R Python Google Earth Engine License: MIT

Reproducible workflow for county-level unemployment small-area estimation in Sweden using Fay–Herriot area-level models (R), with optional Python notebooks for pulling SCB inputs and a Google Earth Engine script for geospatial covariates.

• Thesis PDF: documentation/Msc Thesis II.pdf

.
├── R/ # R scripts (preprocess, visualization, SAE models)
├── Python_pull/ # Python notebooks to pull SCB inputs (PXWeb)
├── data/ # Inputs (SCB, GEE), shapefiles, and intermediate .RData
├── outputs/ # Generated figures/maps/reports (gitignored)
├── renv.lock, renv/ # R dependency lock + bootstrap
├── pyproject.toml, uv.lock # Python project metadata + lock (minimal)
└── qmarkdown/ # Thesis supporting document (Quarto)

1. Restore R dependencies (recommended):

   install.packages("renv")
   renv::restore()

2. Preprocess data + generate direct-estimate maps:

   source("R/3_visualization.R")

3. Fit Fay–Herriot models + write model outputs:

   source("R/test4.R")

4. Build the model summary table:

   source("R/report.R")

Key inputs used by R/2_sweden_preprocess.R:

• data/SWE_adm/SWE_adm1.shp (county boundaries)
• data/direct_estimates.csv (direct unemployment estimates)
• data/geodata.csv (GEE-derived covariates)
• data/popdensity.csv (population density)
• data/vacancies.csv (job vacancies)

Common outputs:

• data/processed_sweden.RData (processed data objects)
• outputs/html/sweden_direct_map.html (interactive map)
• outputs/img/sweden_direct_map_*.png (static maps)
• outputs/sae/ (SAE model artifacts, e.g. fh_models.rds, plots, tables)

outputs/ is generated and ignored by git.

See Python_pull/README.md and run the notebooks in Python_pull/notebooks/. They currently write:

• data/Pop_density.csv
• data/UnempDirect.csv

If you want these to feed the R pipeline as-is, rename them to match the filenames expected by R/2_sweden_preprocess.R (or update the R script).

For questions or suggestions, please open an issue or reach out via https://github.com/joseph-data