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 like spatial vine copulas, Karhunen-Lo猫ve expansion and INLA. In order to investigate the...
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 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 ...
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...
Spatial interpolation and spatiotemporal scanning analysis of human brucellosis in mainland China from 2012 to 2018 Article Open access 03 March 2025 Spatiotemporal distribution and ecological factors of disease burden in Inner Mongolia’s working age population with brucellosis from 2015 to 2020 Art...
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 ...
As any model for digital soil mapping suffers from different types of errors, including interpolation errors, it is important to quantify the uncertainty associated with the maps produced. The most common approach is some form of regression kriging or variation involving geostatistical simulation. ...
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...
Bayesian spatial modelling of soil properties and their uncertainty: The example of soil organic matter in Scotland using R-INLABayesian inferenceremote sensingsoil properties mappinguncertainty propagationAs any model for digital soil mapping suffers from different types of errors, including interpolation ...