By Tomislav Hengl
The aim of this advisor is to aid you in generating caliber maps through the use of totally operational open resource software program programs.
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Extra info for A Practical Guide to Geostatistical Mapping
In addition to estimation of values at new locations, a statistical spatial prediction technique produces a measure of associated uncertainty of making predictions by using a given model. e. the estimated variance of the prediction error. 5). Outputs from any statistical prediction model are commonly two maps: (1) predictions and (2) prediction variance. The mean of the prediction variance at all locations can be termed the overall prediction variance, and can be used as a measure of the overall precision of the final map: if the overall prediction variance gets close to the global variance, then the map is 100% imprecise; if the overall prediction variance tends to zero, then the map is 100% precise20 (see further Fig.
Suppose that there are n point observations, this yields n · (n − 1)/2 pairs for which a semivariance can be calculated. We can then plot all semivariances versus their separation distances, which will produce a variogram cloud as shown in Fig. 9b. Such clouds are not easy to describe visually, so the values are commonly averaged for a standard distance called the “lag”. If we display such averaged data, then we get a standard experimental or sample variogram as shown in Fig. 9c. What we usually expect to see is that semivariances are smaller at shorter distance and then they stabilize at some distance within the extent of a study area.
12: Ordinary kriging explained: EZ-Kriging. J. Walvoort, Wageningen University. J. Walvoort from the Alterra Research institute. The GUI of EZ21 ❤tt♣✿✴✴✇✇✇✳❣st❛t✳♦r❣✴♠❛♥✉❛❧✴♥♦❞❡✷✵✳❤t♠❧ ✷✵ ✷✶ 20 ✶ ✷ ✸ ✹ ✺ ✻ ✼ ✽ ✾ ✶✵ ✶✶ ✶✷ ✶✸ ✶✹ ✶✺ ✶✻ ✶✼ ✶✽ ✶✾ ✷✵ ✷✶ ✷✷ ✷✸ ✷✹ ✷✺ Geostatistical mapping Kriging consists of three panels: (1) data configuration panel, (2) variogram panel, and (3) kriging panel (Fig. 12). This allows you to zoom into ordinary kriging and explore its main characterizes and behavior: how do weights change for different variogram models, how do data values affect the weights, how does block size affect the kriging results etc.
A Practical Guide to Geostatistical Mapping by Tomislav Hengl