From 58a571dfb603bad56976da1fb652b4011cca274d Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Thu, 1 Jan 2026 17:32:21 +0000
Subject: [PATCH 01/31] chore: gitignore data dir and Rproj file

---
 .gitignore | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..9b17743
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,8 @@
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
+
+*.Rproj
+
+data/*

From cd7e64dac708db0d627748b34c9a46d3cadb641d Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Thu, 1 Jan 2026 17:32:52 +0000
Subject: [PATCH 02/31] feat: Add script for package installation

---
 load_required_libraries.R | 41 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)
 create mode 100644 load_required_libraries.R

diff --git a/load_required_libraries.R b/load_required_libraries.R
new file mode 100644
index 0000000..a9edc06
--- /dev/null
+++ b/load_required_libraries.R
@@ -0,0 +1,41 @@
+load_library <- function(library_name) {
+  #' Check if named library installed, if so load, if not install and load
+  #'
+  #' @param library_name (character). The name of the library to be installed/loaded.
+  #'
+  if (library_name %in% rownames(installed.packages()) == FALSE) {
+    print(paste0("The required library '", library_name, "' is not installed, installing now."))
+    install.packages(library_name, dependencies = TRUE)
+    library(library_name, character.only = TRUE)
+  } else {
+    library(library_name, character.only = TRUE)
+  }
+}
+
+load_libraries <- function(library_names) {
+  #' Apply the load_library function across a vector of library names.
+  #'
+  #' @param library_names (vector). A vector of libraries to be loaded (or installed).
+  #'
+  sapply(library_names, load_library)
+}
+
+# Add any required packages to this list
+required_libraries <- c(
+  "sf", # package for handling spatial data
+  "tidyverse", # tidyverse
+  "haven", # package for import foreign statistical formats
+  "terra", # package for spatial data analysis
+  "tictoc", # package for timing R Scripts
+  "exactextractr", # zonal statistics of polygons
+  "gstat", # Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation
+  "spdep", # spatial dependence and weights
+  "car", # Companion to Applied Regression
+  "caret", # Classification And REgression Training
+  "kableExtra", # for html tables
+  "inlabru", # package for Bayesian spatial modelling 
+  "feather" # reading and writing feather files
+)
+
+# Execute code to load (and install) libraries
+load_libraries(required_libraries)

From 550946e30ee6f066a05c009a0dc3998dae2f6bc8 Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Thu, 1 Jan 2026 17:33:16 +0000
Subject: [PATCH 03/31] refactor: Update file paths for ONS versions

---
 00_Data_Processing2.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/00_Data_Processing2.R b/00_Data_Processing2.R
index bbf1c52..8d66ffa 100644
--- a/00_Data_Processing2.R
+++ b/00_Data_Processing2.R
@@ -9,8 +9,8 @@ library(tidyverse)
 options(scipen = 999) # turn off scientific notation for all variables
 
 #Specify Drive Path
-drive_path <- "//Internal/MALAWI/Ortis/"
-input_path <- paste0(drive_path, "Input_Data/Surveys/")
+drive_path <- "./data/"
+input_path <- paste0(drive_path, "mnso_ons_data.20251216.dat/")
 output_path <- paste0(drive_path, "/Output_Data/")
 shapefile_path <- paste0(drive_path, "Input_Data/Shapefiles/")
 

From 67976a765cb9e3327ba2b34ab11ef89aab1103b7 Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Fri, 9 Jan 2026 10:37:07 +0000
Subject: [PATCH 04/31] refactor: Enable execution of 00 script on local
 machines

---
 00_Data_Processing2.R | 29 ++++++++++++++++++++---------
 1 file changed, 20 insertions(+), 9 deletions(-)

diff --git a/00_Data_Processing2.R b/00_Data_Processing2.R
index 8d66ffa..8f3eb10 100644
--- a/00_Data_Processing2.R
+++ b/00_Data_Processing2.R
@@ -10,16 +10,16 @@ options(scipen = 999) # turn off scientific notation for all variables
 
 #Specify Drive Path
 drive_path <- "./data/"
-input_path <- paste0(drive_path, "mnso_ons_data.20251216.dat/")
+input_path <- paste0(drive_path, "MNSO-Data/")
 output_path <- paste0(drive_path, "/Output_Data/")
-shapefile_path <- paste0(drive_path, "Input_Data/Shapefiles/")
+shapefile_path <- paste0(drive_path, "Shapefiles/")
 
 #Load datasets
 mphc_2018 <- read_dta(paste0(input_path, "mphc2018Data_AllRegions.dta"))
 ICT_data <- read_dta(paste0(input_path, "ICT Listing WorldPop.dta"))
 IHS6_data <- read_dta(paste0(input_path, "IHS6 Listing WorldPop.dta"))
 Naca_data <- read_dta(paste0(input_path, "Naca Listing WorldPop.dta"))
-ea <- st_read(file.path(shapefile_path, "EA_Shapefile.shp"))
+ea <- st_read(file.path(shapefile_path, "2018_MPHC_EAs_Final_for_Use.shp")) # replaces "EA_Shapefile.shp"
 
 #####################################################################################
 ####################################################################################
@@ -29,7 +29,7 @@ ea <- st_read(file.path(shapefile_path, "EA_Shapefile.shp"))
 mphc_2018_no_gps <- mphc_2018 %>% 
   filter(is.na(hh_longitude) | is.na(hh_latitude))
 
-#Add additonal digits to EA and TA code
+#Add additional digits to EA and TA code
 
 mphc_2018_no_gps <- mphc_2018_no_gps %>%  
   mutate(new_ta = str_pad(ta, width = 2, pad = 0),
@@ -56,7 +56,7 @@ mphc_pop_no_gps <- mphc_2018_no_gps %>%
             female_count = sum(p03 == 2, na.rm = TRUE))
 
 
-#Create a bin for each age catgeory
+#Create a bin for each age category
 age_summary_no_gps <- mphc_2018_no_gps %>%  
   mutate(age_group = case_when(
     p05 < 1  ~ "age_group_01_less",      #less than 1
@@ -128,10 +128,11 @@ mphc_pop_no_gps <- mphc_pop_no_gps %>%
  mphc_2018_sf$EA_CODE <- nearest_ids
  
  #Write to file
- #st_write(mphc_2018_sf , 
- # dsn = file.path(output_path, "mphc_2018_sf_ea.gpkg"), 
- # driver = "GPKG", 
- # delete_layer = TRUE)
+ st_write(mphc_2018_sf , 
+  dsn = file.path(output_path, "mphc_2018_sf_ea.gpkg"), 
+  driver = "GPKG", 
+  delete_layer = TRUE
+  )
  
  #load dataset
  #mphc_2018_sf <- st_read(paste0(output_path, "mphc_2018_sf_ea.gpkg"))
@@ -143,6 +144,16 @@ mphc_pop_no_gps <- mphc_pop_no_gps %>%
  #check the summary of gps accuracy
  summary(mphc_2018_df$hh_gps_accuracy)
  
+# NOTE: ONS CHANGE: Adding a 'hh_count' column of 1 per row. This is a required 
+# column below but is not in the source data. 
+# This change assumes that each row corresponds to a single resident of Malawi in
+# the census records.
+# Justification: total rows in source data is 17,563,749, matching the published
+# population count for the census. Also this replicates the logic of the no_gps 
+# processing above. 
+mphc_2018_df <- mphc_2018_df %>% 
+   mutate(hh_count = 1)  # Individual observation
+ 
 # Summarize data base on their spatial location
  
 mphc_2018_pop_spatial <- mphc_2018_df %>%  

From 65b4f2959e45f98f8d6610e04e0e07c155b2f532 Mon Sep 17 00:00:00 2001
From: Timothy S Green <62939263+TSGreen@users.noreply.github.com>
Date: Fri, 9 Jan 2026 15:33:12 +0000
Subject: [PATCH 05/31] Add MIT License to the project

---
 LICENSE | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 LICENSE

diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..4d009d2
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Office for National Statistics
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

From 66ab0b264e8e687b3fe106f720960ffba23c0a01 Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Fri, 9 Jan 2026 15:34:24 +0000
Subject: [PATCH 06/31] refactor: Update building mosaic script for
 compatibility with existing data

---
 01_Raster_Mosaicking_Buildings_2018.R | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/01_Raster_Mosaicking_Buildings_2018.R b/01_Raster_Mosaicking_Buildings_2018.R
index 0fc481e..5d96165 100644
--- a/01_Raster_Mosaicking_Buildings_2018.R
+++ b/01_Raster_Mosaicking_Buildings_2018.R
@@ -1,26 +1,26 @@
-#Script to mosaic rasters using neighboring countries
+# Script to mosaic rasters using neighboring countries
 
 library(terra)
 library(sf)
 library(tictoc)
 
 # Specify data path
-drive_path <- "//Working/MALAWI/Ortis/"
-base_path <- paste0(drive_path, "Input_Data/") ## Base path where the folders are located
-shp_path <- paste0(drive_path, "Input_Data/Shapefiles/") ## Shapefile path
-result_path <- paste0(drive_path, "Input_Data/Mosaic_Buildings_2018/") # Result path
-building_path <- paste0(drive_path, "Input_Data/Malawi_Covs/2018_Buildings/")
+drive_path <- "./data"
+base_path <- paste0(drive_path) ## Base path where the folders are located
+shp_path <- paste0(drive_path, "/Shapefiles/") ## Shapefile path
+result_path <- paste0(drive_path, "/Mosaic_Buildings/") # Result path
+building_path <- paste0(drive_path, "/covariate_data/")
 
 #Load data
-boundary <- st_read(paste0(shp_path, "Country_Shapefile_Buffer_10km.shp"))
-r1 <- rast(paste0(building_path, "mwi_buildings_count_2018_glv2_5_t0_5_C_100m_v1.tif"))
+boundary <- st_read(paste0(shp_path, "LU.shp")) # replacing: "Country_Shapefile_Buffer_10km.shp"
+r1 <- rast(paste0(building_path, "mwi_buildings_count_BCB_ms_100m_v1_1.tif")) # replacing: "mwi_buildings_count_2018_glv2_5_t0_5_C_100m_v1.tif"
 
 #Reproject boundary to r1
 boundary <- st_transform(boundary, crs = st_crs(r1))
 
 # Define folder names
-folders <- c("Malawi_Covs/2018_Buildings","Mozambique_Covs/2018_Buildings", 
-             "Tanzania_Covs/2018_Buildings", "Zambia_Covs/2018_Buildings")
+folders <- c("Malawi_Covs/Buildings", "Tanzania_Covs/Buildings",
+             "Mozambique_Covs/Buildings", "Zambia_Covs/Buildings")
 
 
 # Initialize a list to store raster file names

From 14b7eb0062db6095fb0503099bce20bbb231f877 Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Thu, 15 Jan 2026 16:59:21 +0000
Subject: [PATCH 07/31] refactor: Update paths to EA shapefiles and data dir

---
 01_Raster_Mosaicking_Workflow_2018.R | 2 +-
 01_Raster_Mosaicking_Workflow_2024.R | 2 +-
 02_Covariates_Extraction.R           | 6 +++---
 03_HH_Model_Workflow_2024c.R         | 2 +-
 03_Pop_Model_Workflow_2018.R         | 4 ++--
 04_Covs_Stack_Raster_cropping.R      | 6 +++---
 04_Rasterize.R                       | 6 +++---
 7 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/01_Raster_Mosaicking_Workflow_2018.R b/01_Raster_Mosaicking_Workflow_2018.R
index cf0b1b9..e4a2094 100644
--- a/01_Raster_Mosaicking_Workflow_2018.R
+++ b/01_Raster_Mosaicking_Workflow_2018.R
@@ -3,7 +3,7 @@ library(sf)
 library(tictoc)
 
 # Specify data path
-drive_path <- "//Working/MALAWI/Ortis/"
+drive_path <- "./data/"
 base_path <- paste0(drive_path, "Input_Data/") ## Base path where the folders are located
 shp_path <- paste0(drive_path, "Input_Data/Shapefiles/") ## Shapefile path
 result_path <- paste0(drive_path, "Input_Data/Mosaic_Covariates_2018/") # Result path
diff --git a/01_Raster_Mosaicking_Workflow_2024.R b/01_Raster_Mosaicking_Workflow_2024.R
index 63c957b..3824dfd 100644
--- a/01_Raster_Mosaicking_Workflow_2024.R
+++ b/01_Raster_Mosaicking_Workflow_2024.R
@@ -3,7 +3,7 @@ library(sf)
 library(tictoc)
 
 # Specify data path
-drive_path <- "//Working/MALAWI/Ortis/"
+drive_path <- "./data/"
 base_path <- paste0(drive_path, "Input_Data/") ## Base path where the folders are located
 shp_path <- paste0(drive_path, "Input_Data/Shapefiles/") ## Shapefile path
 result_path <- paste0(drive_path, "Input_Data/Mosaic_Covariates_2024/") # Result path
diff --git a/02_Covariates_Extraction.R b/02_Covariates_Extraction.R
index abcfc97..539dbab 100644
--- a/02_Covariates_Extraction.R
+++ b/02_Covariates_Extraction.R
@@ -9,9 +9,9 @@ library(terra)
 library(exactextractr)
 
 #Specify Drive Path
-drive_path <- "//Working/MALAWI/Ortis/"
+drive_path <- "./data/"
 input_path <- paste0(drive_path, "Output_Data/")
-shapefile_path <- paste0(drive_path, "Input_Data/Shapefiles/")
+shapefile_path <- paste0(drive_path, "Shapefiles/")
 covs_path_2018 <- paste0(drive_path, "Input_Data/Mosaic_Covariates_2018/")
 covs_path_2024 <- paste0(drive_path, "Input_Data/Mosaic_Covariates_2024/")
 bcount_path_2018 <- paste0(drive_path, "Input_Data/Mosaic_Buildings_2018/")
@@ -20,7 +20,7 @@ output_path <- paste0(drive_path, "Output_Data/")
 
