1

Sample 50 points that fall within the boundaries of the city of Cologne. You will find the shapefile in the ./data folder.

The shapefile is not a raster file, so you need the sf::read_sf() function to load the data. Sampling is straightforward: Apply the sf::st_sample to the loaded shapefile, but make sure to apply the sf::st_as_sf() function afterward to receive a full-fledged data table (with a geometry column only).

2

Create a new raster layer comprising Cologne’s immigrant rates based on the raster layers from the previous exercises.

You would need the immigrants_cologne.tif and inhabitants_cologne.tif files in the ./data folder.

3

Extract the immigrant rate value at each position of the previously sampled points as a vector. What is your observation?

Remember that the German Census 2011 data could be more sparse due to some severe data protection measures.

4

Use an adequate method of raster extraction to gather information in the geographic surroundings of a point. What is your observation now?

Assume that people move in a 1,000-meter radius around their location. Thus, extracting information on buffers of 1,000 meters around the points might be interesting using the option sf::st_buffer() function. In that case, you should also set a descriptive statistics function, e.g., with the option fun = mean and its helpful companion option to consider missing values na.rm = TRUE.