diff --git a/NEWS.md b/NEWS.md index 2d2da09..df05fe0 100644 --- a/NEWS.md +++ b/NEWS.md @@ -8,7 +8,7 @@ if anything is new or missing. ## Endpoints -- Same endpoints as in v1.1.0 +- Same endpoints as in `v1.1.0` + `get_raster()` communicates with the 4Wings API to get fishing effort + `get_vessel_info()` communicates with the Vessels API + `get_event()` communicates with the Events API @@ -50,6 +50,9 @@ script and copy the string of the request when [filling an issue](https://github + The `region` argument now accepts `sf` polygons rather than a GeoJSON string + Parameters `start_date` and `end_date` replace `date_range` for consistency with other functions +### Additional changes + +- Option `USER_JSON` has been renamed to `USER_SHAPEFILE` for clarity (addresses #161) # gfwr 1.1.0 diff --git a/R/get_endpoint.R b/R/get_endpoint.R index 2cf200b..3763da2 100644 --- a/R/get_endpoint.R +++ b/R/get_endpoint.R @@ -1,7 +1,7 @@ #' #' Function to get API dataset name for given event type #' -#' @param dataset_type Type of dataset to get API dataset name for. It can be +#' @param dataset_type Type of dataset to get API dataset name for. It can be "raster", #' "ENCOUNTER", "LOITERING", "FISHING", "PORT_VISIT", "GAP", "EEZ", "RFMO" or "MPA" #' @param ... Other arguments that would depend on the dataset type. #' @importFrom httr2 request diff --git a/R/get_event.R b/R/get_event.R index 02c7e7d..d7711c6 100644 --- a/R/get_event.R +++ b/R/get_event.R @@ -16,7 +16,7 @@ #' @param confidences Confidence levels (1-4) of events (port visits only) #' @param region sf shape to filter raster or GFW region code (such as an #' EEZ code). See details about formatting the geojson -#' @param region_source source of the region ('EEZ','MPA', 'RFMO' or 'USER_JSON') +#' @param region_source source of the region ('EEZ','MPA', 'RFMO' or 'USER_SHAPEFILE') #' @param gap_intentional_disabling Logical. Whether the Gap events are intentional, #' according to Global Fishing Watch algorithms #' @param key Authorization token. Can be obtained with gfw_auth() function @@ -125,6 +125,18 @@ #' region_source = 'EEZ', #' flags = 'CHN', #' key = gfw_auth()) +#' +#' # 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 = "2020-12-31", +#' region = test_polygon, +#' region_source = 'USER_SHAPEFILE', +#' key = gfw_auth()) #' } #' @export @@ -233,8 +245,9 @@ get_event <- function(event_type, } else if (region_source == 'RFMO' & is.character(region)) { region = rjson::toJSON(list(region = list(dataset = 'public-rfmo', id = region))) - } else if (region_source == 'USER_JSON') { - if (methods::is(region, 'sf') & base::class(region$geometry)[1] %in% c("sfc_POLYGON","sfc_MULTIPOLYGON")) { + } else if (region_source == 'USER_SHAPEFILE') { + if (methods::is(region, 'sf') & any(base::class(sf::st_geometry(region)) %in% c("sfc_POLYGON","sfc_MULTIPOLYGON")) + ) { region <- sf_to_geojson(region, endpoint = 'event') } else { stop('custom region is not an sf polygon') @@ -250,7 +263,7 @@ get_event <- function(event_type, list(startDate = jsonlite::unbox(start)), list(endDate = jsonlite::unbox(end)) )) - } else if (region_source == 'USER_JSON') { + } else if (region_source == 'USER_SHAPEFILE') { body_args <- jsonlite::toJSON(c(body_args, list(startDate = jsonlite::unbox(start)), # removes from array @@ -341,7 +354,7 @@ get_event <- function(event_type, #' @param region_source Optional but mandatory if using the argument region. #' Source of the region. If 'EEZ','MPA', 'RFMO', #' then the value for the argument region must be the code for that region. -#' If 'USER_JSON', then region has to be an sf object +#' If 'USER_SHAPEFILE', then region has to be an sf object #' @param region GFW region code (such as an EEZ, MPA or RFMO code) or a #' formatted geojson shape. See Details about formatting the geojson. #' @param duration duration, in minutes, of the event, ex. 30 @@ -492,7 +505,7 @@ get_event_stats <- function(event_type, } else if (region_source == 'RFMO' & is.character(region)) { region = rjson::toJSON(list(region = list(dataset = 'public-rfmo', id = region))) - } else if (region_source == 'USER_JSON') { + } else if (region_source == 'USER_SHAPEFILE') { if (methods::is(region, 'sf') & base::class(region$geometry)[1] %in% c("sfc_POLYGON","sfc_MULTIPOLYGON")) { region <- sf_to_geojson(region, endpoint = 'event') } else { @@ -510,7 +523,7 @@ get_event_stats <- function(event_type, list(endDate = jsonlite::unbox(end)), list(timeseriesInterval = jsonlite::unbox(interval)) )) - } else if (region_source == 'USER_JSON') { + } else if (region_source == 'USER_SHAPEFILE') { body_args <- jsonlite::toJSON(c(body_args, list(startDate = jsonlite::unbox(start)), # removes from array diff --git a/R/get_raster.R b/R/get_raster.R index c3e17ef..a9c8bd2 100644 --- a/R/get_raster.R +++ b/R/get_raster.R @@ -11,7 +11,7 @@ #' @param end_date End of date range to search events, in YYYY-MM-DD format and excluding this date #' @param region sf shape to filter raster or GFW region code (such as a #' Marine Regions Geographic Identifier or EEZ code). -#' @param region_source source of the region ('EEZ','MPA', 'RFMO' or 'USER_JSON') +#' @param region_source source of the region ('EEZ','MPA', 'RFMO' or 'USER_SHAPEFILE') #' @param key Authorization token. Can be obtained with `gfw_auth()` function #' @param print_request Boolean. Whether to print the request, for debugging #' purposes. When contacting the GFW team it will be useful to send this string @@ -50,7 +50,7 @@ #' start_date = '2021-01-01', #' end_date = '2021-10-01', #' region = test_shape, -#' region_source = 'USER_JSON', +#' region_source = 'USER_SHAPEFILE', #' key = gfw_auth(), #' print_request = TRUE) #' } @@ -90,9 +90,9 @@ if (is.null(region_source)) stop("region_source and region params are required") if (length(region) > 1) stop("only 1 RFMO region must be provided") region <- rjson::toJSON(list(region = list(dataset = 'public-rfmo', id = region))) - } else if (region_source == 'USER_JSON') { - if (methods::is(region, 'sf') & base::class(region$geometry)[1] %in% c("sfc_POLYGON","sfc_MULTIPOLYGON") - ) { + } else if (region_source == 'USER_SHAPEFILE') { + if (methods::is(region, 'sf') & any(base::class(sf::st_geometry(region)) %in% c("sfc_POLYGON","sfc_MULTIPOLYGON")) + ) { region <- sf_to_geojson(region, endpoint = 'raster') } else { stop('custom region is not an sf polygon') diff --git a/R/utils.R b/R/utils.R index 64b1c6a..efdbae7 100644 --- a/R/utils.R +++ b/R/utils.R @@ -152,7 +152,7 @@ get_regions <- function(region_source = 'EEZ', result <- get_endpoint(dataset_type = region_source) %>% httr2::req_headers(Authorization = paste("Bearer", key, sep = " ")) %>% httr2::req_user_agent(gfw_user_agent()) %>% - httr2::req_error(body = parse_response_error) %>% + # httr2::req_error(body = parse_response_error) %>% httr2::req_perform(.) %>% httr2::resp_body_json(.) %>% dplyr::bind_rows() diff --git a/README.Rmd b/README.Rmd index 3bce9a8..5a812f5 100644 --- a/README.Rmd +++ b/README.Rmd @@ -290,12 +290,12 @@ and converts the response to a data frame. In order to use it, you should specif * 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_JSON` (for `sf` shapefiles). +* The source for the specified region. Currently, `EEZ`, `MPA`, `RFMO` or `USER_SHAPEFILE` (for `sf` shapefiles). ### Examples -You can load a sample shapefile inside `gfwr` to see how `'USER_JSON'` works: +You can load a sample shapefile inside `gfwr` to see how `'USER_SHAPEFILE'` works: ```{r example_map_1} data("test_shape") @@ -307,7 +307,7 @@ get_raster( start_date = '2021-01-01', end_date = '2021-02-01', region = test_shape, - region_source = 'USER_JSON', + region_source = 'USER_SHAPEFILE', key = key ) ``` diff --git a/README.md b/README.md index 6891639..bbe9c41 100644 --- a/README.md +++ b/README.md @@ -176,8 +176,8 @@ get_vessel_info(query = 224224000, #> # A tibble: 2 × 9 #> vesselId geartypes_geartype_n…¹ geartypes_geartype_s…² geartypes_geartype_y…³ #> -#> 1 3c99c326… PURSE_SEINE_SUPPORT GFW_VESSEL_LIST 2015 -#> 2 6632c9eb… PURSE_SEINE_SUPPORT GFW_VESSEL_LIST 2019 +#> 1 6632c9eb… PURSE_SEINE_SUPPORT GFW_VESSEL_LIST 2019 +#> 2 3c99c326… PURSE_SEINE_SUPPORT GFW_VESSEL_LIST 2015 #> # ℹ abbreviated names: ¹​geartypes_geartype_name, ²​geartypes_geartype_source, #> # ³​geartypes_geartype_yearFrom #> # ℹ 5 more variables: geartypes_geartype_yearTo , @@ -240,9 +240,9 @@ get_vessel_info(where = "shipname LIKE '%GABU REEFE%' OR imo = '8300949'", #> # A tibble: 3 × 9 #> vesselId geartypes_geartype_n…¹ geartypes_geartype_s…² geartypes_geartype_y…³ #> -#> 1 58cf536b… CARRIER GFW_VESSEL_LIST 2012 +#> 1 1da8dbc2… CARRIER GFW_VESSEL_LIST 2022 #> 2 0b7047cb… CARRIER GFW_VESSEL_LIST 2019 -#> 3 1da8dbc2… CARRIER GFW_VESSEL_LIST 2022 +#> 3 58cf536b… CARRIER GFW_VESSEL_LIST 2012 #> # ℹ abbreviated names: ¹​geartypes_geartype_name, ²​geartypes_geartype_source, #> # ³​geartypes_geartype_yearFrom #> # ℹ 5 more variables: geartypes_geartype_yearTo , @@ -253,7 +253,7 @@ get_vessel_info(where = "shipname LIKE '%GABU REEFE%' OR imo = '8300949'", #> # A tibble: 3 × 13 #> vesselId ssvid shipname nShipname flag callsign imo messagesCounter #> -#> 1 1da8dbc23-3c48-… 6135… GABU RE… GABUREEF… CMR TJMC996 8300… 71775911 +#> 1 1da8dbc23-3c48-… 6135… GABU RE… GABUREEF… CMR TJMC996 8300… 72480839 #> 2 0b7047cb5-58c8-… 2141… GABU RE… GABUREEF… MDA ER2732 8300… 70035084 #> 3 58cf536b1-1fca-… 6168… GABU RE… GABUREEF… COM D6FJ2 8300… 32121624 #> # ℹ 5 more variables: positionsCounter , sourceCode , @@ -303,15 +303,15 @@ get_vessel_info(ids = "8c7304226-6c71-edbe-0b63-c246734b3c01", #> # A tibble: 2 × 6 #> name flag ssvid sourceCode dateFrom dateTo #> -#> 1 COLINER CYP 210631000 2014-01-01T00:16:58Z 2024-04-30T23:41:06Z +#> 1 COLINER CYP 210631000 2014-01-01T00:16:58Z 