 
 # Load dataset ------------------------------------------------------------
-ea <- st_read(file.path(shapefile_path, "EA_Shapefile.shp"))
+ea <- st_read(file.path(shapefile_path, "2018_MPHC_EAs_Final_for_Use.shp")) # replaces "EA_Shapefile.shp"
 pop_data <- read.csv(file.path (input_path, "summarized_survey_data.csv"))
 r1 <- rast(file.path(bcount_path_2024, "MOS_MLW_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
 
diff --git a/03_HH_Model_Workflow_2024c.R b/03_HH_Model_Workflow_2024c.R
index e90df29..39bdccf 100644
--- a/03_HH_Model_Workflow_2024c.R
+++ b/03_HH_Model_Workflow_2024c.R
@@ -17,7 +17,7 @@ options(scipen = 999) # turn off scientific notation for all variables
 #options(digits = 3)
 
 #Specify Drive Path
-drive_path <- "//Working/MALAWI/Ortis/"
+drive_path <- "./data/"
 input_path <- paste0(drive_path, "Output_Data/")
 shapefile_path <- paste0(drive_path, "Input_Data/Shapefiles/")
 output_path <- paste0(drive_path, "Output_Data/")
diff --git a/03_Pop_Model_Workflow_2018.R b/03_Pop_Model_Workflow_2018.R
index f7c9796..e9aa2a7 100644
--- a/03_Pop_Model_Workflow_2018.R
+++ b/03_Pop_Model_Workflow_2018.R
@@ -18,9 +18,9 @@ options(scipen = 999) # turn off scientific notation for all variables
 #options(digits = 3)
 
 #Specify Drive Path
-drive_path <- "//Working/MALAWI/Ortis/"
+drive_path <- "./data/"
 input_path <- paste0(drive_path, "Output_Data/")
-shapefile_path <- paste0(drive_path, "Input_Data/Shapefiles/")
+shapefile_path <- paste0(drive_path, "Shapefiles/")
 output_path <- paste0(drive_path, "Output_Data/")
 output_path1 <- paste0(drive_path, "Output_Data/Pop_Rasters/")
 
diff --git a/04_Covs_Stack_Raster_cropping.R b/04_Covs_Stack_Raster_cropping.R
index 727b45b..ebdb9af 100644
--- a/04_Covs_Stack_Raster_cropping.R
+++ b/04_Covs_Stack_Raster_cropping.R
@@ -6,10 +6,10 @@ library(feather)
 library(sf)
 
 #Specify Drive Path
-drive_path <- "//Working/MALAWI/Ortis/"
+drive_path <- "./data/"
 covs_path_2024 <- paste0(drive_path, "Input_Data/Mosaic_Covariates_2024/")
 output_path <- paste0(drive_path, "Output_Data/")
-shapefile_path <- paste0(drive_path, "Input_Data/Shapefiles/")
+shapefile_path <- paste0(drive_path, "Shapefiles/")
 bcount_path_2024 <- paste0(drive_path, "Input_Data/Mosaic_Buildings_2024/")
 
 
@@ -143,7 +143,7 @@ prediction_covs <- prediction_covs |>
 
 #Read EA shapefiles and join to data
 
-ea <- st_read(file.path(shapefile_path, "EA_Shapefile.shp"))
+ea <- st_read(file.path(shapefile_path, "2018_MPHC_EAs_Final_for_Use.shp")) # replaces "EA_Shapefile.shp"
   
 #create unique id for each district
 district <- ea |> 
diff --git a/04_Rasterize.R b/04_Rasterize.R
index 5ad31c4..4bf0673 100644
--- a/04_Rasterize.R
+++ b/04_Rasterize.R
@@ -8,13 +8,13 @@ library(raster)
 
 
 #Specify Drive Path
-drive_path <- "//Working/MALAWI/Ortis/"
+drive_path <- "./data/"
 output_path <- paste0(drive_path, "Output_Data/")
-shapefile_path <- paste0(drive_path, "Input_Data/Shapefiles/")
+shapefile_path <- paste0(drive_path, "Shapefiles/")
 bcount_path_2024 <- paste0(drive_path, "Input_Data/Mosaic_Buildings_2024/")
 
 #Load datasets
-ea <- st_read(file.path(shapefile_path, "EA_Shapefile.shp"))
+ea <- st_read(file.path(shapefile_path, "2018_MPHC_EAs_Final_for_Use.shp")) # replaces "EA_Shapefile.shp"
 bcount <- rast(file.path(bcount_path_2024, "MOS_MLW_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
 country <- st_read(file.path(shapefile_path, "Country_Shapefile.shp"))
 hh_size <- read.csv(paste0(output_path, "summarized_survey_data.csv"))

From 4cfbede82affee1e9f1e73af9bf2774f9391be58 Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Tue, 27 Jan 2026 14:50:15 +0000
Subject: [PATCH 08/31] List of covariets needed for model and urls to access
 data in WP website

---
 src_covariates/worldpop_covariets.txt |  62 +++++
 src_covariates/worldpop_file_urls.md  | 373 ++++++++++++++++++++++++++
 2 files changed, 435 insertions(+)
 create mode 100644 src_covariates/worldpop_covariets.txt
 create mode 100644 src_covariates/worldpop_file_urls.md

diff --git a/src_covariates/worldpop_covariets.txt b/src_covariates/worldpop_covariets.txt
new file mode 100644
index 0000000..2181c95
--- /dev/null
+++ b/src_covariates/worldpop_covariets.txt
@@ -0,0 +1,62 @@
+buildings_count_BCB_gl_100m_v1_1
+buildings_count_BCB_ms_100m_v1_1
+buildings_cv_area_BCB_gl_100m_v1_1
+buildings_cv_area_BCB_ms_100m_v1_1
+buildings_cv_area_PIB_gl_100m_v1_1
+buildings_cv_area_PIB_ms_100m_v1_1
+buildings_cv_length_BCB_gl_100m_v1_1
+buildings_cv_length_BCB_ms_100m_v1_1
+buildings_cv_length_PIB_gl_100m_v1_1
+buildings_cv_length_PIB_ms_100m_v1_1
+buildings_density_BCB_gl_100m_v1_1
+buildings_density_BCB_ms_100m_v1_1
+buildings_density_PIB_gl_100m_v1_1
+buildings_density_PIB_ms_100m_v1_1
+buildings_mean_area_BCB_gl_100m_v1_1
+buildings_mean_area_BCB_ms_100m_v1_1
+buildings_mean_area_PIB_gl_100m_v1_1
+buildings_mean_area_PIB_ms_100m_v1_1
+buildings_mean_length_BCB_gl_100m_v1_1
+buildings_mean_length_BCB_ms_100m_v1_1
+buildings_mean_length_PIB_gl_100m_v1_1
+buildings_mean_length_PIB_ms_100m_v1_1
+buildings_total_area_BCB_gl_100m_v1_1
+buildings_total_area_BCB_ms_100m_v1_1
+buildings_total_area_PIB_gl_100m_v1_1
+buildings_total_area_PIB_ms_100m_v1_1
+buildings_total_length_BCB_gl_100m_v1_1
+buildings_total_length_BCB_ms_100m_v1_1
+buildings_total_length_PIB_gl_100m_v1_1
+buildings_total_length_PIB_ms_100m_v1_1
+built_S_dist_2024_GHS_MGW_100m_v1
+built_S_GHS_U_wFGW_100m_v1_2024
+built_S_NRES_GHS_U_wFGW_100m_v1_2024
+built_V_GHS_U_wFGW_100m_v1_2024
+built_V_NRES_GHS_U_wFGW_100m_v1_2024
+coastline_dst_100m_v1
+dist_inland_water_100m_esa_2021_v1
+elevation_merit103_100m_v1
+esalc_11_dst_2022_100m_v1
+esalc_130_dst_2022_100m_v1
+esalc_140_dst_2022_100m_v1
+esalc_150_dst_2022_100m_v1
+esalc_160_dst_2022_100m_v1
+esalc_190_dst_2022_100m_v1
+esalc_200_dst_2022_100m_v1
+esalc_210_dst_2022_100m_v1
+esalc_40_dst_2022_100m_v1
+highway_dist_osm_2023_100m_v1
+inland_water_pct_100m_v1
+ms_roads_count_100m_v1
+ms_roads_density_100m_v1
+ms_roads_dst_100m_v1
+ms_roads_total_length_100m_v1
+ppt_2023_yravg_tc_100m_v1
+rd_intrs_dist_osm_2023_100m_v1
+slope_merit103_100m_v1
+tavg_2023_tlst_100m_v1
+viirs_fvf_2023_100m_v1
+viirs_nvf_2023_100m_v1
+waterbodies_dist_osm_2023_100m_v1
+WDPA_pre2022_cat0_dist_100m_v1
+WDPA_pre2022_cat1_dist_100m_v1
\ No newline at end of file
diff --git a/src_covariates/worldpop_file_urls.md b/src_covariates/worldpop_file_urls.md
new file mode 100644
index 0000000..051f814
--- /dev/null
+++ b/src_covariates/worldpop_file_urls.md
@@ -0,0 +1,373 @@
+# WorldPop Covariate File URLs for Malawi, Tanzania, Mozambique, and Zambia
+
+1. buildings_count_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/BCB/v1_1/mwi_buildings_count_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/BCB/v1_1/tza_buildings_count_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/BCB/v1_1/moz_buildings_count_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/BCB/v1_1/zmb_buildings_count_BCB_gl_100m_v1_1.tif
+
+2. buildings_count_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/BCB/v1_1/mwi_buildings_count_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/BCB/v1_1/tza_buildings_count_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/BCB/v1_1/moz_buildings_count_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/BCB/v1_1/zmb_buildings_count_BCB_ms_100m_v1_1.tif
+
+3. buildings_cv_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/BCB/v1_1/mwi_buildings_cv_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/BCB/v1_1/tza_buildings_cv_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/BCB/v1_1/moz_buildings_cv_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/BCB/v1_1/zmb_buildings_cv_area_BCB_gl_100m_v1_1.tif
+
+4. buildings_cv_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/BCB/v1_1/mwi_buildings_cv_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/BCB/v1_1/tza_buildings_cv_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/BCB/v1_1/moz_buildings_cv_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/BCB/v1_1/zmb_buildings_cv_area_BCB_ms_100m_v1_1.tif
+
+5. buildings_cv_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/PIB/v1_1/mwi_buildings_cv_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/PIB/v1_1/tza_buildings_cv_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/PIB/v1_1/moz_buildings_cv_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/PIB/v1_1/zmb_buildings_cv_area_PIB_gl_100m_v1_1.tif
+
+6. buildings_cv_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/PIB/v1_1/mwi_buildings_cv_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/PIB/v1_1/tza_buildings_cv_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/PIB/v1_1/moz_buildings_cv_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/PIB/v1_1/zmb_buildings_cv_area_PIB_ms_100m_v1_1.tif
+
+7. buildings_cv_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/BCB/v1_1/mwi_buildings_cv_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/BCB/v1_1/tza_buildings_cv_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/BCB/v1_1/moz_buildings_cv_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/BCB/v1_1/zmb_buildings_cv_length_BCB_gl_100m_v1_1.tif
+
+8. buildings_cv_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/BCB/v1_1/mwi_buildings_cv_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/BCB/v1_1/tza_buildings_cv_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/BCB/v1_1/moz_buildings_cv_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/BCB/v1_1/zmb_buildings_cv_length_BCB_ms_100m_v1_1.tif
+
+9. buildings_cv_length_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/PIB/v1_1/mwi_buildings_cv_length_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/PIB/v1_1/tza_buildings_cv_length_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/PIB/v1_1/moz_buildings_cv_length_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/PIB/v1_1/zmb_buildings_cv_length_PIB_gl_100m_v1_1.tif
+
+10. buildings_cv_length_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/PIB/v1_1/mwi_buildings_cv_length_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/PIB/v1_1/tza_buildings_cv_length_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/PIB/v1_1/moz_buildings_cv_length_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/PIB/v1_1/zmb_buildings_cv_length_PIB_ms_100m_v1_1.tif
+
+11. buildings_density_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/BCB/v1_1/mwi_buildings_density_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/BCB/v1_1/tza_buildings_density_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/BCB/v1_1/moz_buildings_density_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/BCB/v1_1/zmb_buildings_density_BCB_gl_100m_v1_1.tif
+
+12. buildings_density_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/BCB/v1_1/mwi_buildings_density_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/BCB/v1_1/tza_buildings_density_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/BCB/v1_1/moz_buildings_density_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/BCB/v1_1/zmb_buildings_density_BCB_ms_100m_v1_1.tif
+
+13. buildings_density_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/PIB/v1_1/mwi_buildings_density_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/PIB/v1_1/tza_buildings_density_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/PIB/v1_1/moz_buildings_density_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/PIB/v1_1/zmb_buildings_density_PIB_gl_100m_v1_1.tif
+
+14. buildings_density_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/PIB/v1_1/mwi_buildings_density_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/PIB/v1_1/tza_buildings_density_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/PIB/v1_1/moz_buildings_density_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/PIB/v1_1/zmb_buildings_density_PIB_ms_100m_v1_1.tif
+
+15. buildings_mean_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/BCB/v1_1/mwi_buildings_mean_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/BCB/v1_1/tza_buildings_mean_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/BCB/v1_1/moz_buildings_mean_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/BCB/v1_1/zmb_buildings_mean_area_BCB_gl_100m_v1_1.tif
+
+16. buildings_mean_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/BCB/v1_1/mwi_buildings_mean_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/BCB/v1_1/tza_buildings_mean_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/BCB/v1_1/moz_buildings_mean_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/BCB/v1_1/zmb_buildings_mean_area_BCB_ms_100m_v1_1.tif
+
+17. buildings_mean_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/PIB/v1_1/mwi_buildings_mean_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/PIB/v1_1/tza_buildings_mean_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/PIB/v1_1/moz_buildings_mean_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/PIB/v1_1/zmb_buildings_mean_area_PIB_gl_100m_v1_1.tif
+
+18. buildings_mean_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/PIB/v1_1/mwi_buildings_mean_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/PIB/v1_1/tza_buildings_mean_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/PIB/v1_1/moz_buildings_mean_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/PIB/v1_1/zmb_buildings_mean_area_PIB_ms_100m_v1_1.tif
+
+19. buildings_mean_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/BCB/v1_1/mwi_buildings_mean_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/BCB/v1_1/tza_buildings_mean_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/BCB/v1_1/moz_buildings_mean_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/BCB/v1_1/zmb_buildings_mean_length_BCB_gl_100m_v1_1.tif
+
+20. buildings_mean_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/BCB/v1_1/mwi_buildings_mean_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/BCB/v1_1/tza_buildings_mean_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/BCB/v1_1/moz_buildings_mean_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/BCB/v1_1/zmb_buildings_mean_length_BCB_ms_100m_v1_1.tif
+
+21. buildings_mean_length_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/PIB/v1_1/mwi_buildings_mean_length_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/PIB/v1_1/tza_buildings_mean_length_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/PIB/v1_1/moz_buildings_mean_length_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/PIB/v1_1/zmb_buildings_mean_length_PIB_gl_100m_v1_1.tif
+
+22. buildings_mean_length_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/PIB/v1_1/mwi_buildings_mean_length_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/PIB/v1_1/tza_buildings_mean_length_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/PIB/v1_1/moz_buildings_mean_length_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/PIB/v1_1/zmb_buildings_mean_length_PIB_ms_100m_v1_1.tif
+
+23. buildings_total_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/BCB/v1_1/mwi_buildings_total_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/BCB/v1_1/tza_buildings_total_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/BCB/v1_1/moz_buildings_total_area_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/BCB/v1_1/zmb_buildings_total_area_BCB_gl_100m_v1_1.tif
+
+24. buildings_total_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/BCB/v1_1/mwi_buildings_total_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/BCB/v1_1/tza_buildings_total_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/BCB/v1_1/moz_buildings_total_area_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/BCB/v1_1/zmb_buildings_total_area_BCB_ms_100m_v1_1.tif
+
+25. buildings_total_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/PIB/v1_1/mwi_buildings_total_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/PIB/v1_1/tza_buildings_total_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/PIB/v1_1/moz_buildings_total_area_PIB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/PIB/v1_1/zmb_buildings_total_area_PIB_gl_100m_v1_1.tif
+
+26. buildings_total_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/PIB/v1_1/mwi_buildings_total_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/PIB/v1_1/tza_buildings_total_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/PIB/v1_1/moz_buildings_total_area_PIB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/PIB/v1_1/zmb_buildings_total_area_PIB_ms_100m_v1_1.tif
+
+27. buildings_total_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/gl/BCB/v1_1/mwi_buildings_total_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/gl/BCB/v1_1/tza_buildings_total_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/gl/BCB/v1_1/moz_buildings_total_length_BCB_gl_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/gl/BCB/v1_1/zmb_buildings_total_length_BCB_gl_100m_v1_1.tif
+
+28. buildings_total_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/buildings/ms/BCB/v1_1/mwi_buildings_total_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/buildings/ms/BCB/v1_1/tza_buildings_total_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/buildings/ms/BCB/v1_1/moz_buildings_total_length_BCB_ms_100m_v1_1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/buildings/ms/BCB/v1_1/zmb_buildings_total_length_BCB_ms_100m_v1_1.tif
+
+29. buildings_total_length_PIB_gl_100m_v1_1.tif
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+30. buildings_total_length_PIB_ms_100m_v1_1.tif
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+31. built_S_dist_2024_GHS_MGW_100m_v1.tif
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+32. built_S_GHS_U_wFGW_100m_v1_2024.tif 2024 and 2018
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+33. built_S_NRES_GHS_U_wFGW_100m_v1_2024.tif
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+
+34. built_V_GHS_U_wFGW_100m_v1_2024.tif
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+35. built_V_NRES_GHS_U_wFGW_100m_v1_2024.tif
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+36. coastline_dst_100m_v1.tif
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+37. dist_inland_water_100m_esa_2021_v1.tif
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+38. elevation_merit103_100m_v1.tif
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+39. esalc_11_dst_2022_100m_v1.tif
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+40. esalc_130_dst_2022_100m_v1.tif
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+41. esalc_140_dst_2022_100m_v1.tif
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+42. esalc_150_dst_2022_100m_v1.tif
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+43. esalc_160_dst_2022_100m_v1.tif
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+44. esalc_190_dst_2022_100m_v1.tif
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+45. esalc_200_dst_2022_100m_v1.tif
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+46. esalc_210_dst_2022_100m_v1.tif
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+47. esalc_40_dst_2022_100m_v1.tif
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+48. highway_dist_osm_2023_100m_v1.tif
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+
+49. inland_water_pct_100m_v1.tif
+
+50. ms_roads_count_100m_v1.tif
+
+51. ms_roads_density_100m_v1.tif
+
+52. ms_roads_dst_100m_v1.tif
+
+53. ms_roads_total_length_100m_v1.tif
+
+54. ppt_2023_yravg_tc_100m_v1.tif
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+55. rd_intrs_dist_osm_2023_100m_v1.tif
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+56. slope_merit103_100m_v1.tif
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+
+57. tavg_2023_tlst_100m_v1.tif
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+
+58. viirs_fvf_2023_100m_v1.tif
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+
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+59. viirs_nvf_2023_100m_v1.tif
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+
+60. waterbodies_dist_osm_2023_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/OSM/v1/mwi_waterbodies_dist_osm_2023_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/OSM/v1/tza_waterbodies_dist_osm_2023_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/OSM/v1/moz_waterbodies_dist_osm_2023_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/OSM/v1/zmb_waterbodies_dist_osm_2023_100m_v1.tif
+
+61. WDPA_pre2022_cat0_dist_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/WDPA/v1/cat0//mwi_WDPA_pre2022_cat0_dist_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/WDPA/v1/cat0/tza_WDPA_pre2022_cat0_dist_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/WDPA/v1/cat0/moz_WDPA_pre2022_cat0_dist_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/WDPA/v1/cat0/zmb_WDPA_pre2022_cat0_dist_100m_v1.tif
+
+62. WDPA_pre2022_cat1_dist_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/WDPA/v1/cat1//mwi_WDPA_pre2022_cat1_dist_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/TZA/WDPA/v1/cat1/tza_WDPA_pre2022_cat1_dist_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/WDPA/v1/cat1/moz_WDPA_pre2022_cat1_dist_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/WDPA/v1/cat1/zmb_WDPA_pre2022_cat1_dist_100m_v1.tif

