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.

Creating a spatial Task

To make use of spatial resampling methods, a {mlr3} task that is aware of its spatial characteristic needs to be created. Two child classes exist in {mlr3spatiotempcv} for this purpose:

  • TaskClassifST
  • TaskRegrST

To create one of these, one can either pass a sf object as the “backend” directly:

# create 'sf' object
data_sf = sf::st_as_sf(ecuador, coords = c("x", "y"), crs = 4326)

# create mlr3 task
task = TaskClassifST$new("ecuador_sf",
  backend = data_sf, target = "slides", positive = "TRUE"
)

or use a plain data.frame. In this case, the constructor of TaskClassifST needs a few more arguments:

data = mlr3::as_data_backend(ecuador)
task = TaskClassifST$new("ecuador",
  backend = data, target = "slides",
  positive = "TRUE", extra_args = list(coordinate_names = c("x", "y"))
)

Now this Task can be used as a normal {mlr3} task in any kind of modeling scenario. Have a look at the mlr3book section on “Spatiotemporal Analysis” on how to apply a spatiotemporal resampling method to such a task.

Contributed assets 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

Additional task types:

  • TaskClassifST

  • TaskRegrST

mlr_reflections$task_types
#>       type          package          task        learner        prediction
#> 1: classif             mlr3   TaskClassif LearnerClassif PredictionClassif
#> 2: classif mlr3spatiotempcv TaskClassifST LearnerClassif PredictionClassif
#> 3:    regr             mlr3      TaskRegr    LearnerRegr    PredictionRegr
#> 4:    regr mlr3spatiotempcv    TaskRegrST    LearnerRegr    PredictionRegr
#>           measure
#> 1: MeasureClassif
#> 2: MeasureClassif
#> 3:    MeasureRegr
#> 4:    MeasureRegr

Task Column Roles

Additional column roles:

  • coordinates
mlr_reflections$task_col_roles
#> $regr
#> [1] "feature" "target"  "name"    "order"   "stratum" "group"   "weight" 
#> [8] "uri"    
#> 
#> $classif
#> [1] "feature" "target"  "name"    "order"   "stratum" "group"   "weight" 
#> [8] "uri"    
#> 
#> $classif_st
#> [1] "feature"     "target"      "name"        "order"       "stratum"    
#> [6] "group"       "weight"      "uri"         "coordinates"
#> 
#> $regr_st
#> [1] "feature"     "target"      "name"        "order"       "stratum"    
#> [6] "group"       "weight"      "uri"         "coordinates"

Resampling Methods

Additional resampling methods:

  • spcv_block

  • spcv_buffer

  • spcv_coords

  • spcv_env

  • sptcv_cluto

  • sptcv_cstf

and their respective repeated versions.

as.data.table(mlr_resamplings)
#>                      key                                      params iters
#>  1:            bootstrap                               repeats,ratio    30
#>  2:               custom                                                 0
#>  3:                   cv                                       folds    10
#>  4:              holdout                                       ratio     1
#>  5:             insample                                                 1
#>  6:                  loo                                                NA
#>  7:          repeated_cv                               repeats,folds   100
#>  8:  repeated_spcv_block folds,repeats,rows,cols,range,selection,...    10
#>  9: repeated_spcv_coords                               folds,repeats    10
#> 10:    repeated_spcv_env                      folds,repeats,features    10
#> 11: repeated_sptcv_cluto                               folds,repeats    10
#> 12:  repeated_sptcv_cstf                               folds,repeats    10
#> 13:           spcv_block folds,rows,cols,range,selection,rasterLayer    10
#> 14:          spcv_buffer                   theRange,spDataType,addBG     0
#> 15:          spcv_coords                                       folds    10
#> 16:             spcv_env                              folds,features    10
#> 17:          sptcv_cluto                                       folds    10
#> 18:           sptcv_cstf                                       folds    10
#> 19:          subsampling                               repeats,ratio    30

Examples Tasks

Additional example tasks:

Upstream Packages and Scientific References

The following table lists all methods implemented in {mlr3spatiotempcv}, their upstream R package and scientific references.

Literature Package Reference mlr3 Sugar
Spatial Buffering blockCV Valavi et al. (2018) rsmp("spcv_buffer")
Spatial Blocking blockCV Valavi et al. (2018) rsmp("spcv_block")
Spatial CV sperrorest Brenning (2012) rsmp("spcv_coords")
Environmental Blocking blockCV Valavi et al. (2018) rsmp("spcv_env")
- - - rsmp("sptcv_cluto")
Leave-Location-and-Time-Out CAST Meyer et al. (2018) rsmp("sptcv_cstf")
Spatiotemporal Clustering skmeans Zhao and Karypis (2002) rsmp("repeated_sptcv_cluto")




Repeated Spatial Blocking blockCV Valavi et al. (2018) rsmp("repeated_spcv_block")
Repeated Spatial CV sperrorest Brenning (2012) rsmp("repeated_spcv_coords")
Repeated Env Blocking blockCV Valavi et al. (2018) rsmp("repeated_spcv_env")
- - - rsmp("repeated_sptcv_cluto")
Repeated Leave-Location-and-Time-Out CAST Meyer et al. (2018) | rsmp("repeated_sptcv_cstf")
Repeated Spatiotemporal Clustering skmeans Zhao and Karypis (2002) rsmp("repeated_sptcv_cluto")

References

Brenning, Alexander. 2012. Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE. https://doi.org/10.1109/igarss.2012.6352393.
Meyer, Hanna, Christoph Reudenbach, Tomislav Hengl, Marwan Katurji, and Thomas Nauss. 2018. “Improving Performance of Spatio-Temporal Machine Learning Models Using Forward Feature Selection and Target-Oriented Validation.” Environmental Modelling & Software 101 (March): 1–9. https://doi.org/10.1016/j.envsoft.2017.12.001.
Valavi, Roozbeh, Jane Elith, Jose J. Lahoz-Monfort, and Gurutzeta Guillera-Arroita. 2018. blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv, June. https://doi.org/10.1101/357798.
Zhao, Ying, and George Karypis. 2002. “Evaluation of Hierarchical Clustering Algorithms for Document Datasets.” 11th Conference of Information and Knowledge Management (CIKM), 515–24. http://glaros.dtc.umn.edu/gkhome/node/167.