EnvDATAvizAnimate
Sarah Davidson
May 2019
Purpose
Demonstrate how to build animated tracking maps using tracking data in Movebank, environmental covariates in track and raster annotations from EnvDATA, and the moveVis package written by Jakob Schwalb-Willmann. Subsequent examples become more complete and cover more complicated situations.
Notes: * Consider exploring tracking data in Movebank, DynamoVis (https://doi.org/10.13020/D6PH49) and/or Google Maps first to evaluate datasets you’re not yet familiar with and plan out animations. * See more about the moveVis package at http://movevis.org.
Load libraries
library(ggplot2)
library(moveVis)
library(move)
library(magrittr)
library(raster)
options(max.print=100)
options(width=95)Prepare the tracking data.
Load the Svalbard geese dataset (Griffin, 2014, https://doi.org/10.5441/001/1.5k6b1364).
geese.move <- move("data/annotations/Svalbard geese 1k 16d NDVI-3281758398705165226/Svalbard geese 1k 16d NDVI-3281758398705165226.csv")
geese.move## class : MoveStack
## features : 24488
## extent : -4.029, 22.421, 54.15566, 78.71467 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0
## variables : 6
## names : event.id, timestamp, comments, height.raw, MODIS.Land.Vegetation.Indices.1km.16d.Aqua.NDVI, MODIS.Land.Vegetation.Indices.1km.16d.Terra.NDVI
## min values : 369331563, 2006-04-05 07:00:00, 170563-07, -0.1, 1.000006e-02, 1.000000e-02
## max values : 369356755, 2011-06-18 17:00:00, 86828-09, NULL, -9.997746e-03, -9.998666e-02
## timestamps : 2006-04-05 07:00:00 ... 2011-06-18 17:00:00 Time difference of 1900 days (start ... end, duration)
## sensors : gps
## indiv. data : visible, algorithm.marked.outlier, sensor.type, individual.taxon.canonical.name, tag.local.identifier, individual.local.identifier, study.name
## min ID Data : true, NA, gps, Branta leucopsis, 33102, X170563, Migration timing in barnacle geese (Svalbard) (data from Kölzsch et al. and Shariatinajafabadi et al. 2014)
## max ID Data : true, NA, gps, Branta leucopsis, 186827, X86828, Migration timing in barnacle geese (Svalbard) (data from Kölzsch et al. and Shariatinajafabadi et al. 2014)
## individuals : X33102, X33103, X33104, X33145, X33953, X33954, X64685, X64687, X70564, X70565, X70566, X70567, X70568, X70618, X70619
## study name : Migration timing in barnacle geese (Svalbard) (data from Kölzsch et al. and Shariatinajafabadi et al. 2014)
## date created: 2018-09-12 16:39:55As shown in “timestamps” this study covers 5 years (4/5/2006 to 6/18/2011, 1900 days). Before building animations, check the spread of the data over time.
geese.df <- as.data.frame(geese.move)
ggplot() +
geom_point(data = geese.df, aes(x = timestamp, y = individual.local.identifier)) +
theme(legend.position = "none") # hide legend
Let’s reduce it to the migration with the largest number of birds (spring 2007).
geese.s07 <- subset(geese.move, timestamp >= as.POSIXct('2007-04-01 00:00:00.000')
& timestamp <= as.POSIXct('2007-06-15 00:00:00.000'))Make frames of tracking data for animation.
Evaluate tracking data for sampling rates if unknown. Use this information to help decide the temporal resolution at which to align the data for the animation.