From ca20e6545a2baf28f33ee4225750ecff9b98ba92 Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Tue, 27 Jan 2026 14:51:03 +0000
Subject: [PATCH 09/31] Script to download data using urls and datalog showing
 which ones were downloaded successfully.

---
 src_covariates/covariate_download.log   | 243 ++++++++++++++++++++++++
 src_covariates/request_get_covariets.py |  79 ++++++++
 2 files changed, 322 insertions(+)
 create mode 100644 src_covariates/covariate_download.log
 create mode 100644 src_covariates/request_get_covariets.py

diff --git a/src_covariates/covariate_download.log b/src_covariates/covariate_download.log
new file mode 100644
index 0000000..0403a7b
--- /dev/null
+++ b/src_covariates/covariate_download.log
@@ -0,0 +1,243 @@
+2026-01-27 12:49:36,824 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_count_BCB_gl_100m_v1_1.tif
+2026-01-27 12:49:41,029 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_count_BCB_gl_100m_v1_1.tif
+2026-01-27 12:49:42,821 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_count_BCB_gl_100m_v1_1.tif
+2026-01-27 12:49:44,044 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_count_BCB_gl_100m_v1_1.tif
+2026-01-27 12:49:44,923 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_count_BCB_ms_100m_v1_1.tif
+2026-01-27 12:49:50,178 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_count_BCB_ms_100m_v1_1.tif
+2026-01-27 12:49:52,577 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_count_BCB_ms_100m_v1_1.tif
+2026-01-27 12:49:54,086 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_count_BCB_ms_100m_v1_1.tif
+2026-01-27 12:49:56,312 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_cv_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:50:05,702 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_cv_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:50:09,084 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_cv_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:50:11,176 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_cv_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:50:13,187 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_cv_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:50:15,354 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_cv_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:50:18,276 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_cv_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:50:19,767 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_cv_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:50:21,800 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_cv_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:50:25,073 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_cv_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:50:27,215 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_cv_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:50:29,190 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_cv_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:50:30,425 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_cv_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:50:32,896 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_cv_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:50:35,045 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_cv_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:50:37,464 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_cv_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:50:39,519 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_cv_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:50:43,382 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_cv_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:50:45,902 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_cv_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:50:49,507 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_cv_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:50:51,621 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_cv_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:50:53,952 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_cv_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:50:57,903 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_cv_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:51:00,225 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_cv_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:51:01,772 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_cv_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:51:07,826 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_cv_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:51:11,529 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_cv_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:51:15,833 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_cv_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:51:17,467 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_cv_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:51:19,827 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_cv_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:51:24,562 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_cv_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:51:27,393 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_cv_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:51:29,564 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_density_BCB_gl_100m_v1_1.tif
+2026-01-27 12:51:35,836 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_density_BCB_gl_100m_v1_1.tif
+2026-01-27 12:51:42,686 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_density_BCB_gl_100m_v1_1.tif
+2026-01-27 12:51:45,615 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_density_BCB_gl_100m_v1_1.tif
+2026-01-27 12:51:50,938 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_density_BCB_ms_100m_v1_1.tif
+2026-01-27 12:52:01,288 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_density_BCB_ms_100m_v1_1.tif
+2026-01-27 12:52:11,191 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_density_BCB_ms_100m_v1_1.tif
+2026-01-27 12:52:12,713 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_density_BCB_ms_100m_v1_1.tif
+2026-01-27 12:52:16,519 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_density_PIB_gl_100m_v1_1.tif
+2026-01-27 12:52:25,266 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_density_PIB_gl_100m_v1_1.tif
+2026-01-27 12:52:33,866 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_density_PIB_gl_100m_v1_1.tif
+2026-01-27 12:52:36,570 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_density_PIB_gl_100m_v1_1.tif
+2026-01-27 12:52:39,534 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_density_PIB_ms_100m_v1_1.tif
+2026-01-27 12:52:50,621 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_density_PIB_ms_100m_v1_1.tif
+2026-01-27 12:52:53,304 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_density_PIB_ms_100m_v1_1.tif
+2026-01-27 12:52:55,978 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_density_PIB_ms_100m_v1_1.tif
+2026-01-27 12:52:57,665 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_mean_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:53:13,347 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_mean_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:53:23,118 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_mean_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:53:25,572 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_mean_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:53:26,611 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_mean_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:53:35,911 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_mean_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:53:43,739 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_mean_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:53:45,484 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_mean_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:53:50,086 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_mean_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:53:55,268 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_mean_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:54:02,839 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_mean_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:54:10,322 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_mean_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:54:12,571 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_mean_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:54:24,947 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_mean_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:54:31,645 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_mean_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:54:35,061 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_mean_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:54:38,337 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_mean_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:54:44,364 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_mean_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:54:47,934 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_mean_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:54:49,819 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_mean_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:54:52,262 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_mean_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:54:58,322 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_mean_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:55:03,178 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_mean_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:55:07,951 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_mean_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:55:09,899 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_mean_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:55:18,356 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_mean_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:55:22,281 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_mean_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:55:25,181 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_mean_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:55:26,061 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_mean_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:55:30,642 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_mean_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:55:33,007 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_mean_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:55:35,556 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_mean_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:55:39,424 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_total_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:55:55,873 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_total_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:55:59,967 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_total_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:56:01,895 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_total_area_BCB_gl_100m_v1_1.tif
+2026-01-27 12:56:04,321 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_total_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:56:08,051 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_total_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:56:11,058 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_total_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:56:16,092 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_total_area_BCB_ms_100m_v1_1.tif
+2026-01-27 12:56:19,577 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_total_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:56:33,222 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_total_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:56:43,696 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_total_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:56:50,471 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_total_area_PIB_gl_100m_v1_1.tif
+2026-01-27 12:56:51,740 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_total_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:57:01,108 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_total_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:57:08,326 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_total_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:57:09,892 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_total_area_PIB_ms_100m_v1_1.tif
+2026-01-27 12:57:12,237 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_total_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:57:19,687 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_total_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:57:23,135 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_total_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:57:25,807 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_total_length_BCB_gl_100m_v1_1.tif
+2026-01-27 12:57:26,966 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_total_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:57:37,916 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_total_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:57:39,899 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_total_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:57:41,682 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_total_length_BCB_ms_100m_v1_1.tif
+2026-01-27 12:57:45,193 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_total_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:57:50,818 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_total_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:57:54,677 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_total_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:57:55,961 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_total_length_PIB_gl_100m_v1_1.tif
+2026-01-27 12:57:58,382 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_buildings_total_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:58:01,095 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_buildings_total_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:58:05,058 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_buildings_total_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:58:10,246 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_buildings_total_length_PIB_ms_100m_v1_1.tif
+2026-01-27 12:58:14,385 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_built_S_dist_2024_GHS_MGW_100m_v1.tif
+2026-01-27 12:58:19,985 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_built_S_dist_2018_GHS_MGW_100m_v1.tif
+2026-01-27 12:58:37,248 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_built_S_dist_2024_GHS_MGW_100m_v1.tif
+2026-01-27 12:59:10,253 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_built_S_dist_2018_GHS_MGW_100m_v1.tif
+2026-01-27 12:59:40,257 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_built_S_dist_2024_GHS_MGW_100m_v1.tif
+2026-01-27 13:00:00,675 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_built_S_dist_2018_GHS_MGW_100m_v1.tif
+2026-01-27 13:00:35,048 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_built_S_dist_2024_GHS_MGW_100m_v1.tif
+2026-01-27 13:00:46,245 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_built_S_dist_2018_GHS_MGW_100m_v1.tif