2024-05-31T23:44:00Z #> 2 COLINER CYP 273379740 2015-02-27T10:59:43Z 2018-03-21T07:13:09Z #> #> $registryPublicAuthorizations #> # A tibble: 2 × 4 #> dateFrom dateTo ssvid sourceCode #> -#> 1 2022-12-19T00:00:00Z 2024-05-01T00:00:00Z 210631000 -#> 2 2020-01-01T00:00:00Z 2024-05-01T00:00:00Z 210631000 +#> 1 2022-12-19T00:00:00Z 2024-06-01T00:00:00Z 210631000 +#> 2 2020-01-01T00:00:00Z 2024-06-01T00:00:00Z 210631000 #> #> $combinedSourcesInfo #> # A tibble: 2 × 9 @@ -329,7 +329,7 @@ get_vessel_info(ids = "8c7304226-6c71-edbe-0b63-c246734b3c01", #> # A tibble: 1 × 13 #> vesselId ssvid shipname nShipname flag callsign imo messagesCounter #> -#> 1 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 263226124 +#> 1 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 263878798 #> # ℹ 5 more variables: positionsCounter , sourceCode , #> # matchFields , transmissionDateFrom , transmissionDateTo ``` @@ -354,18 +354,18 @@ get_vessel_info(ids = c("8c7304226-6c71-edbe-0b63-c246734b3c01", #> # A tibble: 3 × 1 #> registryInfoTotalRecords #> -#> 1 1 +#> 1 2 #> 2 1 -#> 3 2 +#> 3 1 #> #> $registryInfo #> # A tibble: 4 × 15 #> id sourceCode ssvid flag shipname nShipname callsign imo #> -#> 1 685862e0626f6234c844… 5480… PHL JOHNREY… JOHNREYN… DUQA7 8118… -#> 2 b82d02e5c2c11e5fe536… 4417… KOR ADRIA ADRIA DTBY3 8919… -#> 3 a8d00ce54b37add7f85a… 2106… CYP FRIO FO… FRIOFORW… 5BWC3 9076… -#> 4 a8d00ce54b37add7f85a… 2733… RUS FRIO FO… FRIOFORW… UCRZ 9076… +#> 1 a8d00ce54b37add7f85a… 2106… CYP FRIO FO… FRIOFORW… 5BWC3 9076… +#> 2 a8d00ce54b37add7f85a… 2733… RUS FRIO FO… FRIOFORW… UCRZ 9076… +#> 3 685862e0626f6234c844… 5480… PHL JOHNREY… JOHNREYN… DUQA7 8118… +#> 4 b82d02e5c2c11e5fe536… 4417… KOR ADRIA ADRIA DTBY3 8919… #> # ℹ 7 more variables: latestVesselInfo , transmissionDateFrom , #> # transmissionDateTo , geartypes , lengthM , tonnageGt , #> # vesselInfoReference @@ -374,34 +374,34 @@ get_vessel_info(ids = c("8c7304226-6c71-edbe-0b63-c246734b3c01", #> # A tibble: 4 × 6 #> name flag ssvid sourceCode dateFrom dateTo #> -#> 1 TRANS PACIFIC JOURNEY FISHING PHL 548012100 2017-02-07T00… 2019-… -#> 2 DONGWON INDUSTRIES KOR 441734000 2014-01-18T19… 2024-… -#> 3 COLINER CYP 210631000 2014-01-01T00… 2024-… -#> 4 COLINER CYP 273379740 2015-02-27T10… 2018-… +#> 1 COLINER CYP 210631000 2014-01-01T00… 2024-… +#> 2 COLINER CYP 273379740 2015-02-27T10… 2018-… +#> 3 TRANS PACIFIC JOURNEY FISHING PHL 548012100 2017-02-07T00… 2019-… +#> 4 DONGWON INDUSTRIES KOR 441734000 2014-01-18T19… 2024-… #> #> $registryPublicAuthorizations #> # A tibble: 6 × 4 #> dateFrom dateTo ssvid sourceCode #> -#> 1 2012-01-01T00:00:00Z 2024-05-01T00:00:00Z 548012100 -#> 2 2012-01-01T00:00:00Z 2017-10-25T00:00:00Z 548012100 -#> 3 2013-09-20T00:00:00Z 2024-05-01T00:00:00Z 441734000 -#> 4 2015-10-08T00:00:00Z 2020-07-21T00:00:00Z 441734000 -#> 5 2022-12-19T00:00:00Z 2024-05-01T00:00:00Z 210631000 -#> 6 2020-01-01T00:00:00Z 2024-05-01T00:00:00Z 210631000 +#> 1 2022-12-19T00:00:00Z 2024-06-01T00:00:00Z 210631000 +#> 2 2020-01-01T00:00:00Z 2024-06-01T00:00:00Z 210631000 +#> 3 2012-01-01T00:00:00Z 2024-05-01T00:00:00Z 548012100 +#> 4 2012-01-01T00:00:00Z 2017-10-25T00:00:00Z 548012100 +#> 5 2013-09-20T00:00:00Z 2024-06-01T00:00:00Z 441734000 +#> 6 2015-10-08T00:00:00Z 2020-07-21T00:00:00Z 441734000 #> #> $combinedSourcesInfo #> # A tibble: 8 × 9 #> vesselId geartypes_geartype_n…¹ geartypes_geartype_s…² geartypes_geartype_y…³ #> -#> 1 55889aef… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2017 -#> 2 71e7da67… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2017 -#> 3 6583c51e… OTHER COMBINATION_OF_REGIST… 2013 -#> 4 6583c51e… OTHER COMBINATION_OF_REGIST… 2013 -#> 5 6583c51e… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2014 -#> 6 6583c51e… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2014 -#> 7 da1cd7e1… CARRIER GFW_VESSEL_LIST 2015 -#> 8 8c730422… CARRIER GFW_VESSEL_LIST 2013 +#> 1 da1cd7e1… CARRIER GFW_VESSEL_LIST 2015 +#> 2 8c730422… CARRIER GFW_VESSEL_LIST 2013 +#> 3 71e7da67… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2017 +#> 4 55889aef… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2017 +#> 5 6583c51e… OTHER COMBINATION_OF_REGIST… 2013 +#> 6 6583c51e… OTHER COMBINATION_OF_REGIST… 2013 +#> 7 6583c51e… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2014 +#> 8 6583c51e… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2014 #> # ℹ abbreviated names: ¹​geartypes_geartype_name, ²​geartypes_geartype_source, #> # ³​geartypes_geartype_yearFrom #> # ℹ 5 more variables: geartypes_geartype_yearTo , @@ -412,9 +412,9 @@ get_vessel_info(ids = c("8c7304226-6c71-edbe-0b63-c246734b3c01", #> # A tibble: 3 × 13 #> vesselId ssvid shipname nShipname flag callsign imo messagesCounter #> -#> 1 71e7da672-2451-… 5480… JOHN RE… JOHNREYN… PHL DUQA-7 8118… 1967237 -#> 2 6583c51e3-3626-… 4417… ADRIA ADRIA KOR DTBY3 8919… 3742574 -#> 3 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 263226124 +#> 1 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 263878798 +#> 2 71e7da672-2451-… 5480… JOHN RE… JOHNREYN… PHL DUQA-7 8118… 1967237 +#> 3 6583c51e3-3626-… 4417… ADRIA ADRIA KOR DTBY3 8919… 3742574 #> # ℹ 5 more variables: positionsCounter , sourceCode , #> # matchFields , transmissionDateFrom , transmissionDateTo ``` @@ -567,8 +567,8 @@ get_event(event_type = 'FISHING', end_date = "2020-02-01", key = key ) -#> [1] "Downloading 34 events from GFW" -#> # A tibble: 34 × 11 +#> [1] "Downloading 37 events from GFW" +#> # A tibble: 37 × 11 #> start end id type lat lon regions #> #> 1 2020-01-05 04:58:45 2020-01-05 06:31:45 379d… fish… 43.7 -124. @@ -581,7 +581,7 @@ get_event(event_type = 'FISHING', #> 8 2020-01-10 18:21:53 2020-01-12 03:13:04 6739… fish… 38.0 -73.9 #> 9 2020-01-13 12:45:32 2020-01-13 15:38:38 46f8… fish… 38.0 -73.9 #> 10 2020-01-13 13:20:55 2020-01-13 15:07:53 2333… fish… 43.7 -124. -#> # ℹ 24 more rows +#> # ℹ 27 more rows #> # ℹ 4 more variables: boundingBox , distances , vessel , #> # event_info ``` @@ -617,12 +617,12 @@ should specify: - 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_JSON` (for `sf` shapefiles). + or `USER_SHAPEFILE` (for `sf` shapefiles). ### Examples -You can load a sample shapefile inside `gfwr` to see how `'USER_JSON'` -works: +You can load a sample shapefile inside `gfwr` to see how +`'USER_SHAPEFILE'` works: ``` r data("test_shape") @@ -634,7 +634,7 @@ get_raster( start_date = '2021-01-01', end_date = '2021-02-01', region = test_shape, - region_source = 'USER_JSON', + region_source = 'USER_SHAPEFILE', key = key ) #> Rows: 2526 Columns: 6 @@ -689,16 +689,16 @@ get_raster(spatial_resolution = 'LOW', #> # A tibble: 611 × 6 #> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours` #> -#> 1 4.8 -5.9 2021 CHN 2 46.9 -#> 2 5.2 -4 2021 CPV 1 0.15 -#> 3 5.2 -4 2021 SLV 3 9.07 -#> 4 5.2 -4 2021 ESP 9 15.8 -#> 5 5.2 -4 2021 LBR 2 58.7 -#> 6 5.1 -4 2021 GHA 6 4.82 -#> 7 3 -3.9 2021 SLV 1 1.49 -#> 8 2.5 -5.4 2021 FRA 1 8.92 -#> 9 2.7 -4.1 2021 ESP 1 3.77 -#> 10 3.7 -7 2021 FRA 1 2.39 +#> 1 5 -5.5 2021 CHN 1 3.66 +#> 2 5.2 -4 2021 SLV 3 9.07 +#> 3 5.2 -4 2021 LBR 2 58.7 +#> 4 4.5 -4 2021 SLV 2 9.14 +#> 5 4.5 -3.8 2021 SLV 1 7.15 +#> 6 2.5 -5.4 2021 FRA 1 8.92 +#> 7 2 -4.2 2021 FRA 1 7.98 +#> 8 4.1 -7 2021 ESP 1 2.72 +#> 9 3.8 -5.9 2021 BLZ 1 7.67 +#> 10 3 -5.7 2021 ESP 1 0.57 #> # ℹ 601 more rows ``` @@ -738,16 +738,16 @@ get_raster(spatial_resolution = 'LOW', #> # A tibble: 5,660 × 6 #> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours` #> -#> 1 50.9 1.4 2021 FRA 30 831. -#> 2 50.8 1.3 2021 BEL 1 5.92 -#> 3 51.4 2.1 2021 BEL 3 3.14 -#> 4 51.2 2.1 2021 NLD 21 123. -#> 5 51.3 2 2021 NLD 16 68.9 -#> 6 51.2 2 2021 FRA 22 465. -#> 7 51.5 2.2 2021 NLD 20 546. -#> 8 51.5 2.2 2021 BEL 3 62.6 -#> 9 51.4 2.3 2021 BEL 6 8.07 -#> 10 51.3 2.2 2021 NLD 13 269. +#> 1 49 -6.2 2021 FRA 20 216. +#> 2 49.1 -6.1 2021 FRA 14 66.6 +#> 3 48.9 -6.2 2021 FRA 14 104. +#> 4 49 -6 2021 FRA 18 264. +#> 5 49 -6.1 2021 BLZ 1 1.49 +#> 6 49 -5.9 2021 FRA 19 244. +#> 7 49.1 -5.7 2021 FRA 20 313. +#> 8 49.1 -5.8 2021 BLZ 1 0.17 +#> 9 49 -5.8 2021 FRA 21 389. +#> 10 48.9 -5.8 2021 FRA 15 209. #> # ℹ 5,650 more rows ``` @@ -777,16 +777,16 @@ get_raster(spatial_resolution = 'LOW', #> # A tibble: 40 × 6 #> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours` #> -#> 1 -4.7 -176. 2015 KOR 3 15.8 -#> 2 -4.7 -176. 2015 KIR 1 0.36 +#> 1 -3.9 -173. 2015 KOR 1 0.01 +#> 2 -4.7 -176. 2015 KOR 3 15.8 #> 3 -2.2 -176. 2015 KIR 1 1.89 -#> 4 -3.6 -176. 2015 KIR 1 5.99 -#> 5 -3.1 -176. 2015 KOR 1 0.91 -#> 6 -3.5 -176. 2015 KOR 1 10.8 -#> 7 -3.4 -176. 2015 KOR 1 1.37 -#> 8 -3.6 -176. 2015 KOR 1 7.9 -#> 9 -3.6 -176. 2015 KOR 1 1.98 -#> 10 -4.2 -176. 2015 KOR 1 0.05 +#> 4 -2.5 -176. 2015 KOR 1 6.54 +#> 5 -2.6 -176. 2015 TWN 1 0.35 +#> 6 -2.2 -176. 2015 KIR 1 0.53 +#> 7 -2.6 -176. 2015 KOR 1 5.58 +#> 8 -2.8 -176. 2015 KOR 1 9.29 +#> 9 -2.8 -176. 2015 KOR 2 21.6 +#> 10 -2.9 -176. 2015 KOR 2 9.74 #> # ℹ 30 more rows ``` @@ -851,8 +851,8 @@ get_event(event_type = 'FISHING', region_source = 'EEZ', key = key)$label) %>% dplyr::select(-start, -end) -#> [1] "Downloading 34 events from GFW" -#> # A tibble: 34 × 6 +#> [1] "Downloading 37 events from GFW" +#> # A tibble: 37 × 6 #> # Rowwise: #> id type lat lon eez eez_name #> @@ -866,7 +866,7 @@ get_event(event_type = 'FISHING', #> 8 6739137b68e5fb477de38226f57892f7 fishing 38.0 -73.9 8456 United States #> 9 46f8debd1e55a894ca26ac74faf11162 fishing 38.0 -73.9 8456 United States #> 10 23330ffa0e1bbab43ead8328456c45aa fishing 43.7 -124. 8456 United States -#> # ℹ 24 more rows +#> # ℹ 27 more rows ``` ### When your API request times out diff --git a/docs/articles/gfwr.html b/docs/articles/gfwr.html index d9e3fcc..73b735e 100644 --- a/docs/articles/gfwr.html +++ b/docs/articles/gfwr.html @@ -265,8 +265,8 @@

