Spatial blocks
The function cv_spatial creates spatial blocks/polygons
then assigns blocks to the training and testing folds with
random, checkerboard pattern or a systematic
way (with the selection argument). When
selection = "random", the function tries to find evenly
distributed records in training and testing folds. Spatial blocks can be
defined either by size or number of rows and columns.
Consistent with other functions, the distance (size)
should be in metres, regardless of the unit of the
reference system of the input data. When the input map has
geographic coordinate system (i.e. decimal degrees), the block
size is calculated based on dividing size by 111325 (the
standard distance of a degree in metres, on the Equator) to change metre
to degree. In reality, this value varies by a factor of the cosine of
the latitude. So, an alternative sensible value could be
cos(mean(sf::st_bbox(x)[c(2,4)]) * pi/180) * 111325.
The offset argument can be used to shift the spatial
position of the blocks in horizontal and vertical axes, respectively.
This only works when the block have been built based on
size, and the extend option allows user to
enlarge the blocks ensuring all points fall inside the blocks (most
effectve when rows_cols is used). The blocks argument
allows users to define an external spatial polygon as blocking
layer.
Here are some spatial block settings:
sb1 <- cv_spatial(x = pa_data,
column = "occ", # the response column (binary or multi-class)
k = 5, # number of folds
size = 350000, # size of the blocks in metres
selection = "random", # random blocks-to-fold
iteration = 50, # find evenly dispersed folds
biomod2 = TRUE) # also create folds for biomod2
The output object is an R S3 object and you can get its elements by a
$. Explore sb1$folds_ids,
sb1$folds_list, and sb1$biomod_table for the
three types of generated folds from the cv_spatial object
sb1. Use the one suitable for you modelling practice to
evaluate your models. See the explanation of all other outputs/elements
of the function in the help file of the function.
The same setting from previous code can be used to create square
blocks by using hexagon = FALSE. You can optionally add a
raster layer (using r argument) for to be used for creating
blocks and be used in the background of the plot (raster can also be
added later only for visualising blocks using cv_plot).
sb2 <- cv_spatial(x = pa_data,
column = "occ",
r = rasters, # optionally add a raster layer
k = 5,
size = 350000,
hexagon = FALSE, # use square blocks
selection = "random",
progress = FALSE, # turn off progress bar for vignette
iteration = 50,
biomod2 = TRUE)
##
## train_0 train_1 test_0 test_1
## 1 214 197 43 46
## 2 218 211 39 32
## 3 216 198 41 45
## 4 194 208 63 35
## 5 186 158 71 85
The assignment of folds to each block can also be done in a
systematic manner using selection = "systematic", or a
checkerboard pattern using selection = "checkerboard". The
blocks can also be created by number of rows and columns when no
size is supplied by
e.g. rows_cols = c(12, 10).
# systematic fold assignment
# and also use row/column for creating blocks instead of size
sb3 <- cv_spatial(x = pa_data,
column = "occ",
rows_cols = c(12, 10),
hexagon = FALSE,
selection = "systematic")
##
## train_0 train_1 test_0 test_1
## 1 227 203 30 40
## 2 204 232 53 11
## 3 215 125 42 118
## 4 194 220 63 23
## 5 188 192 69 51
The function’s output report reveals that setting the selection to
‘random’ results in a more even distribution of presence/absence
instances between the train and test folds compared to ‘systematic’.
This is because the random assignment process is repeated multiple
times, controlled by the iteration parameter, to ensure
that the folds are evenly distributed.
# checkerboard block to CV fold assignment
sb4 <- cv_spatial(x = pa_data,
column = "occ",
size = 350000,
hexagon = FALSE,
selection = "checkerboard")
##
## train_0 train_1 test_0 test_1
## 1 125 143 132 100
## 2 132 100 125 143
Let’s visualise the checkerboard blocks with tmap:
tm_shape(sb4$blocks) +
tm_fill(col = "folds", style = "cat")
Spatial and environemntal clustering
The function cv_cluster uses clustering methods
to specify sets of similar environmental conditions based on the input
covariates. Species data corresponding to any of these groups or
clusters are assigned to a fold. Alternatively, the clusters can be
based on spatial coordinates of sample points (the x
argument).
Using spatial coordinate values for clustering:
# spatial clustering
set.seed(6)
scv <- cv_cluster(x = pa_data,
column = "occ", # optional: counting number of train/test records
k = 5)
## train_0 train_1 test_0 test_1
## 1 230 240 27 3
## 2 171 142 86 101
## 3 232 228 25 15
## 4 203 227 54 16
## 5 192 135 65 108
The clustering can be done in environmental space by supplying
r. Notice, this could be an extreme case of
cross-validation as the testing folds could possibly fall in novel
environmental conditions than what the training points are (check
cv_similarity for testing this). Note that the input raster
layer should cover all the species points, otherwise an error will rise.
The records with no raster value should be deleted prior to the analysis
or a different raster be used.
# environmental clustering
set.seed(6)
ecv <- cv_cluster(x = pa_data,
column = "occ",
r = rasters,
k = 5,
scale = TRUE)
## train_0 train_1 test_0 test_1
## 1 249 172 8 71
## 2 215 234 42 9
## 3 211 211 46 32
## 4 216 145 41 98
## 5 137 210 120 33
When r is supplied, all the input rasters are first
centred and scaled to avoid one raster variable dominate the clusters
using scale = TRUE option.
By default, the clustering will be done based only on the values of
the predictors at the sample points. In this case, and the number of the
folds will be the same as k. If
raster_cluster = TRUE, the clustering is done in the raster
space. In this approach, the clusters will be consistent throughout the
region and across species (in the same region). However, this may result
in cluster(s) that cover none of the species records especially when
species data is not dispersed throughout the region (or environmental
ranges) or the number of clusters (k or folds) is high.
Buffering LOO (also known as Spatial LOO)
The function cv_buffer generates spatially separated
training and testing folds by considering buffers of specified distance
around each observation point. This approach is a form of leave-one-out
(LOO) cross-validation. Each fold is generated by excluding nearby
observations around each testing point within the specified distance
(ideally the range of spatial autocorrelation). In this method the test
set never directly abuts a training set.
Using buffering to create CV folds:
bloo <- cv_buffer(x = pa_data,
column = "occ",
size = 350000)
When using species presence-background data (or
presence and pseudo-absence), you need to supply the column
and set presence_bg = TRUE. In this case, only presence
points (1s) are considered as target points. For more information read
the details section in the help of the function
(i.e. help(cv_buffer)).
For species presence-absence data and any other
types of data (such as continuous,
counts, and multi-class targets) keep
presence_bg = FALSE (default). In this case, all sample
points other than the target point within the buffer are excluded, and
the training set comprises all points outside the buffer.
