Exercise 1_4_2: Fancier Raster Operations - Solution

1

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

Clues

The shapefile is no raster file, so that you would 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).

solution

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

cologne <-
  sf::read_sf("../data/cologne.shp")

cologne_50_points <-
  cologne %>% 
  sf::st_sample(50) %>% 
  sf::st_as_sf()

2

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

Clues

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

solution

library(raster)

## Loading required package: sp

## 
## Attaching package: 'raster'

## The following object is masked from 'package:dplyr':
## 
##     select

immigrant_rates <-
  raster::raster("../data/immigrants_cologne.tif") * 100 /
  raster::raster("../data/inhabitants_cologne.tif")

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition

3

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

Clues

Remember that the German Census 2011 data are relatively sparse due to some severe data protection measures.

solution

immigrant_rates_at_point <-
  raster::extract(immigrant_rates, cologne_50_points)

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition

## Warning in showSRID(SRS_string, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum European Terrestrial Reference System 1989 in CRS
## definition

# There may be a lot of missing values.

4

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

Clues

Assume that people move in a 500 meters radius around their location. Thus, extracting information on buffers of 500 meters around the points might be interesting using the option buffer = 500. In that case, you should also set a descriptive statistics function, e.g., with the option fun = mean.

solution

immigrant_rates_500m_buffer <-
  raster::extract(immigrant_rates, cologne_50_points, buffer = 500, fun = mean)

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition

## Warning in showSRID(SRS_string, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum European Terrestrial Reference System 1989 in CRS
## definition

# There may be a lot of missing values.