 17:27:26 2018-11-11 09:43:00 a1e988d9827685… port_vis…  5.23 -4.02
#>  8 2019-07-09 09:31:01 2019-07-13 13:19:15 557fe44f868f3d… port_vis…  5.23 -3.97
#>  9 2019-05-09 10:03:41 2019-05-11 16:22:04 db32557dc74e7c… port_vis…  5.26 -4.01
#> 10 2015-12-29 14:52:13 2016-01-03 16:38:59 ecd93cc08b5219… 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_date and end_date if the request is too large and returns a time out error (524)

When a date range is provided to gfw_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, the id field for encounter events includes an additional suffix (1 or 2) separated by a period. The vessel field 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 <- gfw_vessel_info(
  where = "flag='USA' AND geartypes='TRAWLERS'",
  search_type = "search",
  quiet = TRUE 
  )
# Set quiet = TRUE if you want the output to return silently

This list returns 6660 vesselIds belonging to 4270 vessels.

usa_trawlers$selfReportedInfo
#> # A tibble: 6,791 × 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>           445303
#>  5     3 15cea26f… 3680… PELICAN  PELICAN   USA   <NA>     <NA>              407
#>  6     4 242fa3fb… 3670… TAUNY A… TAUNYANN  USA   WDC4097  <NA>          2636656
#>  7     5 bc29946f… 3667… ALEX     ALEX      USA   WDA2216  <NA>           901292
#>  8     5 0e4052b1… 3667… <NA>     <NA>      USA   WDA2216  <NA>             8403
#>  9     5 8d68317d… 3667… ALEX     ALEX      USA   <NA>     <NA>            46135
#> 10     6 0dddd2a8… 3673… SHAMROCK SHAMROCK  USA   WDD8722  <NA>             2720
#> # ℹ 6,781 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 gfw_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: gfw_event() can receive several vesselIds at 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 many vesselIds are requested, this value can be around 2,800 vesselIds depending 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 242fa3fbf-fa03-eb47-5855-f0880b8e7acf
#>  7     5 bc29946f2-2b0b-9613-054a-cd59327226d9
#>  8     5 0e4052b12-2e16-969f-d3e3-2a266be74255
#>  9     5 8d68317d6-6610-59c4-c99a-ef4cd41acd1a
#> 10     6 0dddd2a83-3626-24f1-0fe6-3c4d45bbb409
#> # ℹ 41 more rows