Examples#> # A tibble: 2 × 9 #> vesselId geartypes_geartype_n…¹ geartypes_geartype_s…² geartypes_geartype_y…³ #> <chr> <chr> <chr> <int> -#> 1 3c99c326… PURSE_SEINE_SUPPORT GFW_VESSEL_LIST 2015 -#> 2 6632c9eb… PURSE_SEINE_SUPPORT GFW_VESSEL_LIST 2019 +#> 1 6632c9eb… PURSE_SEINE_SUPPORT GFW_VESSEL_LIST 2019 +#> 2 3c99c326… PURSE_SEINE_SUPPORT GFW_VESSEL_LIST 2015 #> # ℹ abbreviated names: ¹​geartypes_geartype_name, ²​geartypes_geartype_source, #> # ³​geartypes_geartype_yearFrom #> # ℹ 5 more variables: geartypes_geartype_yearTo <int>, @@ -327,9 +327,9 @@

Examples#> # A tibble: 3 × 9 #> vesselId geartypes_geartype_n…¹ geartypes_geartype_s…² geartypes_geartype_y…³ #> <chr> <chr> <chr> <int> -#> 1 58cf536b… CARRIER GFW_VESSEL_LIST 2012 +#> 1 1da8dbc2… CARRIER GFW_VESSEL_LIST 2022 #> 2 0b7047cb… CARRIER GFW_VESSEL_LIST 2019 -#> 3 1da8dbc2… CARRIER GFW_VESSEL_LIST 2022 +#> 3 58cf536b… CARRIER GFW_VESSEL_LIST 2012 #> # ℹ abbreviated names: ¹​geartypes_geartype_name, ²​geartypes_geartype_source, #> # ³​geartypes_geartype_yearFrom #> # ℹ 5 more variables: geartypes_geartype_yearTo <int>, @@ -340,7 +340,7 @@

Examples#> # A tibble: 3 × 13 #> vesselId ssvid shipname nShipname flag callsign imo messagesCounter #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> -#> 1 1da8dbc23-3c48-… 6135… GABU RE… GABUREEF… CMR TJMC996 8300… 71775911 +#> 1 1da8dbc23-3c48-… 6135… GABU RE… GABUREEF… CMR TJMC996 8300… 72480839 #> 2 0b7047cb5-58c8-… 2141… GABU RE… GABUREEF… MDA ER2732 8300… 70035084 #> 3 58cf536b1-1fca-… 6168… GABU RE… GABUREEF… COM D6FJ2 8300… 32121624 #> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>, @@ -389,15 +389,15 @@

Examples#> # A tibble: 2 × 6 #> name flag ssvid sourceCode dateFrom dateTo #> <chr> <chr> <chr> <list> <chr> <chr> -#> 1 COLINER CYP 210631000 <chr [1]> 2014-01-01T00:16:58Z 2024-04-30T23:41:06Z +#> 1 COLINER CYP 210631000 <chr [1]> 2014-01-01T00:16:58Z 2024-05-31T23:44:00Z #> 2 COLINER CYP 273379740 <chr [1]> 2015-02-27T10:59:43Z 2018-03-21T07:13:09Z #> #> $registryPublicAuthorizations #> # A tibble: 2 × 4 #> dateFrom dateTo ssvid sourceCode #> <chr> <chr> <chr> <list> -#> 1 2022-12-19T00:00:00Z 2024-05-01T00:00:00Z 210631000 <chr [1]> -#> 2 2020-01-01T00:00:00Z 2024-05-01T00:00:00Z 210631000 <chr [1]> +#> 1 2022-12-19T00:00:00Z 2024-06-01T00:00:00Z 210631000 <chr [1]> +#> 2 2020-01-01T00:00:00Z 2024-06-01T00:00:00Z 210631000 <chr [1]> #> #> $combinedSourcesInfo #> # A tibble: 2 × 9 @@ -415,7 +415,7 @@

Examples#> # A tibble: 1 × 13 #> vesselId ssvid shipname nShipname flag callsign imo messagesCounter #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> -#> 1 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 263226124 +#> 1 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 263878798 #> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>, #> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>

To specify more than one vesselId, you can submit a @@ -438,18 +438,18 @@

Examples#> # A tibble: 3 × 1 #> registryInfoTotalRecords #> <int> -#> 1 1 +#> 1 2 #> 2 1 -#> 3 2 +#> 3 1 #> #> $registryInfo #> # A tibble: 4 × 15 #> id sourceCode ssvid flag shipname nShipname callsign imo #> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr> -#> 1 b82d02e5c2c11e5fe536… <chr [5]> 4417… KOR ADRIA ADRIA DTBY3 8919… -#> 2 685862e0626f6234c844… <chr [5]> 5480… PHL JOHNREY… JOHNREYN… DUQA7 8118… -#> 3 a8d00ce54b37add7f85a… <chr [6]> 2106… CYP FRIO FO… FRIOFORW… 5BWC3 9076… -#> 4 a8d00ce54b37add7f85a… <chr [2]> 2733… RUS FRIO FO… FRIOFORW… UCRZ 9076… +#> 1 a8d00ce54b37add7f85a… <chr [6]> 2106… CYP FRIO FO… FRIOFORW… 5BWC3 9076… +#> 2 a8d00ce54b37add7f85a… <chr [2]> 2733… RUS FRIO FO… FRIOFORW… UCRZ 9076… +#> 3 685862e0626f6234c844… <chr [5]> 5480… PHL JOHNREY… JOHNREYN… DUQA7 8118… +#> 4 b82d02e5c2c11e5fe536… <chr [5]> 4417… KOR ADRIA ADRIA DTBY3 8919… #> # ℹ 7 more variables: latestVesselInfo <lgl>, transmissionDateFrom <chr>, #> # transmissionDateTo <chr>, geartypes <list>, lengthM <dbl>, tonnageGt <dbl>, #> # vesselInfoReference <chr> @@ -458,34 +458,34 @@