unique(timestamps(geese.s07))## [1] "2007-04-01 10:00:00 UTC" "2007-04-01 12:00:00 UTC" "2007-04-01 14:00:00 UTC"
## [4] "2007-04-01 16:00:00 UTC" "2007-04-01 18:00:00 UTC" "2007-04-02 04:00:00 UTC"
## [7] "2007-04-02 06:00:00 UTC" "2007-04-02 08:00:00 UTC" "2007-04-02 10:00:00 UTC"
## [10] "2007-04-02 12:00:00 UTC" "2007-04-02 14:00:00 UTC" "2007-04-02 16:00:00 UTC"
## [13] "2007-04-02 18:00:00 UTC" "2007-04-02 20:00:00 UTC" "2007-04-03 06:00:00 UTC"
## [16] "2007-04-03 08:00:00 UTC" "2007-04-03 10:00:00 UTC" "2007-04-03 12:00:00 UTC"
## [19] "2007-04-03 14:00:00 UTC" "2007-04-03 16:00:00 UTC" "2007-04-03 18:00:00 UTC"
## [22] "2007-04-04 04:00:00 UTC" "2007-04-04 06:00:00 UTC" "2007-04-04 08:00:00 UTC"
## [25] "2007-04-04 10:00:00 UTC" "2007-04-04 12:00:00 UTC" "2007-04-04 14:00:00 UTC"
## [28] "2007-04-04 16:00:00 UTC" "2007-04-04 18:00:00 UTC" "2007-04-04 20:00:00 UTC"
## [31] "2007-04-05 04:00:00 UTC" "2007-04-05 06:00:00 UTC" "2007-04-05 08:00:00 UTC"
## [34] "2007-04-05 16:00:00 UTC" "2007-04-05 18:00:00 UTC" "2007-04-05 20:00:00 UTC"
## [37] "2007-04-06 04:00:00 UTC" "2007-04-06 06:00:00 UTC" "2007-04-06 08:00:00 UTC"
## [40] "2007-04-06 10:00:00 UTC" "2007-04-06 12:00:00 UTC" "2007-04-06 14:00:00 UTC"
## [43] "2007-04-06 16:00:00 UTC" "2007-04-06 18:00:00 UTC" "2007-04-07 04:00:00 UTC"
## [46] "2007-04-07 06:00:00 UTC" "2007-04-07 08:00:00 UTC" "2007-04-07 10:00:00 UTC"
## [49] "2007-04-07 12:00:00 UTC" "2007-04-07 14:00:00 UTC" "2007-04-08 04:00:00 UTC"
## [52] "2007-04-08 06:00:00 UTC" "2007-04-08 08:00:00 UTC" "2007-04-08 10:00:00 UTC"
## [55] "2007-04-08 12:00:00 UTC" "2007-04-08 14:00:00 UTC" "2007-04-08 16:00:00 UTC"
## [58] "2007-04-08 18:00:00 UTC" "2007-04-09 10:00:00 UTC" "2007-04-09 12:00:00 UTC"
## [61] "2007-04-09 14:00:00 UTC" "2007-04-09 16:00:00 UTC" "2007-04-09 18:00:00 UTC"
## [64] "2007-04-09 20:00:00 UTC" "2007-04-10 06:00:00 UTC" "2007-04-10 08:00:00 UTC"
## [67] "2007-04-10 10:00:00 UTC" "2007-04-10 12:00:00 UTC" "2007-04-10 14:00:00 UTC"
## [70] "2007-04-10 16:00:00 UTC" "2007-04-10 18:00:00 UTC" "2007-04-10 20:00:00 UTC"
## [73] "2007-04-11 10:00:00 UTC" "2007-04-11 12:00:00 UTC" "2007-04-11 14:00:00 UTC"
## [76] "2007-04-11 16:00:00 UTC" "2007-04-11 18:00:00 UTC" "2007-04-11 20:00:00 UTC"
## [79] "2007-04-12 04:00:00 UTC" "2007-04-12 06:00:00 UTC" "2007-04-12 08:00:00 UTC"
## [82] "2007-04-12 10:00:00 UTC" "2007-04-12 12:00:00 UTC" "2007-04-12 14:00:00 UTC"
## [85] "2007-04-12 16:00:00 UTC" "2007-04-12 18:00:00 UTC" "2007-04-12 20:00:00 UTC"
## [88] "2007-04-14 04:00:00 UTC" "2007-04-14 06:00:00 UTC" "2007-04-14 08:00:00 UTC"
## [91] "2007-04-14 16:00:00 UTC" "2007-04-14 18:00:00 UTC" "2007-04-14 20:00:00 UTC"
## [94] "2007-04-15 10:00:00 UTC" "2007-04-15 12:00:00 UTC" "2007-04-15 14:00:00 UTC"
## [97] "2007-04-15 16:00:00 UTC" "2007-04-15 18:00:00 UTC" "2007-04-15 20:00:00 UTC"
## [100] "2007-04-16 04:00:00 UTC"
## [ reached 'max' / getOption("max.print") -- omitted 1040 entries ]timeLag(geese.s07, unit = "hours")## $X64687
## [1] 