This repository contains a structured workflow for geospatial data processing, raster preparation, covariate extraction and modelling workflow for population modeling in Malawi. The scripts are organized in sequential stages to ensure a reproducible and streamlined processing pipeline.
| File | Description |
|---|---|
00_Data_Processing2.R |
Initial preprocessing of household survey or enumeration data. |
01_Raster_Mosaicking_Buildings_2018.R |
Mosaicking of 2018 Google footprint rasters. |
01_Raster_Mosaicking_Buildings_2024.R |
Mosaicking of 2024 updated building footprint data. |
01_Raster_Mosaicking_Workflow_2018.R |
Full workflow script for 2018 covariates raster mosaicking automation. |
01_Raster_Mosaicking_Workflow_2024.R |
Automated workflow for mosaicking 2024 covariates raster data. |
02_Covariates_Extraction.R |
Extraction of geospatial covariates for modelling at the EA. |
04_Rasterize.R |
Converts vector geospatial layers into raster format for analysis. |
04_Covs_Stack_Raster_cropping.R |
Creating the prediction grid. |
README.md |
Overview and usage instructions (this file). |
The main goal of this project is to model household count and population for census preparation in Malawi
- Data Cleaning & Setup (
00_*.R) - Raster Mosaicking (
01_*.R)- Process and harmonize building footprints
- Covariate Preparation (
02_*.R) - Rasterization of Vector Inputs (
04_Rasterize.R) - Covariate Raster Stacking & Cropping (
04_Covs_*.R)
Make sure the following R packages are installed:
install.packages(c("tidyverse", "sf", "terra", "raster", "exactextractr"))