wdpa_clean: Clean data
In prioritizr/wdpar: Interface to the World Database on Protected Areas

View source: R/wdpa_clean.R

wdpa_clean

R Documentation

Clean data

Description

Clean data obtained from Protected Planet. Specifically, this function is designed to clean data obtained from the World Database on Protected Areas (WDPA) and the World Database on Other Effective Area-Based Conservation Measures (WDOECM). For recommended practices on cleaning large datasets (e.g. datasets that span multiple countries or a large geographic area), please see below.

Usage

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"),
  exclude_unesco = TRUE,
  retain_status = c("Designated", "Inscribed", "Established"),
  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).
`exclude_unesco`	`logical` should UNESCO Biosphere Reserves be excluded? Defaults to `TRUE`.
`retain_status`	`character` vector containing the statuses for protected areas that should be retained during the cleaning process. Available statuses include: `"Proposed"`, `"Inscribed"`, `"Adopted"`, `"Designated"`, and `"Established"`. Additionally, a `NULL` argument can be specified to ensure that no protected areas are excluded according to their status. The default argument is a `character` vector containing `"Designated"`, `"Inscribed"`, and `"Established"`. This default argument ensures that protected areas that are not currently implemented are excluded.
`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`.

Details

This function cleans data following best practices (Butchart et al. 2015; Protected Planet 2021; Runge et al. 2015). 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).

Exclude protected areas according to their status (i.e. "STATUS" field). Specifically, protected areas that have a status not specified in the argument to retain_status are excluded. By default, only protected areas that have a "Designated", "Inscribed", or "Established" status are retained. This means that the default behavior is to exclude protected that are not currently implemented.
Exclude United Nations Educational, Scientific and Cultural Organization (UNESCO) Biosphere Reserves (Coetzer et al. 2014). This step is only performed if the argument to exclude_unesco is TRUE.
Create a field ("GEOMETRY_TYPE") indicating if areas are represented as point localities ("POINT") or as polygons ("POLYGON").
Exclude areas represented as point localities that do not have a reported spatial extent (i.e. missing data for the field
Geometries are wrapped to the dateline (using sf::st_wrap_dateline() with the options "WRAPDATELINE=YES" and "DATELINEOFFSET=180").
Reproject data to coordinate system specified in argument to crs (using sf::st_transform()).
Repair any invalid geometries that have manifested (using st_repair_geometry()).
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).
Snap the geometries to a grid to fix any remaining geometry issues (using argument to snap_tolerance and lwgeom::st_snap_to_grid()).
Repair any invalid geometries that have manifested (using st_repair_geometry()).
Simplify the protected area geometries to reduce computational burden (using argument to simplify_tolerance and sf::st_simplify()).
Repair any invalid geometries that have manifested (using st_repair_geometry()).
The "MARINE" field is converted from integer codes to descriptive names (i.e. 0 = "terrestrial", 1 = "partial", 2 = "marine").
The "PA_DEF" field is converted from integer codes to descriptive names (i.e. 0 = "OECM", and 1 = "PA").
Zeros in the "STATUS_YR" field are replaced with missing values (i.e. NA_real_ values).
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).
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()).
Slivers are removed (geometries with areas less than 0.1 square meters).
The size of areas are calculated in square kilometers and stored in the field "AREA_KM2".

Value

sf::sf() object.

Recommended practices for large datasets

This function can be used to clean large datasets assuming that sufficient computational resources and time are available. Indeed, it can clean data spanning large countries, multiple countries, and even the full global dataset. When processing the full global dataset, it is recommended to use a computer system with at least 32 GB RAM available and to allow for at least one full day for the data cleaning procedures to complete. It is also recommended to avoid using the computer system for any other tasks while the data cleaning procedures are being completed, because they are very computationally intensive. Additionally, when processing large datasets – and especially for the global dataset – it is strongly recommended to disable the procedure for erasing overlapping areas. This is because the built-in procedure for erasing overlaps is very time consuming when processing many protected areas, so that information on each protected area can be output (e.g. IUCN category, year established). Instead, when cleaning large datasets, it is recommended to run the data cleaning procedures with the procedure for erasing overlapping areas disabled (i.e. with erase_overlaps = FALSE). After the data cleaning procedures have completed, the protected area data can be manually dissolved to remove overlapping areas (e.g. using wdpa_dissolve()). For an example of processing a large protected area dataset, please see the vignette.

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.

Protected Planet (2021) Calculating protected and OECM area coverage. Available at: https://www.protectedplanet.net/en/resources/calculating-protected-area-coverage.

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.

Examples

## Not run: 
# 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)


## End(Not run)

prioritizr/wdpar documentation built on Nov. 5, 2023, 4:02 a.m.