2 2 2 2 10 2 2 2 2 2 2 2 2 10 2 2 2 2 2 2 10 2 2 2 2 2 2 2 2 8
## [31] 2 2 8 2 2 8 2 2 2 2 2 2 2 10 2 2 2 2 2 14 2 2 2 2 2 2 2 16 2 2
## [61] 2 2 2 10 2 2 2 2 2 2 2 14 2 2 2 2 2 8 2 2 2 2 2 2 2 2 32 2 2 8
## [91] 2 2 14 2 2 2 2 2 8 2
## [ reached getOption("max.print") -- omitted 333 entries ]
##
## $X70564
## [1] 1 1 1 1 1 3 2 8 2 2 2 2 2 2 2 20 2 2 14 2 2 2 2 2 2 8 2 8 2 2
## [31] 2 8 2 2 2 2 2 2 2 2 8 2 2 2 2 44 2 2 2 2 2 2 10 2 2 2 2 2 2 2
## [61] 8 2 2 2 2 2 2 2 14 2 2 8 8 2 2 2 2 2 2 2 14 2 2 2 2 2 2 8 2 2
## [91] 2 2 2 2 2 14 2 2 2 2
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##
## $X70565
## [1] 2 2 2 2 2 2 2 8 2 2 2 2 2 2 2 2 14 30 2 2 10 2 2 2 2 2 2 2 10 2
## [31] 2 2 2 2 2 2 10 2 2 2 2 16 2 8 2 12 2 2 2 2 2 2 2 8 2 2 8 2 16 2
## [61] 2 2 2 2 10 2 2 2 2 2 2 2 8 2 2 2 2 2 2 2 2 10 2 2 2 2 2 2 2 10
## [91] 2 2 2 2 2 2 2 10 2 2
## [ reached getOption("max.print") -- omitted 471 entries ]
##
## $X70566
## [1] 2 2 68 2 2 8 2 2 2 2 2 2 2 2 8 2 2 44 2 2 8 2 2 8 2 2 2 2 2 2
## [31] 2 2 10 2 2 2 2 2 2 2 14 2 2 2 2 2 8 2 2 2 2 2 2 2 2 8 2 2 2 2
## [61] 2 2 2 2 8 2 2 2 2 2 14 2 2 26 2 2 2 2 2 8 2 4 2 2 2 2 2 10 2 8
## [91] 2 2 32 2 2 20 2 22 2 2
## [ reached getOption("max.print") -- omitted 413 entries ]
##
## $X70567
## [1] 2 2 2 2 40 2 2 2 2 2 8 2 2 2 2 2 2 2 2 8 2 2 26 2 2 44 2 2 2 2
## [31] 10 2 2 2 2 2 50 2 16 2 2 2 2 2 8 2 2 2 2 2 2 2 2 10 2 26 2 2 14 2
## [61] 2 2 2 2 2 2 2 14 2 2 2 2 2 8 2 2 56 2 2 32 2 2 50 2 2 2 2 2 20 2
## [91] 2 8 2 2 2 2 2 2 2 2
## [ reached getOption("max.print") -- omitted 410 entries ]
##
## $X70568
## [1] 2 2 2 2 2 14 2 2 2 2 2 8 2 2 2 2 2 2 2 2 62 2 2 2 2 2 8 2 2 2
## [31] 2 2 2 12 2 2 20 2 2 2 2 2 2 2 2 8 2 2 2 2 2 2 2 2 8 2 2 2 2 2
## [61] 2 2 2 8 2 2 26 2 2 2 2 2 8 2 2 2 2 2 2 2 2 8 6 2 2 2 2 2 20 2
## [91] 2 8 2 2 50 2 2 74 2 2
## [ reached getOption("max.print") -- omitted 215 entries ]
##
## $X70618
## [1] 2 2 2 2 2 2 2 2 8 2 2 20 2 2 2 2 2 14 2 2 2 2 2 2 2 2 8 2 2 2
## [31] 2 2 2 2 2 8 2 2 20 2 2 26 2 2 2 2 2 8 2 2 2 2 2 2 2 16 2 2 2 2
## [61] 2 10 2 8 2 2 8 2 2 2 2 2 2 2 2 8 2 2 2 2 2 14 2 2 2 2 2 20 2 2
## [91] 2 2 2 8 2 2 2 2 2 2
## [ reached getOption("max.print") -- omitted 524 entries ]
##
## $X70619
## [1] 2 2 2 2 2 2 2 10 2 2 2 2 2 14 2 2 2 2 2 2 2 2 8 2 2 2 2 2 2 2
## [31] 2 8 2 2 2 2 2 2 2 2 8 2 2 2 2 2 2 2 2 8 2 2 68 2 2 2 2 2 2 2
## [61] 2 10 2 32 2 2 8 2 2 2 2 2 2 2 2 8 2 2 2 2 2 2 2 2 8 2 2 8 2 2
## [91] 8 2 2 2 2 2 2 2 2 20
## [ reached getOption("max.print") -- omitted 471 entries ]
##
## $X170563
## [1] 4 1 1 2 2 8 2 2 2 8 2 8 2 2 2 2 2 2 20 2 2 2 10 2 2 2 2 2 2 2
## [31] 10 2 2 2 2 2 2 2 2 8 2 2 2 2 2 2 26 2 8 2 2 2 2 2 2 2 2 10 2 2
## [61] 2 2 2 2 2 10 2 2 32 2 8 2 2 2 8 2 22 2 8 8 2 2 38 2 2 8 2 8 2 2
## [91] 2 2 2 2 2 2 8 2 2 2
## [ reached getOption("max.print") -- omitted 439 entries ]Align tracking data to uniform temporal resolution for interpretation by frames_spatial.
geese <- align_move(geese.s07, res = 12, unit = "hours", spaceMethod = "greatcircle")Could use 2-hr resolution for a final product but 12-hr should speed up processing.
