Interactive maps of extrapolation — map

Produces interactive html maps of extrapolation values in the prediction area. The function relies on the leaflet package (Cheng et al. 2018), and thus requires an internet connection (i.e. will not work offline).

map_extrapolation(
  map.type = NULL,
  extrapolation.object = NULL,
  base.layer = "ocean",
  sightings = NULL,
  tracks = NULL,
  verbose = TRUE
)

Arguments

map.type	Character string. Type of map to be returned. Either `extrapolation` for an extrapolation map, `mic` for a map of the most influential covariates, or `nearby` for a map of the percentage of data nearby.
extrapolation.object	List object as returned by compute_extrapolation or compute_nearby.
base.layer	Base layer used for mapping. The default is `ocean`, which uses the ESRI.OceanBasemap. Use `world` for ESRI.WorldImagery and `gray` for ESRI.WorldGrayCanvas. Available map tiles can be viewed at https://leaflet-extras.github.io/leaflet-providers/preview/.
sightings	Species observations (optional). Can be supplied as a `matrix` of coordinates, a `data.frame`, a `SpatialPoints` object or a `SpatialPointsDataFrame` object. Circle markers will be proportional to group size if the data contain a column labelled `size`.
tracks	Survey tracks (optional). Can be supplied as a `matrix` of coordinates, a `data.frame`, a `SpatialLines` object or a `SpatialLinesDataFrame` object. A `TransectID` field is required for matrix or data.frame inputs.
verbose	Logical. Show or hide possible warnings and messages.

Value

An interactive html map.

References

Bouchet PJ, Miller DL, Roberts JJ, Mannocci L, Harris CM and Thomas L (2019). From here and now to there and then: Practical recommendations for extrapolating cetacean density surface models to novel conditions. CREEM Technical Report 2019-01, 59 p. https://research-repository.st-andrews.ac.uk/handle/10023/18509

Cheng J, Karambelkar B, Xie Y (2018). leaflet: Create Interactive Web Maps with the JavaScript 'Leaflet' Library. R package version 2.0.2. https://CRAN.R-project.org/package=leaflet

Examples

library(dsmextra)

# Load the Mid-Atlantic sperm whale data (see ?spermwhales)
data(spermwhales)

# Extract the data
segs <- spermwhales$segs
predgrid <- spermwhales$predgrid

# Define relevant coordinate system
my_crs <- sp::CRS("+proj=aea +lat_1=38 +lat_2=30 +lat_0=34 +lon_0=-73 +x_0=0
 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0")

 # Define covariates of interest
 my_cov <- c("Depth", "DistToCAS", "SST", "EKE", "NPP")

# Assess extrapolation in the multivariate space defined by five covariates
spermw.extrapolation <- compute_extrapolation(samples = segs,
      covariate.names = c("Depth", "DistToCAS", "SST", "EKE", "NPP"),
      prediction.grid = predgrid,
      coordinate.system = my_crs)
#> Computing ...
#> Done!

# Assess the percentage of data nearby
spermw.nearby <- compute_nearby(samples = segs,
                               prediction.grid = predgrid,
                               coordinate.system = my_crs,
                               covariate.names = my_cov,
                               nearby = 1)
#> Preprocessing data ...
#> Calculating distances ....
#> Calculating the geometric variance...
#> Calculating cumulative frequencies ...
#> Finishing up ...
#> Done!

# Generate maps
map_extrapolation(map.type = "extrapolation", extrapolation.object = spermw.extrapolation)
#> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from extrapolation object returned by <compute_extrapolation> and visualised using alternative packages such as ggplot2.
map_extrapolation(map.type = "mic", extrapolation.object = spermw.extrapolation)
#> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from extrapolation object returned by <compute_extrapolation> and visualised using alternative packages such as ggplot2.
map_extrapolation(map.type = "nearby", extrapolation.object = spermw.nearby)
#> Warning: map_extrapolation relies on the leaflet package, which is built around a Web Mercator projection (EPSG:3857), and therefore requires rasters to be reprojected for plotting. As a result, minor discrepancies may  occur between the interactive maps shown in the viewer, and the underlying raw data. The latter can be accessed directly from extrapolation object returned by <compute_extrapolation> and visualised using alternative packages such as ggplot2.