Description Usage Arguments Details Value Author(s) References Examples

View source: R/smooth_sphere.R

Spline interpolation and smoothing on the sphere.

1 2 3 4 5 6 7 8 9 10 | ```
interpolate_spline(
observations,
targets,
value,
lon_obs = lon,
lat_obs = lat,
lon_targets = lon,
lat_targets = lat,
k = 50
)
``` |

`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 |

`lon_obs` |
Column in |

`lat_obs` |
Column in |

`lon_targets` |
Column in |

`lat_targets` |
Column in |

`k` |
(default 50) is the basis dimension. For small data sets reduce |

`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.

A smooth of the general type discussed in Duchon (1977) is used: the sphere is embedded in a 3D Euclidean space, but smoothing employs a penalty based on second derivatives (so that locally as the smoothing parameter tends to zero we recover a "normal" thin plate spline on the tangent space). This is an unpublished suggestion of Jean Duchon.

See `ordinary kriging`

for interpolation and smoothing on the sphere by means of kriging.

Object equal to object `targets`

including an extra column with predicted values.

Martin Haringa

`Splines on the sphere`

1 2 3 4 5 6 7 8 | ```
## Not run:
target <- sf::st_drop_geometry(nl_postcode3)
obs <- dplyr::sample_n(insurance, 1000)
pop_df <- interpolate_spline(obs, target, population_pc4, k = 20)
pop_sf <- left_join(nl_postcode3, pop_df)
choropleth(pop_sf, value = "population_pc4_pred", n = 13)
## End(Not run)
``` |

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