Create animation with static web basemap.
Create map frames for animation (see p.25 of MoveVis manual). Note that equidistant = TRUE does not mean that the map will be displayed in an equidistant projection (i.e. that preserves distances). Instead it causes frames_spatial to stretch the displayed area to an square extent. If equidistant = FALSE, the extent is displayed in the projection-native axis ratio.
frames <- frames_spatial(geese, map_service = "osm", map_type = "watercolor",
equidistant = FALSE, path_legend = T, path_legend_title = "Geese",
alpha = 0.5)## Processing movement data...
## Retrieving and compositing basemap imagery...
## Assigning raster maps to frames...
## Creating frames...Have a look at one of the frames.
frames[[100]]
Note: Working with the frames I sometimes get a message “Error in grid.Call….polygon edge not found” but if I rerun the same code the command succeeds. Add labels and a progress bar.
frames.l <- add_labels(frames, x = "Longitude", y = "Latitude") %>% # axis labels
add_progress() %>% # progress bar
add_scalebar() %>% # scale bar
add_northarrow() %>% # north arrow
add_timestamps(geese, type = "label") # timestampsSee the changes.
frames.l[[100]]
Record an animation using defaults shown in in moveVis manual p.18.
animate_frames(frames.l, "/Users/sarahdavidson/Desktop/Svalbard_geese_osm_test.gif", fps = 25,
width = 500, height = 800, res = 100,
display = TRUE, overwrite = TRUE, verbose = TRUE)## Rendering animation...##
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## Finalizing encoding... done!Running animate_frames takes a while, you can go grab a coffee. When it finishes, check out your gif. That was easy, thanks moveVis! Now what if we have our own raster basemap? Let’s try it with the ETOPO DEM we got from EnvDATA (see https://doi.org/10.7289/V5C8276M).
Prepare raster basemaps.
See EnvDataGridRequestWorksheet_SvalbardGeese.pdf for details on the requests submitted for this example and EnvDATAvizRaster.R for more about working with rasters. If you need to merge multiple rasters per basemap, create a separate folder for each set of raster files to merge and rename files if that helps keep things organized. Merge ETOPO files into a single background raster using the raster package.
DEMs <- lapply(list.files("data/annotations/ETOPO/rasters_ETOPO/", full.names = TRUE), raster)
DEM.basemap <- do.call(merge, DEMs)View stats.
DEM.basemap## class : RasterLayer
## dimensions : 1800, 1800, 3240000 (nrow, ncol, ncell)
## resolution : 0.01666667, 0.01666667 (x, y)
## extent : -6, 24, 50, 80 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## data source : in memory
## names : layer
## values : -5558.5, 2127.75 (min, max)hist(DEM.basemap, main = "Elevation (m)",
col = "blue",
maxpixels = 3240000) # if ncell > 100000, add this
Could use this histogram to help define optional breaks to use in colourmap, more below. Plot.
plot(DEM.basemap)
Create animation with static raster basemap.
