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Title:

Angular Distance Weighting Interpolation

Version:

0.4.0

Maintainer:

Panfeng Zhang <zhangpanfeng@jlnu.edu.cn>

Description:

The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius. In addition to this, we also provide a simple way (Jones and Hulme, 1996) to grid the irregularly-spaced data points onto regular latitude-longitude grids by averaging all stations in grid-boxes.

URL:

https://github.com/PanfengZhang/adw

BugReports:

https://github.com/PanfengZhang/adw/issues

Depends:

R (≥ 4.2.0)

Imports:

methods, sf, terra, cnmap

License:

GPL-3

Encoding:

UTF-8

RoxygenNote:

7.3.1

Suggests:

knitr, rmarkdown, ggplot2

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2024-04-15 16:34:07 UTC; zhang

Author:

Panfeng Zhang

[aut, cre], Guoyu Ren [ctb], Yun Qin [ctb], Chenchen Ren [ctb], Xiang Zheng [ctb]

Repository:

CRAN

Date/Publication:

2024-04-15 19:10:16 UTC

Angular Distance Weighting Interpolation.

Description

The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius.

Usage

adw(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)

Arguments

ds

a input dataframe which contains the column names of lon, lat, value.

extent

a extent numeric vector (latitude and longitude) of length 4 in the order c(xmin, xmax, ymin, ymax), or a polygon object with class 'sf' (package 'sf'), or a polygon object with class 'SpatVector' (package 'terra'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326).

gridsize

the grid size, i.e. the grid resolution. units: degree.

cdd

correlation decay distance, i.e. the maximum search radius. unit: kilometer. default value: 1000km.

m

is used to adjust the weighting function further, higher values of m increase the rate at which the weight decays with distance. default value 4.

nmin

the minimum number of observation points required to interpolate a grid within the search radius (i.e. cdd); if the number of stations within the search ridius (cdd) is less than nmin, a missing value will be generated to fill this grid. default value 3.

nmax

The number of nearest points within the search radius to use for interpolation. default value 10.

Value

a regular latitude-longitude dataframe grid (interpoled values).

References

Caesar, J., L. Alexander, and R. Vose, 2006: Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. Journal of Geophysical Research, 111, https://doi.org/10.1029/2005JD006280.

Examples

set.seed(2)
dd <- data.frame(lon = runif(100, min = 110, max = 117),
                 lat = runif(100, min = 31, max = 37),
                 value = runif(100, min = -10, max = 10))
head(dd)

# example 1
grd <- adw(dd, extent = c(110, 117, 31, 37), gridsize = 0.5, cdd = 500)
head(grd)

# example 2
hmap <- cnmap::getMap(code = "410000") |> sf::st_make_valid() # return a 'sf' object.
grd <- adw(dd, extent = hmap, gridsize = 0.5, cdd = 500)
head(grd)

# example 3
hmap <- cnmap::getMap(code = "410000", returnClass = "sv") # return a 'SpatVector' object.
grd <- adw(dd, extent = hmap, gridsize = 0.5, cdd = 500)
head(grd)

Angular Distance Weighting Interpolation for the extent of 'simple feature'.

Description

The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius.

Usage

adw_sf(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)

Arguments

ds

a input dataframe which contains the column names of lon, lat, value.

extent

a polygon object with class 'sf' (package 'sf'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326).

gridsize

the grid size, i.e. the grid resolution. units: degree.

cdd

correlation decay distance, i.e. the maximum search radius. unit: kilometer. default value: 1000km.

m

is used to adjust the weighting function further, higher values of m increase the rate at which the weight decays with distance. default value 4.

nmin

nmax

The number of nearest points within the search radius to use for interpolation. default value 10.

Value

a regular latitude-longitude dataframe grid (interpoled values).

