Contents
What is spatio temporal kriging?
Stochastic spatio-temporal variability is often observed in naturally occurring phenomena. Kriging interpolates the best linear unbiased estimate of a value at an unobserved point in space, based on the weighted linear combination of surrounding observations, minimizing the prediction error.
Why normal distribution is important in kriging?
When considering only predictors that are formed from weighted averages, kriging is the best unbiased predictor whether or not your data is normally distributed. However, if the data is normally distributed, kriging is the best predictor among all unbiased predictors, not only those that are weighted averages.
Does kriging assume normal distribution?
Normalizing the distribution of data can only improve the conditions for the stationary assumption. Therefore, the normal distribution of data is not a necessary condition for the kriging technique.
Does kriging assume normality?
Although normality of data is often mentioned in the literature, you can use kriging also for the data in non-normal distribution in case you choose an appropriate method. For linear modeling approaches the distributional or Gaussian assumption is not required.
How is spatio temporal kriging used in GStat?
The package spacetime provides ways of creating objects where the time component is taken into account, and gstat uses these formats for its space-time analysis. Here I will present an example of spatio-temporal kriging using sensors’ data.
Which is more accurate spatial or spatio temporal interpolation?
Spatio-temporal interpolation can potentially provide more accurate predictions than spatial interpolation because observations taken at other times can be included. In addition, spatio-temporal interpolation allows predictions to be made at single locations or entire fields in between and beyond observation times.
When to use 2D kriging on temporal slices?
There are cases and datasets in which performing 2D kriging on “temporal slices” may be appropriate. However, there are other instances where this is not possible and therefore the only solution is take time into account during kriging.
When is time considered in the kriging model?
Time is clearly considered in these studies, but not explicitly during the interpolation phase. If we want to compute daily averages we first perform the averaging and then kriging. However, the temporal interactions are not considered in the kriging model.