Create map frames for animation (see p.25 of MoveVis manual). Using the start time for r_times since there is just 1 raster.
starttime <- as.POSIXct('2007-04-15 00:00:00.000')
frames <- frames_spatial(geese, r_list = DEM.basemap, r_times = starttime,
r_type = "gradient", fade_raster = FALSE, equidistant = FALSE,
path_legend = T, path_legend_title = "Geese", alpha = 0.9)## Processing movement data...
## Assigning raster maps to frames...
## Creating frames...Check a frame.
frames[[10]]
Add labels and colorscale for the raster, clean up a bit from the previous example.
frames.l <- add_labels(frames, x = "Longitude", y = "Latitude") %>%
add_progress() %>%
add_scalebar(distance = 600, position = "bottomright") %>%
add_timestamps(geese, x = 4, y = 78.5, type = "label") %>% # x & y in degrees
add_colourscale(type = "gradient",
colours = c("-5600" = "steelblue4", "-0.01" = "steelblue1", "0" = "white",
"0.01" = "wheat1", "2200" = "sienna4"),
legend_title = "Elevation\n(m amsl)")Check a frame.
frames.l[[100]]
Currently the scale and color assignments are off: white should occur at 0 elevation but instead is in the middle of the range of values. This bug is fixed with v0.10.2 of moveVis, currently you can install this from GitHub using
remotes::install_github(“16eagle/moveVis”).
Record an animation.
animate_frames(frames.l, "animations/Svalbard_geese_dem_test.gif", fps = 25, width = 500, height = 800, res = 100, display = TRUE, overwrite = TRUE, verbose = TRUE)## Rendering animation...##
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## Finalizing encoding... done!Hurray, now we can watch our animals move over any environmental layer in EnvDATA! But what about all the variables that change over time?
Create animation with multiple raster basemaps.
Let’s try this with 16-day NDVI from MODIS (see https://doi.org/10.5067/MODIS/MYD13A2.006).
First merge NDVI geotif files into a single background raster for each date (if needed).
scenes <- c("rasters_NDVI_MYD13A2_20070330", "rasters_NDVI_MYD13A2_20070415",
"rasters_NDVI_MYD13A2_20070430", "rasters_NDVI_MYD13A2_20070515",
"rasters_NDVI_MYD13A2_20070531", "rasters_NDVI_MYD13A2_20070615")
scenes <- paste0("data/annotations/NDVI/", scenes)
NDVIs.all <- stack(lapply(scenes, function(x)
do.call(merge, lapply(list.files(x, full = TRUE), raster))))
names(NDVIs.all) <- paste("NDVI", c("30 March", "15 April", "30 April", "15 May",
"31 May", "15 June"))Prepare lists of rasters and times of the NDVI basemaps for frames_spatial.
NDVI.times <- list("2007-03-30 00:00:00", "2007-04-15 00:00:00",
"2007-04-30 00:00:00", "2007-05-15 00:00:00",
"2007-05-31 00:00:00", "2007-06-15 00:00:00")
NDVI.times <- as.POSIXct(strptime(NDVI.times, format = "%Y-%m-%d %H:%M:%OS"), tz="UTC")Create map frames for animation.
frames <- frames_spatial(geese, r_list = unstack(NDVIs.all), r_times = NDVI.times,
r_type = "gradient", fade_raster = FALSE, equidistant = FALSE,
tail_length = 1, path_legend = TRUE,
path_legend_title = "Geese", alpha = 0.9)## Processing movement data...
## Assigning raster maps to frames...
## Creating frames...Check a frame.
frames[[100]]
Add labels and colorscale for the rasters.
frames.l <- add_labels(frames, x = "Longitude", y = "Latitude") %>%
add_progress() %>%
add_scalebar(distance = 600, position = "bottomright") %>%
add_timestamps(geese, x = 4, y = 78.5, type = "label") %>%
add_colourscale(type= "gradient", colours = c("wheat", "forestgreen"), legend_title = "NDVI")Check a frame.
frames.l[[100]]
Record an animation, slowing down the animation a bit compared to the others.
animate_frames(frames.l, "animations/Svalbard_geese_ndvi_test.gif", fps = 10, width = 500,
height = 800, res = 100, display = TRUE, overwrite = TRUE, verbose = TRUE)## Rendering animation...##
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## Finalizing encoding... done!Awesome, now we can animate both our tracks and raster base layers over time. For these geese, we have the remaining problem that they are migrating to very high latitudes and the default projection has a lot of distortion. Let’s try using a different projection.