+2026-01-27 13:00:48,065 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_built_S_GHS_U_wFGW_100m_v1_2024.tif
+2026-01-27 13:00:50,465 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_built_S_GHS_U_wFGW_100m_v1_2018.tif
+2026-01-27 13:01:00,486 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_built_S_GHS_U_wFGW_100m_v1_2024.tif
+2026-01-27 13:01:12,537 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_built_S_GHS_U_wFGW_100m_v1_2018.tif
+2026-01-27 13:01:18,570 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_built_S_GHS_U_wFGW_100m_v1_2024.tif
+2026-01-27 13:01:24,153 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_built_S_GHS_U_wFGW_100m_v1_2018.tif
+2026-01-27 13:01:27,289 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_built_S_GHS_U_wFGW_100m_v1_2024.tif
+2026-01-27 13:01:32,930 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_built_S_GHS_U_wFGW_100m_v1_2018.tif
+2026-01-27 13:01:32,930 INFO: Exists: data\covariate_data\Malawi_Covs\mwi_built_S_GHS_U_wFGW_100m_v1_2018.tif
+2026-01-27 13:01:36,823 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_built_V_GHS_U_wFGW_100m_v1_2024.tif
+2026-01-27 13:01:40,574 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_built_V_GHS_U_wFGW_100m_v1_2018.tif
+2026-01-27 13:01:45,890 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_built_V_GHS_U_wFGW_100m_v1_2024.tif
+2026-01-27 13:01:53,228 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_built_V_GHS_U_wFGW_100m_v1_2018.tif
+2026-01-27 13:02:01,572 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_built_V_GHS_U_wFGW_100m_v1_2024.tif
+2026-01-27 13:02:05,863 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_built_V_GHS_U_wFGW_100m_v1_2018.tif
+2026-01-27 13:02:11,324 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_built_V_GHS_U_wFGW_100m_v1_2024.tif
+2026-01-27 13:02:16,863 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_built_V_GHS_U_wFGW_100m_v1_2018.tif
+2026-01-27 13:02:22,910 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_coastline_dst_100m_v1.tif
+2026-01-27 13:03:00,314 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_coastline_dst_100m_v1.tif
+2026-01-27 13:03:38,155 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_coastline_dst_100m_v1.tif
+2026-01-27 13:04:04,769 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_coastline_dst_100m_v1.tif
+2026-01-27 13:04:12,954 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_dist_inland_water_100m_esa_2021_v1.tif
+2026-01-27 13:04:53,446 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_dist_inland_water_100m_esa_2021_v1.tif
+2026-01-27 13:05:19,979 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_dist_inland_water_100m_esa_2021_v1.tif
+2026-01-27 13:05:39,720 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_dist_inland_water_100m_esa_2021_v1.tif
+2026-01-27 13:05:48,013 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_elevation_merit103_100m_v1.tif
+2026-01-27 13:06:16,523 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_elevation_merit103_100m_v1.tif
+2026-01-27 13:06:46,734 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_elevation_merit103_100m_v1.tif
+2026-01-27 13:07:28,283 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_elevation_merit103_100m_v1.tif
+2026-01-27 13:07:31,801 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_esalc_11_dst_2022_100m_v1.tif
+2026-01-27 13:08:06,887 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_esalc_11_dst_2022_100m_v1.tif
+2026-01-27 13:08:43,465 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_esalc_11_dst_2022_100m_v1.tif
+2026-01-27 13:09:07,761 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_esalc_11_dst_2022_100m_v1.tif
+2026-01-27 13:09:14,107 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_esalc_130_dst_2022_100m_v1.tif
+2026-01-27 13:09:34,972 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_esalc_130_dst_2022_100m_v1.tif
+2026-01-27 13:10:04,174 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_esalc_130_dst_2022_100m_v1.tif
+2026-01-27 13:10:24,497 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_esalc_130_dst_2022_100m_v1.tif
+2026-01-27 13:10:30,004 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_esalc_140_dst_2022_100m_v1.tif
+2026-01-27 13:11:03,795 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_esalc_140_dst_2022_100m_v1.tif
+2026-01-27 13:11:28,889 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_esalc_140_dst_2022_100m_v1.tif
+2026-01-27 13:11:53,139 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_esalc_140_dst_2022_100m_v1.tif
+2026-01-27 13:11:57,011 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_esalc_150_dst_2022_100m_v1.tif
+2026-01-27 13:12:27,831 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_esalc_150_dst_2022_100m_v1.tif
+2026-01-27 13:13:06,681 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_esalc_150_dst_2022_100m_v1.tif
+2026-01-27 13:13:55,522 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_esalc_150_dst_2022_100m_v1.tif
+2026-01-27 13:14:02,787 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_esalc_160_dst_2022_100m_v1.tif
+2026-01-27 13:14:53,788 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_esalc_160_dst_2022_100m_v1.tif
+2026-01-27 13:15:28,592 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_esalc_160_dst_2022_100m_v1.tif
+2026-01-27 13:16:27,661 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_esalc_160_dst_2022_100m_v1.tif
+2026-01-27 13:16:42,126 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_esalc_190_dst_2022_100m_v1.tif
+2026-01-27 13:18:19,393 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_esalc_190_dst_2022_100m_v1.tif
+2026-01-27 13:20:14,835 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_esalc_190_dst_2022_100m_v1.tif
+2026-01-27 13:22:16,640 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_esalc_190_dst_2022_100m_v1.tif
+2026-01-27 13:22:48,713 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_esalc_200_dst_2022_100m_v1.tif
+2026-01-27 13:27:21,855 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_esalc_200_dst_2022_100m_v1.tif
+2026-01-27 13:28:50,316 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_esalc_200_dst_2022_100m_v1.tif
+2026-01-27 13:29:58,628 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_esalc_200_dst_2022_100m_v1.tif
+2026-01-27 13:30:18,380 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_esalc_210_dst_2022_100m_v1.tif
+2026-01-27 13:31:10,284 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_esalc_210_dst_2022_100m_v1.tif
+2026-01-27 13:31:48,987 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_esalc_210_dst_2022_100m_v1.tif
+2026-01-27 13:32:42,518 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_esalc_210_dst_2022_100m_v1.tif
+2026-01-27 13:32:52,344 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_esalc_40_dst_2022_100m_v1.tif
+2026-01-27 13:33:27,517 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_esalc_40_dst_2022_100m_v1.tif
+2026-01-27 13:34:00,520 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_esalc_40_dst_2022_100m_v1.tif
+2026-01-27 13:34:19,810 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_esalc_40_dst_2022_100m_v1.tif
+2026-01-27 13:34:30,898 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_highway_dist_osm_2023_100m_v1.tif
+2026-01-27 13:35:20,610 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_highway_dist_osm_2023_100m_v1.tif
+2026-01-27 13:36:01,330 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_highway_dist_osm_2023_100m_v1.tif
+2026-01-27 13:36:34,029 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_highway_dist_osm_2023_100m_v1.tif
+2026-01-27 13:36:44,790 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_ppt_2023_yravg_tc_100m_v1.tif
+2026-01-27 13:36:52,509 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_ppt_2018_yravg_tc_100m_v1.tif
+2026-01-27 13:37:34,912 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_ppt_2023_yravg_tc_100m_v1.tif
+2026-01-27 13:38:09,865 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_ppt_2023_yravg_tc_100m_v1.tif
+2026-01-27 13:38:46,856 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_ppt_2023_yravg_tc_100m_v1.tif
+2026-01-27 13:38:54,665 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_rd_intrs_dist_osm_2023_100m_v1.tif
+2026-01-27 13:39:27,964 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_rd_intrs_dist_osm_2023_100m_v1.tif
+2026-01-27 13:40:05,626 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_rd_intrs_dist_osm_2023_100m_v1.tif
+2026-01-27 13:40:38,807 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_rd_intrs_dist_osm_2023_100m_v1.tif
+2026-01-27 13:40:48,058 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_slope_merit103_100m_v1.tif
+2026-01-27 13:41:44,110 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_slope_merit103_100m_v1.tif
+2026-01-27 13:42:51,653 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_slope_merit103_100m_v1.tif
+2026-01-27 13:43:48,065 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_slope_merit103_100m_v1.tif
+2026-01-27 13:43:49,690 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_tavg_2023_tlst_100m_v1.tif
+2026-01-27 13:43:51,704 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_tavg_2018_tlst_100m_v1.tif
+2026-01-27 13:44:02,201 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_tavg_2023_tlst_100m_v1.tif
+2026-01-27 13:44:20,973 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_tavg_2023_tlst_100m_v1.tif
+2026-01-27 13:44:39,109 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_tavg_2023_tlst_100m_v1.tif
+2026-01-27 13:44:42,405 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_viirs_fvf_2023_100m_v1.tif
+2026-01-27 13:44:44,367 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_viirs_fvf_2018_100m_v1.tif
+2026-01-27 13:44:53,693 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_viirs_fvf_2023_100m_v1.tif
+2026-01-27 13:44:57,889 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_viirs_fvf_2018_100m_v1.tif
+2026-01-27 13:45:01,575 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_viirs_fvf_2023_100m_v1.tif
+2026-01-27 13:45:05,654 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_viirs_fvf_2018_100m_v1.tif
+2026-01-27 13:45:13,204 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_viirs_fvf_2023_100m_v1.tif
+2026-01-27 13:45:18,196 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_viirs_fvf_2018_100m_v1.tif
+2026-01-27 13:45:21,021 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_viirs_nvf_2023_100m_v1.tif
+2026-01-27 13:45:22,741 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_viirs_nvf_2018_100m_v1.tif
+2026-01-27 13:45:29,462 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_viirs_nvf_2023_100m_v1.tif
+2026-01-27 13:45:33,225 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_viirs_nvf_2018_100m_v1.tif
+2026-01-27 13:45:36,783 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_viirs_nvf_2023_100m_v1.tif
+2026-01-27 13:45:40,731 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_viirs_nvf_2018_100m_v1.tif
+2026-01-27 13:45:45,423 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_viirs_nvf_2023_100m_v1.tif
+2026-01-27 13:45:48,241 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_viirs_nvf_2018_100m_v1.tif
+2026-01-27 13:45:57,472 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_waterbodies_dist_osm_2023_100m_v1.tif
+2026-01-27 13:47:59,520 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_waterbodies_dist_osm_2023_100m_v1.tif
+2026-01-27 13:50:25,636 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_waterbodies_dist_osm_2023_100m_v1.tif
+2026-01-27 13:52:02,712 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_waterbodies_dist_osm_2023_100m_v1.tif
+2026-01-27 13:52:27,766 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_WDPA_pre2022_cat0_dist_100m_v1.tif
+2026-01-27 13:54:55,663 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_WDPA_pre2022_cat0_dist_100m_v1.tif
+2026-01-27 13:58:21,723 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_WDPA_pre2022_cat0_dist_100m_v1.tif
+2026-01-27 13:59:45,208 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_WDPA_pre2022_cat0_dist_100m_v1.tif
+2026-01-27 14:00:03,667 INFO: Downloaded to data\covariate_data\Malawi_Covs\mwi_WDPA_pre2022_cat1_dist_100m_v1.tif
+2026-01-27 14:01:52,446 INFO: Downloaded to data\covariate_data\Tanzania_Covs\tza_WDPA_pre2022_cat1_dist_100m_v1.tif
+2026-01-27 14:03:17,146 INFO: Downloaded to data\covariate_data\Mozambique_Covs\moz_WDPA_pre2022_cat1_dist_100m_v1.tif
+2026-01-27 14:04:26,513 INFO: Downloaded to data\covariate_data\Zambia_Covs\zmb_WDPA_pre2022_cat1_dist_100m_v1.tif
diff --git a/src_covariates/request_get_covariets.py b/src_covariates/request_get_covariets.py
new file mode 100644
index 0000000..b756618
--- /dev/null
+++ b/src_covariates/request_get_covariets.py
@@ -0,0 +1,79 @@
+# Using requests directly
+
+import requests
+import os
+import logging
+
+file_containing_urls = "worldpop_file_urls.md"
+
+def get_country_folder(filename):
+    if filename.startswith("mwi_"):
+        return "Malawi_Covs"
+    elif filename.startswith("tza_"):
+        return "Tanzania_Covs"
+    elif filename.startswith("moz_"):
+        return "Mozambique_Covs"
+    elif filename.startswith("zmb_"):
+        return "Zambia_Covs"
+    else:
+        return None
+
+def download_file(url, out_path):
+    # Send HTTP GET request to the URL, streaming the response
+    response = requests.get(url, stream=True)
+    # Raise an error if the request failed (e.g., 404 or 500)
+    response.raise_for_status()
+    # Create the output directory if it doesn't exist
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+    # Open the output file in binary write mode
+    with open(out_path, 'wb') as f:
+        # Write the file in chunks to avoid loading the whole file into memory
+        for chunk in response.iter_content(chunk_size=8192):
+            f.write(chunk)
+    # Log confirmation of download
+    logging.info(f"Downloaded to {out_path}")
+
+def main():
+    # Set up logging to file
+    logging.basicConfig(filename='covariate_download.log',
+                        filemode='a',
+                        format='%(asctime)s %(levelname)s: %(message)s',
+                        level=logging.INFO)
+
+    # Path to the markdown file containing URLs
+    md_path = file_containing_urls
+    # Base directory for covariate data
+    base_dir = os.path.join("data", "covariate_data")
+    # Open the markdown file for reading
+    with open(md_path, 'r', encoding='utf-8') as md:
+        # Iterate over each line in the file
+        for line in md:
+            # Remove whitespace from the line
+            url = line.strip()
+            # Skip lines that do not start with 'http' (not a URL)
+            if not url.startswith("http"):
+                continue
+            # Extract the filename from the URL
+            filename = url.split("/")[-1]
+            # Determine the country folder based on filename prefix
+            country_folder = get_country_folder(filename)
+            # Skip if the filename does not match any country prefix
+            if not country_folder:
+                continue
+            # Build the output directory path for the country
+            out_dir = os.path.join(base_dir, country_folder)
+            # Build the full output file path
+            out_path = os.path.join(out_dir, filename)
+            # If the file already exists, log and skip download
+            if os.path.exists(out_path):
+                logging.info(f"Exists: {out_path}")
+                continue
+            try:
+                # Attempt to download the file
+                download_file(url, out_path)
+            except Exception as e:
+                # Log any errors encountered during download
+                logging.error(f"Failed to download {url}: {e}")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file

