anipaths

Animating animal trajectories

The package anipaths contains a collection of telemetry observations for turkey vultures originally analyzed in:

Dodge S, Bohrer G, Bildstein K, Davidson SC, Weinzierl R, Mechard MJ, Barber D, Kays R, Brandes D, Han J (2014) Environmental drivers of variability in the movement ecology of turkey vultures (Cathartes aura) in North and South America. Philosophical Transactions of the Royal Society B 20130195.

To animate the locations, we first need to create a time stamp variable of class numeric or POSIX. One advantage to using the POSIX class is that we can specify the gaps in the interpolation (delta.t) using convenient character strings like "hour" or "week".

library(anipaths)
vultures$POSIX <- as.POSIXct(vultures$timestamp, tz = "UTC")
vultures_paths <- vultures[format(vultures$POSIX, "%Y") == 2009, ]
delta.t <- "day"
animate_paths(paths = vultures_paths, 
              delta.t = delta.t,
              coord = c("location.long", "location.lat"),
              Time.name = "POSIX",
              ID.name = "individual.local.identifier")

The animation isn’t much good without context, so we add a simple map. There are lots of ways to incorporate a map. The simplest way is to set background to TRUE, in which case anipaths will do the best it can to select a map based on the data. In the next example, we’ve changed the time step to help the animations load a little faster.

delta.t <- "week"
animate_paths(paths = vultures_paths, 
              delta.t = delta.t,
              coord = c("location.long", "location.lat"),
              Time.name = "POSIX",
              ID.name = "individual.local.identifier", 
              background = TRUE)

You can also give a long/lat location, zoom level (3-21; see ?ggmap::get_map()), and maptype (satellite, terrain, hybrid) to be passed to ggmap::get_map(), and anipaths will make a background for you. As shown below, the value of delta.t can be specified as a numeric, which will be interpreted in whatever units used by as.numeric(paths['Time.name']) (in our case, this is seconds).

vultures_paths <- vultures[format(vultures$POSIX, "%Y") == 2009:2010, ]
delta.t <- 3600*24*2 ## number of seconds in two days
background <- list(location = c(-90, 10), 
                   zoom = 3, 
                   maptype = "satellite")
animate_paths(paths = vultures_paths, 
              delta.t = delta.t,
              coord = c("location.long", "location.lat"),
              Time.name = "POSIX",
              ID.name = "individual.local.identifier", 
              background = background)

Finally, you can also supply your own background image. The projection and units should match the data.

delta.t <- "week"
background <- rworldmap::getMap(resolution = "coarse")
sp::proj4string(background) ## matches default in animate_paths()
animate_paths(paths = vultures_paths, 
              delta.t = delta.t,
              coord = c("location.long", "location.lat"),
              Time.name = "POSIX",
              ID.name = "individual.local.identifier", 
              background = background)

The function animate_paths() must project the data to match Google’s map tiles, so if your data aren’t in long/lat format, make sure you update the variable with the correct string.

If you have ffmpeg installed on your system, you can set method = "mp4" to create a stand-alone video that is easy to share with others. For more information, see https://www.ffmpeg.org/ for instructions.

Check interpolation

As a way to check that anipaths is producing reasonable interpolations of the telemetry observations, a generic plot() function is provided that takes an argument of class paths_animation produced by calling animate_paths() with return.paths = TRUE. See example(plot.paths_animation). Adjusting the parameters of the interpolation can be done by modifying the bs and max.knots arguments. As a general rule, if the interpolated paths look “too smooth”, try increasing max.knots.