Examples#> # A tibble: 4 × 6 #> name flag ssvid sourceCode dateFrom dateTo #> <chr> <chr> <chr> <list> <chr> <chr> -#> 1 DONGWON INDUSTRIES KOR 441734000 <chr [2]> 2014-01-18T19… 2024-… -#> 2 TRANS PACIFIC JOURNEY FISHING PHL 548012100 <chr [3]> 2017-02-07T00… 2019-… -#> 3 COLINER CYP 210631000 <chr [1]> 2014-01-01T00… 2024-… -#> 4 COLINER CYP 273379740 <chr [1]> 2015-02-27T10… 2018-… +#> 1 COLINER CYP 210631000 <chr [1]> 2014-01-01T00… 2024-… +#> 2 COLINER CYP 273379740 <chr [1]> 2015-02-27T10… 2018-… +#> 3 TRANS PACIFIC JOURNEY FISHING PHL 548012100 <chr [3]> 2017-02-07T00… 2019-… +#> 4 DONGWON INDUSTRIES KOR 441734000 <chr [2]> 2014-01-18T19… 2024-… #> #> $registryPublicAuthorizations #> # A tibble: 6 × 4 #> dateFrom dateTo ssvid sourceCode #> <chr> <chr> <chr> <list> -#> 1 2013-09-20T00:00:00Z 2024-05-01T00:00:00Z 441734000 <chr [1]> -#> 2 2015-10-08T00:00:00Z 2020-07-21T00:00:00Z 441734000 <chr [1]> +#> 1 2022-12-19T00:00:00Z 2024-06-01T00:00:00Z 210631000 <chr [1]> +#> 2 2020-01-01T00:00:00Z 2024-06-01T00:00:00Z 210631000 <chr [1]> #> 3 2012-01-01T00:00:00Z 2024-05-01T00:00:00Z 548012100 <chr [1]> #> 4 2012-01-01T00:00:00Z 2017-10-25T00:00:00Z 548012100 <chr [1]> -#> 5 2022-12-19T00:00:00Z 2024-05-01T00:00:00Z 210631000 <chr [1]> -#> 6 2020-01-01T00:00:00Z 2024-05-01T00:00:00Z 210631000 <chr [1]> +#> 5 2013-09-20T00:00:00Z 2024-06-01T00:00:00Z 441734000 <chr [1]> +#> 6 2015-10-08T00:00:00Z 2020-07-21T00:00:00Z 441734000 <chr [1]> #> #> $combinedSourcesInfo #> # A tibble: 8 × 9 #> vesselId geartypes_geartype_n…¹ geartypes_geartype_s…² geartypes_geartype_y…³ #> <chr> <chr> <chr> <int> -#> 1 6583c51e… OTHER COMBINATION_OF_REGIST… 2013 -#> 2 6583c51e… OTHER COMBINATION_OF_REGIST… 2013 -#> 3 6583c51e… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2014 -#> 4 6583c51e… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2014 -#> 5 55889aef… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2017 -#> 6 71e7da67… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2017 -#> 7 da1cd7e1… CARRIER GFW_VESSEL_LIST 2015 -#> 8 8c730422… CARRIER GFW_VESSEL_LIST 2013 +#> 1 da1cd7e1… CARRIER GFW_VESSEL_LIST 2015 +#> 2 8c730422… CARRIER GFW_VESSEL_LIST 2013 +#> 3 71e7da67… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2017 +#> 4 55889aef… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2017 +#> 5 6583c51e… OTHER COMBINATION_OF_REGIST… 2013 +#> 6 6583c51e… OTHER COMBINATION_OF_REGIST… 2013 +#> 7 6583c51e… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2014 +#> 8 6583c51e… TUNA_PURSE_SEINES COMBINATION_OF_REGIST… 2014 #> # ℹ abbreviated names: ¹​geartypes_geartype_name, ²​geartypes_geartype_source, #> # ³​geartypes_geartype_yearFrom #> # ℹ 5 more variables: geartypes_geartype_yearTo <int>, @@ -496,9 +496,9 @@

Examples#> # A tibble: 3 × 13 #> vesselId ssvid shipname nShipname flag callsign imo messagesCounter #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> -#> 1 6583c51e3-3626-… 4417… ADRIA ADRIA KOR DTBY3 8919… 3742574 +#> 1 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 263878798 #> 2 71e7da672-2451-… 5480… JOHN RE… JOHNREYN… PHL DUQA-7 8118… 1967237 -#> 3 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 263226124 +#> 3 6583c51e3-3626-… 4417… ADRIA ADRIA KOR DTBY3 8919… 3742574 #> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>, #> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>

This is useful especially because a vessel can have different @@ -640,8 +640,8 @@

Examples= "2020-02-01", key = key ) -#> [1] "Downloading 34 events from GFW" -#> # A tibble: 34 × 11 +#> [1] "Downloading 37 events from GFW" +#> # A tibble: 37 × 11 #> start end id type lat lon regions #> <dttm> <dttm> <chr> <chr> <dbl> <dbl> <list> #> 1 2020-01-05 04:58:45 2020-01-05 06:31:45 379d… fish… 43.7 -124. <named list> @@ -654,7 +654,7 @@

Examples#> 8 2020-01-10 18:21:53 2020-01-12 03:13:04 6739… fish… 38.0 -73.9 <named list> #> 9 2020-01-13 12:45:32 2020-01-13 15:38:38 46f8… fish… 38.0 -73.9 <named list> #> 10 2020-01-13 13:20:55 2020-01-13 15:07:53 2333… fish… 43.7 -124. <named list> -#> # ℹ 24 more rows +#> # ℹ 27 more rows #> # ℹ 4 more variables: boundingBox <list>, distances <list>, vessel <list>, #> # event_info <list>

When no events are available, the get_event() function @@ -691,14 +691,14 @@

Fishing effort API

Examples

You can load a sample shapefile inside gfwr to see how -'USER_JSON' works:

+'USER_SHAPEFILE' works:

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

@@ -805,16 +805,16 @@

Examples#> # A tibble: 5,660 × 6 #> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours` #> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> -#> 1 50.9 1.4 2021 FRA 30 831. -#> 2 50.8 1.3 2021 BEL 1 5.92 -#> 3 51.4 2.1 2021 BEL 3 3.14 -#> 4 51.2 2.1 2021 NLD 21 123. -#> 5 51.3 2 2021 NLD 16 68.9 -#> 6 51.2 2 2021 FRA 22 465. -#> 7 51.5 2.2 2021 NLD 20 546. -#> 8 51.5 2.2 2021 BEL 3 62.6 -#> 9 51.4 2.3 2021 BEL 6 8.07 -#> 10 51.3 2.2 2021 NLD 13 269. +#> 1 49 -6.2 2021 FRA 20 216. +#> 2 49.1 -6.1 2021 FRA 14 66.6 +#> 3 48.9 -6.2 2021 FRA 14 104. +#> 4 49 -6 2021 FRA 18 264. +#> 5 49 -6.1 2021 BLZ 1 1.49 +#> 6 49 -5.9 2021 FRA 19 244. +#> 7 49.1 -5.7 2021 FRA 20 313. +#> 8 49.1 -5.8 2021 BLZ 1 0.17 +#> 9 49 -5.8 2021 FRA 21 389. +#> 10 48.9 -5.8 2021 FRA 15 209. #> # ℹ 5,650 more rows

A similar approach can be used to search for a specific Marine Protected Area, in this case the Phoenix Island Protected Area @@ -842,16 +842,16 @@

Examples#> # A tibble: 40 × 6 #> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours` #> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> -#> 1 -4.7 -176. 2015 KOR 3 15.8 -#> 2 -4.7 -176. 2015 KIR 1 0.36 +#> 1 -3.9 -173. 2015 KOR 1 0.01 +#> 2 -4.7 -176. 2015 KOR 3 15.8 #> 3 -2.2 -176. 2015 KIR 1 1.89 -#> 4 -3.6 -176. 2015 KIR 1 5.99 -#> 5 -3.1 -176. 2015 KOR 1 0.91 -#> 6 -3.5 -176. 2015 KOR 1 10.8 -#> 7 -3.4 -176. 2015 KOR 1 1.37 -#> 8 -3.6 -176. 2015 KOR 1 7.9 -#> 9 -3.6 -176. 2015 KOR 1 1.98 -#> 10 -4.2 -176. 2015 KOR 1 0.05 +#> 4 -2.5 -176. 2015 KOR 1 6.54 +#> 5 -2.6 -176. 2015 TWN 1 0.35 +#> 6 -2.2 -176. 2015 KIR 1 0.53 +#> 7 -2.6 -176. 2015 KOR 1 5.58 +#> 8 -2.8 -176. 2015 KOR 1 9.29 +#> 9 -2.8 -176. 2015 KOR 2 21.6 +#> 10 -2.9 -176. 2015 KOR 2 9.74 #> # ℹ 30 more rows

It is also possible to filter rasters to one of the five regional fisheries management organizations (RFMO) that manage tuna and tuna-like @@ -912,8 +912,8 @@

Examples= 'EEZ', key = key)$label) %>% dplyr::select(-start, -end) -#> [1] "Downloading 34 events from GFW" -#> # A tibble: 34 × 6 +#> [1] "Downloading 37 events from GFW" +#> # A tibble: 37 × 6 #> # Rowwise: #> id type lat lon eez eez_name #> <chr> <chr> <dbl> <dbl> <chr> <chr> @@ -927,7 +927,7 @@

Examples#> 8 6739137b68e5fb477de38226f57892f7 fishing 38.0 -73.9 8456 United States #> 9 46f8debd1e55a894ca26ac74faf11162 fishing 38.0 -73.9 8456 United States #> 10 23330ffa0e1bbab43ead8328456c45aa fishing 43.7 -124. 8456 United States -#> # ℹ 24 more rows +#> # ℹ 27 more rows

When your API request times out diff --git a/docs/articles/identity.html b/docs/articles/identity.html index 7bc7175..42d2139 100644 --- a/docs/articles/identity.html +++ b/docs/articles/identity.html @@ -384,36 +384,34 @@

Matching AIS and registries: # # A tibble: 1 × 13 # vesselId ssvid shipname nShipname flag callsign imo messagesCounter # <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> -# 1 8e930bac5-594b-… 7010… ATLANTI… ATLANTIC… ARG LW3233 8615… 975700351 +# 1 8e930bac5-594b-… 7010… ATLANTI… ATLANTIC… ARG LW3233 8615… 985759069 # # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>, -# # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>