From 5975b6f844b9814ff585078b7c8ed669734d3d6c Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Wed, 4 Feb 2026 10:07:43 +0000
Subject: [PATCH 10/31] Added path to covariate

---
 src_covariates/worldpop_file_urls.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src_covariates/worldpop_file_urls.md b/src_covariates/worldpop_file_urls.md
index 051f814..0791a31 100644
--- a/src_covariates/worldpop_file_urls.md
+++ b/src_covariates/worldpop_file_urls.md
@@ -298,6 +298,8 @@ https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MOZ/OSM/v1/moz_highway
 https://data.worldpop.org/GIS/Covariates/Global_2015_2030/ZMB/OSM/v1/zmb_highway_dist_osm_2023_100m_v1.tif
 
 49. inland_water_pct_100m_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/Inland_water/esa_worldcover/v1/Dist/mwi_dist_inland_water_100m_esa_2021_v1.tif
+https://data.worldpop.org/GIS/Covariates/Global_2015_2030/MWI/OSM/v1/mwi_waterbodies_dist_osm_2023_100m_v1.tif
 
 50. ms_roads_count_100m_v1.tif
 

From 2df0710201c045bf85be4a4e7475f2dd2beeee05 Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Thu, 5 Feb 2026 15:03:42 +0000
Subject: [PATCH 11/31] feat: Output 10km buffered country boundary geometry

Include check for file and create if not
---
 01_Raster_Mosaicking_Buildings_2018.R | 10 +++++++++-
 01_Raster_Mosaicking_Buildings_2024.R |  9 ++++++++-
 01_Raster_Mosaicking_Workflow_2018.R  | 10 +++++++++-
 01_Raster_Mosaicking_Workflow_2024.R  | 10 +++++++++-
 utils.R                               | 16 ++++++++++++++++
 5 files changed, 51 insertions(+), 4 deletions(-)
 create mode 100644 utils.R

diff --git a/01_Raster_Mosaicking_Buildings_2018.R b/01_Raster_Mosaicking_Buildings_2018.R
index 5d96165..075344e 100644
--- a/01_Raster_Mosaicking_Buildings_2018.R
+++ b/01_Raster_Mosaicking_Buildings_2018.R
@@ -4,6 +4,8 @@ library(terra)
 library(sf)
 library(tictoc)
 
+source("utils.R")
+
 # Specify data path
 drive_path <- "./data"
 base_path <- paste0(drive_path) ## Base path where the folders are located
@@ -12,7 +14,13 @@ result_path <- paste0(drive_path, "/Mosaic_Buildings/") # Result path
 building_path <- paste0(drive_path, "/covariate_data/")
 
 #Load data
-boundary <- st_read(paste0(shp_path, "LU.shp")) # replacing: "Country_Shapefile_Buffer_10km.shp"
+boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
+if(file.exists(paste0(shp_path, boundary_data_filename))) {
+  boundary <- st_read(paste0(shp_path, boundary_data_filename))
+} else {
+  boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
+}
+
 r1 <- rast(paste0(building_path, "mwi_buildings_count_BCB_ms_100m_v1_1.tif")) # replacing: "mwi_buildings_count_2018_glv2_5_t0_5_C_100m_v1.tif"
 
 #Reproject boundary to r1
diff --git a/01_Raster_Mosaicking_Buildings_2024.R b/01_Raster_Mosaicking_Buildings_2024.R
index 45140c7..5d42dbc 100644
--- a/01_Raster_Mosaicking_Buildings_2024.R
+++ b/01_Raster_Mosaicking_Buildings_2024.R
@@ -2,6 +2,8 @@ library(terra)
 library(sf)
 library(tictoc)
 
+source("utils.R")
+
 # Specify data path
 drive_path <- "//Working/MALAWI/Ortis/"
 base_path <- paste0(drive_path, "Input_Data/") ## Base path where the folders are located
@@ -10,7 +12,12 @@ result_path <- paste0(drive_path, "Input_Data/Mosaic_Buildings_2024/") # Result
 building_path <- paste0(drive_path, "Input_Data/Malawi_Covs/2024_Buildings/")
 
 #Load data
-boundary <- st_read(paste0(shp_path, "Country_Shapefile_Buffer_10km.shp"))
+boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
+if(file.exists(paste0(shp_path, boundary_data_filename))) {
+  boundary <- st_read(paste0(shp_path, boundary_data_filename))
+} else {
+  boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
+}
 r1 <- rast(paste0(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
 
 #Reproject boundary to r1
diff --git a/01_Raster_Mosaicking_Workflow_2018.R b/01_Raster_Mosaicking_Workflow_2018.R
index e4a2094..bffc57b 100644
--- a/01_Raster_Mosaicking_Workflow_2018.R
+++ b/01_Raster_Mosaicking_Workflow_2018.R
@@ -2,6 +2,8 @@ library(terra)
 library(sf)
 library(tictoc)
 
+source("utils.R")
+
 # Specify data path
 drive_path <- "./data/"
 base_path <- paste0(drive_path, "Input_Data/") ## Base path where the folders are located
@@ -10,7 +12,13 @@ result_path <- paste0(drive_path, "Input_Data/Mosaic_Covariates_2018/") # Result
 building_path <- paste0(drive_path, "Input_Data/Malawi_Covs/2024_Buildings/")
 
 #Load data
-boundary <- st_read(paste0(shp_path, "Country_Shapefile_Buffer_10km.shp"))
+boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
+if(file.exists(paste0(shp_path, boundary_data_filename))) {
+  boundary <- st_read(paste0(shp_path, boundary_data_filename))
+} else {
+  boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
+}
+
 r1 <- rast(paste0(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
 
 #Reproject boundary to r1
diff --git a/01_Raster_Mosaicking_Workflow_2024.R b/01_Raster_Mosaicking_Workflow_2024.R
index 3824dfd..c7de191 100644
--- a/01_Raster_Mosaicking_Workflow_2024.R
+++ b/01_Raster_Mosaicking_Workflow_2024.R
@@ -2,6 +2,8 @@ library(terra)
 library(sf)
 library(tictoc)
 
+source("utils.R")
+
 # Specify data path
 drive_path <- "./data/"
 base_path <- paste0(drive_path, "Input_Data/") ## Base path where the folders are located
@@ -10,7 +12,13 @@ result_path <- paste0(drive_path, "Input_Data/Mosaic_Covariates_2024/") # Result
 building_path <- paste0(drive_path, "Input_Data/Malawi_Covs/2024_Buildings/")
 
 #Load data
-boundary <- st_read(paste0(shp_path, "Country_Shapefile_Buffer_10km.shp"))
+boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
+if(file.exists(paste0(shp_path, boundary_data_filename))) {
+  boundary <- st_read(paste0(shp_path, boundary_data_filename))
+} else {
+  boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
+}
+
 r1 <- rast(paste0(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
 
 #Reproject boundary to r1
diff --git a/utils.R b/utils.R
new file mode 100644
index 0000000..18fb336
--- /dev/null
+++ b/utils.R
@@ -0,0 +1,16 @@
+library(sf)
+
+generate_buffered_country_boundary <- function(
+    shape_path,
+    file_name = "Country_Shapefile_Buffer_10km.shp"
+){
+  #' Produce buffered country boundary shapefile by dissolving EA geometries and
+  #' adding 10km buffer
+  #' 
+  ea_geoms <- st_read(file.path(shape_path, "2018_MPHC_EAs_Final_for_Use.shp"))
+  country_buffer <- ea_geoms %>% 
+    st_union() %>% 
+    st_buffer(10E3)
+  st_write(country_buffer, file.path(shape_path, file_name))
+  return(country_buffer)
+}

From 3415fcd800d885ca5200d41a7da6c8c5937b2f35 Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Fri, 6 Feb 2026 11:55:49 +0000
Subject: [PATCH 12/31] refactor: Enable building raster mosaicing

---
 01_Raster_Mosaicking_Buildings_2018.R | 21 +++++++++++++--------
 01_Raster_Mosaicking_Buildings_2024.R | 21 +++++++++++++--------
 2 files changed, 26 insertions(+), 16 deletions(-)

diff --git a/01_Raster_Mosaicking_Buildings_2018.R b/01_Raster_Mosaicking_Buildings_2018.R
index 075344e..733b75a 100644
--- a/01_Raster_Mosaicking_Buildings_2018.R
+++ b/01_Raster_Mosaicking_Buildings_2018.R
@@ -9,26 +9,26 @@ source("utils.R")
 # Specify data path
 drive_path <- "./data"
 base_path <- paste0(drive_path) ## Base path where the folders are located
-shp_path <- paste0(drive_path, "/Shapefiles/") ## Shapefile path
-result_path <- paste0(drive_path, "/Mosaic_Buildings/") # Result path
-building_path <- paste0(drive_path, "/covariate_data/")
+shp_path <- file.path(drive_path, "Shapefiles") ## Shapefile path
+result_path <- file.path(drive_path, "Mosaic_Buildings_2018") # Result path
+building_path <- file.path(drive_path, "Malawi_Covs", "2018_Buildings")
 
 #Load data
 boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
-if(file.exists(paste0(shp_path, boundary_data_filename))) {
-  boundary <- st_read(paste0(shp_path, boundary_data_filename))
+if(file.exists(file.path(shp_path, boundary_data_filename))) {
+  boundary <- st_read(file.path(shp_path, boundary_data_filename))
 } else {
   boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
 }
 
-r1 <- rast(paste0(building_path, "mwi_buildings_count_BCB_ms_100m_v1_1.tif")) # replacing: "mwi_buildings_count_2018_glv2_5_t0_5_C_100m_v1.tif"
+r1 <- rast(file.path(building_path, "mwi_buildings_count_2018_glv2_5_t0_5_C_100m_v1.tif")) 
 
 #Reproject boundary to r1
 boundary <- st_transform(boundary, crs = st_crs(r1))
 
 # Define folder names
-folders <- c("Malawi_Covs/Buildings", "Tanzania_Covs/Buildings",
-             "Mozambique_Covs/Buildings", "Zambia_Covs/Buildings")
+folders <- c("Malawi_Covs/2018_Buildings", "Tanzania_Covs/2018_Buildings",
+             "Mozambique_Covs/2018_Buildings", "Zambia_Covs/2018_Buildings")
 
 
 # Initialize a list to store raster file names
@@ -73,6 +73,11 @@ process_raster <- function(raster_name) {
   
   # Save the masked raster to a file with a name based on the original raster file name
   output_name <- paste0("MOS_MLW", raster_name)
+  
+  if (!file.exists(result_path)){
+    dir.create(file.path(result_path))
+  }
+  
   writeRaster(masked_raster, file.path(result_path, output_name), overwrite = TRUE)
   
   # Display a message after saving the raster
diff --git a/01_Raster_Mosaicking_Buildings_2024.R b/01_Raster_Mosaicking_Buildings_2024.R
index 5d42dbc..d810846 100644
--- a/01_Raster_Mosaicking_Buildings_2024.R
+++ b/01_Raster_Mosaicking_Buildings_2024.R
@@ -5,20 +5,20 @@ library(tictoc)
 source("utils.R")
 
 # Specify data path
-drive_path <- "//Working/MALAWI/Ortis/"
-base_path <- paste0(drive_path, "Input_Data/") ## Base path where the folders are located
-shp_path <- paste0(drive_path, "Input_Data/Shapefiles/") ## Shapefile path
-result_path <- paste0(drive_path, "Input_Data/Mosaic_Buildings_2024/") # Result path
-building_path <- paste0(drive_path, "Input_Data/Malawi_Covs/2024_Buildings/")
+drive_path <- "./data/"
+base_path <- file.path(drive_path) ## Base path where the folders are located
+shp_path <- file.path(drive_path, "Shapefiles") ## Shapefile path
+result_path <- file.path(drive_path, "Mosaic_Buildings_2024") # Result path
+building_path <- file.path(drive_path, "Malawi_Covs", "2024_Buildings")
 
 #Load data
 boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
-if(file.exists(paste0(shp_path, boundary_data_filename))) {
-  boundary <- st_read(paste0(shp_path, boundary_data_filename))
+if(file.exists(file.path(shp_path, boundary_data_filename))) {
+  boundary <- st_read(file.path(shp_path, boundary_data_filename))
 } else {
   boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
 }
-r1 <- rast(paste0(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
+r1 <- rast(file.path(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
 
