Introduction

This package adds resampling methods for the {mlr3} package framework suited for spatial, temporal and spatiotemporal data. These methods can help to reduce the influence of autocorrelation on performance estimates when performing cross-validation. While this article gives a rather technical introduction to the package, a more applied approach can be found in the mlr3book section on “Spatiotemporal Analysis”.

After loading the package via library("mlr3spatiotempcv"), the spatiotemporal resampling methods and example tasks provided by {mlr3spatiotempcv} are available to the user alongside the default {mlr3} resampling methods and tasks.

# create 'sf' object
data_sf = sf::st_as_sf(ecuador, coords = c("x", "y"), crs = 32717)

# create `TaskClassifST` from `sf` object
task = as_task_classif_st(data_sf, id = "ecuador_task", target = "slides", positive = "TRUE")

task = as_task_classif_st(ecuador, id = "ecuador_task", target = "slides",
  positive = "TRUE", coordinate_names = c("x", "y"), crs = 32717)

print(task)
#> <TaskClassifST:ecuador_task> (751 x 11)
#> * Target: slides
#> * Properties: twoclass
#> * Features (10):
#>   - dbl (10): carea, cslope, dem, distdeforest, distroad,
#>     distslidespast, hcurv, log.carea, slope, vcurv
#> * Coordinates:
#>             x       y
#>         <num>   <num>
#>   1: 712882.5 9560002
#>   2: 715232.5 9559582
#>   3: 715392.5 9560172
#>   4: 715042.5 9559312
#>   5: 715382.5 9560142
#>  ---                 
#> 747: 714472.5 9558482
#> 748: 713142.5 9560992
#> 749: 713322.5 9560562
#> 750: 715392.5 9557932
#> 751: 713802.5 9560862

Contributed reflections by {mlr3spatiotempcv}

In {mlr3}, dictionaries are used for overview purposes of available methods. The following sections show which dictionaries get appended with new entries when loading {mlr3spatiotempcv}.

Task Type

• TaskClassifST

• TaskRegrST

mlr_reflections$task_types
#> Key: <type>
#>            type          package             task        learner
#>          <char>           <char>           <char>         <char>
#> 1:      classif             mlr3      TaskClassif LearnerClassif
#> 2:   classif_st mlr3spatiotempcv    TaskClassifST LearnerClassif
#> 3:         regr             mlr3         TaskRegr    LearnerRegr
#> 4:      regr_st mlr3spatiotempcv       TaskRegrST    LearnerRegr
#> 5: unsupervised             mlr3 TaskUnsupervised        Learner
#>           prediction       prediction_data        measure
#>               <char>                <char>         <char>
#> 1: PredictionClassif PredictionDataClassif MeasureClassif
#> 2: PredictionClassif PredictionDataClassif MeasureClassif
#> 3:    PredictionRegr    PredictionDataRegr    MeasureRegr
#> 4:    PredictionRegr    PredictionDataRegr    MeasureRegr
#> 5:              <NA>                  <NA>           <NA>

Task Column Roles

• coordinate

• space

• time

mlr_reflections$task_col_roles
#> $regr
#> [1] "feature" "target"  "name"    "order"   "stratum" "group"   "weight" 
#> 
#> $classif
#> [1] "feature" "target"  "name"    "order"   "stratum" "group"   "weight" 
#> 
#> $unsupervised
#> [1] "feature" "name"    "order"  
#> 
#> $classif_st
#>  [1] "feature"    "target"     "name"       "order"      "stratum"   
#>  [6] "group"      "weight"     "coordinate" "space"      "time"      
#> 
#> $regr_st
#>  [1] "feature"    "target"     "name"       "order"      "stratum"   
#>  [6] "group"      "weight"     "coordinate" "space"      "time"

Resampling Methods

• mlr_resampling_spcv_block

• mlr_resampling_spcv_buffer

• mlr_resampling_spcv_coords

• mlr_resampling_spcv_knndm

• mlr_resampling_spcv_disc

• mlr_resampling_spcv_tiles

• mlr_resampling_spcv_env

• mlr_resampling_sptcv_cstf

and their respective repeated versions. See as.data.table(mlr_resamplings) for the full dictionary.

Examples Tasks

• tsk("ecuador") (spatial, classif)

• tsk("cookfarm_mlr3") (spatiotemp, regr)

Upstream Packages and Scientific References

The following table lists all spatiotemporal methods implemented in {mlr3spatiotempcv} (or {mlr3}), their upstream R package and scientific references. All methods besides "spcv_buffer" also have a corresponding “repeated” method.