mapKDE: Make simple maps of Kernel Density Estimates

In track2KBA: Identifying Important Areas from Animal Tracking Data

Make simple maps of Kernel Density Estimates

Description

mapKDE uses output from estSpaceUse to create maps illustrating utilization distributions for each ID.

Usage

mapKDE(KDE, colony = NULL, show = TRUE)

Arguments

KDE	Simple feature MULTIPOLYGON or estUDm object. Must be output of estSpaceUse function).
colony	data.frame. Optional.'Latitude' and 'Longitude' locations to display reference point of, for example, a breeding or tagging site.
show	logical. show plot, or just save it. Note, saving plot only works for Simple Features input. Default is TRUE.

Details

If the input is simple features polygons, these will be displayed for all IDs on same map. If input estUDm utilization distribution surface, each ID level gets its own facet displaying the full UD.

Value

Returns a figure of either single map with all core ranges displayed together, or a series of facetted maps, each of which shows a utilization distribution corresponding to a level of ID in KDE.

See Also

estSpaceUse

Examples

## make some play data
dataGroup <- data.frame(Longitude = c(1, 1.01, 1.02, 1.04, 1.05, 1.03, 1), 
                       Latitude =  c(1, 1.01, 1.02, 1.03, 1.021, 1.01, 1),
                       ID = rep("A", 7),
                       DateTime = format(
                         lubridate::ymd_hms("2021-01-01 00:00:00") + 
                         lubridate::hours(0:6)
                         )
)
## project tracks
tracks <- projectTracks(dataGroup, projType = "azim", custom = TRUE)
## estimate utilization distributions for each track
KDE <- estSpaceUse(tracks, scale=10, levelUD = 50)
## map it
mapKDE(KDE)

track2KBA documentation built on July 3, 2024, 5:10 p.m.