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Global patterns of deforestation

⛔ WARNING: This repository is deprecated and not maintained!

Background

In my 2014 master's thesis I adressed the issue of global deforestation ("Ursachen globaler Entwaldung – eine empirische Untersuchung unter Nutzung multivariater Analysemethoden"). I performed a factor-cluster analysis to identify patterns that drive global deforestation. The data is based on various FAO statistics which are publicly available, but are also included in the repository data\raw.

How to use it?

The code is written in R. As I produced it in my early R beginner times, it is certainly not the most appealing code and could use some major refactoring. Anyhow, it still works (last tested: February 2021).

Where to start?

You can simply perform the analysis by running the script run_analysis.R and it will call all relevant functions.

# This master script is responsible for launching and orchestrating the
# functions defined in the src folder

# Load libraries ----------------------------------------------------------

library(here)

# Load scripts ------------------------------------------------------------

# "01_data_engineering.R", 
# "02_factoranalysis.R", 
# "03_clusterBeschreiben.R", 
# "04_regressionanalysis.R"

path = here("src/")
pathnames <- list.files(pattern = "[.]R$", path = path, full.names = TRUE)

sapply(pathnames, FUN = source)

Of course, it is also possible to run the analysis step-by-step and execute the scripts manually. You then just have to do it in the right order: 01_data_engineering.R, 02_factoranalysis.R, 03_clusterBeschreiben.R and last 04_regressionanalysis.R.

Structure

├───config
├───data
│   ├───interim
│   ├───processed
│   └───raw
├───docs
├───output
│   ├───data
│   ├───plots
│   └───tex
├───rmd
└───src
    ├───dataeng
    └───plot

data

In data you will find the raw, interim and processed data. raw takes only the unaltered original statistics retrieved mainly from FAO. interim is used as a temporary storage for intermediate calculation. processed holds the cleaned and prepared dataset which is used for further analysis.

src

src contains all R scripts with subfolders for those dedicated to data engineering (dataeng) and plotting (plot).

output

Results of the analysis are stored in output. This can be .csv files (data), graphics (plots) or LaTeX snippets like tables (tex).

rmd and docs

R notebooks with some additional tests and plots to explore the data can be found in rmd. The knit result is stored as html file in docs.