Interpolation and smoothing on the sphere by means of ordinary kriging.

interpolate_krige(
  observations,
  targets,
  value,
  lon_obs = lon,
  lat_obs = lat,
  lon_targets = lon,
  lat_targets = lat
)

Arguments

observations

data.frame of observations.

targets

data.frame of locations to calculate the interpolated and smoothed values for (target points).

value

Column with values in observations.

lon_obs

Column in observations with longitude (lon is default).

lat_obs

Column in observations with latitude (lat is default).

lon_targets

Column in targets with longitude (lon is default).

lat_targets

Column in targets with latitude (lat is default).

Value

Object equal to object targets including extra columns for the predicted value and the variance.

Details

observations should include at least columns for longitude and latitude.

targets should include at least columns for longitude, latitude and value of interest to interpolate and smooth.

Kriging can be considered as linear regression with spatially correlated residuals. Kriging is most appropriate when it is known there is a spatially correlated distance or directional bias in the data. It is often used in soil science and geology.

See splines on the sphere for interpolation and smoothing on the sphere by means of splines.

References

gstat::krige

Author

Martin Haringa

Examples

if (FALSE) { target <- sf::st_drop_geometry(nl_postcode3) obs <- insurance %>% dplyr::sample_n(1000) pop_df <- interpolate_krige(obs, target, population_pc4) pop_sf <- left_join(nl_postcode3, pop_df) choropleth(pop_sf, value = "population_pc4_pred", n = 13) choropleth(pop_sf, value = "population_pc4_var", n = 13) }