Tidy and geospatial interpolated grids

Tidy and geospatial interpolated grids for 2-dimensional gridded data.

Usage

tidy_intergrid(data, attrib, cellsize, verbose=FALSE)
st_intergrid(x, attrib, cellsize, verbose=FALSE)

Arguments

data,x

sf object, where attribute is estimate value and incomplete/irregular estimation grid is polygon geometry

attrib

name or position of estimate variable. Default is 1.

cellsize

cell size. If missing then automatically calculated from data.

verbose

flag for verbose output. Default is FALSE.

Value

The input gridded data is interpolated to a complete regular rectangular grid.

Details

Any missing grid cells are inferred so there are no gaps in the output, and the attribute value to set to 0. For any other grid cells with missing attribute values, the attributate is also set to 0. tidy_overgrid/st_overgrid is usually deployed on gridded data from third parties, where geometries are excluded/varying to reduce storage requirements, but tidy_as_kde/st_as_kde require complete regular rectangular grids.

Examples

## geospatial quasi density estimate
library(ggplot2)
data(ales_grid, package="eks")

## incomplete 1 km x 1 km grid
## ind = #individuals in grid cells 
gs <- ggplot() + theme_sf() + 
    colorspace::scale_fill_continuous_sequential(palette="Heat", breaks=seq(0,6000,by=1000))  
gs + geom_sf(data=ales_grid, aes(fill=ind))


## complete regular interpolated 1 km x 1 km grid
ales_sgrid <- st_intergrid(ales_grid, attrib="ind", cellsize=c(1000,1000))
gs + geom_sf(data=ales_sgrid, aes(fill=ind)) 


## geom_sf KDE plot
ales_skde <- st_as_kde(ales_sgrid)
ggplot(ales_skde) + theme_sf() + 
    geom_sf(data=st_get_contour(ales_skde), aes(fill=contperc))