Tutorial
Here we will provide a short introduction to the wdpar R package. First, we will load the wdpar R package. We will also load the dplyr and ggmap R packages to help explore the data.
# load packages
library(wdpar)
library(dplyr)
library(ggmap)Now we will download protected area data for Malta from Protected Planet. We can achieve this by specifying Malta’s country name (i.e. "Malta") or Malta’s ISO3 code (i.e. "MLT"). Since data are downloaded to a temporary directory by default, we will specify that the data should be downloaded to a persistent directory. This means that R won’t have to re-download the same dataset every time we restart our R session, and R can simply re-load previously downloaded datasets as needed.
# download protected area data for Malta
# (excluding areas represented as point localities)
mlt_raw_pa_data <- wdpa_fetch("Malta", wait = TRUE,
download_dir = rappdirs::user_data_dir("wdpar"))## Warning in wdpa_fetch("Malta", wait = TRUE, download_dir =
## rappdirs::user_data_dir("wdpar")): local data is out of date: Jul 2021Next, we will clean the data set. Briefly, the cleaning steps include: excluding protected areas that are not yet implemented, excluding protected areas with limited conservation value, replacing missing data codes (e.g. "0") with missing data values (i.e. NA), replacing protected areas represented as points with circular protected areas that correspond to their reported extent, repairing any topological issues with the geometries, and erasing overlapping areas. For more information, see ?wdpa_clean.
# clean Malta data
mlt_pa_data <- wdpa_clean(mlt_raw_pa_data)After cleaning the data set, we will perform an additional step that involves clipping the terrestrial protected areas to Malta’s coastline. Ideally, we would also clip the marine protected areas to Malta’s Exclusive Economic Zone (EEZ) but such data are not as easy to obtain on a per country basis (but see https://www.marineregions.org/eez.php)).
# download Malta boundary from Global Administrative Areas dataset
file_path <- tempfile(fileext = "rds")
download.file("https://biogeo.ucdavis.edu/data/gadm3.6/Rsf/gadm36_MLT_0_sf.rds",
file_path)
# import Malta's boundary
mlt_boundary_data <- readRDS(file_path)
# repair any geometry issues, dissolve the border, reproject to same
# coordinate system as the protected area data, and repair the geometry again
mlt_boundary_data <-
mlt_boundary_data %>%
st_set_precision(1000) %>%
sf::st_make_valid() %>%
st_set_precision(1000) %>%
st_combine() %>%
st_union() %>%
st_set_precision(1000) %>%
sf::st_make_valid() %>%
st_transform(st_crs(mlt_pa_data)) %>%
sf::st_make_valid()## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()# clip Malta's protected areas to the coastline
mlt_pa_data <-
mlt_pa_data %>%
filter(MARINE == "terrestrial") %>%
st_intersection(mlt_boundary_data) %>%
rbind(mlt_pa_data %>%
filter(MARINE == "marine") %>%
st_difference(mlt_boundary_data)) %>%
rbind(mlt_pa_data %>% filter(!MARINE %in% c("terrestrial", "marine")))## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries# recalculate the area of each protected area
mlt_pa_data <-
mlt_pa_data %>%
mutate(AREA_KM2 = as.numeric(st_area(.)) * 1e-6)Now that we have finished cleaning the data, let’s preview the data. For more information on what these columns mean, please refer to the official manual (available in English, French, Spanish, and Russian).