References

Examples

set.seed(2)
dd <- data.frame(lon = runif(100, min = 110, max = 117),
                 lat = runif(100, min = 31, max = 37),
                 value = runif(100, min = -10, max = 10))
head(dd)
hmap <- cnmap::getMap(code = "410000") |> sf::st_make_valid() # return a 'sf' object.
grd <- adw_sf(dd, extent = hmap, gridsize = 0.5, cdd = 500)
head(grd)

Angular Distance Weighting Interpolation for the extent of 'SpatVector'.

Description

The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius.

Usage

adw_sv(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)

Arguments

ds

a input dataframe which contains the column names of lon, lat, value.

extent

a polygon object with class 'SpatVector' (package 'terra'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326).

gridsize

the grid size, i.e. the grid resolution. units: degree.

cdd

correlation decay distance, i.e. the maximum search radius. unit: kilometer. default value: 1000km.

m

is used to adjust the weighting function further, higher values of m increase the rate at which the weight decays with distance. default value 4.

nmin

nmax

The number of nearest points within the search radius to use for interpolation. default value 10.

Value

a regular latitude-longitude dataframe grid (interpoled values).

References

Examples

set.seed(2)
dd <- data.frame(lon = runif(100, min = 110, max = 117),
                 lat = runif(100, min = 31, max = 37),
                 value = runif(100, min = -10, max = 10))
head(dd)
# example
hmap <- cnmap::getMap(code = "410000", returnClass = "sv") # return a 'SpatVector' object.
grd <- adw_sv(dd, extent = hmap, gridsize = 0.5, cdd = 500)
head(grd)

Angular Distance Weighting Interpolation for the extent of vector.

Description

The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius.

Usage

adw_vector(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)

Arguments

ds

a input dataframe which contains the column names of lon, lat, value.

extent

a extent numeric vector (latitude and longitude) of length 4 in the order c(xmin, xmax, ymin, ymax).

gridsize

the grid size, i.e. the grid resolution. units: degree.

cdd

correlation decay distance, i.e. the maximum search radius. unit: kilometer. default value: 1000km.

m

is used to adjust the weighting function further, higher values of m increase the rate at which the weight decays with distance. default value 4.

nmin

nmax

The number of nearest points within the search radius to use for interpolation. default value 10.

Value

a regular latitude-longitude dataframe grid (interpoled values).

References

Examples

set.seed(2)
dd <- data.frame(lon = runif(100, min = 110, max = 117),
                 lat = runif(100, min = 31, max = 37),
                 value = runif(100, min = -10, max = 10))
head(dd)
# example
grd <- adw_vector(dd, extent = c(110, 117, 31, 37), gridsize = 0.5, cdd = 500)
head(grd)

Area weighted average.

Description

The large area, or hemispheric, or global averages can be calculated dependent on the area represented by the grid-point or grid-box. The weight of latitude-longitude grid-points-boxes should be the cosine of the latitude of the ith grid-point-box.

Usage

awa(dat, lat)

Arguments

dat

a numeric vector of grid data. The missing values are not allowed.

lat

a latitude numeric vector of grid data. The cosine of latitude is used as the weight coefficient.

Value

a scalar value, i.e the value of area weighted average.

References

Jones, P. D., and M. Hulme, 1996: Calculating regional climatic time series for temperature and precipitation: Methods and illustrations. Int. J. Climatol., 16, 361–377, https://doi.org/10.1002/(SICI)1097-0088(199604)16:4<361::AID-JOC53>3.0.CO;2-F.

Examples

# set.seed(2)
# dd <- data.frame(lon = runif(100, min = 110, max = 117),
#                  lat = runif(100, min = 31, max = 37),
#                  value = runif(100, min = -10, max = 10))
# grd <- points2grid(dd, extent = c(110, 117, 31, 37), gridsize = 0.5)
# grd <- na.omit(grd)
# awa(grd$value, grd$lat) # area weighted average

Points were to converted grids using a local gridding method.

Description

the irregularly-spaced data of points are converted onto regular latitude-longitude grids by averaging all stations in grid-boxes.

