| Title: | Angular Distance Weighting Interpolation |
| Version: | 0.4.1 |
| Maintainer: | Panfeng Zhang <zhangpf08@163.com> |
| 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 (≥ 4.3.0) |
| Imports: | methods, sf, terra, cnmap |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| Suggests: | knitr, rmarkdown, ggplot2, tibble |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-09-13 02:59:17 UTC; zhang |
| Author: | Panfeng Zhang 
[aut, cre],
Guoyu Ren [ctb],
Yun Qin [ctb],
Chenchen Ren [ctb],
Xiang Zheng [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2025-09-13 22:00:09 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 |
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)
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 |
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
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 |
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
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 |
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. |
Value
a regular latitude-longitude dataframe grid (grid values).
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))
# 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
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))
# 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
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))
# 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
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))
# head(dd)
# # example
# grd <- points2grid(dd, extent = c(110, 117, 31, 37), gridsize = 0.5)
# head(grd)