-
-#check registry info:
+# #   matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>
+#check registry info:
 one_AIS$registryInfo %>% relocate(transmissionDateFrom, transmissionDateTo) #changing column order for visualization
 # # A tibble: 1 × 15
 #   transmissionDateFrom transmissionDateTo  id    sourceCode ssvid flag  shipname
 #   <chr>                <chr>               <chr> <list>     <chr> <chr> <chr>   
-# 1 2014-01-01T03:28:33Z 2024-04-30T21:44:3… 4550… <chr [1]>  7010… ARG   ATLANTI…
+# 1 2014-01-01T03:28:33Z 2024-05-31T23:56:3… 4550… <chr [1]>  7010… ARG   ATLANTI…
 # # ℹ 8 more variables: nShipname <chr>, callsign <chr>, imo <chr>,
 # #   latestVesselInfo <lgl>, geartypes <list>, lengthM <dbl>, tonnageGt <int>,
 # #   vesselInfoReference <chr>

This other vessel has had three identities based on AIS, but these are easy to reconstruct:

-
+
 three_AIS <- get_vessel_info(where= "ssvid='412217989'") #one registry, three AIS 
 # see AIS-based indentities:
 three_AIS$selfReportedInfo %>% relocate(transmissionDateFrom, transmissionDateTo) 
 # # A tibble: 3 × 13
 #   transmissionDateFrom transmissionDateTo   vesselId    ssvid shipname nShipname
 #   <chr>                <chr>                <chr>       <chr> <chr>    <chr>    
-# 1 2021-11-29T06:20:07Z 2024-06-28T23:58:36Z b373b6306-… 4122… HAO YAN… HAOYANG77
+# 1 2021-11-29T06:20:07Z 2024-07-20T23:59:49Z b373b6306-… 4122… HAO YAN… HAOYANG77
 # 2 2014-03-29T00:32:05Z 2021-11-27T20:13:55Z 305097c65-… 4122… JIN LIA… JINLIAOY…
 # 3 2012-08-01T01:47:23Z 2014-03-29T00:14:05Z 95a173191-… 4316… HAKKO M… HAKKOMAR…
 # # ℹ 7 more variables: flag <chr>, callsign <chr>, imo <chr>,
 # #   messagesCounter <int>, positionsCounter <int>, sourceCode <list>,
-# #   matchFields <chr>

-
-#check registry info:
+# #   matchFields <chr>
+#check registry info:
 three_AIS$registryInfo 
 # # A tibble: 1 × 15
 #   id                    sourceCode ssvid flag  shipname nShipname callsign imo  
@@ -424,7 +422,7 @@ 
Matching AIS and registries:
 # #   vesselInfoReference <chr>

 
However, sometimes a vessel found in AIS has no registry information
 and the registry fields come back empty.

-
+
 get_vessel_info(where = "ssvid='71000036'")
 # $dataset
 # # A tibble: 1 × 1
@@ -470,15 +468,14 @@ 
Matching AIS and registries:
 # #   matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>

 
It is also possible that a search returns a vessel with no matching
 AIS information and no registry

-
+
 noAIS_noReg <- get_vessel_info(where = "imo='44201155'") #no AIS, no registry information
 noAIS_noReg$registryInfoTotalRecords
 # # A tibble: 1 × 1
 #   registryInfoTotalRecords
 #                      <int>
-# 1                        0

-
-noAIS_noReg$selfReported
+# 1                        0
+noAIS_noReg$selfReported
 # # A tibble: 1 × 13
 #   vesselId         ssvid shipname nShipname flag  callsign imo   messagesCounter
 #   <chr>            <chr> <chr>    <chr>     <lgl> <chr>    <chr>           <int>
diff --git a/docs/articles/making-maps.html b/docs/articles/making-maps.html
index 49c0990..e11efa3 100644
--- a/docs/articles/making-maps.html
+++ b/docs/articles/making-maps.html
@@ -150,26 +150,23 @@ 
Setup
 #>     filter, lag
 #> The following objects are masked from 'package:base':
 #> 
-#>     intersect, setdiff, setequal, union

-
-library(tidyr)
+#>     intersect, setdiff, setequal, union
+library(tidyr)
 library(sf)
-#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE

-
-library(rnaturalearth)
+#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
+library(rnaturalearth)
 library(rnaturalearthdata)
 #> 
 #> Attaching package: 'rnaturalearthdata'
 #> The following object is masked from 'package:rnaturalearth':
 #> 
-#>     countries110

-
-library(glue)
+#>     countries110
+library(glue)
 library(ggplot2)

 
To make our maps a little nicer, let’s define a custom
 ggplot2 theme and a color palette for our heatmaps of
 apparent fishing effort.

-
+
 # Map theme with dark background
 map_theme <- ggplot2::theme_minimal() + 
   ggplot2::theme(
@@ -188,7 +185,7 @@ 
Setup
 # Palette for fishing activity
 map_effort_light <- c("#ffffff", "#eeff00", "#3b9088","#0c276c")

 
We’ll also define a common date range to use for querying data.

-
+
 start_date <- '2021-01-01'
 end_date <- '2021-03-31'

 

@@ -199,16 +196,16 @@ 
Making heatm
 

 
The gfwr function get_raster() provides
 aggregated gridded (e.g. raster) data for AIS-based apparent fishing
-effort (SAR). It was designed to provide data for a specific region,
-offering users the ability to select from multiple built-in region types
-by specifying a specific Exclusive Economic Zone (EEZ), Marine Protected
+effort. It was designed to provide data for a specific region, offering
+users the ability to select from multiple built-in region types by
+specifying a specific Exclusive Economic Zone (EEZ), Marine Protected
 Area (MPA), or Regional Fisheries Management Organization (RFMO).

 
The list of available regions for each type, and their
 label and id, can be accessed with the
 get_regions() function.

-
+
 eez_regions <- get_regions(region_source = 'EEZ', key = gfw_key)

-
+
 eez_regions
 #> # A tibble: 282 × 3
 #>       id label                                                         iso3 
@@ -244,7 +241,7 @@ 
Making heatm
 later). We could look up the Italian EEZ id in the
 eez_regions table we just created, but let’s use
 get_region_id().

-
+
 # Use get_region_id function to get EEZ code for Italy
 ita_eez_code <- get_region_id(region_name = "ITA", region_source = "EEZ", key = gfw_auth())

 
#> # A tibble: 1 × 3
@@ -258,7 +255,7 @@ 
Making heatm
 APIs for more info about these parameter options.

 
In this case, let’s request data during our time range at 100th
 degree resolution and grouped by flag State:

-
+
 # Download data for the Italian EEZ
 eez_fish_df <- get_raster(
   spatial_resolution = "HIGH",
@@ -281,26 +278,26 @@ 
Making heatm
 #>      Lat   Lon `Time Range` flag  `Vessel IDs` `Apparent Fishing Hours`
 #>    <dbl> <dbl>        <dbl> <chr>        <dbl>                    <dbl>
 #>  1  44.2  9.19         2021 ITA             12                     54.0
-#>  2  37.5 12.9          2021 ITA             12                     19.0
-#>  3  37.4 13.2          2021 ITA             13                     22.6
-#>  4  37.4 13.1          2021 ITA             13                     15.9
-#>  5  37.4 13.1          2021 ITA             19                     24.4
-#>  6  37.4 13.2          2021 ITA             16                     15.7
-#>  7  37.4 13.2          2021 ITA             14                     23.0
-#>  8  37.3 13.3          2021 ITA             13                     14.7
-#>  9  37.3 13.4          2021 ITA             13                     10.5
-#> 10  37.0 13.9          2021 ITA             21                     30.2
+#>  2  45.1 12.4          2021 ITA             13                     39.3
+#>  3  45.1 12.4          2021 ITA             17                     43.8
+#>  4  45.1 12.5          2021 ITA             12                     46.8
+#>  5  45.1 12.5          2021 ITA             15                     58.2
+#>  6  45.1 12.5          2021 ITA             19                     77.4
+#>  7  45.1 12.5          2021 ITA             16                     57.8
+#>  8  45.0 12.5          2021 ITA             12                     34.4
+#>  9  45.1 12.5          2021 ITA             14                     54.3
+#> 10  45.0 12.6          2021 ITA             14                     30.6
 #> # ℹ 69,132 more rows

 
Because the data includes fishing by all flag states, to make a map
 of all activity, we first need to summarize activity by grid cell.

-
+
 eez_fish_all_df <- eez_fish_df %>% 
   group_by(Lat, Lon) %>% 
   summarize(fishing_hours = sum(`Apparent Fishing Hours`, na.rm = T))
 #> `summarise()` has grouped output by 'Lat'. You can override using the `.groups`
 #> argument.

 
Now we can use ggplot2 to plot the data:

-
+
 eez_fish_all_df %>% 
   filter(fishing_hours >= 1) %>% 
   ggplot() +
@@ -327,7 +324,7 @@ 
Making heatm
 
As another example, let’s request low resolution apparent fishing
 effort data within the jurisdiction of the Indian Ocean Tuna Commission
 (IOTC), grouped yearly by gear type:

-
+
 # Download data for the IOTC
 iotc_fish_df <- get_raster(
   spatial_resolution = "LOW",
@@ -347,22 +344,23 @@ 
Making heatm
 #> ℹ Use `spec()` to retrieve the full column specification for this data.
 #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

 
#> # A tibble: 106,229 × 6
-#>      Lat   Lon `Time Range` geartype `Vessel IDs` `Apparent Fishing Hours`
-#>    <dbl> <dbl>        <dbl> <chr>           <dbl>                    <dbl>
-#>  1   7.7  98.3         2021 trawlers           10                     548.
-#>  2   7.7  98.4         2021 trawlers           10                     586.
-#>  3   6.4  99.5         2021 trawlers            9                     356.
-#>  4   5.6  99.8         2021 trawlers           11                     130.
-#>  5   6.2 100           2021 trawlers           22                    2365.
-#>  6   6.2  99.9         2021 trawlers           10                     146.
-#>  7   6.1 100           2021 trawlers           23                    1443.
-#>  8   6.2 100.          2021 trawlers           24                    2127.
-#>  9   6.1 100.          2021 trawlers           25                    1382.
-#> 10   6.2 100.          2021 trawlers           10                     219.
-#> # ℹ 106,219 more rows

+#>      Lat   Lon `Time Range` geartype         `Vessel IDs` Apparent Fishing Hou…¹
+#>    <dbl> <dbl>        <dbl> <chr>                   <dbl>                  <dbl>
+#>  1 -32.3  98.9         2021 drifting_longli…           13                   68.4
+#>  2 -32.3  99.1         2021 drifting_longli…           12                   90.4
+#>  3 -32.4  99.1         2021 drifting_longli…           12                   98.2
+#>  4 -32.4  99.7         2021 drifting_longli…            9                   38.2
+#>  5 -32.4  99.6         2021 drifting_longli…           12                   50.8
+#>  6 -32.9  98.8         2021 drifting_longli…           14                   60.9
+#>  7 -33    99           2021 drifting_longli…           26                  292. 
+#>  8 -33.1  99           2021 drifting_longli…           14                   98.5
+#>  9 -33.1  99.1         2021 drifting_longli…           14                   96.0
+#> 10 -32.9  99.5         2021 drifting_longli…           14                   73.3
+#> # ℹ 106,219 more rows
+#> # ℹ abbreviated name: ¹​`Apparent Fishing Hours`

 
This time, instead of aggregating all activity, let’s plot the
 activity of a specific gear type:

-
+
 iotc_p1 <- iotc_fish_df %>% 
   filter(geartype == 'drifting_longlines') %>% 
   filter(`Apparent Fishing Hours` >= 1) %>% 
@@ -445,7 +443,7 @@ 
Plotting vessel eventsregion and
 region_source arguments like we did in the previous
 example.

