In the subsequent sections we are going to predict values of a spatially distributed variable at locations where it was not observed based on a set of observations. In the first example we make inferences on the mean annual rainfall in Germany based on a set of observations at 586 DWD weather stations. In the second example we predict the spatial distribution of Zinc in Lake Rangsdorf based on 32 sediment samples taken during a field survey in 2017.

The general procedure is outlined below. First, if necessary, we prepare the data for subsequent analysis. Then we investigate the sample variogram and propose a variogram model that fits the observational data. Then we evaluate the different models using the root mean squared error (RMSE) as model assessment metric. Finally we use the `scikit-gstat`

package for spatial prediction.

- Data preparation
- Sample variogram
- Variogram modelling
- Model evaluation
- Spatial prediction

Before we continue recall **Tobler's first law of geography**:

"Everything is related to everything else, but near things are more related than distant things."

**Citation**

The E-Learning project SOGA-Py was developed at the Department of Earth Sciences by Annette Rudolph, Joachim Krois and Kai Hartmann. You can reach us via mail by soga[at]zedat.fu-berlin.de.

You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: *Rudolph, A., Krois, J., Hartmann, K. (2023): Statistics and Geodata Analysis
using Python (SOGA-Py). Department of Earth Sciences, Freie Universitaet Berlin.*