Areal Interpolation — AI • smile

This function estimates variables observed at a "source" region into a "target" region. "Source" and "target" regions represent two different ways to divide a city, for example. For more details, see https://lcgodoy.me/smile/articles/sai.html.

Usage

ai(source, target, vars)

ai_var(source, target, vars, vars_var, sc_vars = FALSE, var_method = "CS")

Arguments

source: a sf object - source spatial data.
target: a sf object - target spatial data.
vars: a character representing the variables (observed at the source) to be estimated at the target data.
vars_var: a scalar of type character representing the name of the variable in the source dataset that stores the variances of the variable to be estimated at the target data.
sc_vars: boolean indicating whether vars should be scaled by its observed variance (if available).
var_method: a character representing the method to approximate the variance of the AI estimates. Possible values are "CS" (Cauchy-Schwartz) or "MI" (Moran's I).

Value

the target (of type sf) with estimates of the variables observed at the source data.

Examples

data(nyc_surv)
data(nyc_comd)

## creating variables that store the variance for each area
## this is done to exemplify the functionality of the package
nyc_surv <- transform(nyc_surv,
                      my_var = moe / qnorm(p = .975))
nyc_surv <- transform(nyc_surv, my_var = my_var * my_var)

if (FALSE) {
## areal interpolation
estimate_comd <-
   ai(source = nyc_surv, target = nyc_comd,
      vars = "estimate")

## areal interpolation with uncertainty estimation
estimate_comd <-
   ai_var(source = nyc_surv, target = nyc_comd,
          vars = "estimate", vars_var = "my_var",
          var_method = "MI")
}