 #Reproject boundary to r1
 boundary <- st_transform(boundary, crs = st_crs(r1))
@@ -70,6 +70,11 @@ process_raster <- function(raster_name) {
   
   # Save the masked raster to a file with a name based on the original raster file name
   output_name <- paste0("MOS_MLW", raster_name)
+  
+  if (!file.exists(result_path)){
+    dir.create(file.path(result_path))
+  }
+  
   writeRaster(masked_raster, file.path(result_path, output_name), overwrite = TRUE)
   
   # Display a message after saving the raster

From 5e20b8a00e3111e26ea1af379a243db63d7bb23f Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Mon, 9 Feb 2026 14:09:43 +0000
Subject: [PATCH 13/31] refactor: Enable covariate mosaicing

---
 01_Raster_Mosaicking_Workflow_2018.R | 21 +++++++++++++--------
 01_Raster_Mosaicking_Workflow_2024.R | 21 +++++++++++++--------
 2 files changed, 26 insertions(+), 16 deletions(-)

diff --git a/01_Raster_Mosaicking_Workflow_2018.R b/01_Raster_Mosaicking_Workflow_2018.R
index bffc57b..4362ab7 100644
--- a/01_Raster_Mosaicking_Workflow_2018.R
+++ b/01_Raster_Mosaicking_Workflow_2018.R
@@ -6,20 +6,20 @@ source("utils.R")
 
 # Specify data path
 drive_path <- "./data/"
-base_path <- paste0(drive_path, "Input_Data/") ## Base path where the folders are located
-shp_path <- paste0(drive_path, "Input_Data/Shapefiles/") ## Shapefile path
-result_path <- paste0(drive_path, "Input_Data/Mosaic_Covariates_2018/") # Result path
-building_path <- paste0(drive_path, "Input_Data/Malawi_Covs/2024_Buildings/")
+base_path <- file.path(drive_path) ## Base path where the folders are located
+shp_path <- file.path(drive_path, "Shapefiles") ## Shapefile path
+result_path <- file.path(drive_path, "Mosaic_Covariates_2018") # Result path
+building_path <- file.path(drive_path, "Malawi_Covs", "2024_Buildings")
 
 #Load data
 boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
-if(file.exists(paste0(shp_path, boundary_data_filename))) {
-  boundary <- st_read(paste0(shp_path, boundary_data_filename))
+if(file.exists(file.path(shp_path, boundary_data_filename))) {
+  boundary <- st_read(file.path(shp_path, boundary_data_filename))
 } else {
   boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
 }
 
-r1 <- rast(paste0(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
+r1 <- rast(file.path(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
 
 #Reproject boundary to r1
 boundary <- st_transform(boundary, crs = st_crs(r1))
@@ -82,7 +82,12 @@ process_raster <- function(raster_name) {
   
   # Save the mosaicked raster to a file with a name based on the original raster file name
   output_name <- paste0("MOS_MLW", raster_name)
-  writeRaster(masked_raster, paste0(result_path, output_name), overwrite = TRUE)
+  
+  if (!file.exists(result_path)){
+    dir.create(file.path(result_path))
+  }
+  
+  writeRaster(masked_raster, file.path(result_path, output_name), overwrite = TRUE)
   
   message("Saved ", output_name)
   
diff --git a/01_Raster_Mosaicking_Workflow_2024.R b/01_Raster_Mosaicking_Workflow_2024.R
index c7de191..e798833 100644
--- a/01_Raster_Mosaicking_Workflow_2024.R
+++ b/01_Raster_Mosaicking_Workflow_2024.R
@@ -6,20 +6,20 @@ source("utils.R")
 
 # Specify data path
 drive_path <- "./data/"
-base_path <- paste0(drive_path, "Input_Data/") ## Base path where the folders are located
-shp_path <- paste0(drive_path, "Input_Data/Shapefiles/") ## Shapefile path
-result_path <- paste0(drive_path, "Input_Data/Mosaic_Covariates_2024/") # Result path
-building_path <- paste0(drive_path, "Input_Data/Malawi_Covs/2024_Buildings/")
+base_path <- file.path(drive_path) ## Base path where the folders are located
+shp_path <- file.path(drive_path, "Shapefiles") ## Shapefile path
+result_path <- file.path(drive_path, "Mosaic_Covariates_2024") # Result path
+building_path <- file.path(drive_path, "Malawi_Covs", "2024_Buildings/")
 
 #Load data
 boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
-if(file.exists(paste0(shp_path, boundary_data_filename))) {
-  boundary <- st_read(paste0(shp_path, boundary_data_filename))
+if(file.exists(file.path(shp_path, boundary_data_filename))) {
+  boundary <- st_read(file.path(shp_path, boundary_data_filename))
 } else {
   boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
 }
 
-r1 <- rast(paste0(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
+r1 <- rast(file.path(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
 
 #Reproject boundary to r1
 boundary <- st_transform(boundary, crs = st_crs(r1))
@@ -82,7 +82,12 @@ process_raster <- function(raster_name) {
   
   # Save the mosaicked raster to a file with a name based on the original raster file name
   output_name <- paste0("MOS_MLW", raster_name)
-  writeRaster(masked_raster, paste0(result_path, output_name), overwrite = TRUE)
+  
+  if (!file.exists(result_path)){
+    dir.create(file.path(result_path))
+  }
+  
+  writeRaster(masked_raster, file.path(result_path, output_name), overwrite = TRUE)
   
   message("Saved ", output_name)
   

From 89e76850eff465e46604e97cb432503985410a94 Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Wed, 11 Feb 2026 17:32:40 +0000
Subject: [PATCH 14/31] refactor: Enable run through of 00 preprocessing
 scripts

---
 00_Data_Processing.R      |  71 ++++----
 00_Data_Processing2.R     | 352 ++++++++++++++++++++++----------------
 load_required_libraries.R |   3 +-
 3 files changed, 241 insertions(+), 185 deletions(-)

diff --git a/00_Data_Processing.R b/00_Data_Processing.R
index 787ecfa..89e27f4 100644
--- a/00_Data_Processing.R
+++ b/00_Data_Processing.R
@@ -1,4 +1,4 @@
-# Data exploration workflow for dataset recived
+# Data exploration workflow for dataset received
 
 #load packages
 library(sf)
@@ -9,21 +9,41 @@ library(tidyverse)
 options(scipen = 999) # turn off scientific notation for all variables
 
 #Specify Drive Path
-drive_path <- "//Internal_Path/"
-input_path <- paste0(drive_path, "Input_Data/Surveys/")
-output_path <- paste0(drive_path, "/Output_Data/")
-shapefile_path <- paste0(drive_path, "Input_Data/Shapefiles/")
+drive_path <- "./data"
+input_path <- file.path(drive_path, "MNSO-Data")
+output_path <- file.path(drive_path, "Output_Data")
+shapefile_path <- file.path(drive_path, "Shapefiles")
 
 #Load datasets
-mphc_2018 <- read_dta(paste0(input_path, "mphc2018Data_AllRegions.dta"))
-mphc_structures_2018 <- read_dta(paste0(input_path, "mphc2018Data_structures.dta"))
-ICT_data <- read_dta(paste0(input_path, "ICT Listing WorldPop.dta"))
-IHS6_data <- read_dta(paste0(input_path, "IHS6 Listing WorldPop.dta"))
-Naca_data <- read_dta(paste0(input_path, "Naca Listing WorldPop.dta"))
-ea <- st_read(file.path(shapefile_path, "2018_MPHC_EAs_Final_for_Use_Corrected.shp"))
-mphc_structures_2018 <- st_read(paste0(output_path, "mphc_structures_points.gpkg"))
-dhs_data <- read_dta(paste0(input_path, "MDHS_2024_NoDZLK_anonymized.dta"))
-dhs_listing <- read_dta(paste0(input_path, "FINAL MDHS LISTING DATA_Annon.dta"))
+mphc_2018 <- read_dta(file.path(input_path, "mphc2018Data_AllRegions.dta"))
+mphc_structures_2018 <- read_dta(file.path(input_path, "mphc2018Data_structures.dta"))
+ICT_data <- read_dta(file.path(input_path, "ICT Listing WorldPop.dta"))
+IHS6_data <- read_dta(file.path(input_path, "IHS6 Listing WorldPop.dta"))
+Naca_data <- read_dta(file.path(input_path, "Naca Listing WorldPop.dta"))
+ea <- st_read(file.path(shapefile_path, "2018_MPHC_EAs_Final_for_Use.shp")) # replaces "2018_MPHC_EAs_Final_for_Use_Corrected.shp"
+dhs_data <- read_dta(file.path(input_path, "MDHS_2024_NoDZLK_anonymized.dta"))
+dhs_listing <- read_dta(file.path(input_path, "FINAL MDHS LISTING DATA_Annon.dta"))
+
+mphc_structures_2018_filepath <- file.path(
+  output_path, "mphc_structures_points.gpkg")
+
+if (!file.exists(mphc_structures_2018_filepath)) {
+  #Structures
+  #Convert to sf object
+  mphc_structures_sf <- mphc_structures_2018 |>
+    drop_na(st_longitude, st_latitude) |>
+    st_as_sf(coords = c("st_longitude", "st_latitude"))
+
+  #set the spatial reference
+  st_crs(mphc_structures_sf ) <- 4326
+
+  # Write to GPKG file
+  st_write(mphc_structures_sf ,
+           dsn = file.path(output_path, "mphc_structures_points.gpkg"),
+           driver = "GPKG",
+           delete_layer = TRUE)
+}
+mphc_structures_2018 <- st_read(file.path(output_path, "mphc_structures_points.gpkg"))
 
 # Write each data as a shapefile  -----------------------------------------
 
@@ -41,20 +61,7 @@ dhs_listing <- read_dta(paste0(input_path, "FINAL MDHS LISTING DATA_Annon.dta"))
 #          driver = "GPKG", 
 #          delete_layer = TRUE)  # overwrite if layer already exists
 # 
-# #Structures
-# #Convert to sf object
-# mphc_structures_sf <- mphc_structures_2018 |> 
-#   drop_na(st_longitude, st_latitude) |> 
-#   st_as_sf(coords = c("st_longitude", "st_latitude"))
-# 
-# #set the spatial reference
-# st_crs(mphc_structures_sf ) <- 4326
-# 
-# # Write to GPKG file
-# st_write(mphc_structures_sf , 
-#          dsn = file.path(output_path, "mphc_structures_points.gpkg"), 
-#          driver = "GPKG", 
-#          delete_layer = TRUE)  
+
 # 
 # #ICT
 # ICT_sf <- ICT_data |> 
@@ -179,7 +186,7 @@ mphc_pop_per_ea <- mphc_2018 |>
             female_count = sum(p03 == 2, na.rm = TRUE))
   
 
-#Create a bin for each age catgeory
+#Create a bin for each age category
 age_summary <- mphc_2018 |> 
   mutate(age_group = case_when(
     p05 < 1  ~ "age_group_01_less",      #less than 1
@@ -333,13 +340,13 @@ combined_data <- combined_data |>
   
 #Write to file
 
-write.csv(combined_data, paste0(output_path, "summarized_survey_data.csv"), row.names = F)
+write.csv(combined_data, file.path(output_path, "summarized_survey_data.csv"), row.names = F)
 
 
 #join combined data to EA shapefile and export
 #Join to ea data
 combined_data <- combined_data |> 
-  mutate(EA_CODE = as.character(EA_CODE))  #convert EA code to interger
+  mutate(EA_CODE = as.character(EA_CODE))  #convert EA code to integer
 
 #select hh size
 hh_size <- combined_data |> 
@@ -352,7 +359,7 @@ hh_ea <- hh_ea |>
   select(EA_CODE, mphc_total_pop, hh_size)
 
 #write to file
-st_write(hh_ea, paste(output_path, "hh_size_data.shp"))
+st_write(hh_ea, file.path(output_path, "hh_size_data.shp"))
 
 
 
diff --git a/00_Data_Processing2.R b/00_Data_Processing2.R
index d4c5b02..d7b5aa5 100644
--- a/00_Data_Processing2.R
+++ b/00_Data_Processing2.R
@@ -1,4 +1,6 @@
 # Summarizing data at the EA level using EA-CODE and Spatial location of the points
+# Should be run after 00_Data_Processing.R
+# NOTE: This is currently omitting the Zomba, Malemia and DHS Segmented data due to availability.
 
