Clean data obtained from the World Database on Protected Areas (WDPA).

wdpa_clean(
  x,
  crs = paste("+proj=cea +lon_0=0 +lat_ts=30 +x_0=0",
    "+y_0=0 +datum=WGS84 +ellps=WGS84 +units=m +no_defs"),
  snap_tolerance = 1,
  simplify_tolerance = 0,
  geometry_precision = 1500,
  erase_overlaps = TRUE,
  verbose = interactive()
)

Arguments

x

sf::sf() object containing protected area data.

crs

character or codeinteger object representing a coordinate reference system. Defaults to World Behrmann (ESRI:54017).

snap_tolerance

numeric tolerance for snapping geometry to a grid for resolving invalid geometries. Defaults to 1 meter.

simplify_tolerance

numeric simplification tolerance. Defaults to 0 meters.

geometry_precision

numeric level of precision for processing the spatial data (used with sf::st_set_precision()). The default argument is 1500 (higher values indicate higher precision). This level of precision is generally suitable for analyses at the national-scale. For analyses at finer-scale resolutions, please consider using a greater value (e.g. 10000).

erase_overlaps

logical should overlapping boundaries be erased? This is useful for making comparisons between individual protected areas and understanding their "effective" geographic coverage. On the other hand, this processing step may not be needed (e.g. if the protected area boundaries are going to be rasterized), and so processing time can be substantially by skipping this step and setting the argument to FALSE. Defaults to TRUE.

verbose

logical should progress on data cleaning be reported? Defaults to TRUE in an interactive session, otherwise FALSE.

Value

sf::sf() object.

Details

This function cleans data from World Database on Protected Areas following best practices (Butchart et al. 2015, Runge et al. 2015, https://www.protectedplanet.net/en/resources/calculating-protected-area-coverage). To obtain accurate protected area coverage statistics for a country, please note that you will need to manually clip the cleaned data to the countries' coastline and its Exclusive Economic Zone (EEZ). Although this function can in theory be used to clean the global dataset, this process can take several weeks to complete. Therefore, it is strongly recommended to use alternative methods for cleaning the global dataset.

  1. Repair invalid geometry (using sf::st_make_valid()).

  2. Exclude protected areas that are not currently implemented (i.e. exclude areas without the status "Designated", "Inscribed", "Established").

  3. Exclude United Nations Educational, Scientific and Cultural Organization (UNESCO) Biosphere Reserves (Coetzer et al. 2014).

  4. Create a field ("GEOMETRY_TYPE") indicating if areas are represented as point localities ("POINT") or as polygons ("POLYGON").

  5. Exclude areas represented as point localities that do not have a reported spatial extent (i.e. missing data for the field

  6. Geometries are wrapped to the dateline (using sf::st_wrap_dateline() with the options "WRAPDATELINE=YES" and "DATELINEOFFSET=180").

  7. Reproject data to coordinate system specified in argument to crs (using sf::st_transform()).

  8. Fix any invalid geometries that have manifested (using sf::st_make_valid()).

  9. Buffer areas represented as point localities to circular areas using their reported spatial extent (using data in the field "REP_AREA" and sf::st_buffer(); see Visconti et al. 2013).

  10. Snap the geometries to a grid to fix any remaining geometry issues (using argument to snap_tolerance and lwgeom::st_snap_to_grid()).

  11. Fix any invalid geometries that have manifested (using sf::st_make_valid()).

  12. Simplify the protected area geometries to reduce computational burden (using argument to simplify_tolerance and sf::st_simplify()).

  13. Fix any invalid geometries that have manifested (using sf::st_make_valid()).

  14. The "MARINE" field is converted from integer codes to descriptive names (i.e. 0 = "terrestrial", 1 = "partial", 2 = "marine").

  15. Zeros in the "STATUS_YR" field are replaced with missing values (i.e. NA_real_ values).

  16. Zeros in the "NO_TK_AREA" field are replaced with NA values for areas where such data are not reported or applicable (i.e. areas with the values "Not Applicable" or "Not Reported" in the "NO_TK_AREA" field).

  17. Overlapping geometries are erased from the protected area data (discussed in Deguignet et al. 2017). Geometries are erased such that areas associated with more effective management categories ("IUCN_CAT") or have historical precedence are retained (using sf::st_difference()).

  18. Slivers are removed (geometries with areas less than 0.1 square meters).

  19. The size of areas are calculated in square kilometers and stored in the field "AREA_KM2".

References

Butchart SH, Clarke M, Smith RJ, Sykes RE, Scharlemann JP, Harfoot M, ... & Brooks TM (2015) Shortfalls and solutions for meeting national and global conservation area targets. Conservation Letters, 8: 329--337.

Coetzer KL, Witkowski ET, & Erasmus BF (2014) Reviewing Biosphere Reserves globally: Effective conservation action or bureaucratic label? Biological Reviews, 89: 82--104.

Deguignet M, Arnell A, Juffe-Bignoli D, Shi Y, Bingham H, MacSharry B & Kingston N (2017) Measuring the extent of overlaps in protected area designations. PloS One, 12: e0188681.

Runge CA, Watson JEM, Butchart HM, Hanson JO, Possingham HP & Fuller RA (2015) Protected areas and global conservation of migratory birds. Science, 350: 1255--1258.

Visconti P, Di Marco M, Alvarez-Romero JG, Januchowski-Hartley SR, Pressey, RL, Weeks R & Rondinini C (2013) Effects of errors and gaps in spatial data sets on assessment of conservation progress. Conservation Biology, 27: 1000--1010.

See also

Examples

# \dontrun{ # fetch data for the Liechtenstein lie_raw_data <- wdpa_fetch("LIE", wait = TRUE) # clean data lie_data <- wdpa_clean(lie_raw_data) # plot cleaned dataset plot(lie_data)
#> Warning: plotting the first 9 out of 32 attributes; use max.plot = 32 to plot all
# }