Hello
The most important question: is the (virogram) used in the (ordinary kriging) Is it the (traditional virogram) or (The Residual Maximum Likelihood Method virogram)?
Does (ordinary kriging OK) include the trend, or does it not depend on the trend?
My greetings
Thank you doctor, I do not use it with R, I use it with QGIS in SMART MAP (ordinary kriging)
It is true, as I liked, that (GIS, QGIS) provide the statistical model, but I ask whether (Spherical), (Gauusin) and (Expontional) are affiliated with (Residual Maximum Likelihood Method virogram) or of (traditional virogram).
In empirical modeling, kriging is a popular interpolation technique used to approximate the functional relationships between impact variables and system response. The interpolation is based on a statistical analysis of the provided data, and a priori set trend functions are an optional addition. However, only trend functions that are linear with respect to the parameters can currently be employed with kriging. In this article, we provide a Kriging extension for dealing with trend functions with nonlinear parameters.
Kriging is the most commonly used geostatistical approach for spatial interpolation. Kriging techniques rely on a spatial model between observations (defined by a variogram) to predict attribute values at unsampled locations. One of the specificity of kriging methods is that they do not only consider the distance between observations but they also intend to capture the spatial structure in the data by comparing observations separated by specific spatial distances two at a time. The objective is to understand the relationships between observations separated by different lag distances. All this knowledge is accounted for in the variogram. Kriging methods then derive spatial weights for the observations based on this variogram. It must be noted that kriging techniques will preserve the values of the initial samples in the interpolated map.
Kriging methods consider that the process that originated the data can be divided into two major components: a deterministic trend (large scale variations) and an autocorrelated error (the residuals).
For further reading and answer to your question, please click on this link
https://www.aspexit.com/spatial-data-interpolation-tin-idw-kriging-block-kriging-co-kriging-what-are-the-differences/
- Badges
- Science topic
- Similar topics
- Geoscience
- Cartography
- Mapping
- Maps