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This function adds to tag the parameters defining the 3D grid of the map. The spatial parameters (extent and scale) define the geographical dimensions of the map, and the temporal dimension is defined based on the stationary periods built using the labels. include_stap_id and include_min_duration can be used to limit which stationary periods are computed and modelled in the analysis. By default, all stationary periods are included.

In addition, tag offers the possibility to define known locations (e.g., equipment or retrieval site). These can only be defined at the level of a stationary period (i.e., assuming constant position during the whole stationary period) but you can define as many known stationary periods as you wish. Because the index of the last stationary period is generally unknown, you can use negative indexing in known, i.e., known$stap_id = -1 will be converted to nrow(tag$stap).

By default, no likelihood map will be computed for these stationary periods and the trajectory model will be more constrained, saving significant computational time. You can change this using the compute_known parameter in geopressure_map().

Usage

tag_set_map(
  tag,
  extent,
  scale = 10,
  known = data.frame(stap_id = integer(), known_lat = double(), known_lon = double()),
  include_stap_id = NULL,
  include_min_duration = 0
)

Arguments

tag

a GeoPressureR tag object.

extent

geographical extent of the map on which the likelihood and graph model will be computed. Vector of length 4 c(xmin, xmax, ymin, ymax) or c(W, E, S, N).

scale

number of pixels per 1° latitude-longitude. For instance, scale = 10 for a resolution of 0.1° (~10km) and scale=4 for a resolution of 0.25° (~30km). To avoid interpolating the ERA5 data, the scale should be equal to or smaller than 10. Read more about scale on the Google earth Engine documentation .

known

data.frame containing the known positions of the bird (e.g., equipment or retrieval site) with columns stap_id, known_lat and known_lon. You can set position of the last stationary period using stap_id = -1. Also accept list which are converted as data.frame.

include_stap_id

vector of stap_id defining which stationary period to model, that is, to compute in the likelihood map and use in the graph.

include_min_duration

minimum duration threshold of stationary periods to include (in hours).

Value

A GeoPressureR tag object with:

  • stap: Data.frame of all stationary periods with three new columns: known_lat and known_lon define the known position during these stationary periods, and include defines whether the likelihood map of this stationary period should be computed and later used in the graph.

  • extent same as input parameter extent

  • scale same as input parameter scale

See also

Examples

withr::with_dir(system.file("extdata", package = "GeoPressureR"), {
  tag <- tag_create("18LX", quiet = TRUE) |> tag_label(quiet = TRUE)
})

# Default tag
tag_default <- tag_set_map(tag, c(-16, 23, 0, 50))

print(tag_default)
#> 
#> ── GeoPressureR `tag` object for 18LX ──────────────────────────────────────────
#> Note: All green texts are fields of `tag` (i.e., `tag$field`).
#> 
#> ── Parameter param 
#> Run `tag$param` to display full table
#> 
#> ── Sensors data 
#> Manufacturer: soi
#> Date range: 2017-07-27 to 2017-08-09 23:30:00
#>  pressure: 672 datapoints
#>  acceleration: 4,032 datapoints
#>  light: 4,032 datapoints
#>  temperature_external: 2,448 datapoints
#> 
#> ── Stationary periods stap 
#> 5 stationary periods
#>   stap_id               start                 end known_lat known_lon include
#> 1       1 2017-07-26 23:57:30 2017-08-04 19:47:30        NA        NA    TRUE
#> 2       2 2017-08-04 23:17:30 2017-08-05 19:27:30        NA        NA    TRUE
#> 3       3 2017-08-06 02:52:30 2017-08-06 19:12:30        NA        NA    TRUE
#> ...
#> Run `tag$stap` to see full stap table
#> 
#> ── Map 
#>  Extent (W, E, S, N): -16°, 23°, 0°, 50°
#>  Dimensions (lat x lon): 500 x 390 (res. 0.1°)
#>  No pressure likelihood computed yet. Use `geopressure_map()`.

# Customized tag, with coarse grid scale, known position for the first stationary
#  period and considering only the stationary periods lasting more than 20hours.
tag_custom <- tag_set_map(tag,
  extent = c(-16, 23, 0, 50),
  scale = 1,
  include_min_duration = 20,
  known = data.frame(
    stap_id = 1,
    known_lon = 17.05,
    known_lat = 48.9
  )
)

print(tag_custom)
#> 
#> ── GeoPressureR `tag` object for 18LX ──────────────────────────────────────────
#> Note: All green texts are fields of `tag` (i.e., `tag$field`).
#> 
#> ── Parameter param 
#> Run `tag$param` to display full table
#> 
#> ── Sensors data 
#> Manufacturer: soi
#> Date range: 2017-07-27 to 2017-08-09 23:30:00
#>  pressure: 672 datapoints
#>  acceleration: 4,032 datapoints
#>  light: 4,032 datapoints
#>  temperature_external: 2,448 datapoints
#> 
#> ── Stationary periods stap 
#> 5 stationary periods
#>   stap_id               start                 end known_lat known_lon include
#> 1       1 2017-07-26 23:57:30 2017-08-04 19:47:30      48.9     17.05    TRUE
#> 2       2 2017-08-04 23:17:30 2017-08-05 19:27:30        NA        NA    TRUE
#> 3       3 2017-08-06 02:52:30 2017-08-06 19:12:30        NA        NA   FALSE
#> ...
#> Run `tag$stap` to see full stap table
#> 
#> ── Map 
#>  Extent (W, E, S, N): -16°, 23°, 0°, 50°
#>  Dimensions (lat x lon): 50 x 39 (res. 1°)
#>  No pressure likelihood computed yet. Use `geopressure_map()`.