1 PRIO-Grid (10 points)

In this section, we will work with the PRIO-Grid dataset, a spatial grid with static and dynamic variables. The dataset provides information at a 0.5° x 0.5° resolution, allowing us to explore geographic features and socioeconomic indicators globally.

Download and prepare PRIO-Grid data (3 points)

Visit grid.prio.org and download the required data:1

  • Static variables
  • Dynamic variables for the year 2000
  • Geodata (under “Extensions”) .
  • The codebook and main paper

Also add the references to your bibliography and cite them. Briefly describe the structure of the dataset and the available information.

Save all files in your project directory and follow this code to combine all into one object:

Show code 
library("tidyverse")
library("janitor")
library("here")
library("sf")
library("RColorBrewer")

# Load shapefile
priogrid = sf::read_sf(here("data","PRIO-GRID","priogrid_cell.shp")) 

# load and combine prio grid data
priodata = read_csv(here("data","PRIO-GRID","PRIO-GRID Static Variables - 2024-12-16.csv"))

priodatadyn = read_csv(here("data","PRIO-GRID","PRIO-GRID Yearly Variables for 2000-2000 - 2024-12-16.csv"))

priogrid =  priogrid |>
  left_join(priodata, join_by(gid)) |> 
  left_join(priodatadyn, join_by(gid)) |>
  clean_names()

rm(priodata, priodatadyn)

Geographical features (3 points)

Make a visually pleasing map of one of the available geographic variables (e.g., forest cover, temperature, or mountains). Use a suitable projection. The goal is to show the resolution of the grid on a global scale rather than any particular insight, feel free to be creative.

Gross cell product (4 points)

  • Focus on one continent/large region and make two maps illustrating the distribution of nightlights and population.

  • For both maps, plot the natural logarithm of the respective variable and choose an appropriate color scale for your grid.

Footnotes

  1. Check the rest of the assignment and decide what variables you need or just download everything and select later.↩︎