Usage

points2grid(dd, extent, gridsize = 0.5)

Arguments

dd

a input dataframe which contains the column names of lon, lat, value.

extent

gridsize

the grid size, i.e. the grid resolution. units: degree.

Value

a regular latitude-longitude dataframe grid (grid values).

References

Examples

# set.seed(2)
# dd <- data.frame(lon = runif(100, min = 110, max = 117),
#                  lat = runif(100, min = 31, max = 37),
#                  value = runif(100, min = -10, max = 10))
# head(dd)
# 
# # example 1
# grd <- points2grid(dd, extent = c(110, 117, 31, 37), gridsize = 0.5)
# head(grd)
# 
# # example 2
# hmap <- cnmap::getMap(code = "410000", return = "sf") |> sf::st_make_valid()
# grd <- points2grid(dd, extent = hmap, gridsize = 0.5)
# head(grd)
# 
# # example 3
# hmap <- cnmap::getMap(code = "410000", return = "sv")
# grd <- points2grid(dd, extent = hmap, gridsize = 0.5)
# head(grd)

Points were to converted grids using a local gridding method.

Description

the irregularly-spaced data of points are converted onto regular latitude-longitude grids by averaging all stations in grid-boxes.

Usage

points2grid_sf(dd, extent, gridsize = 5)

Arguments

dd

a input dataframe which contains the column names of lon, lat, value.

extent

a polygon object of simple feature (come from package 'sf'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326).

gridsize

the grid size, i.e. the grid resolution. units: degree.

Value

a regular latitude-longitude dataframe grid (grid values).

References

Examples

# set.seed(2)
# dd <- data.frame(lon = runif(100, min = 110, max = 117),
#                  lat = runif(100, min = 31, max = 37),
#                  value = runif(100, min = -10, max = 10))
# head(dd)
# # example
# hmap <- cnmap::getMap(code = 410000) |> sf::st_make_valid()
# grd <- points2grid_sf(dd, extent = hmap, gridsize = 0.5)
# head(grd)

Points were to converted grids using a local gridding method.

Description

the irregularly-spaced data of points are converted onto regular latitude-longitude grids by averaging all stations in grid-boxes.

Usage

points2grid_sv(dd, extent, gridsize = 5)

Arguments

dd

a input dataframe which contains the column names of lon, lat, value.

extent

a polygon object of SpatVector (from package 'terra'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326).

gridsize

the grid size, i.e. the grid resolution. units: degree.

Value

a regular latitude-longitude dataframe grid (grid values).

References

Examples

# set.seed(2)
# dd <- data.frame(lon = runif(100, min = 110, max = 117),
#                  lat = runif(100, min = 31, max = 37),
#                  value = runif(100, min = -10, max = 10))
# head(dd)
# # example
# hmap <- cnmap::getMap(code = 410000, returnClass = "sv")
# grd <- points2grid_sv(dd, extent = hmap, gridsize = 0.5)
# head(grd)

Points were to converted grids using a local gridding method.

Description

The irregularly-spaced data of points are converted onto regular latitude-longitude grids by averaging all stations in grid-boxes.

Usage

points2grid_vector(dd, extent, gridsize = 5)

Arguments

dd

a input dataframe which contains the column names of lon, lat, value.

extent

a extent numeric vector (latitude and longitude) of length 4 in the order c(xmin, xmax, ymin, ymax).

gridsize

the grid size, i.e. the grid resolution. units: degree.

Value

a regular latitude-longitude dataframe grid (grid values).

References

Examples

# set.seed(2)
# dd <- data.frame(lon = runif(100, min = 110, max = 117),
#                  lat = runif(100, min = 31, max = 37),
#                  value = runif(100, min = -10, max = 10))
# head(dd)
# # example
# grd <- points2grid(dd, extent = c(110, 117, 31, 37), gridsize = 0.5)
# head(grd)