F Exercise 04

Last updated: 2026-02-12 16:04:45

F.1 Materials

The materials for this exercise are:

  • Vector layers (Chapter 7)
  • Geometric operations with vector layers (Chapter 8)

F.2 Question 1

  • Load the built-in data.frame object named world.cities from the maps package, as follows:
library(maps)
head(world.cities)
##                 name country.etc   pop   lat  long capital
## 1 'Abasan al-Jadidah   Palestine  5629 31.31 34.34       0
## 2 'Abasan al-Kabirah   Palestine 18999 31.32 34.35       0
## 3       'Abdul Hakim    Pakistan 47788 30.55 72.11       0
## 4 'Abdullah-as-Salam      Kuwait 21817 29.36 47.98       0
## 5              'Abud   Palestine  2456 32.03 35.07       0
## 6            'Abwein   Palestine  3434 32.03 35.20       0
  • The world.cities object is a table with information about world cities. The table includes the longitude and latitude of each city in the long and lat columns, respectively.
  • Choose a city which starts with the same letter as your first name. (In your code, you can use the city name and the country name for subsetting.)
  • Find the 5 nearest cities (excluding self) to the city you selected. (Hint: use st_distance to find the distances to all cities, then sort the cities by distance.)
  • Plot (Figure F.1):
    • the selected city,
    • the five other nearest cities,
    • text labels of the 6 city names, and
    • a convex hull polygon of all 6 cities combined.
  • Use axes=TRUE to show the axis coordinates in the plot.
Mexico City and five nearest other cities

Figure F.1: Mexico City and five nearest other cities

(50 points)

F.3 Question 2

  • Read 'rainfall.csv' into a data.frame, and transform it into an sf layer (See 7.4)
  • Read 'nafot.shp', which includes polygons of “Nafa” administrative regions in Israel
  • Calculate the average density of rainfall stations (stations per \(km^2\)) per “Nafa” polygon, i.e., the count of the stations falling into each “Nafa”, divided by the area of the “Nafa” in \(km^2\)
  • Plot the densities. Use a sequential color palette, going from white to dark shades of a specific color, such as pal=function(n) hcl.colors(n,'Reds',rev=TRUE) (F.2).
Average population density ($1/km^2$) in each "Nafa"

Figure F.2: Average population density (\(1/km^2\)) in each “Nafa”

(50 points)