Area Weighted Intersection (areal implementation)

Description

Returns the fractional percent of each feature in x that is covered by each intersecting feature in y. These can be used as the weights in an area-weighted mean overlay analysis where x is the data source and area- weighted means are being generated for the target, y.

This function is a lightwieght wrapper around the functions aw_intersect aw_total and aw_weight from the areal package.

Usage

calculate_area_intersection_weights(x, y, allow_lonlat = FALSE)

Arguments

Value

data.frame containing fraction of each feature in x that is covered by each feature in y. e.g. If a feature from x is entirely within a feature in y, w will be 1. If a feature from x is 50 will be two rows, one for each x/y pair of features with w = 0.5 in each.

Examples

b1 = sf::st_polygon(list(rbind(c(-1,-1), c(1,-1),
                           c(1,1), c(-1,1),
                           c(-1,-1))))
b2 = b1 + 2
b3 = b1 + c(-0.2, 2)
b4 = b1 + c(2.2, 0)
b = sf::st_sfc(b1, b2, b3, b4)
a1 = b1 * 0.8
a2 = a1 + c(1, 2)
a3 = a1 + c(-1, 2)
a = sf::st_sfc(a1,a2,a3)
plot(b, border = 'red')
plot(a, border = 'green', add = TRUE)

sf::st_crs(b) <- sf::st_crs(a) <- sf::st_crs(5070)

b <- sf::st_sf(b, data.frame(idb = c(1, 2, 3, 4)))
a <- sf::st_sf(a, data.frame(ida = c(1, 2, 3)))

sf::st_agr(a) <- sf::st_agr(b) <- "constant"

a_b <- calculate_area_intersection_weights(a, b)
b_a <- calculate_area_intersection_weights(b, a)

Create Cell Geometry

Description

Creates cell geometry from vectors of X and Y positions.

Usage

create_cell_geometry(
  X_coords,
  Y_coords,
  prj,
  geom = NULL,
  buffer_dist = 0,
  regularize = FALSE,
  eps = 1e-10
)

Arguments

Details

Intersection is performed with cell centers then geometry is constructed. A buffer may be required to fully cover geometry with cells.

Examples

dir <- tempdir()
ncf <- file.path(dir, "metdata.nc") 

try(zip::unzip(system.file("extdata/metdata.zip", package = "ncdfgeom"), exdir = dir))

if(file.exists(ncf)) {

nc <- RNetCDF::open.nc(ncf)
ncmeta::nc_vars(nc)
variable_name <- "precipitation_amount"
cv <- ncmeta::nc_coord_var(nc, variable_name)

x <- RNetCDF::var.get.nc(nc, cv$X, unpack = TRUE)
y <- RNetCDF::var.get.nc(nc, cv$Y, unpack = TRUE)

prj <- ncmeta::nc_gm_to_prj(ncmeta::nc_grid_mapping_atts(nc))

geom <- sf::read_sf(system.file("shape/nc.shp", package = "sf"))
geom <- sf::st_transform(geom, 5070)

cell_geometry <- create_cell_geometry(x, y, prj, geom, 0)

plot(sf::st_geometry(cell_geometry), lwd = 0.25)
plot(sf::st_transform(sf::st_geometry(geom), prj), add = TRUE)

}

Read attribute dataframe from NetCDF-DSG file

Description

Gets attribute data from a NetCDF-DSG file and returns it in a data.frame. This function is intended as a convenience to be used within workflows where the netCDF file is already open and well understood.

Usage

read_attribute_data(nc, instance_dim)

Arguments

Examples

hucPolygons <- sf::read_sf(system.file('extdata','example_huc_eta.json', package = 'ncdfgeom'))
hucPolygons_nc <- ncdfgeom::write_geometry(tempfile(), hucPolygons)

read_attribute_data(hucPolygons_nc, "instance")

Read NetCDF-CF spatial geometries

Description

Attempts to convert a NetCDF-CF DSG Simple Geometry file into a sf data.frame.

Usage

read_geometry(nc_file)

Arguments

Value

sf data.frame containing spatial geometry of type found in the NetCDF-CF DSG file.

References

http://cfconventions.org/index.html

1. http://cfconventions.org/cf-conventions/cf-conventions.html#_features_and_feature_types

Examples

huc_eta_nc <- tempfile()
file.copy(system.file('extdata','example_huc_eta.nc', package = 'ncdfgeom'), 
         huc_eta_nc, overwrite = TRUE)
         
vars <- ncmeta::nc_vars(huc_eta_nc)

hucPolygons <- sf::read_sf(system.file('extdata','example_huc_eta.json', package = 'ncdfgeom'))
plot(sf::st_geometry(hucPolygons))
names(hucPolygons)

hucPolygons_nc <- ncdfgeom::write_geometry(nc_file=huc_eta_nc, 
                                          geom_data = hucPolygons, 
                                          instance_dim_name = "station", 
                                          variables = vars$name)
huc_poly <- read_geometry(huc_eta_nc)
plot(sf::st_geometry(huc_poly))
names(huc_poly)

Read NetCDF-CF timeSeries featuretype

Description

This function reads a timeseries discrete sampling geometry NetCDF file and returns a list containing the file's contents.