 #load packages
 library(sf)
@@ -10,24 +12,20 @@ library(tidyverse)
 options(scipen = 999) # turn off scientific notation for all variables
 
 #Specify Drive Path
-drive_path <- "./data/"
-input_path <- paste0(drive_path, "MNSO-Data/")
-output_path <- paste0(drive_path, "/Output_Data/")
-shapefile_path <- paste0(drive_path, "Shapefiles/")
+drive_path <- "./data"
+input_path <- file.path(drive_path, "MNSO-Data")
+output_path <- file.path(drive_path, "Output_Data")
+shapefile_path <- file.path(drive_path, "Shapefiles")
 
 #Load datasets
-mphc_2018 <- read_dta(paste0(input_path, "mphc2018Data_AllRegions.dta"))
-#mphc_structures_2018 <- read_dta(paste0(input_path, "mphc2018Data_structures.dta"))
-ICT_data <- read_dta(paste0(input_path, "ICT Listing WorldPop.dta"))
-IHS6_data <- read_dta(paste0(input_path, "IHS6 Listing WorldPop.dta"))
-Naca_data <- read_dta(paste0(input_path, "Naca Listing WorldPop.dta"))
+mphc_2018 <- read_dta(file.path(input_path, "mphc2018Data_AllRegions.dta"))
+ICT_data <- read_dta(file.path(input_path, "ICT Listing WorldPop.dta"))
+IHS6_data <- read_dta(file.path(input_path, "IHS6 Listing WorldPop.dta"))
+Naca_data <- read_dta(file.path(input_path, "Naca Listing WorldPop.dta"))
 ea <- st_read(file.path(shapefile_path, "2018_MPHC_EAs_Final_for_Use.shp")) # replaces "2018_MPHC_EAs_Final_for_Use_Corrected.shp"
-#mphc_structures_2018 <- st_read(paste0(output_path, "mphc_structures_points.gpkg"))
-dhs_data <- read_dta(paste0(input_path, "MDHS_2024_NoDZLK_anonymized.dta"))
-dhs_listing <- read_dta(paste0(input_path, "FINAL MDHS LISTING DATA_Annon.dta"))
-dhs_file <- read.csv(paste0(input_path, "DHS_Segmented_File.csv"))
-zomba_data <- read.csv(paste0(output_path, "zomba_rbind_data.csv"))
-malemia_data <- read.csv(paste0(input_path, "malemia_hh_without_IDs.csv"))
+dhs_data <- read_dta(file.path(input_path, "MDHS_2024_NoDZLK_anonymized.dta"))
+dhs_listing <- read_dta(file.path(input_path, "FINAL MDHS LISTING DATA_Annon.dta"))
+
 
 #####################################################################################
 ####################################################################################
@@ -110,42 +108,47 @@ mphc_pop_no_gps <- mphc_pop_no_gps %>%
 
 # ============================================================== ----------
 
-# Convert remaining mphc_2018 data to shapefiles
-
-# #Convert to sf object
-# mphc_2018_sf <- mphc_2018 %>%  
-#  drop_na(hh_longitude, hh_latitude) %>%  
-#  st_as_sf(coords = c("hh_longitude", "hh_latitude"))
-# 
-# #set the spatial reference
-# st_crs(mphc_2018_sf) <- 4326
-
-#Fix corrupt geometries
-#st_make_valid(ea)
+#load dataset
+mphc_2018_sf_filepath <- file.path(output_path, "mphc_2018_sf_ea.gpkg")
+if (!file.exists(mphc_2018_sf_filepath)) {
+  print("The geopackage version of mphc_2018 is not available, creating dataframe and saving to disk.")
+  
+  # Convert remaining mphc_2018 data to shapefiles
+  
+  #Convert to sf object
+  mphc_2018_sf <- mphc_2018 %>%
+  drop_na(hh_longitude, hh_latitude) %>%
+  st_as_sf(coords = c("hh_longitude", "hh_latitude"))
+  
+  #set the spatial reference
+  st_crs(mphc_2018_sf) <- 4326
 
-#Turn off invalid geometries
-# sf::sf_use_s2(FALSE)
+  #Fix corrupt geometries
+  st_make_valid(ea)
 
-#transform
-# mphc_2018_sf <- st_transform(mphc_2018_sf, crs = st_crs(ea))
+  #Turn off invalid geometries
+  sf::sf_use_s2(FALSE)
 
-# EA Nearest Neighbor Assignment 
-# nearest_indices <- st_nearest_feature(mphc_2018_sf, ea)
+  #transform
+  mphc_2018_sf <- st_transform(mphc_2018_sf, crs = st_crs(ea))
 
-# Extract the EA_CODE  of the nearest polygons
-# nearest_ids <- ea$EA_CODE[nearest_indices]
+  # EA Nearest Neighbor Assignment
+  nearest_indices <- st_nearest_feature(mphc_2018_sf, ea)
 
-# Add the EA_CODE to data
-# mphc_2018_sf$EA_CODE <- nearest_ids
+  # Extract the EA_CODE  of the nearest polygons
+  nearest_ids <- ea$EA_CODE[nearest_indices]
 
-#Write to file
-#st_write(mphc_2018_sf , 
-# dsn = file.path(output_path, "mphc_2018_sf_ea.gpkg"), 
-# driver = "GPKG", 
-# delete_layer = TRUE)
+  # Add the EA_CODE to data
+  mphc_2018_sf$EA_CODE <- nearest_ids
 
-#load dataset
-mphc_2018_sf <- st_read(paste0(output_path, "mphc_2018_sf_ea.gpkg"))
+  #Write to file
+  st_write(mphc_2018_sf ,
+   dsn = file.path(output_path, "mphc_2018_sf_ea.gpkg"),
+   driver = "GPKG",
+   delete_layer = TRUE
+   )
+}
+mphc_2018_sf <- st_read(mphc_2018_sf_filepath)
 
 #convert to dataframe
 mphc_2018_df <- mphc_2018_sf %>%  
@@ -186,7 +189,7 @@ summary(mphc_2018_df$hh_gps_accuracy)
 #   )
  
 #  #load dataset
-#  #mphc_2018_sf <- st_read(paste0(output_path, "mphc_2018_sf_ea.gpkg"))
+#  #mphc_2018_sf <- st_read(file.path(output_path, "mphc_2018_sf_ea.gpkg"))
  
 #  #convert to dataframe
 #  mphc_2018_df <- mphc_2018_sf %>%  
@@ -589,27 +592,42 @@ sum(Naca_data$hh_count)
 #####################################################################################
 ####################################################################################
 ######### PROCESS DHS Listing DATA ################################################ 
-#Not segmented clusters
-unique(dhs_file$Cluster.Segmented)
 
-#Get non-segmented cluster
-non_seg_cluster <- dhs_file %>% 
-  filter(grepl("^no\\b", Cluster.Segmented, ignore.case = TRUE))
 
-#Unique cluster id
-unique(non_seg_cluster$DHScluster)
-
-# Clusters in non_seg_cluster and not present in dhs listing
-missing_clusters <- setdiff(unique(non_seg_cluster$DHScluster), unique(dhs_listing$QHCLUST))
-missing_clusters
-
-#Add a new column to data called hh_count
-dhs_listing <- dhs_listing %>%  
-  mutate(hh_count = 1)
+dhs_file_path <- file.path(input_path, "DHS_Segmented_File.csv")
+if (!file.exists(dhs_file_path)) {
+  print("'DHS_Segmented_File.csv' cannot be found. Skipping processing of this data.")
+  
+  #Add a new column to data called hh_count
+  dhs_listing <- dhs_listing %>%  
+    mutate(hh_count = 1)
+  
+} else {
+  dhs_file <- read.csv(dhs_file_path)  ## FILE MISSING!
+  
+  #Not segmented clusters
+  unique(dhs_file$Cluster.Segmented)
+  
+  #Get non-segmented cluster
+  non_seg_cluster <- dhs_file %>% 
+    filter(grepl("^no\\b", Cluster.Segmented, ignore.case = TRUE))
+  
+  #Unique cluster id
+  unique(non_seg_cluster$DHScluster)
+  
+  # Clusters in non_seg_cluster and not present in dhs listing
+  missing_clusters <- setdiff(unique(non_seg_cluster$DHScluster), unique(dhs_listing$QHCLUST))
+  missing_clusters
+  
+  #Add a new column to data called hh_count
+  dhs_listing <- dhs_listing %>%  
+    mutate(hh_count = 1)
+  
+  # Subset dhs_listing using the DHScluster IDs in non_seg_cluster
+  dhs_listing <- dhs_listing %>%
+    filter(QHCLUST %in% unique(non_seg_cluster$DHScluster)) 
 
-# Subset dhs_listing using the DHScluster IDs in non_seg_cluster
-dhs_listing <- dhs_listing %>%
-  filter(QHCLUST %in% unique(non_seg_cluster$DHScluster)) 
+}
 
 #Summarize total number of hhold per dhs cluster
 dhs_hh_summary <- dhs_listing %>% 
@@ -681,7 +699,7 @@ dhs_hh_count<-  dhs_centroids_sf %>%
 #####################################################################################
 ####################################################################################
 ######### PROCESS DHS SUrvey DATA ################################################ 
-dhs_data <- read_dta(paste0(input_path, "MDHS_2024_NoDZLK_anonymized.dta"))
+dhs_data <- read_dta(file.path(input_path, "MDHS_2024_NoDZLK_anonymized.dta"))
 
 #Calculate hh size per hh
 dhs_size <- dhs_data %>%
@@ -766,94 +784,106 @@ dhs_hh_size<-  dhs_centroids_sf %>%
 ####################################################################################
 ######### PROCESS ZOMBA DISTRICT DATA ################################################ 
 
-#Add a new column to data called hh_count
-zomba_data <- zomba_data %>%  
-  mutate(hh_count = 1)
-
-#Convert to sf object
-zomba_sf <- zomba_data %>%  
-  drop_na(gps_longitude, gps_latitude) %>%  
-  st_as_sf(coords = c("gps_longitude", "gps_latitude"))
-
-# #set the spatial reference
-st_crs(zomba_sf) <- 4326
-
-#transform
-zomba_sf <- st_transform(zomba_sf, crs = st_crs(ea))
-
-# EA Nearest Neighbor Assignment 
-nearest_indices <- st_nearest_feature(zomba_sf, ea)
-
-# Extract the EA_CODE  of the nearest polygons
-nearest_ids <- ea$EA_CODE[nearest_indices]
-
-# Add the EA_CODE to data
-zomba_sf$EA_CODE <- nearest_ids
-
-#Write to file
-#st_write(zomba_sf , 
-#dsn = file.path(output_path, "zomba_point.gpkg"), 
-#driver = "GPKG", 
-#delete_layer = TRUE)
-
-#convert data to tibble
-zomba_tibble <- zomba_sf %>%  
-  as_tibble()
-
-#Summarize data
-zomba_tibble <- zomba_tibble %>% 
-  group_by(EA_CODE) %>% 
-  summarise(zomba_hh_count = sum(hh_count, na.rm = T),
-            zomba_pop = sum(household_size, na.rm = T)) %>%  
-  ungroup() 
+zomba_data_path <- file.path(output_path, "zomba_rbind_data.csv")
+if (file.exists(zomba_data_path)){
+  zomba_data <- read.csv(zomba_data_path) ## FILE MISSING!
+  
+  #Add a new column to data called hh_count
+  zomba_data <- zomba_data %>%  
+    mutate(hh_count = 1)
+  
+  #Convert to sf object
+  zomba_sf <- zomba_data %>%  
+    drop_na(gps_longitude, gps_latitude) %>%  
+    st_as_sf(coords = c("gps_longitude", "gps_latitude"))
+  
+  # #set the spatial reference
+  st_crs(zomba_sf) <- 4326
+  
+  #transform
+  zomba_sf <- st_transform(zomba_sf, crs = st_crs(ea))
+  
+  # EA Nearest Neighbor Assignment 
+  nearest_indices <- st_nearest_feature(zomba_sf, ea)
+  
+  # Extract the EA_CODE  of the nearest polygons
+  nearest_ids <- ea$EA_CODE[nearest_indices]
+  
+  # Add the EA_CODE to data
+  zomba_sf$EA_CODE <- nearest_ids
+  
+  #Write to file
+  #st_write(zomba_sf , 
+  #dsn = file.path(output_path, "zomba_point.gpkg"), 
+  #driver = "GPKG", 
+  #delete_layer = TRUE)
+  
+  #convert data to tibble
+  zomba_tibble <- zomba_sf %>%  
+    as_tibble()
+  
+  #Summarize data
+  zomba_tibble <- zomba_tibble %>% 
+    group_by(EA_CODE) %>% 
+    summarise(zomba_hh_count = sum(hh_count, na.rm = T),
+              zomba_pop = sum(household_size, na.rm = T)) %>%  
+    ungroup() 
+} else {
+  print("The Zomba data cannot be found. Skipping processing of this data for now.")
+}
 
 #####################################################################################
 ####################################################################################
 ######### PROCESS MALEMA DISTRICT DATA ################################################ 
 
-#Add a new column to data called hh_count
-malemia_data <- malemia_data %>%  
-  mutate(hh_count = 1)
-
-#Convert to sf object
-malemia_sf <- malemia_data %>%  
-  drop_na(hh_longitude, hh_latitude) %>%  
-  st_as_sf(coords = c("hh_longitude", "hh_latitude"))
-
-# #set the spatial reference
-st_crs(malemia_sf) <- 4326
-
-#transform
-malemia_sf <- st_transform(malemia_sf, crs = st_crs(ea))
-
-# EA Nearest Neighbor Assignment 
-nearest_indices <- st_nearest_feature(malemia_sf, ea)
-
-# Extract the EA_CODE  of the nearest polygons
-nearest_ids <- ea$EA_CODE[nearest_indices]
-
-# Add the EA_CODE to data
-malemia_sf$EA_CODE <- nearest_ids
-
-#Write to file
-#st_write(malemia_sf , 
-#dsn = file.path(output_path, "malemia_point.gpkg"), 
-#driver = "GPKG", 
-#delete_layer = TRUE)
-
-#convert data to tibble
-malemia_tibble <- malemia_sf %>%  
-  as_tibble()
-
-#Summarize data
-malemia_tibble <- malemia_tibble %>% 
-  group_by(EA_CODE) %>% 
-  summarise(malemia_hh_count = sum(hh_count, na.rm = T)) %>% 
-  #zomba_pop = sum(household_size, na.rm = T)) %>%  
-  ungroup() 
-
-
+malemia_data_path <- file.path(input_path, "malemia_hh_without_IDs.csv") ## FILE MISSING!
 
