sandwich mapping model — sandwich • spEcula

Spatial prediction based on spatial stratified heterogeneity using sandwich mapping model.

Usage

sandwich(
  sampling,
  stratification,
  reporting,
  sampling_attr,
  ssh_zone,
  reporting_id,
  weight_type = "area"
)

Arguments

sampling: Sampling layer, spatial point vector object which is sf or can be converted to sf object.
stratification: Stratification layer, spatial polygon vector object which is sf or can be converted to sf object.
reporting: Reporting layer, spatial polygon vector object which is sf or can be converted to sf object.
sampling_attr: The attribute column for the sampling point in sampling layer.
ssh_zone: The zone column for the stratification layer.
reporting_id: The id column for the reporting layer.
weight_type: (optional) Geographic area based on weight(area) or indicate human population size(population) , Default is area.

Value

A sf object with estimated mean sandwichest_mean and standard error sandwichest_standarderror.

References

Lin, Y., Xu, C., & Wang, J. (2023). sandwichr: Spatial prediction in R based on spatial stratified heterogeneity. Transactions in GIS: TG, 27(5), 1579–1598. https://doi.org/10.1111/tgis.13088

Author

Wenbo Lv lyu.geosocial@gmail.com

Examples

library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
simpath = system.file("extdata", "sim.gpkg", package="spEcula")
sampling = read_sf(simpath,layer = 'sim_sampling')
ssh = read_sf(simpath,layer = 'sim_ssh')
reporting = read_sf(simpath,layer = 'sim_reporting')
sandwich(sampling = sampling,stratification = ssh,reporting = reporting,
        sampling_attr = 'Value',ssh_zone = 'X',reporting_id = 'Y',
        weight_type = 'population')
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
#> Simple feature collection with 7 features and 3 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 5.684342e-14 ymin: 2 xmax: 4 ymax: 6
#> Geodetic CRS:  WGS 84
#> # A tibble: 7 × 4
#>       Y sandwichest_mean sandwichest_standarderror                      geometry
#>   <dbl>            <dbl>                     <dbl>                 <POLYGON [°]>
#> 1     1            NaN                      NaN    ((0.8 4, 0.8 4, 1 4, 1.2 4, …
#> 2     2            266.                       2.11 ((2.8 6, 2.6 6, 2.4 6, 2.2 6…
#> 3     3            311.                       2.42 ((2.4 3, 2.4 2.8, 2.2 2.8, 2…
#> 4     4            413.                       3.06 ((4 3.6, 4 3.8, 4 4, 4 4.2, …
#> 5     5            NaN                      NaN    ((1 3.6, 1 3.4, 1.2 3.4, 1.4…
#> 6     6            NaN                      NaN    ((1.6 3, 1.6 2.8, 1.8 2.8, 2…
#> 7     7             93.8                      2.85 ((0.6 5, 0.6 5, 0.6 5.2, 0.6…
sandwich(sampling = sampling,stratification = ssh,reporting = reporting,
        sampling_attr = 'Value',ssh_zone = 'X',reporting_id = 'Y',
        weight_type = 'area')
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
#> Simple feature collection with 7 features and 3 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 5.684342e-14 ymin: 2 xmax: 4 ymax: 6
#> Geodetic CRS:  WGS 84
#> # A tibble: 7 × 4
#>       Y sandwichest_mean sandwichest_standarderror                      geometry
#>   <dbl>            <dbl>                     <dbl>                 <POLYGON [°]>
#> 1     1             381.                      2.43 ((0.8 4, 0.8 4, 1 4, 1.2 4, …
#> 2     2             262.                      2.10 ((2.8 6, 2.6 6, 2.4 6, 2.2 6…
#> 3     3             298.                      2.49 ((2.4 3, 2.4 2.8, 2.2 2.8, 2…
#> 4     4             401.                      2.88 ((4 3.6, 4 3.8, 4 4, 4 4.2, …
#> 5     5             390.                      2.53 ((1 3.6, 1 3.4, 1.2 3.4, 1.4…
#> 6     6             357.                      2.15 ((1.6 3, 1.6 2.8, 1.8 2.8, 2…
#> 7     7             203.                      2.40 ((0.6 5, 0.6 5, 0.6 5.2, 0.6…