Spatial predictions of drift deposits on soil surface were conducted using eight different spatial interpolation methods i.e. classical approaches like the Thiessen method and kriging, and some advanced methods
Reanalysis methods use additional data combination structures that can affect the estimation process, in particular spatially smoothing the observed data through the use of the theoretical climate model in spatial interpolation in the construction of the data grid. Our analysis allows us to directly esti...
To include spatial dependence, the Bayesian spatial zero-inflated Poisson model was fitted using integrated nested Laplace approximations (INLA). INLA is an estimation tool for Bayesian analysis, which eases the computational burden but approximates accurate posterior distribution. In INLA, fitting logisti...
To calculate population at risk by age group, year and spatial unit (1 km2 grid cell or municipality), we performed linear interpolation of age, year and spatial unit specific weights, see Additional file 1: Text S1 and Figures S1, S2. The 1 km2 grid size was selected as a compromise ...
Spatial interpolation was adapted to face the fact that the domain area was elliptical with radial transects (Fig. 3a,b). The geographic refer- ence system was thus irrelevant to describe the orientation between observations. For instance, North–South did not mean the same thing in different...
Overall, the four models are better at local interpolation than extrapolation over large regions without data. The predictions in Fig. 6(iii) generated using the randomly distributed data recover the prevalence structure of the MAP raster in Fig. 3d much more faithfully than the predictions in Fig...
12-Interpolation.qmd 13-Geostatistics.qmd 14-Areal.qmd 15-Measures.qmd 16-SpatialRegression.qmd 17-Econometrics.qmd 97-allcode.qmd 99-references.qmd DESCRIPTION ERRATA.qmd LICENSE.md README.md _quarto.yml book.bib index.qmd krantz.cls
Key Features: Describes R packages for retrieval, manipulation, and visualization of spatial data Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes. Provides detailed ...
The cross-validation summary statistics of interpolation for the models based on RF, GBT and QRF are shown in Table2and Fig.9. These statistics show that RF produced more accurate estimations than GBT. Our results showed significant overestimation in lower values of DTB, which is a common probl...
Interpolations are calculated using the Bayesian inference approach integrated nested Laplace approximation. We empirically tested this method on a continuous population of brown bears (Ursus arctos) spanning two counties in Sweden.Results:Two areas were identified as differentiated from the remaining ...