Create a basemap

Johannes Krietsch

This article shows how to make different basemaps to plot the movement data on. Here we present three options: A simple basemap using polygons provided with the package (see also article: Basemap data), a simple basemap based on bathymetry data and flexible approach using the package OpenStreetMap (e.g. to plot on a satellite image).

Load packages

# packages
library(tools4watlas)
library(ggplot2)

Using ggplot2 and a basemap layer

One can create a simple basemaps of the study area by using the function atl_create_bm(). This function uses a data.table with x- and y-coordinates to check the required bounding box (which can be extended with a buffer in meters) and spatial features (polygons) of land, lakes, mudflats and rivers. Without adding spatial data, it will default to the spatial data provided in the package. By changing asp the desired aspect ratio can be chosen (default is “16:9”). The resulting map is always in EPSG:32631 (WGS 84 / UTM zone 31N), but the data can be provided in other projections, which then needs to be specified as projection.

Basemap independent of movement data

This can be useful when one wants to zoom into a specific area of the plot or has an area of interest, but the movement data also go out of this range. If no data are provided the function creates a map around Griend (our main study site) with a specified buffer.

# create basemap
bm <- atl_create_bm(buffer = 30000)

# plot
bm

Alternatively, one can provide a table with one or multiple locations, which will then be used to buffer the map. This can for example be a location in EPSG:4326 (WGS 84 in degrees) that can be exacted from Google Maps by a right click on a specific location. Here I choose a point a bit east of Griend, to include Richel and Ballastplaat in the map.

# define location
location <- data.table(x = c(5.275), y = c(53.2523))

# create basemap
bm <- atl_create_bm(location, buffer = 7000, projection = sf::st_crs(4326))

# plot
bm

Basemap with extend of movement data

# load example data
data <- data_example

# create basemap
bm <- atl_create_bm(data, buffer = 1000)

# plot
bm +
  geom_point(data = data, aes(x, y), alpha = 0.1, color = "darkorange")

Basemap with bathymetry data

Bathymetry data can be found in the “Birds, fish ’n chips” SharePoint folder: Documents/data/GIS/rasters/. To run the script set the file path (fp) to the local copy of the folder on your computer. The data can also be downloaded from the Waddenregister.

# additional packages
library(terra)

# file path to Birds, fish 'n chips GIS/rasters folder
fp <- atl_file_path("rasters")

# load bathymetry data
bat <- rast(paste0(fp, "bathymetry/2024/bodemhoogte_20mtr_UTM31_int.tif"))

# create base map with bathymetry data
bm <- atl_create_bm(
  buffer = 5000, raster_data = bat, option = "bathymetry"
)

# plot
bm

We can also add some shading (shade = TRUE) to the bathymetry data to highlight the water depth better. Note that calculating the shade can take a while, especially for large maps. So, only use this option for polished maps.

# additional packages
library(terra)

# file path to Birds, fish 'n chips GIS/rasters folder
fp <- atl_file_path("rasters")

# load bathymetry data
bat <- rast(paste0(fp, "bathymetry/2024/bodemhoogte_20mtr_UTM31_int.tif"))

# create base map with bathymetry data
bm <- atl_create_bm(
  buffer = 5000, raster_data = bat, option = "bathymetry", shade = TRUE
)

# plot
bm

Basemap with OpenStreetMap

Provides a range of different basemap options. For this we first have to transform the data to WGS 84 to extract the basemap with the bounding box and then transform our data to a Mercator projection to plot the data on top of the map.

Unfortunately, sometimes the type = "bing" (satellite image) does not work.

# additional packages
library(OpenStreetMap)
library(sf)

# load example data
data <- data_example

# make data spatial and transform projection to WGS 84 (used in osm)
d_sf <- atl_as_sf(data, additional_cols = c("tag", "datetime"))
d_sf <- st_transform(d_sf, crs = st_crs(4326))

# get bounding box
bbox <- atl_bbox(d_sf, asp = "16:9", buffer = 10000)

# extract openstreetmap
# other 'type' options are "osm", "maptoolkit-topo", "bing", "stamen-toner",
# "stamen-watercolor", "esri", "esri-topo", "nps", "apple-iphoto", "skobbler";
map <- openmap(
  c(bbox["ymax"], bbox["xmin"]),
  c(bbox["ymin"], bbox["xmax"]),
  type = "osm", mergeTiles = TRUE
)

bm <- autoplot.OpenStreetMap(map)

# transform points to Mercator and add transformed coordinates to data
d_sf <- st_transform(d_sf, crs = sf::st_crs(3857))
osm_coords <- st_coordinates(d_sf)
data[, `:=`(x_osm = osm_coords[, 1], y_osm = osm_coords[, 2])]

# plot
bm +
  geom_point(
    data = data, aes(x_osm, y_osm), alpha = 0.1, color = "darkorange"
  ) +
  coord_sf(crs = 3857, expand = FALSE) +
  theme(
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )

Additional features

How to add a WATLAS logo or receivers to a base map.

Add WATLAS logo to a basemap

# additional packages
library(ggimage)

# load example data
data <- data_example

# create basemap
bm <- atl_create_bm(data, buffer = 1000)

# path to WATLAS logo
logo_path <- system.file(
  "extdata", "watlas_logo.png",
  package = "tools4watlas"
)

# define position based on data (here upper left corner)
# adjust as desired
x_pos <- min(data$x) - 2500
y_pos <- max(data$y)

# create table with image of the logo
di <- data.table(x = x_pos, y = y_pos, image = logo_path)

# plot basemap with logo
bm +
  geom_image(data = di, aes(x, y, image = image), size = 0.2)

Add WATLAS receivers to a basemap

Receiver data are managed by Allert. They are located in the “WATLAS” SharePoint folder: Documents/data/. Either specify the path to your local copy of this folder or add the path for your user in the atl_file_path() function.

# load example data
data <- data_example

# create basemap
bm <- atl_create_bm(data, buffer = 20000)

# file path to WATLAS teams data folder
fp <- atl_file_path("watlas_teams")

# load receivers data
dr <- readxl::read_excel(
  paste0(fp, "receivers/receiver specifications.xlsx"),
  sheet = "receivers"
) |>
  data.table()

# end date for active receivers as system date
dr[active == "ja", date_removed := as.POSIXct(Sys.time(), tz = "UTC")]

# subset all active in period of tracking data
start <- min(data$datetime)
end <- max(data$datetime)

# Check if each row includes period of tracking data
dr[, includes_data := (date_placed <= end & date_removed >= start)]
dr <- dr[includes_data == TRUE]

# plot basemap with receivers active during tracking period
bm +
  geom_point(data = dr, aes(x, y), pch = 17)

One can also add the name of the receiver.

# additional packages
library(ggrepel)

# plot basemap with receivers and label
bm +
  geom_point(data = dr, aes(x, y), pch = 17, size = 1.5) +
  geom_text_repel(
    data = dr, aes(x, y, label = location),
    max.overlaps = Inf,
    size = 3,
    point.padding = 0.5,
    segment.color = "grey50"
  )