README.md

In highamm/sptotal: Predicting Totals and Weighted Sums from Spatial Data

sptotal implements finite population block kriging (FPBK, Ver Hoef (2008)), a geostatistical approach to predicting means and totals of count data for finite populations.

See sptotal's Website for more information.

The accepted Journal of Open Source Software paper on sptotal can be found at

Statement of Need

The primary purpose of sptotal is to provide an implementation of the Finite Population Block Kriging (FPBK) methods developed in Ver Hoef (2002) and Ver Hoef (2008). The method is useful when

• there are a finite number of spatial locations (or sites)
• only a subset of the spatial locations are sampled
• there is expected to be some spatial correlation

Examples of settings where FPBK is useful include wildlife abundance surveys performed on a finite number of spatial locations. In these surveys, it is not uncommon to only sample a subset of the region.

Installation Instructions

sptotal can be installed from CRAN

install.packages("sptotal")

or using devtools

library(devtools)
install_git("https://github.com/highamm/sptotal.git")

Simple Example

The sptotal package can be used for spatial prediction in settings where there are a finite number of sites and some of these sites were not sampled. Note that, to keep this example simple, we are simulating response values that are spatially independent. In a real example, we assume that there is some spatial dependence in the response.

set.seed(102910)
spatial_coords <- expand.grid(1:10, 1:10)
toy_df <- data.frame(xco = spatial_coords[ ,1],
yco = spatial_coords[ ,2], counts = sample(c(rpois(50, 15),
rep(NA, 50)), size = 100, replace = TRUE))

mod <- slmfit(formula = counts ~ 1, xcoordcol = "xco",
ycoordcol = "yco", data = toy_df)
summary(mod)

pred <- predict(mod)

We can look at the predictions with

pred$Pred_df[1:6, c("xco", "yco", "counts", "counts_pred_count")]

Methods and Basic Functions

sptotal Main Functions:

slmfit() fits a spatial linear model to the response on the observed/sampled sites. \code{check.variogram} can be used to construct an empirical variogram of the residuals of the spatial linear model.

predict.slmfit() uses the spatial linear model fitted with slmfit() and finite population block kriging to predict counts/densities at unobserved locations. A prediction for the total count as well as a prediction variance are given by default.

For more details on how to use these functions and for a real world example applying the methods to a moose data set, please see the Vignette at https://highamm.github.io/sptotal/articles/sptotal-vignette.html .

Community Guidelines

We encourage users to submit GitHub issues and enhancement requests at https://github.com/highamm/sptotal so we may continue to improve sptotal.

Citation

To cite this package in the literature, run the following line:

citation("sptotal")

The methods in this package are based on the following references:

Ver Hoef, J. M. (2008). "Spatial methods for plot-based sampling of wildlife populations." Environmental and Ecological Statistics, 15(1), 3–13.

Ver Hoef, J. M. (2002). "Sampling and geostatistics for spatial data." Ecoscience, 9(2), 152– 161.

highamm/sptotal documentation built on Aug. 12, 2024, 2:04 a.m.