# print first six rows of the data
head(mlt_pa_data)## Simple feature collection with 6 features and 32 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: 1382584 ymin: 4288196 xmax: 1399759 ymax: 4299269
## CRS: +proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +ellps=WGS84 +units=m +no_defs
## # A tibble: 6 × 33
## WDPAID WDPA_PID PA_DEF NAME ORIG_NAME DESIG DESIG_ENG DESIG_TYPE IUCN_CAT
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 555588631 555588631 1 Il-M… Il-Majji… Park… National… National II
## 2 174757 174757 1 Il-Ġ… Il-Ġonna… List… List Of … National III
## 3 174758 174758 1 Bidn… Bidnija,… List… List Of … National III
## 4 194415 194415 1 Il-Ġ… Il-Ġonna… List… List Of … National III
## 5 194416 194416 1 Il-B… Il-Buske… List… List Of … National III
## 6 194417 194417 1 Il-W… Il-Wied … List… List Of … National III
## # … with 24 more variables: INT_CRIT <chr>, MARINE <chr>, REP_M_AREA <dbl>,
## # GIS_M_AREA <dbl>, REP_AREA <dbl>, GIS_AREA <dbl>, NO_TAKE <chr>,
## # NO_TK_AREA <dbl>, STATUS <chr>, STATUS_YR <dbl>, GOV_TYPE <chr>,
## # OWN_TYPE <chr>, MANG_AUTH <chr>, MANG_PLAN <chr>, VERIF <chr>,
## # METADATAID <int>, SUB_LOC <chr>, PARENT_ISO <chr>, ISO3 <chr>,
## # SUPP_INFO <chr>, CONS_OBJ <chr>, GEOMETRY_TYPE <chr>, AREA_KM2 <dbl>,
## # geometry <GEOMETRY [m]>We will now reproject the data to longitude/latitude coordinates (EPSG:4326) for visualization purposes.
# reproject data
mlt_pa_data <- st_transform(mlt_pa_data, 4326)Next, we can plot a map showing the boundaries of Malta’s protected area system.
# download basemap for making the map
bg <- get_stamenmap(unname(st_bbox(mlt_pa_data)), zoom = 8,
maptype = "watercolor", force = TRUE)
# print map
ggmap(bg) +
geom_sf(data = mlt_pa_data, fill = "#31A35480", inherit.aes = FALSE) +
theme(axis.title = element_blank())
We can also create a histogram showing the year when each protected area was established.
hist(mlt_pa_data$STATUS_YR, main = "Malta's protected areas", xlab = "Year established")
Now let’s calculate some statistics. We can calculate the total amount of land and ocean inside Malta’s protected area system (km2).
# calculate total amount of area inside protected areas (km^2)
statistic <-
mlt_pa_data %>%
as.data.frame() %>%
select(-geometry) %>%
group_by(MARINE) %>%
summarize(area_km = sum(AREA_KM2)) %>%
ungroup() %>%
arrange(desc(area_km))
# print statistic
print(statistic)## # A tibble: 3 × 2
## MARINE area_km
## <chr> <dbl>
## 1 marine 7641.
## 2 terrestrial 128.
## 3 partial 16.9We can also calculate the percentage of land inside its protected area system that are managed under different categories (i.e. using the protected area management categories defined by The International Union for Conservation of Nature).
# calculate percentage of land inside protected areas (km^2)
statistic <-
mlt_pa_data %>%
as.data.frame() %>%
select(-geometry) %>%
group_by(IUCN_CAT) %>%
summarize(area_km = sum(AREA_KM2)) %>%
ungroup() %>%
mutate(percentage = (area_km / sum(area_km)) * 100) %>%
arrange(desc(area_km))
# print statistic
print(statistic)## # A tibble: 7 × 3
## IUCN_CAT area_km percentage
## <chr> <dbl> <dbl>
## 1 IV 4534. 58.2
## 2 Not Reported 3205. 41.2
## 3 V 34.9 0.448
## 4 Not Assigned 8.77 0.113
## 5 II 2.38 0.0306
## 6 III 0.180 0.00231
## 7 Ia 0.167 0.00215We can also plot a map showing Malta’s protected areas and color each area according to it’s management category.
ggmap(bg) +
geom_sf(aes(fill = IUCN_CAT), data = mlt_pa_data, inherit.aes = FALSE) +
theme(axis.title = element_blank(), legend.position = "bottom")