-
+
 # using same example as above
 encounters_df <- get_event(event_type = "ENCOUNTER",
                            encounter_types = "CARRIER-FISHING",
@@ -460,7 +458,7 @@ 
Plotting vessel eventsevent_id to remove duplicate positions. We’ll also use the
 lon and lat coordinates to create a
 sf object for each encounter event.

-
+
 encounters_sf_df <- encounters_df %>% 
   tidyr::separate(id, c("event_id","vessel_number")) %>% 
   filter(vessel_number == 1) %>% 
@@ -468,12 +466,12 @@ 
Plotting vessel events  select(event_id, type, geometry)

 
To assist with plotting, let’s get the bounding box of the encounter
 events.

-
+
 enc_bbox <- st_bbox(encounters_sf_df)

 
Now let’s add the encounters layer to our previous map of drifting
 longline effort in the IOTC and use the bounding box to restrict the
 plot to the area with encounters.

-
+
 iotc_p1 +
   geom_sf(data = encounters_sf_df, 
           aes(color = type), 
@@ -501,7 +499,7 @@ 
Making maps for custom regions
+
 my_shp <- tibble(
   lon = c(-96,-96,-66,-66,-96),
   lat = c(-24,4,4,-24,-24)
@@ -516,7 +514,7 @@ 
Making maps for custom regions#> deprecated. It might return a CRS with a non-EPSG compliant axis order.

 
Plot the sf object to confirm it was created
 successfully.

-
+
 ggplot() +
   geom_sf(data = ne_countries(returnclass = "sf", scale = "small")) +
   geom_sf(
@@ -528,11 +526,11 @@ 
Making maps for custom regions
+
 
Now we’re ready to request data in our custom region from
 get_raster() and get_event().

-
+
 
For events, we’ll request high-confidence port visits by fishing
 vessels

-
+
 my_port_events_df <- get_event(event_type = "PORT_VISIT",
                           confidences = 4,
                           vessel_types = "FISHING",
                           start_date = start_date,
                           end_date = end_date,
                           region = my_shp,
-                          region_source = "USER_JSON") 
-#> [1] "Downloading 3983 events from GFW"

+                          region_source = "USER_SHAPEFILE") 
+#> [1] "Downloading 3985 events from GFW"

 
and loitering events by refrigerated cargo vessels

-
+
 my_loitering_events_df <- get_event(event_type = "LOITERING",
                                     vessel_types = "CARRIER",
                                     start_date = start_date,
                                     end_date = end_date,
                                     region = my_shp,
-                                    region_source = "USER_JSON") 
+                                    region_source = "USER_SHAPEFILE") 
 #> [1] "Downloading 50 events from GFW"

 
As before, let’s summarize the raster to plot all fishing activity by
 fishing vessels

-
+
 my_raster_all_df <- my_raster_df %>% 
   group_by(Lat, Lon) %>% 
   summarize(fishing_hours = sum(`Apparent Fishing Hours`, na.rm = T))
@@ -580,7 +578,7 @@ 
Making maps for custom regions#> argument.

 
and combine our two event datasets and create sf objects
 for each event.

-
+
 my_events_sf <- my_port_events_df %>% 
   select(id, lon, lat, type) %>% 
   bind_rows(
@@ -591,7 +589,7 @@ 
Making maps for custom regionsselect(id, type, geometry)

 
Finally, let’s plot the fishing effort raster and overlay the
 loitering events and port visits.

-
+
 my_raster_all_df %>% 
   filter(fishing_hours > 1) %>% 
   ggplot() +
diff --git a/docs/articles/making-maps_files/figure-html/ita_plot-1.png b/docs/articles/making-maps_files/figure-html/ita_plot-1.png
index 291f303..884f896 100644
Binary files a/docs/articles/making-maps_files/figure-html/ita_plot-1.png and b/docs/articles/making-maps_files/figure-html/ita_plot-1.png differ
diff --git a/docs/articles/making-maps_files/figure-html/loitering_ports_fishing_map-1.png b/docs/articles/making-maps_files/figure-html/loitering_ports_fishing_map-1.png
index a506c4e..9d49d21 100644
Binary files a/docs/articles/making-maps_files/figure-html/loitering_ports_fishing_map-1.png and b/docs/articles/making-maps_files/figure-html/loitering_ports_fishing_map-1.png differ
diff --git a/docs/index.html b/docs/index.html
index f99a004..3214578 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -215,8 +215,8 @@ 
Examples#> # A tibble: 2 × 9
 #>   vesselId  geartypes_geartype_n…¹ geartypes_geartype_s…² geartypes_geartype_y…³
 #>   <chr>     <chr>                  <chr>                                   <int>
-#> 1 3c99c326… PURSE_SEINE_SUPPORT    GFW_VESSEL_LIST                          2015
-#> 2 6632c9eb… PURSE_SEINE_SUPPORT    GFW_VESSEL_LIST                          2019
+#> 1 6632c9eb… PURSE_SEINE_SUPPORT    GFW_VESSEL_LIST                          2019
+#> 2 3c99c326… PURSE_SEINE_SUPPORT    GFW_VESSEL_LIST                          2015
 #> # ℹ abbreviated names: ¹​geartypes_geartype_name, ²​geartypes_geartype_source,
 #> #   ³​geartypes_geartype_yearFrom
 #> # ℹ 5 more variables: geartypes_geartype_yearTo <int>,
@@ -273,9 +273,9 @@ 
Examples#> # A tibble: 3 × 9
 #>   vesselId  geartypes_geartype_n…¹ geartypes_geartype_s…² geartypes_geartype_y…³
 #>   <chr>     <chr>                  <chr>                                   <int>
-#> 1 58cf536b… CARRIER                GFW_VESSEL_LIST                          2012
+#> 1 1da8dbc2… CARRIER                GFW_VESSEL_LIST                          2022
 #> 2 0b7047cb… CARRIER                GFW_VESSEL_LIST                          2019
-#> 3 1da8dbc2… CARRIER                GFW_VESSEL_LIST                          2022
+#> 3 58cf536b… CARRIER                GFW_VESSEL_LIST                          2012
 #> # ℹ abbreviated names: ¹​geartypes_geartype_name, ²​geartypes_geartype_source,
 #> #   ³​geartypes_geartype_yearFrom
 #> # ℹ 5 more variables: geartypes_geartype_yearTo <int>,
@@ -286,7 +286,7 @@ 
Examples#> # A tibble: 3 × 13
 #>   vesselId         ssvid shipname nShipname flag  callsign imo   messagesCounter
 #>   <chr>            <chr> <chr>    <chr>     <chr> <chr>    <chr>           <int>
-#> 1 1da8dbc23-3c48-… 6135… GABU RE… GABUREEF… CMR   TJMC996  8300…        71775911
+#> 1 1da8dbc23-3c48-… 6135… GABU RE… GABUREEF… CMR   TJMC996  8300…        72480839
 #> 2 0b7047cb5-58c8-… 2141… GABU RE… GABUREEF… MDA   ER2732   8300…        70035084
 #> 3 58cf536b1-1fca-… 6168… GABU RE… GABUREEF… COM   D6FJ2    8300…        32121624
 #> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
@@ -328,15 +328,15 @@ 
Examples#> # A tibble: 2 × 6
 #>   name    flag  ssvid     sourceCode dateFrom             dateTo              
 #>   <chr>   <chr> <chr>     <list>     <chr>                <chr>               
-#> 1 COLINER CYP   210631000 <chr [1]>  2014-01-01T00:16:58Z 2024-04-30T23:41:06Z
+#> 1 COLINER CYP   210631000 <chr [1]>  2014-01-01T00:16:58Z 2024-05-31T23:44:00Z
 #> 2 COLINER CYP   273379740 <chr [1]>  2015-02-27T10:59:43Z 2018-03-21T07:13:09Z
 #> 
 #> $registryPublicAuthorizations
 #> # A tibble: 2 × 4
 #>   dateFrom             dateTo               ssvid     sourceCode
 #>   <chr>                <chr>                <chr>     <list>    
-#> 1 2022-12-19T00:00:00Z 2024-05-01T00:00:00Z 210631000 <chr [1]> 
-#> 2 2020-01-01T00:00:00Z 2024-05-01T00:00:00Z 210631000 <chr [1]> 
+#> 1 2022-12-19T00:00:00Z 2024-06-01T00:00:00Z 210631000 <chr [1]> 
+#> 2 2020-01-01T00:00:00Z 2024-06-01T00:00:00Z 210631000 <chr [1]> 
 #> 
 #> $combinedSourcesInfo
 #> # A tibble: 2 × 9
@@ -354,7 +354,7 @@ 
Examples#> # A tibble: 1 × 13
 #>   vesselId         ssvid shipname nShipname flag  callsign imo   messagesCounter
 #>   <chr>            <chr> <chr>    <chr>     <chr> <chr>    <chr>           <int>
-#> 1 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP   5BWC3    9076…       263226124
+#> 1 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP   5BWC3    9076…       263878798
 #> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
 #> #   matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>

 
To specify more than one vesselId, you can submit a vector:

