library(dplyr)
library(sf)
library(trip)
dat <- akbs_locs %>%
ungroup() %>%
arrange(deploy_id, date)
dat_tr <- trip(dat, c("date","deploy_id"), correct_all = FALSE)
keep <- sda(
dat_tr,
smax = 27 #km/hour or 7.5m/sec *3.6
)
akbs_locs <- dat %>%
mutate(sda_keep = keep) %>%
filter(sda_keep) %>%
dplyr::select(-sda_keep)Removing Outlier Locations
Filtering Obviously Wrong Locations
Location estimates from bio-loggers can sometimes provide extremely erroneous locations. This is especially true for the Argos system and the error estimates provided do not provide a realistic mechanism for removal (e.g. remove all locations with an error estimate above some threshold). If these extreme outliers are not removed from the data set prior to model fitting, issues with model convergence or other errors can arise.
Speed, Distance, and Angle Filtering
To identify and remove these obviously erroneous observations we will rely on a speed, distance, and angle filter from the trip package to identify locations that would require traveling speeds that exceed 2x or more what would be expected from the study species. In our case, with bearded seals, we’ll use 7.5m/second (the likely maximum sustained speed for a bearded seal is 2.5m/second).