R/join_mpa_data.R

In dafishr: Download, Wrangle, and Analyse Vessel Monitoring System Data

#' Detect fishing vessel presence within Marine Protected Areas polygons in Mexico
#'
#' The function spatially joins the Vessels Monitoring System, VMS, points with the Marine Protected Area, MPAs, polygons in Mexico.
#'
#' It adds three columns `zone`, `mpa_decree`, `state`, `municipality`, `region`, which are data from the
#' MPAs polygon. `zone` contains the name of the MPA (in Spanish) and when the vessel is outside an MPA polygon is dubbed as `open area`,
#' `mpa_decree` contains the type of MPA (such as National Park, etc.),
#' `state` contains the Mexican state with jurisdiction on the MPA, `municipality` contains the Mexican municipality with jurisdiction over the MPA,
#' and `region` contains the overall location of the MPA (in Spanish)
#'
#' @param x A data.frame with VMS data that must contain columns longitude and latitude
#' @param all_mpas A shape file that contains all MPA polygons in Mexico you can upload this using `data("all_mpas")`
#' @return A data.frame
#' @export
#' @importFrom dplyr %>%
#' @importFrom rlang .data
#'
#' @examples
#'
#'
#' # Use sample_dataset
#' data("sample_dataset")
#' data("all_mpas")
#' vms_cleaned <- vms_clean(sample_dataset)
#' vms_mpas <- join_mpa_data(vms_cleaned, all_mpas)
#'
#'
#' # Plotting data
#' # Points NOT inside MPA are removed to reduce data size
#' vms_mpas_sub <- vms_mpas  |>
#'   dplyr::filter(zone != "open area")
#'
#' vms_mpas_sf <- sf::st_as_sf(vms_mpas_sub, coords = c("longitude", "latitude"), crs = 4326)
#'
#' # Loading Mexico shapefile
#' data("mx_shape")
#'
#' # Map
#' library(ggplot2)
#' ggplot(mx_shape, col = "gray90") +
#'   geom_sf(data = all_mpas, fill = "gray60") +
#'   geom_sf(data = vms_mpas_sf, aes(col = zone)) +
#'   theme_void() +
#'   theme(legend.position = "")
join_mpa_data <- function(x, all_mpas = all_mpas) {
  x_sf <- sf::st_as_sf(x, coords = c("longitude", "latitude"), crs = 4326, remove = F)

  res <- sf::st_join(x_sf, all_mpas, left = T)

  res <- res |>
    dplyr::rename(
      zone = .data$NOMBRE,
      mpa_decree = .data$CAT_DECRET,
      state = .data$ESTADOS,
      municipality = .data$MUNICIPIOS,
      region = .data$REGION
    ) |>
    dplyr::mutate(zone = tidyr::replace_na(.data$zone, "open area"))  |>
    as.data.frame()  |>
    dplyr::select(-.data$geometry)
  res
}