@@ -376,18 +376,18 @@ 
Examples#> # A tibble: 3 × 1
 #>   registryInfoTotalRecords
 #>                      <int>
-#> 1                        1
+#> 1                        2
 #> 2                        1
-#> 3                        2
+#> 3                        1
 #> 
 #> $registryInfo
 #> # A tibble: 4 × 15
 #>   id                    sourceCode ssvid flag  shipname nShipname callsign imo  
 #>   <chr>                 <list>     <chr> <chr> <chr>    <chr>     <chr>    <chr>
-#> 1 685862e0626f6234c844… <chr [5]>  5480… PHL   JOHNREY… JOHNREYN… DUQA7    8118…
-#> 2 b82d02e5c2c11e5fe536… <chr [5]>  4417… KOR   ADRIA    ADRIA     DTBY3    8919…
-#> 3 a8d00ce54b37add7f85a… <chr [6]>  2106… CYP   FRIO FO… FRIOFORW… 5BWC3    9076…
-#> 4 a8d00ce54b37add7f85a… <chr [2]>  2733… RUS   FRIO FO… FRIOFORW… UCRZ     9076…
+#> 1 a8d00ce54b37add7f85a… <chr [6]>  2106… CYP   FRIO FO… FRIOFORW… 5BWC3    9076…
+#> 2 a8d00ce54b37add7f85a… <chr [2]>  2733… RUS   FRIO FO… FRIOFORW… UCRZ     9076…
+#> 3 685862e0626f6234c844… <chr [5]>  5480… PHL   JOHNREY… JOHNREYN… DUQA7    8118…
+#> 4 b82d02e5c2c11e5fe536… <chr [5]>  4417… KOR   ADRIA    ADRIA     DTBY3    8919…
 #> # ℹ 7 more variables: latestVesselInfo <lgl>, transmissionDateFrom <chr>,
 #> #   transmissionDateTo <chr>, geartypes <list>, lengthM <dbl>, tonnageGt <dbl>,
 #> #   vesselInfoReference <chr>
@@ -396,34 +396,34 @@ 
Examples#> # A tibble: 4 × 6
 #>   name                          flag  ssvid     sourceCode dateFrom       dateTo
 #>   <chr>                         <chr> <chr>     <list>     <chr>          <chr> 
-#> 1 TRANS PACIFIC JOURNEY FISHING PHL   548012100 <chr [3]>  2017-02-07T00… 2019-…
-#> 2 DONGWON INDUSTRIES            KOR   441734000 <chr [2]>  2014-01-18T19… 2024-…
-#> 3 COLINER                       CYP   210631000 <chr [1]>  2014-01-01T00… 2024-…
-#> 4 COLINER                       CYP   273379740 <chr [1]>  2015-02-27T10… 2018-…
+#> 1 COLINER                       CYP   210631000 <chr [1]>  2014-01-01T00… 2024-…
+#> 2 COLINER                       CYP   273379740 <chr [1]>  2015-02-27T10… 2018-…
+#> 3 TRANS PACIFIC JOURNEY FISHING PHL   548012100 <chr [3]>  2017-02-07T00… 2019-…
+#> 4 DONGWON INDUSTRIES            KOR   441734000 <chr [2]>  2014-01-18T19… 2024-…
 #> 
 #> $registryPublicAuthorizations
 #> # A tibble: 6 × 4
 #>   dateFrom             dateTo               ssvid     sourceCode
 #>   <chr>                <chr>                <chr>     <list>    
-#> 1 2012-01-01T00:00:00Z 2024-05-01T00:00:00Z 548012100 <chr [1]> 
-#> 2 2012-01-01T00:00:00Z 2017-10-25T00:00:00Z 548012100 <chr [1]> 
-#> 3 2013-09-20T00:00:00Z 2024-05-01T00:00:00Z 441734000 <chr [1]> 
-#> 4 2015-10-08T00:00:00Z 2020-07-21T00:00:00Z 441734000 <chr [1]> 
-#> 5 2022-12-19T00:00:00Z 2024-05-01T00:00:00Z 210631000 <chr [1]> 
-#> 6 2020-01-01T00:00:00Z 2024-05-01T00:00:00Z 210631000 <chr [1]> 
+#> 1 2022-12-19T00:00:00Z 2024-06-01T00:00:00Z 210631000 <chr [1]> 
+#> 2 2020-01-01T00:00:00Z 2024-06-01T00:00:00Z 210631000 <chr [1]> 
+#> 3 2012-01-01T00:00:00Z 2024-05-01T00:00:00Z 548012100 <chr [1]> 
+#> 4 2012-01-01T00:00:00Z 2017-10-25T00:00:00Z 548012100 <chr [1]> 
+#> 5 2013-09-20T00:00:00Z 2024-06-01T00:00:00Z 441734000 <chr [1]> 
+#> 6 2015-10-08T00:00:00Z 2020-07-21T00:00:00Z 441734000 <chr [1]> 
 #> 
 #> $combinedSourcesInfo
 #> # A tibble: 8 × 9
 #>   vesselId  geartypes_geartype_n…¹ geartypes_geartype_s…² geartypes_geartype_y…³
 #>   <chr>     <chr>                  <chr>                                   <int>
-#> 1 55889aef… TUNA_PURSE_SEINES      COMBINATION_OF_REGIST…                   2017
-#> 2 71e7da67… TUNA_PURSE_SEINES      COMBINATION_OF_REGIST…                   2017
-#> 3 6583c51e… OTHER                  COMBINATION_OF_REGIST…                   2013
-#> 4 6583c51e… OTHER                  COMBINATION_OF_REGIST…                   2013
-#> 5 6583c51e… TUNA_PURSE_SEINES      COMBINATION_OF_REGIST…                   2014
-#> 6 6583c51e… TUNA_PURSE_SEINES      COMBINATION_OF_REGIST…                   2014
-#> 7 da1cd7e1… CARRIER                GFW_VESSEL_LIST                          2015
-#> 8 8c730422… CARRIER                GFW_VESSEL_LIST                          2013
+#> 1 da1cd7e1… CARRIER                GFW_VESSEL_LIST                          2015
+#> 2 8c730422… CARRIER                GFW_VESSEL_LIST                          2013
+#> 3 71e7da67… TUNA_PURSE_SEINES      COMBINATION_OF_REGIST…                   2017
+#> 4 55889aef… TUNA_PURSE_SEINES      COMBINATION_OF_REGIST…                   2017
+#> 5 6583c51e… OTHER                  COMBINATION_OF_REGIST…                   2013
+#> 6 6583c51e… OTHER                  COMBINATION_OF_REGIST…                   2013
+#> 7 6583c51e… TUNA_PURSE_SEINES      COMBINATION_OF_REGIST…                   2014
+#> 8 6583c51e… TUNA_PURSE_SEINES      COMBINATION_OF_REGIST…                   2014
 #> # ℹ abbreviated names: ¹​geartypes_geartype_name, ²​geartypes_geartype_source,
 #> #   ³​geartypes_geartype_yearFrom
 #> # ℹ 5 more variables: geartypes_geartype_yearTo <int>,
@@ -434,9 +434,9 @@ 
Examples#> # A tibble: 3 × 13
 #>   vesselId         ssvid shipname nShipname flag  callsign imo   messagesCounter
 #>   <chr>            <chr> <chr>    <chr>     <chr> <chr>    <chr>           <int>
-#> 1 71e7da672-2451-… 5480… JOHN RE… JOHNREYN… PHL   DUQA-7   8118…         1967237
-#> 2 6583c51e3-3626-… 4417… ADRIA    ADRIA     KOR   DTBY3    8919…         3742574
-#> 3 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP   5BWC3    9076…       263226124
+#> 1 8c7304226-6c71-… 2106… FRIO FO… FRIOFORW… CYP   5BWC3    9076…       263878798
+#> 2 71e7da672-2451-… 5480… JOHN RE… JOHNREYN… PHL   DUQA-7   8118…         1967237
+#> 3 6583c51e3-3626-… 4417… ADRIA    ADRIA     KOR   DTBY3    8919…         3742574
 #> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
 #> #   matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>

 
This is useful especially because a vessel can have different vesselIds in time. Check the function documentation for examples with the other function arguments and our dedicated vignette for more information about vessel identity .

@@ -550,8 +550,8 @@ 
Examples= "2020-02-01",
           key = key
           )
-#> [1] "Downloading 34 events from GFW"
-#> # A tibble: 34 × 11
+#> [1] "Downloading 37 events from GFW"
+#> # A tibble: 37 × 11
 #>    start               end                 id    type    lat    lon regions     
 #>    <dttm>              <dttm>              <chr> <chr> <dbl>  <dbl> <list>      
 #>  1 2020-01-05 04:58:45 2020-01-05 06:31:45 379d… fish…  43.7 -124.  <named list>
@@ -564,7 +564,7 @@ 
Examples#>  8 2020-01-10 18:21:53 2020-01-12 03:13:04 6739… fish…  38.0  -73.9 <named list>
 #>  9 2020-01-13 12:45:32 2020-01-13 15:38:38 46f8… fish…  38.0  -73.9 <named list>
 #> 10 2020-01-13 13:20:55 2020-01-13 15:07:53 2333… fish…  43.7 -124.  <named list>
-#> # ℹ 24 more rows
+#> # ℹ 27 more rows
 #> # ℹ 4 more variables: boundingBox <list>, distances <list>, vessel <list>,
 #> #   event_info <list>

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

@@ -591,12 +591,12 @@ 
Fishing effort API
 
Examples
 

-
You can load a sample shapefile inside gfwr to see how 'USER_JSON' works:

+
You can load a sample shapefile inside gfwr to see how 'USER_SHAPEFILE' works:

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

 
 
A similar approach can be used to search for a specific Marine Protected Area, in this case the Phoenix Island Protected Area (PIPA)

 
 
It is also possible to filter rasters to one of the five regional fisheries management organizations (RFMO) that manage tuna and tuna-like species. These include "ICCAT", "IATTC","IOTC", "CCSBT" and "WCPFC".

 
@@ -794,8 +794,8 @@ 
Examples= 'EEZ',
                                          key = key)$label) %>% 
   dplyr::select(-start, -end)
-#> [1] "Downloading 34 events from GFW"
-#> # A tibble: 34 × 6
+#> [1] "Downloading 37 events from GFW"
+#> # A tibble: 37 × 6
 #> # Rowwise: 
 #>    id                               type      lat    lon eez   eez_name     
 #>    <chr>                            <chr>   <dbl>  <dbl> <chr> <chr>        
@@ -809,7 +809,7 @@ 
Examples#>  8 6739137b68e5fb477de38226f57892f7 fishing  38.0  -73.9 8456  United States
 #>  9 46f8debd1e55a894ca26ac74faf11162 fishing  38.0  -73.9 8456  United States
 #> 10 23330ffa0e1bbab43ead8328456c45aa fishing  43.7 -124.  8456  United States
-#> # ℹ 24 more rows

+#> # ℹ 27 more rows

 

 

 
When your API request times out
diff --git a/docs/news/index.html b/docs/news/index.html
index d159e9b..a8bad74 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -68,7 +68,7 @@ 
gfwr 2.0.0the API documentation and the migration guide if anything is new or missing.

