# Getting Started

# 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:

- `tsk("ecuador")` (spatial, classif)
- `tsk("cookfarm")` (spatiotemp, regr)

# 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.