There 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 <- gfw_event(event_type = "FISHING",
                            vessels = twenty_usa_trawlers$vesselId,
                            start_date = "2020-01-01",
                            end_date = "2020-02-01")
#> [1] "Downloading 57 events from GFW"
fishing_events
#> # A tibble: 57 × 17
#>    start               end                 eventId        eventType   lat    lon
#>    <dttm>              <dttm>              <chr>          <chr>     <dbl>  <dbl>
#>  1 2020-01-31 05:25:15 2020-01-31 06:43:40 ea857a15cef2e… fishing    42.8 -125. 
#>  2 2020-01-13 17:38:55 2020-01-13 21:52:25 ca7ea60b731e7… fishing    38.0  -73.9
#>  3 2020-01-17 09:10:03 2020-01-17 17:57:00 e5d2760a2cd5e… fishing    41.5  -70.1
#>  4 2019-12-31 01:38:26 2020-01-02 05:06:56 b630325a08b11… fishing    41.4  -68.7
#>  5 2020-01-31 15:05:36 2020-01-31 21:32:05 32c27b8be6040… fishing    43.0 -125. 
#>  6 2020-01-17 06:46:00 2020-01-17 08:53:00 1789e3addb7fc… fishing    43.7 -124. 
#>  7 2020-01-21 10:19:37 2020-01-21 15:31:33 b07cba0b120d9… fishing    38.1  -73.9
#>  8 2020-01-10 18:21:53 2020-01-12 03:13:04 6739137b68e5f… fishing    38.0  -73.9
#>  9 2020-01-18 08:32:40 2020-01-19 20:25:48 cf7977e6a3a4c… fishing    41.4  -69.3
#> 10 2020-01-14 00:09:56 2020-01-14 07:27:11 dc8646fae556a… fishing    38.0  -74.0
#> # ℹ 47 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: 57 × 7
#>    vesselId                     vessel_name vessel_ssvid vessel_flag vessel_type
#>    <chr>                        <chr>       <chr>        <chr>       <chr>      
#>  1 454a28f85-56e4-93cb-efa6-ff… BERNADETTE  366233570    USA         fishing    
#>  2 64907178b-b02a-f401-afa1-b3… SUSAN L     366211560    USA         fishing    
#>  3 35eb371c0-088a-1382-098a-c7… STARBRITE   367707710    USA         fishing    
#>  4 35eb371c0-088a-1382-098a-c7… STARBRITE   367707710    USA         fishing    
#>  5 454a28f85-56e4-93cb-efa6-ff… BERNADETTE  366233570    USA         fishing    
#>  6 8d68317d6-6610-59c4-c99a-ef… ALEX        366788850    USA         fishing    
#>  7 64907178b-b02a-f401-afa1-b3… SUSAN L     366211560    USA         fishing    
#>  8 64907178b-b02a-f401-afa1-b3… SUSAN L     366211560    USA         fishing    
#>  9 35eb371c0-088a-1382-098a-c7… STARBRITE   367707710    USA         fishing    
#> 10 64907178b-b02a-f401-afa1-b3… SUSAN L     366211560    USA         fishing    
#> # ℹ 47 more rows
#> # ℹ 2 more variables: vessel_publicAuthorizations <list>, vessel_nextPort <lgl>

When no events are available, the gfw_event() function returns nothing.

gfw_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"
#> NULL

Apparent fishing hours

The gfw_ais_fishing_hours() 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) or HIGH (0.01 degree)
  • The temporal resolution, which can be HOURLY, DAILY, MONTHLY, YEARLY or ENTIRE.
  • The variable to group by: FLAG, GEARTYPE, FLAGANDGEARTYPE, MMSI or VESSEL_ID
  • The date range note: this must be 366 days or less
  • The region polygon in sf format or the region code (such as an EEZ code) to filter the raster
  • The source for the specified region. Currently, EEZ, MPA, RFMO or USER_SHAPEFILE (for sf shapefiles).

User-defined shapefile

You can load an sf shapefile with your area of interest and fetch apparent the data of interest. 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("gfw_test_shape")

gfw_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...

gfw_ais_fishing_hours(spatial_resolution = "LOW",
               temporal_resolution = "YEARLY",
               group_by = "FLAG",
               start_date = "2021-01-01",
               end_date = "2021-02-01",
               region_source = "USER_SHAPEFILE",
               region = gfw_test_shape)
#> # A tibble: 2,686 × 6
#>      Lat   Lon `Time Range` flag  `Vessel IDs` `Apparent Fishing Hours`
#>    <dbl> <dbl>        <dbl> <chr>        <dbl>                    <dbl>
#>  1  12.5  62.9         2021 CHN              1                     3.75
#>  2  15.9  62.6         2021 CHN              1                     7.66
#>  3   0.7  68           2021 LKA              1                     3.08
#>  4  15.2  63.8         2021 CHN              2                    22.4 
#>  5  15.1  62.8         2021 CHN              1                     4.82
#>  6   4.6  65.9         2021 LKA              1                    11.8 
#>  7  12    62.3         2021 CHN              1                     5.89
#>  8  15.7  63.4         2021 CHN              2                    13.0 
#>  9  15.6  62.4         2021 CHN              1                     3.37
#> 10   4.4  67.5         2021 LKA              1                     3.39
#> # ℹ 2,676 more rows

Apparent fishing hours in preloaded EEZ, RFMOs and MPAs

EEZ

If you want raster data from a particular EEZ, you can use the gfw_region_id() function to get the EEZ id, and enter that code in the region argument of gfw_ais_fishing_hours() instead of the region shapefile (with region_source = "EEZ"):

