The modelling work is still at an evaluation stage.

expanse_multiyear

Version 0.1.0

This is a place to explore spatio-temporal modelling for multiple years at an annual scale

Project organization

``` . ├── .gitignore ├── CITATION.md ├── LICENSE.md ├── README.md ├── requirements.txt ├── bin <- Compiled and external code, ignored by git (PG) │ └── external <- Any external source code, ignored by git (RO) ├── config <- Configuration files (HW) ├── data <- All project data, ignored by git │ ├── processed <- The final, canonical data sets for modeling. (PG) │ ├── raw <- The original, immutable data dump. (RO) │ └── temp <- Intermediate data that has been transformed. (PG) ├── docs <- Documentation notebook for users (HW) │ ├── manuscript <- Manuscript source, e.g., LaTeX, Markdown, etc. (HW) │   └── reports <- Other project reports and notebooks (e.g. Jupyter, .Rmd) (HW) ├── results │ ├── figures <- Figures for the manuscript or reports (PG) │   └── output <- Other output for the manuscript or reports (PG) └── src <- Source code for this project (HW)

```

Code

In the folder src, you can find R code for implementing the multi-year modelling for annual average exposures for the entire Europe.

SLR, RF

00_ model_structure_clean.R gives the modelling process for supervised linear regression and random forests.

GTWR

src/00_gtwr_optimize3.R tunes the parameters for GTWR (lamda, ksi, and the equivalent temporal distance) using 5-fold CV.

src/01_gtwr_five_fold.R outputs 5-fold predictions for GTWR with the optimized parameters.

src/02_gtwr_all.R implements the geographically and temporally weighted regression using all data. This script also outputs the coefficient values in geotiff files.

Output random forests predictions at random points

01_combineRandomPointsPredictors.R 02_rf_randomAndEscape.R

Then visualize the results (combined with the predictions obtained from GEE for SLR, GWR, GTWR)

03_vis_escape2.R 03_vis_randomPoints2.R

License

This project is licensed under the terms of the MIT License

Citation

Please cite this project as described here.