 

 
Endpoints

-
  • Same endpoints as in v1.1.0
+
    • Same endpoints as in v1.1.0
 
      • 
 get_raster() communicates with the 4Wings API to get fishing effort
        • 
 
      • 
@@ -114,10 +114,14 @@ 
        Major changes and new features
 get_raster()
 
        • All parameters must now be specified using UPPER CASE (ex. spatial_resolution = "LOW" instead of spatial_resolution = "low")
          • 
-
        • The region argument now accepts sf polygons rather than a GeoJSON string
          • 
+
        • The region argument now accepts sf polygons rather than a GeoJSON string.
          • 
 
        • Parameters start_date and end_date replace date_range for consistency with other functions
          • 
 
        • 
+
      
+
      Additional changes
      
+
      • Option USER_JSON has been renamed to USER_SHAPEFILE for clarity (addresses #161)
        • 
 
      
+

 

     

 
gfwr 1.1.0

diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 3febec2..012a6ce 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   gfwr: gfwr.html
   identity: identity.html
   making-maps: making-maps.html
-last_built: 2024-07-02T01:22Z
+last_built: 2024-07-22T21:21Z
 urls:
   reference: https://globalfishingwatch.github.io/gfwr/reference
   article: https://globalfishingwatch.github.io/gfwr/articles
diff --git a/docs/reference/get_endpoint.html b/docs/reference/get_endpoint.html
index c66fb1f..5bb024d 100644
--- a/docs/reference/get_endpoint.html
+++ b/docs/reference/get_endpoint.html
@@ -75,7 +75,7 @@ 
Function to get API dataset name for given event type

     

     
Arguments

     
dataset_type

-
Type of dataset to get API dataset name for. It can be
+
Type of dataset to get API dataset name for. It can be "raster",
 "ENCOUNTER", "LOITERING", "FISHING", "PORT_VISIT", "GAP", "EEZ", "RFMO" or "MPA"

 
 
diff --git a/docs/reference/get_event.html b/docs/reference/get_event.html
index f93ccff..afe3277 100644
--- a/docs/reference/get_event.html
+++ b/docs/reference/get_event.html
@@ -133,7 +133,7 @@ 
Arguments

 
 
 
region_source

-
source of the region ('EEZ','MPA', 'RFMO' or 'USER_JSON')

+
source of the region ('EEZ','MPA', 'RFMO' or 'USER_SHAPEFILE')

 
 
 
gap_intentional_disabling

@@ -261,6 +261,18 @@ 
Examples

               region_source = 'EEZ',
               flags = 'CHN',
               key = gfw_auth())
+
+# 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 = "2020-12-31",
+              region = test_polygon,
+              region_source = 'USER_SHAPEFILE',
+              key = gfw_auth())
               }
 

     

diff --git a/docs/reference/get_event_stats.html b/docs/reference/get_event_stats.html
index 9a57d03..3ec435c 100644
--- a/docs/reference/get_event_stats.html
+++ b/docs/reference/get_event_stats.html
@@ -123,7 +123,7 @@ 
Arguments

 
Optional but mandatory if using the argument region.
 Source of the region. If 'EEZ','MPA', 'RFMO',
 then the value for the argument region must be the code for that region.
-If 'USER_JSON', then region has to be an sf object

+If 'USER_SHAPEFILE', then region has to be an sf object

 
 
 
region

diff --git a/docs/reference/get_raster.html b/docs/reference/get_raster.html
index ed472b1..0eb5835 100644
--- a/docs/reference/get_raster.html
+++ b/docs/reference/get_raster.html
@@ -118,7 +118,7 @@ 
Arguments

 
 
 
region_source

-
source of the region ('EEZ','MPA', 'RFMO' or 'USER_JSON')

+
source of the region ('EEZ','MPA', 'RFMO' or 'USER_SHAPEFILE')

 
 
 
key

@@ -154,7 +154,7 @@ 
Examples

             start_date = '2021-01-01',
             end_date = '2021-10-01',
             region = test_shape,
-            region_source = 'USER_JSON',
+            region_source = 'USER_SHAPEFILE',
             key = gfw_auth(),
             print_request = TRUE)
 }
diff --git a/man/get_endpoint.Rd b/man/get_endpoint.Rd
index 36dc9d8..73336da 100644
--- a/man/get_endpoint.Rd
+++ b/man/get_endpoint.Rd
@@ -7,7 +7,7 @@
 get_endpoint(dataset_type, ...)
 }
 \arguments{
-\item{dataset_type}{Type of dataset to get API dataset name for. It can be
+\item{dataset_type}{Type of dataset to get API dataset name for. It can be "raster",
 "ENCOUNTER", "LOITERING", "FISHING", "PORT_VISIT", "GAP", "EEZ", "RFMO" or "MPA"}
 
 \item{...}{Other arguments that would depend on the dataset type.}
diff --git a/man/get_event.Rd b/man/get_event.Rd
index 3cde7b0..5c0cb5b 100644
--- a/man/get_event.Rd
+++ b/man/get_event.Rd
@@ -49,7 +49,7 @@ vector with any combination of: "CARRIER-FISHING", "FISHING-CARRIER",
 \item{region}{sf shape to filter raster or GFW region code (such as an
 EEZ code). See details about formatting the geojson}
 
-\item{region_source}{source of the region ('EEZ','MPA', 'RFMO' or 'USER_JSON')}
+\item{region_source}{source of the region ('EEZ','MPA', 'RFMO' or 'USER_SHAPEFILE')}
 
 \item{gap_intentional_disabling}{Logical. Whether the Gap events are intentional,
 according to Global Fishing Watch algorithms}
@@ -157,5 +157,17 @@ get_event(event_type = 'FISHING',
               region_source = 'EEZ',
               flags = 'CHN',
               key = gfw_auth())
+
+# 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 = "2020-12-31",
+              region = test_polygon,
+              region_source = 'USER_SHAPEFILE',
+              key = gfw_auth())
               }
 }
diff --git a/man/get_event_stats.Rd b/man/get_event_stats.Rd
index e7b8b85..18e517c 100644
--- a/man/get_event_stats.Rd
+++ b/man/get_event_stats.Rd
@@ -43,7 +43,7 @@ vector with any combination of: "CARRIER-FISHING", "FISHING-CARRIER",
 \item{region_source}{Optional but mandatory if using the argument region.
 Source of the region. If 'EEZ','MPA', 'RFMO',
 then the value for the argument region must be the code for that region.
-If 'USER_JSON', then region has to be an sf object}
+If 'USER_SHAPEFILE', then region has to be an sf object}
 
 \item{region}{GFW region code (such as an EEZ, MPA or RFMO code) or a
 formatted geojson shape. See Details about formatting the geojson.}
diff --git a/man/get_raster.Rd b/man/get_raster.Rd
index d3e7404..4daba71 100644
--- a/man/get_raster.Rd
+++ b/man/get_raster.Rd
@@ -36,7 +36,7 @@ or "HIGH" = 0.01 degree}
 \item{region}{sf shape to filter raster or GFW region code (such as a
 Marine Regions Geographic Identifier or EEZ code).}
 
-\item{region_source}{source of the region ('EEZ','MPA', 'RFMO' or 'USER_JSON')}
+\item{region_source}{source of the region ('EEZ','MPA', 'RFMO' or 'USER_SHAPEFILE')}
 
 \item{key}{Authorization token. Can be obtained with \code{gfw_auth()} function}
 
@@ -68,7 +68,7 @@ get_raster(spatial_resolution = 'LOW',
             start_date = '2021-01-01',
             end_date = '2021-10-01',
             region = test_shape,
-            region_source = 'USER_JSON',
+            region_source = 'USER_SHAPEFILE',
             key = gfw_auth(),
             print_request = TRUE)
 }
diff --git a/vignettes/articles/gfwr.Rmd b/vignettes/articles/gfwr.Rmd
index ebf5429..deb705a 100644
--- a/vignettes/articles/gfwr.Rmd
+++ b/vignettes/articles/gfwr.Rmd
@@ -292,12 +292,12 @@ and converts the response to a data frame. In order to use it, you should specif
 * 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_JSON` (for `sf` shapefiles).
+* The source for the specified region. Currently, `EEZ`, `MPA`, `RFMO` or `USER_SHAPEFILE` (for `sf` shapefiles).
 
 
 ### Examples
 
-You can load a sample shapefile inside `gfwr` to see how `'USER_JSON'` works:
+You can load a sample shapefile inside `gfwr` to see how `'USER_SHAPEFILE'` works:
 
 ```{r example_map_1}
 data("test_shape")
@@ -309,7 +309,7 @@ get_raster(
   start_date = '2021-01-01',
   end_date = '2021-02-01',
   region = test_shape,
-  region_source = 'USER_JSON',
+  region_source = 'USER_SHAPEFILE',
   key = key
   )
 ```
diff --git a/vignettes/articles/making-maps.Rmd b/vignettes/articles/making-maps.Rmd
index faece91..f48c283 100644
--- a/vignettes/articles/making-maps.Rmd
+++ b/vignettes/articles/making-maps.Rmd
@@ -86,7 +86,7 @@ end_date <- '2021-03-31'
 
 ## Making heatmaps of apparent fishing effort with `get_raster()`
 
-The `gfwr` function `get_raster()` provides aggregated gridded (e.g. raster) data for AIS-based apparent fishing effort (SAR). It was designed to provide data for a specific region, offering users the ability to select from multiple built-in region types by specifying a specific Exclusive Economic Zone (EEZ), Marine Protected Area (MPA), or Regional Fisheries Management Organization (RFMO). 
+The `gfwr` function `get_raster()` provides aggregated gridded (e.g. raster) data for AIS-based apparent fishing effort. It was designed to provide data for a specific region, offering users the ability to select from multiple built-in region types by specifying a specific Exclusive Economic Zone (EEZ), Marine Protected Area (MPA), or Regional Fisheries Management Organization (RFMO). 
 
 The list of available regions for each type, and their `label` and `id`, can be accessed with the `get_regions()` function.
 
@@ -316,7 +316,7 @@ my_raster_df <- get_raster(
   start_date = start_date,
   end_date = end_date,
   region = my_shp,
-  region_source = "USER_JSON"
+  region_source = "USER_SHAPEFILE"
   )
 ```
 
@@ -329,7 +329,7 @@ my_port_events_df <- get_event(event_type = "PORT_VISIT",
                           start_date = start_date,
                           end_date = end_date,
                           region = my_shp,
-                          region_source = "USER_JSON") 
+                          region_source = "USER_SHAPEFILE") 
 ```
 
 and loitering events by refrigerated cargo vessels
@@ -340,7 +340,7 @@ my_loitering_events_df <- get_event(event_type = "LOITERING",
                                     start_date = start_date,
                                     end_date = end_date,
                                     region = my_shp,
-                                    region_source = "USER_JSON") 
+                                    region_source = "USER_SHAPEFILE") 
 ```
 
 As before, let's summarize the raster to plot all fishing activity by fishing vessels