# use EEZ function to get EEZ code of Cote d'Ivoire
code_eez <- gfw_region_id(region = "CIV", region_source = "EEZ")

gfw_ais_fishing_hours(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   4.7  -5.8         2021 CHN              1                     3.62
#>  2   4.4  -3.7         2021 BLZ              1                     0.72
#>  3   4.5  -3.8         2021 PAN              1                     2.24
#>  4   5.1  -3.9         2021 GHA              1                     0.58
#>  5   1.9  -6.8         2021 GHA              1                     2.98
#>  6   5.1  -4.8         2021 CHN              1                     9.3 
#>  7   3.8  -3.8         2021 FRA              1                     2.15
#>  8   1.6  -6.9         2021 BLZ              1                     1.02
#>  9   4.3  -3.9         2021 SLV              1                     1.59
#> 10   1.8  -6.4         2021 BLZ              1                     0.35
#> # ℹ 585 more rows

You could search for just one word in the name of the EEZ and then decide which one you want:

(gfw_region_id(region = "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… 200NM

From the results above, let’s say we’re interested in the French Exclusive Economic Zone, 5677

gfw_ais_fishing_hours(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  47.3  -6.1         2021 FRA             12                   390.  
#>  2  49    -1.7         2021 FRA             18                   213.  
#>  3  45.9  -4.9         2021 FRA              2                     6.68
#>  4  47.4  -3.1         2021 FRA              7                    16.2 
#>  5  48.4  -6.3         2021 FRA              8                    16.2 
#>  6  46.7  -4.7         2021 ESP             11                    70.8 
#>  7  46.8  -5.6         2021 FRA              1                     1.07
#>  8  45.5  -5           2021 ESP             26                   133.  
#>  9  46.5  -4.6         2021 ESP              3                     4.67
#> 10  45.8  -4           2021 FRA              1                     6.56
#> # ℹ 5,608 more rows

Marine 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 <- gfw_region_id(region = "Phoenix",
                          region_source = "MPA")
code_mpa
#> # A tibble: 2 × 2
#>   id        label                                                               
#>   <chr>     <chr>                                                               
#> 1 309888    Phoenix Islands Protected Area - Protected Area                     
#> 2 555512002 Aire protégée des îles Phoenix - World Heritage Site (natural or mi…
gfw_ais_fishing_hours(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  -2.8 -176.         2015 KOR              1                    10.4 
#>  3  -1   -170.         2015 KOR              1                     2.39
#>  4  -3.5 -176.         2015 KOR              1                     3.11
#>  5  -3.6 -176.         2015 KIR              1                     6.07
#>  6  -2.9 -176.         2015 FSM              1                     2.77
#>  7  -2.6 -176.         2015 KOR              1                    13.9 
#>  8  -3   -176.         2015 FSM              1                     2.16
#>  9  -4.7 -176.         2015 KOR              2                    13.7 
#> 10  -4.1 -176.         2015 KOR              1                     1.57
#> # ℹ 28 more rows

Regional Fisheries Management Organizations (RFMOs)

It is also possible to filter rasters to regional fisheries management organizations (RFMO) like "ICCAT", "IATTC", "IOTC", "CCSBT" and "WCPFC".

gfw_ais_fishing_hours(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 rows

Note: For a complete list of MPAs, RFMOs and EEZ, check the function gfw_regions()

When your API request times out

For API performance reasons, the gfw_ais_fishing_hours() 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 gfw_ais_fishing_hours() call will return an HTTP 524 error, and subsequent API requests using any gfwr gfw_ 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 gfw_last_report() function, which lets users request the results of their last API request with gfw_ais_fishing_hours().

The gfw_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 gfw_ais_fishing_hours(). For more information, see the Get last report generated endpoint documentation on the Global Fishing Watch API page.

