Simple Training/Test Set Splitting for spatial data
Source:R/spatial_initial_split.R
spatial_initial_split.Rdspatial_initial_split creates a single binary split of the data into a training
set and testing set. All strategies from the package spatialsample are available;
a random split from that strategy will be used to generate the initial split.
Value
An rsplit object that can be used with the rsample::training and rsample::testing
functions to extract the data in each split.
Examples
set.seed(123)
block_initial <- spatial_initial_split(boston_canopy, prop = 1 / 5, spatial_block_cv)
testing(block_initial)
#> Simple feature collection with 153 features and 18 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 745098 ymin: 2915630 xmax: 805045.8 ymax: 2969840
#> Projected CRS: NAD83 / Massachusetts Mainland (ftUS)
#> # A tibble: 153 × 19
#> grid_id land_area canopy_gain canopy_loss canopy_no_change canopy_area_2014
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 M-9 2690727. 52443. 53467. 304239. 357706.
#> 2 Q-21 2690727. 54712. 101816. 1359305. 1461121.
#> 3 AB-23 725043. 13737. 13278. 52628. 65906.
#> 4 AC-15 1175032. 24517. 24010. 111148. 135158.
#> 5 U-25 2691491. 83740. 117496. 601040. 718536.
#> 6 Y-13 2691490. 79215. 41676. 312299. 353975.
#> 7 M-10 2578879. 27026. 41240. 161115. 202355.
#> 8 T-22 2691490. 80929. 140490. 573628. 714118.
#> 9 AO-16 1717547. 64863. 52390. 465563. 517953.
#> 10 X-23 2690728. 85198. 109044. 458205. 567249.
#> # ℹ 143 more rows
#> # ℹ 13 more variables: canopy_area_2019 <dbl>, change_canopy_area <dbl>,
#> # change_canopy_percentage <dbl>, canopy_percentage_2014 <dbl>,
#> # canopy_percentage_2019 <dbl>, change_canopy_absolute <dbl>,
#> # mean_temp_morning <dbl>, mean_temp_evening <dbl>, mean_temp <dbl>,
#> # mean_heat_index_morning <dbl>, mean_heat_index_evening <dbl>,
#> # mean_heat_index <dbl>, geometry <MULTIPOLYGON [US_survey_foot]>
training(block_initial)
#> Simple feature collection with 529 features and 18 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 739826.9 ymin: 2908294 xmax: 812069.7 ymax: 2970073
#> Projected CRS: NAD83 / Massachusetts Mainland (ftUS)
#> # A tibble: 529 × 19
#> grid_id land_area canopy_gain canopy_loss canopy_no_change canopy_area_2014
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 AB-4 795045. 15323. 3126. 53676. 56802.
#> 2 I-33 265813. 8849. 11795. 78677. 90472.
#> 3 AO-9 270153 6187. 1184. 26930. 28114.
#> 4 H-10 2691490. 73098. 80362. 345823. 426185.
#> 5 V-7 107890. 219. 3612. 240. 3852.
#> 6 Q-22 2648089. 122211. 154236. 1026632. 1180868.
#> 7 X-4 848558. 8275. 1760. 6872. 8632.
#> 8 P-18 2690726. 110928. 113146. 915137. 1028283.
#> 9 J-29 2574479. 38069. 15530. 2388638. 2404168.
#> 10 G-28 2641525. 87024. 39246. 1202528. 1241774.
#> # ℹ 519 more rows
#> # ℹ 13 more variables: canopy_area_2019 <dbl>, change_canopy_area <dbl>,
#> # change_canopy_percentage <dbl>, canopy_percentage_2014 <dbl>,
#> # canopy_percentage_2019 <dbl>, change_canopy_absolute <dbl>,
#> # mean_temp_morning <dbl>, mean_temp_evening <dbl>, mean_temp <dbl>,
#> # mean_heat_index_morning <dbl>, mean_heat_index_evening <dbl>,
#> # mean_heat_index <dbl>, geometry <MULTIPOLYGON [US_survey_foot]>