 Hello, I'm Hans van der Kraas, senior lecturer at IT Doft Institute for Water Education. Today in the QGIS Open Day, I learned in a session hosted by QGIS Brazil from Sydney Govea how to do cridging in QGIS. In previous videos we've used two interpolation methods, the IDW, or inverse distance weighing, and the t-cent polygon method to interpolate these temperatures from meteor stations in the Netherlands. In this video we are going to use cridging. For this we need to install a plugin. The plugin is a so called experimental plugin, therefore we need to check this box. It's called the smart map plugin. In order to install it, we need to install skykit learn in python in the osgo4w shell. We open the location of the osgo4w shell and then we click right on it to run it as an administrator. Here we can run the setup to install python 3 in the osgo4w shell. So expand the command line section and check if you have python 3 installed, like in this case, otherwise you need to install it by clicking on those arrows. I already did that, so I am aborting this. Now I need to start the python 3 environment by typing py3 underscore n and now I can use python. And as the manual says I need to install skykit learn, which was already installed on my system. Now we can correctly install the plugin. Now we have this icon to start the plugin interface and you can define here the output folder where the raster layers of the interpolation will be saved. And as an input layer I use my stations and I choose the temperature field and I click import to import the table. And here we see a graphical result of that and I can define here the output pixel size. I'll put it on 1 km. Then I go to the interpolation tab and there I can get the semi-variogram, which it calculates from the points and I can change the settings such as the lag and I can change the different experimental variogram models. And this is an advantage over other gringing tools that are in QGIS because you need to fit a variogram to these points, so you need to have this graphical fitting window that we have here, which is very useful. We can graphically see what happens if you change the parameters. It also gives you the RMSE and the R-square of the fit. When you click interpolate it will do the interpolation. It will take some time and here's the interpolation result. It adds it to the layers panel and it's stored in the folder and we have this figure that is presented here. And if you click on it, it opens the picture and then you can also save the picture. Let's close the window and I copy the style from the others, drag it to the top and there we see our interpolation result. Keep in mind that for gringing you need a lot of points because it needs to calculate the lag distance to get the spatial autocorrelation. So it's always the question which interpolation method works best with your dataset. It's not that the most sophisticated one gives the best results.