Reverse region id search

The gfw_region_id() function also works in reverse. If a region id is passed as a numeric to the function as the region, 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 <- gfw_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 57 events from GFW"

fishing_events
#> # A tibble: 57 × 7
#>    eez   eventId  eventType start               end                   lat    lon
#>    <chr> <chr>    <chr>     <dttm>              <dttm>              <dbl>  <dbl>
#>  1 8456  ea857a1… fishing   2020-01-31 05:25:15 2020-01-31 06:43:40  42.8 -125. 
#>  2 8456  ca7ea60… fishing   2020-01-13 17:38:55 2020-01-13 21:52:25  38.0  -73.9
#>  3 8456  e5d2760… fishing   2020-01-17 09:10:03 2020-01-17 17:57:00  41.5  -70.1
#>  4 8456  b630325… fishing   2019-12-31 01:38:26 2020-01-02 05:06:56  41.4  -68.7
#>  5 8456  32c27b8… fishing   2020-01-31 15:05:36 2020-01-31 21:32:05  43.0 -125. 
#>  6 8456  1789e3a… fishing   2020-01-17 06:46:00 2020-01-17 08:53:00  43.7 -124. 
#>  7 8456  b07cba0… fishing   2020-01-21 10:19:37 2020-01-21 15:31:33  38.1  -73.9
#>  8 8456  6739137… fishing   2020-01-10 18:21:53 2020-01-12 03:13:04  38.0  -73.9
#>  9 8456  cf7977e… fishing   2020-01-18 08:32:40 2020-01-19 20:25:48  41.4  -69.3
#> 10 8456  dc8646f… fishing   2020-01-14 00:09:56 2020-01-14 07:27:11  38.0  -74.0
#> # ℹ 47 more rows

We can apply gfw_region_id() to the numeric vector to extract the labels:

fishing_events %>% 
  mutate(eez_name = purrr::map_df(as.numeric(fishing_events$eez),
                                  ~gfw_region_id(region = .x,
                                                 region_source = "EEZ"))$label) %>% 
  dplyr::relocate(eez, eez_name)
#> # A tibble: 57 × 8
#>    eez   eez_name      eventId eventType start               end                
#>    <chr> <chr>         <chr>   <chr>     <dttm>              <dttm>             
#>  1 8456  United States ea857a… fishing   2020-01-31 05:25:15 2020-01-31 06:43:40
#>  2 8456  United States ca7ea6… fishing   2020-01-13 17:38:55 2020-01-13 21:52:25
#>  3 8456  United States e5d276… fishing   2020-01-17 09:10:03 2020-01-17 17:57:00
#>  4 8456  United States b63032… fishing   2019-12-31 01:38:26 2020-01-02 05:06:56
#>  5 8456  United States 32c27b… fishing   2020-01-31 15:05:36 2020-01-31 21:32:05
#>  6 8456  United States 1789e3… fishing   2020-01-17 06:46:00 2020-01-17 08:53:00
#>  7 8456  United States b07cba… fishing   2020-01-21 10:19:37 2020-01-21 15:31:33
#>  8 8456  United States 673913… fishing   2020-01-10 18:21:53 2020-01-12 03:13:04
#>  9 8456  United States cf7977… fishing   2020-01-18 08:32:40 2020-01-19 20:25:48
#> 10 8456  United States dc8646… fishing   2020-01-14 00:09:56 2020-01-14 07:27:11
#> # ℹ 47 more rows
#> # ℹ 2 more variables: lat <dbl>, lon <dbl>

About

R package for accessing data from Global Fishing Watch APIs

globalfishingwatch.github.io/gfwr/

Topics

r mapping api-wrapper ais ais-data globalfishingwatch

Resources

Readme

License

Apache-2.0 license

Contributing

Contributing
Activity
Custom properties

Stars

77 stars

Watchers

18 watching

Forks

13 forks
Report repository

Releases 7

gfwr 2.0.4 Latest
Aug 6, 2025
+ 6 releases

Contributors 9

Languages