Usage

read_timeseries_dsg(nc_file, read_data = TRUE)

Arguments

Details

The current implementation checks several NetCDF-CF specific conventions prior to attempting to read the file. The Conventions and featureType global attributes are checked but not strictly required.

Variables with standard_name and/or cf_role of station_id and/or timeseries_id are searched for to indicate which variable is the 'timeseries identifier'. The function stops if one is not found.

All variables are introspected for a coordinates attribute. This attribute is used to determine which variables are coordinate variables. If none are found an attempt to infer data variables by time and timeseries_id dimensions is made.

The coordinates variables are introspected and their standard_names used to determine which coordinate they are. Lat, lon, and time are required, height is not.

Variables with a coordinates attribute are assumed to be the 'data variables'.

Data variables are traversed and their metadata and data content put into lists within the main response list.

See the timeseries vignette for more information.

Value

list containing the contents of the NetCDF file.

References

https://www.unidata.ucar.edu/software/netcdf-java/v4.6/reference/FeatureDatasets/CFpointImplement.html

Write attribute data to NetCDF-CF

Description

Creates a NetCDF file with an instance dimension, and any attributes from a data frame. Use to create the start of a NetCDF-DSG file. One character length dimension is created long enough to contain the longest provided character string. This function does not implement any CF convention attributes or standard names. Any columns of class date will be converted to character.

Usage

write_attribute_data(
  nc_file,
  att_data,
  instance_dim_name = "instance",
  units = rep("unknown", ncol(att_data)),
  overwrite = FALSE
)

Arguments

Examples

sample_data <- sf::st_set_geometry(sf::read_sf(system.file("shape/nc.shp", 
                                                           package = "sf")), 
                                   NULL)
example_file <-write_attribute_data(tempfile(), sample_data,
                                    units = rep("unknown", ncol(sample_data)))

try({
  ncdump <- system(paste("ncdump -h", example_file), intern = TRUE)
  cat(ncdump ,sep = "\n")
}, silent = TRUE)

Write geometries and attributes to NetCDF-CF

Description

Creates a file with point, line or polygon instance data ready for the extended NetCDF-CF timeSeries featuretype format.

Will also add attributes if provided data has them.

Usage

write_geometry(
  nc_file,
  geom_data,
  instance_dim_name = NULL,
  variables = list()
)

Arguments

References

1. http://cfconventions.org/cf-conventions/cf-conventions.html

Examples

hucPolygons <- sf::read_sf(system.file('extdata','example_huc_eta.json', package = 'ncdfgeom'))

hucPolygons_nc <- ncdfgeom::write_geometry(nc_file=tempfile(), 
                                           geom_data = hucPolygons)
try({
  ncdump <- system(paste("ncdump -h", hucPolygons_nc), intern = TRUE)
  cat(ncdump ,sep = "\n")
}, silent = TRUE)

Write time series to NetCDF-CF

Description

This function creates a timeseries discrete sampling geometry NetCDF file. It uses the orthogonal array encoding to write one data.frame per function call. This encoding is best suited to data with the same number of timesteps per instance (e.g. geometry or station).

Usage

write_timeseries_dsg(
  nc_file,
  instance_names,
  lats,
  lons,
  times,
  data,
  alts = NA,
  data_unit = "",
  data_prec = "double",
  data_metadata = list(name = "data", long_name = "unnamed data"),
  time_units = "days since 1970-01-01 00:00:00",
  instance_dim_name = "instance",
  dsg_timeseries_id = "instance_name",
  coordvar_long_names = list(instance = "Station Names", time = "time of measurement",
    lat = "latitude of the measurement", lon = "longitude of the measurement", alt =
    "altitude of the measurement"),
  attributes = list(),
  add_to_existing = FALSE,
  overwrite = FALSE
)

Arguments

Details

Suggested Global Variables: c(title = "title", abstract = "history", provider site = "institution", provider name ="source", description = "description")

Note regarding add_to_existing: add_to_existing = TRUE should only be used to add variables to an existing NetCDF discrete sampling geometry file. All other inputs should be the same as are already in the file. If the functions is called with add_to_existing=FALSE (the default), it will overwrite an existing file with the same name. The expected usage is to call this function repeatedly only changing the data, data_unit, data_prec and data_metadata inputs.

See the timeseries vignette for more information.

References

1. https://www.unidata.ucar.edu/software/netcdf-java/v4.6/reference/FeatureDatasets/CFpointImplement.html

2. http://cfconventions.org/cf-conventions/cf-conventions.html#_orthogonal_multidimensional_array_representation

3. http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/cf-conventions.html#time-series-data