chopin (version 0.9.4)

summarize_sedc: Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates

Description

Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates

Usage

summarize_sedc(
  point_from = NULL,
  point_to = NULL,
  id = NULL,
  sedc_bandwidth = NULL,
  threshold = NULL,
  target_fields = NULL,
  extent_from = NULL,
  extent_to = NULL,
  ...
)

Value

data.frame object with input field names with a suffix "_sedc" where the sums of EDC are stored. Additional attributes are attached for the EDC information.

• attr(result, "sedc_bandwidth"): the bandwidth where concentration reduces to approximately five percent

• attr(result, "sedc_threshold"): the threshold distance at which emission source points are excluded beyond that

Arguments

point_from

SpatVector object. Locations where the sum of SEDCs are calculated.

point_to

SpatVector object. Locations where each SEDC is calculated.

id

character(1). Name of the unique id field in point_to.

sedc_bandwidth

numeric(1). Distance at which the source concentration is reduced to exp(-3) (approximately -95 %)

threshold

numeric(1). For computational efficiency, the nearest points in threshold will be selected. 2 * sedc_bandwidth is applied if this value remains NULL.

target_fields

character. Field names to calculate SEDC.

extent_from

numeric(4) or SpatExtent. Extent of clipping point_from. It only works with point_from of character(1) file path. See terra::ext for more details. Coordinate systems should match.

extent_to

numeric(4) or SpatExtent. Extent of clipping point_to.

...

Placeholder.

Author

Insang Song

Details

The SEDC is specialized in vector to vector summary of covariates with exponential decay. Decaying slope will be defined by sedc_bandwidth, where the concentration of the source is reduced to $\exp(-3)$ (approximately 5 \ of the attenuating concentration with the distance from the sources. It can be thought of as a fixed bandwidth kernel weighted sum of covariates, which encapsulates three steps:

• Calculate the distance between each source and target points.

• Calculate the weight of each source point with the exponential decay.

• Summarize the weighted covariates.

References

• Messier KP, Akita Y, Serre ML. (2012). Integrating Address Geocoding, Land Use Regression, and Spatiotemporal Geostatistical Estimation for Groundwater Tetrachloroethylene. Environmental Science & Technology 46(5), 2772-2780.(tools:::Rd_expr_doi("10.1021/es203152a"))

• Wiesner C. (n.d.). Euclidean Sum of Exponentially Decaying Contributions Tutorial.

See Also

Other Macros for calculation: extract_at(), kernelfunction(), summarize_aw()

Examples

library(terra)
library(sf)
set.seed(101)
ncpath <- system.file("gpkg/nc.gpkg", package = "sf")
nc <- terra::vect(ncpath)
nc <- terra::project(nc, "EPSG:5070")
pnt_from <- terra::centroids(nc, inside = TRUE)
pnt_from <- pnt_from[, "NAME"]
pnt_to <- terra::spatSample(nc, 100L)
pnt_to$pid <- seq(1, 100)
pnt_to <- pnt_to[, "pid"]
pnt_to$val1 <- rgamma(100L, 1, 0.05)
pnt_to$val2 <- rgamma(100L, 2, 1)

vals <- c("val1", "val2")
suppressWarnings(
  summarize_sedc(pnt_from, pnt_to, "NAME", 1e5, 2e5, vals)
)