Reproject tracking data and rasters.
# WGS84/North Pole Lambert Azimuthal Equal Area (http://spatialreference.org/ref/sr-org/7250/)
crs.new = CRS("+proj=laea +lat_0=90 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs")Reproject the tracks.
geese.polar <- spTransform(geese, crs.new)
geese.polar # check "extent" and "coord. ref."## class : MoveStack
## features : 1285
## extent : -248653.5, 621786.6, -3897316, -1179404 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=laea +lat_0=90 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs
## variables : 9
## names : event.id, timestamp, comments, height.raw, MODIS.Land.Vegetation.Indices.1km.16d.Aqua.NDVI, MODIS.Land.Vegetation.Indices.1km.16d.Terra.NDVI, x, y, time
## min values : 369331564, 2007-04-04 13:00:00, 170563-07, 10, 0.015120786, 0.02013790, 0.004035225, 54.49865, 2007-04-01 13:00:00
## max values : 369351132, 2007-06-14 01:00:00, 70619-07, neg alt, -0.064717468, -0.11660282, 21.110325713, 78.66116, 2007-06-14 01:00:00
## timestamps : 2007-04-01 13:00:00 ... 2007-06-14 01:00:00 Time difference of 74 days (start ... end, duration)
## sensors : interpolateTime, gps
## indiv. data : visible, algorithm.marked.outlier, sensor.type, individual.taxon.canonical.name, tag.local.identifier, individual.local.identifier, study.name
## min ID Data : true, NA, gps, Branta leucopsis, 64687, X170563, Migration timing in barnacle geese (Svalbard) (data from Kölzsch et al. and Shariatinajafabadi et al. 2014)
## max ID Data : true, NA, gps, Branta leucopsis, 170563, X70619, Migration timing in barnacle geese (Svalbard) (data from Kölzsch et al. and Shariatinajafabadi et al. 2014)
## individuals : X64687, X70564, X70565, X70566, X70567, X70568, X70618, X70619, X170563
## date created: 2018-09-12 16:39:55Reproject the NDVI rasters.
NDVIs.all.polar = raster::projectRaster(NDVIs.all, crs = crs.new, method = "bilinear")Create map frames for animation. Note: moveVis 0.10.2 will allow you to assign a color for missing raster values: na.colour for continuous variables and na.show for discrete variables.
frames <- frames_spatial(geese.polar, r_list = unstack(NDVIs.all.polar), r_times = NDVI.times,
r_type = "gradient", fade_raster = FALSE, equidistant = FALSE,
tail_length = 1, path_legend = TRUE,
path_legend_title = "Geese", alpha = 0.9)## Processing movement data...
## Assigning raster maps to frames...
## Creating frames...Check a frame.
frames[[100]]
Add labels and raster colorscale.
frames.l <- add_labels(frames, x = "Easting (m)", y = "Northing (m)") %>%
add_progress() %>%
#add_scalebar(distance = 500, position = "bottomright") %>% # causes an error
add_timestamps(geese.polar, type = "label") %>%
add_colourscale(type= "gradient", colours = c("wheat", "forestgreen"), legend_title = "NDVI")Check a frame.
frames.l[[100]]
Record a final animation, slowing down the animation a bit compared to the others.
animate_frames(frames.l, "animations/Svalbard_geese_ndvi_polar.gif", fps = 10, width = 500, height = 800, res = 100, display = TRUE, overwrite = TRUE, verbose = TRUE)## Rendering animation...##
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## Finalizing encoding... done!As final note: One thing missing here is an animation with the animal locations colored based on an annotated covariate. The trick here is that when we use align_move to align the dataset to a consistent temporal resolution so that we can create frames to animate, our times and locations are interpolated, and we lose most of the information in the other columns of our dataset, including our annotated values. This might be solved by finding a a way to do something like align_move that retains or interpolates covariate values from the dataset, or interpolating values from the original dataset to the aligned one. Remember this will lead to some reduction in accuracy.