By Tomislav Hengl
The aim of this advisor is to help you in generating caliber maps through the use of absolutely operational open resource software program programs.
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Additional resources for A Practical Guide to Geostatistical Mapping
The most common techniques that belong to this group are: ✶ ✷ ✸ ✹ ✺ ✻ ✼ ✽ ✾ ✶✵ ✶✶ ✶✷ Thiessen polygons; ✶✸ Inverse distance interpolation; ✶✹ Regression on coordinates; ✶✺ Natural neighbors; ✶✻ Splines; ✶✼ ... ) LINEAR STATISTICAL (PROBABILITY) MODELS — In the case of statistical models, the model parameters are commonly estimated in an objective way, following probability theory. The predictions are accompanied with an estimate of the prediction error. A drawback is that the input data set usually need to satisfy strict statistical assumptions.
It is also important to note that there is a difference between the range factor and the range of spatial dependence, also known as the practical range. g. e. that is distance at which the semivariance is close to 95% of the sill (Fig. 10b). The target variable is said to be stationary if several sample variograms are ‘similar’ (if they do not differ statistically), which is referred to as the covariance stationarity or second order stationarity. In summary, three important requirements for ordinary kriging are: (1) the trend function is constant (µ =constant); (2) the variogram is constant in the whole area of interest; (3) the target variable follows (approximately) a normal distribution.
21) using objective criteria. 2), geographically weighted regression can be compared with regression-kriging with a moving window where variograms and regression models are estimated locally. The main benefit of geographically weighted regression (GWR) is that this method enables researchers to study local differences in responses to input variables. It therefore more often focuses on coefficients’ explanation than on interpolation of the endogenous variables. By setting up the search radius (bandwidth) one can investigate the impact of spatial proximity between the samples on the regression parameters.
A Practical Guide to Geostatistical Mapping by Tomislav Hengl