Visualizing utilization distributions, again01 Jun 2016
I’ve been banging my head against the wall trying to figure out an easy way to wrap utilization distribution estimation and visualization into easier to use functions. Mostly for my benefit in developing a Shiny application to visualize animal movement data, however I know these functions will be useful for wildlife biologists I work with. The banging my head agains a wall is due to the nuances of the
adehabitatHR package and trying to get them to play nicely with leaflet.
Workspace and data
dplyr is loaded after
adehabitatHR 2 functions will be masked from
MASS::select), or a dependency. I recommend loading
maptools will install several geographic dependencies if they aren’t already installed.
The data is from a animal with a GPS collar several years ago. The
sp package doesn’t play nicely with
tbl_df classes from
dplyr so convert to a dataframe, then a SpatialPointsDataFrame for use in
adehabitatHR. I specify the projection of the data with the EPSG ID. I find this to be a shortcut over specifying the entire Coordinate Reference System. 26911 is for UTM Zone 11, 4326 is for WGS84 longlat. Then estimate a kernel density utilization distribution.
Plot the points and utilization distribution last. I use the colors from
viridis rather than base R because they look better, and are easier to see.
SpatialPixelsDataFrame or a
A utilization distribution is a surface of utilization probabilities. To create this surface a extent is generated based on the X and Y coordinates of the data, the box is then divided into n uniform cells. The size of these cells is specified with the
grid parameter. The smaller
grid values correspond to smaller cell sizes (therefore more cells). The
adehabitatHR package vignette goes into detail. The
kernelUD function returns an object of class
estUD, which extends a
SpatialPixelsDataFrame by storing the smoothing parameter.
kernelUD actually returns a list with an informal class
estUDm. I think this is a minor bug in the package, but it works in my favor! I mostly use
kernelUD in data visualization, rarely do I need to know the smoothing information, especially the method as I specify this in the function call. I would much rather work with an easier gridded class. Moreover, I am generally only interested in contour levels derived from the
Problem 2: only 1 contour at a time?
The following function estimates the selected percent contours for the given kernel density (
estUD). The function works ideally with that small bug in
adehabitatHR that returns a list when estimating one the kernel density for one animal.
getContours function is essentially a rewrite of
adehabitatHR::getverticesHR. However, this function will return contours for multiple percentiles.
getverticesHR works for multiple animals. To use
getContours for multiple animals
lapply over the list of
estUD returned with
Still confused by all the different polygon calls in this function. This is the clearest explanation of a
SpatialPolygonsDataFrame I’ve seen. The figure is from Applied Spatial Analysis with R (chapter 2). There are figures like this for every Spatial*DataFrame class in
There is a minor bug in
getContours (well an intentional feature, perhaps). I skip the extent validation of
getverticesHR so that I can the
The grid is too small to allow the estimation of home-range. You should rerun kernelUD with a larger extent parameter. error that is occasionally thrown. In the context of a shiny application I can’t easily rerun the
kernelUD. On the occasions that this error would be thrown by
getContours returns skewed polygons. I’m working on this.
Problem 3: I want a geoJSON
When I map with leaflet I prefer to use a geoJSON rather than a SpatialPolygonsDataFrame with the
leaflet call. I find it more flexible when layering leaflet maps.
Again, if you have a list of multiple animals that you want contours of use
lapply(spdf, function(x) geojson_json(x)). I’ve written a few functions to plot the polygons and and points as different colors. The functions required for the code below can be found in this Gist.
This particular example isn’t that great as all the points are from the same animal and the kernel density is estimated with the
dateid field we created. Each layer can be turned on or off with the layer controls panel in the upper right.
Problem 4: problem 1 revisited,
kernelbb is used to estimate the Brownian Bridge utilization distribution (methods and description beyond the scope of this post, check the vignette).
kernelbb are similar function in that they return the same class. However, running
kernelbb with one animal returns an object of class
estUD, multiple animals returns a list of
estUDm). This differs from
kernelUD which always returns a list of
estUD, one for each animal. The consequences of this bug are minor. Mainly having to specify
getConts(kd[], pcts). In the context of my Shiny application I always
lapply(kd, function(x) getContours(x, pct)) to return the polygons of contours, so I will use
list(bb), only if there is one animal, to force
estUD to a list I can use with
lapply. A little convoluted, I admit, but it solved a few days of frustration.
Hopefully this is helpful for mapping adehabitat objects in leaflet. These methods can also be used to make plotting spatial data in ggplot easier too. Let me know if there are any errors or questions. I’ll respond when I can.