+if (file.exists(malemia_data_path)) {
+  malemia_data <- read.csv(malemia_data_path) ## FILE MISSING!
+  
+  #Add a new column to data called hh_count
+  malemia_data <- malemia_data %>%  
+    mutate(hh_count = 1)
+  
+  #Convert to sf object
+  malemia_sf <- malemia_data %>%  
+    drop_na(hh_longitude, hh_latitude) %>%  
+    st_as_sf(coords = c("hh_longitude", "hh_latitude"))
+  
+  # #set the spatial reference
+  st_crs(malemia_sf) <- 4326
+  
+  #transform
+  malemia_sf <- st_transform(malemia_sf, crs = st_crs(ea))
+  
+  # EA Nearest Neighbor Assignment 
+  nearest_indices <- st_nearest_feature(malemia_sf, ea)
+  
+  # Extract the EA_CODE  of the nearest polygons
+  nearest_ids <- ea$EA_CODE[nearest_indices]
+  
+  # Add the EA_CODE to data
+  malemia_sf$EA_CODE <- nearest_ids
+  
+  #Write to file
+  #st_write(malemia_sf , 
+  #dsn = file.path(output_path, "malemia_point.gpkg"), 
+  #driver = "GPKG", 
+  #delete_layer = TRUE)
+  
+  #convert data to tibble
+  malemia_tibble <- malemia_sf %>%  
+    as_tibble()
+  
+  #Summarize data
+  malemia_tibble <- malemia_tibble %>% 
+    group_by(EA_CODE) %>% 
+    summarise(malemia_hh_count = sum(hh_count, na.rm = T)) %>% 
+    #zomba_pop = sum(household_size, na.rm = T)) %>%  
+    ungroup() 
+} else {
+  print("Malemia data cannot be found. Skipping processing of this for now.")
+}
 
 
 ############################################################################
@@ -866,9 +896,25 @@ combined_data <- mphc_rbind %>%
   left_join(IHS_rbind, by = "EA_CODE") %>%  
   left_join(Naca_rbind, by = c("EA_CODE" ="EA_Number")) %>% 
   left_join(dhs_hh_size, by = "EA_CODE") %>% 
-  left_join(dhs_hh_count, by = "EA_CODE") %>% 
-  left_join(zomba_tibble, by = "EA_CODE") %>% 
+  left_join(dhs_hh_count, by = "EA_CODE") 
+
+if (exists("zomba_tibble")) {
+  combined_data <- combined_data %>%
+    left_join(zomba_tibble, by = "EA_CODE")
+} else {
+  # add dummy placeholder column full of NAs
+  combined_data <- combined_data %>%
+    mutate(malemia_hh_count = NA)
+}
+
+if (exists("malemia_tibble")) {
+  combined_data <- combined_data %>%
   left_join(malemia_tibble, by = "EA_CODE")
+} else {
+  # add dummy placeholder column full of NAs
+  combined_data <- combined_data %>%
+    mutate(zomba_hh_count = NA)  
+}
 
 #create observed hh_count based on priority conditions
 combined_data <- combined_data %>%  
@@ -896,17 +942,19 @@ combined_data <- combined_data %>%
   select(EA_CODE, mphc_total_pop, mphc_median_hh_size, mphc_mean_hh_size, 
          dhs_median_hh_size, dhs_mean_hh_size, observed_hh_count,dhs_hh_count,
          mphc_hh_count, ict_hh_count, ihs_hh_count,
-         naca_hh_count, zomba_hh_count, malemia_hh_count, zomba_pop, female_count, male_count, starts_with("age_"))
+         naca_hh_count, zomba_hh_count, malemia_hh_count, 
+         # zomba_pop, ### excluding here as commented out in processed code above so not created by default
+         female_count, male_count, starts_with("age_"))
 
 #Write to file
 
-write.csv(combined_data, paste0(output_path, "summarized_survey_data.csv"), row.names = F)
+write.csv(combined_data, file.path(output_path, "summarized_survey_data.csv"), row.names = F)
 
 
 #join combined data to EA shapefile and export
 #Join to ea data
 combined_data <- combined_data %>%  
-  mutate(EA_CODE = as.character(EA_CODE))  #convert EA code to interger
+  mutate(EA_CODE = as.character(EA_CODE))  #convert EA code to integer
 
 #select hh size
 hh_size <- combined_data %>%  
@@ -919,7 +967,7 @@ hh_ea <- hh_ea %>%
   select(EA_CODE, mphc_total_pop, mphc_median_hh_size, mphc_mean_hh_size)
 
 #write to file
-st_write(hh_ea, paste(output_path, "hh_size_data.gpkg"), append = T)
+st_write(hh_ea, file.path(output_path, "hh_size_data.gpkg"), append = T)
 
 
 #################### END OF SCRIPT #########################################
diff --git a/load_required_libraries.R b/load_required_libraries.R
index a9edc06..c687ddd 100644
--- a/load_required_libraries.R
+++ b/load_required_libraries.R
@@ -34,7 +34,8 @@ required_libraries <- c(
   "caret", # Classification And REgression Training
   "kableExtra", # for html tables
   "inlabru", # package for Bayesian spatial modelling 
-  "feather" # reading and writing feather files
+  "feather", # reading and writing feather files
+  "nngeo" # nearest neighbour in geospatial
 )
 
 # Execute code to load (and install) libraries

From 066bd840c5f9c094a0476605fe2f22efd693458f Mon Sep 17 00:00:00 2001
From: Baizan Edge <Amanda.Baizan.Edge@ons.gov.uk>
Date: Thu, 26 Mar 2026 11:42:37 +0000
Subject: [PATCH 15/31] Changed shapefile from
 "2018_MPHC_EAs_Final_for_Use_Corrected.shp" to
 "2018_MPHC_EAs_Final_for_Use.shp" in line with the rest of the code on this
 branch

---
 02_Covariates_Extraction.R | 84 +++++++++++++++++++++++++++-----------
 1 file changed, 60 insertions(+), 24 deletions(-)

diff --git a/02_Covariates_Extraction.R b/02_Covariates_Extraction.R
index 539dbab..39ceb90 100644
--- a/02_Covariates_Extraction.R
+++ b/02_Covariates_Extraction.R
@@ -61,14 +61,18 @@ bcount_2024_extract <- bcount_2024_extract |>
          microsoft_PIB = sum.buildings_count_PIB_ms_100m_v1_1)
 
 #Extract non residential bcount
-non_res <- rast(paste0(input_path, "non_res_raster.tif"))
-
-non_res_extract <- exactextractr::exact_extract(non_res, ea, fun = 'sum')
-
-#convert to tibille
-non_res_extract <- non_res_extract |> 
-  as_tibble() |> 
-  rename(non_res_bcount = value)
+# Check if non_res_raster.tif exists before loading
+non_res_path <- paste0(input_path, "non_res_raster.tif")
+if (file.exists(non_res_path)) {
+  non_res <- rast(non_res_path)
+  non_res_extract <- exactextractr::exact_extract(non_res, ea, fun = 'sum')
+  non_res_extract <- non_res_extract |> 
+    as_tibble() |> 
+    rename(non_res_bcount = value)
+} else {
+  non_res_extract <- NULL
+  message("non_res_raster.tif not found, skipping non-res extraction.")
+}
 
 
 #####################################################################################
@@ -83,9 +87,16 @@ non_res_extract <- non_res_extract |>
 raster_list <-list.files(path=covs_path_2024, pattern= ".tif$", all.files=TRUE, full.names=FALSE)
 raster_list
 
-#Stack all covariates 
+#Stack all unique covariates
 raster_2024_covariates <- rast(paste0(covs_path_2024, c(raster_list)))
 
+# Find duplicate layer names
+dup_names <- names(raster_2024_covariates)[duplicated(names(raster_2024_covariates))]
+
+# Remove layers with duplicate names (keep only the first occurrence)
+unique_layers <- !duplicated(names(raster_2024_covariates))
+raster_2024_covariates <- raster_2024_covariates[[unique_layers]]
+
 #Extract rasters using their mean values
 tic()
 
@@ -97,7 +108,7 @@ toc()
 var_names <- names(raster_2024_extract)
 
 #Change names
-colnames(raster_2024_extract) <- c(paste0('x', 1:64))
+colnames(raster_2024_extract) <- c(paste0('x', 1:length(raster_2024_extract)))
 
 #Extract names of raster
 var_names2<- names(raster_2024_extract)
@@ -127,9 +138,15 @@ lat_long <- lat_long %>%
 
 
 #Cbind raster_extract to ea
-ea_2024 <- ea %>% 
-  cbind(bcount_2024_extract, non_res_extract, raster_2024_extract, lat_long) |> 
-  as_tibble()
+if (file.exists(non_res_path)) {
+  ea_2024 <- ea %>% 
+    cbind(bcount_2024_extract, non_res_extract, raster_2024_extract, lat_long) |> 
+    as_tibble()
+} else {
+  ea_2024 <- ea %>% 
+    cbind(bcount_2024_extract, raster_2024_extract, lat_long) |> 
+    as_tibble()
+}
 
 #convert pop data to character
 pop_data <- pop_data |> 
@@ -173,14 +190,22 @@ rename(google_v2_5 = sum.buildings_count_2018_glv2_5_t0_5_C_100m_v1,
  microsoft_BCB = sum.buildings_count_BCB_ms_100m_v1_1)
 
 #Extract non residential bcount
-non_res <- rast(paste0(input_path, "non_res_raster.tif"))
-
-non_res_extract <- exactextractr::exact_extract(non_res, ea, fun = 'sum')
+# Check if non_res_raster.tif exists before loading
+non_res_path <- paste0(input_path, "non_res_raster.tif")
+if (file.exists(non_res_path)) {
+  non_res <- rast(paste0(input_path, "non_res_raster.tif"))
+  
+  non_res_extract <- exactextractr::exact_extract(non_res, ea, fun = 'sum')
+  
+  #convert to tibille
+  non_res_extract <- non_res_extract |> 
+    as_tibble() |> 
+    rename(non_res_bcount = value)
+} else {
+  non_res_extract <- NULL
+  message("non_res_raster.tif not found, skipping non-res extraction.")
+}
 
-#convert to tibille
-non_res_extract <- non_res_extract |> 
-  as_tibble() |> 
-  rename(non_res_bcount = value)
 
 # Extract 2018 covariates -------------------------------------------------
 
@@ -192,6 +217,10 @@ raster_list
 #Stack all covariates 
 raster_2018_covariates <- rast(paste0(covs_path_2018, c(raster_list)))
 
+# Remove layers with duplicate names (keep only the first occurrence)
+unique_layers_2018 <- !duplicated(names(raster_2018_covariates))
+raster_2018_covariates <- raster_2018_covariates[[unique_layers_2018]]
+
 #Extract rasters using their mean values
 tic()
 
@@ -203,7 +232,7 @@ toc()
 var_names <- names(raster_2018_extract)
 
 #Change names
-colnames(raster_2018_extract) <- c(paste0('x', 1:64))
+colnames(raster_2018_extract) <- c(paste0('x', 1:length(raster_2018_extract)))
 
 #Extract names of raster
 var_names2<- names(raster_2018_extract)
@@ -233,9 +262,16 @@ lat_long <- lat_long %>%
 
 
 #Cbind raster_extract to ea
-ea_2018 <- ea %>% 
-  cbind(bcount_2018_extract, non_res_extract, raster_2018_extract, lat_long) |> 
-  as_tibble()
+if (file.exists(non_res_path)) {
+  ea_2018 <- ea %>% 
+    cbind(bcount_2018_extract, non_res_extract, raster_2018_extract, lat_long) |> 
+    as_tibble()
+} else {
+  ea_2018 <- ea %>% 
+    cbind(bcount_2018_extract, raster_2018_extract, lat_long) |> 
+    as_tibble()
+  
+}
 
 #convert pop data to character
 pop_data <- pop_data |> 

From 64d4cb869a64faa3672fc383684b16122eb2905c Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Thu, 26 Mar 2026 12:29:42 +0000
Subject: [PATCH 16/31] Added correct file name for shapefile

---
 03_HH_Model_Workflow_2024c.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03_HH_Model_Workflow_2024c.R b/03_HH_Model_Workflow_2024c.R
index 39bdccf..7722cc0 100644
--- a/03_HH_Model_Workflow_2024c.R
+++ b/03_HH_Model_Workflow_2024c.R
@@ -25,7 +25,7 @@ output_path1 <- paste0(drive_path, "Output_Data/Pop_Rasters/")
 
 #Load data
 pop_data <-  read.csv(paste0(input_path, "Malawi_2024_data.csv"))
-shapefile <- st_read(paste0(shapefile_path, "2018_MPHC_EAs_Final_for_Use_Corrected.shp"))
+shapefile <- st_read(paste0(shapefile_path, "2018_MPHC_EAs_Final_for_Use.shp"))
 
 #create unique id for each district
 pop_data <- pop_data %>% 

From b7128d4d8f13e62e9ae3e171949d129e0cfbce7a Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Thu, 26 Mar 2026 12:30:13 +0000
Subject: [PATCH 17/31] Changed readme to be more useful

---
 README.md | 79 ++++++++++++++++++++++++++++++++-----------------------
 1 file changed, 46 insertions(+), 33 deletions(-)

diff --git a/README.md b/README.md
index e43313e..832c3f5 100644
--- a/README.md
+++ b/README.md
@@ -1,47 +1,60 @@
-# Malawi_Project
-# 📌 Malawi_Project Workflow
+# WorldPop-Malawi-fork (ons-compatability-updates branch)
 
-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.
+## Project Overview
 
----
+This project provides scripts and workflows for processing, analyzing, and modeling population and building data for Malawi, following the WorldPop methodology and adapted for ONS compatibility.
 
-## 📂 Repository Structure
+## Branch Information
 
-| 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). |
+This is the `ons-compatability-updates` branch, which includes updates for compatibility with ONS workflows and standards.
 
----
+## Dependencies
 
-## 🎯 Objective
+This project requires R and the following R packages:
 
-The main goal of this project is to **model household count** and **population** for census preparation in Malawi
+-   tidyverse
+-   sf
+-   tictoc
+-   terra
+-   exactextractr
+-   haven
+-   nngeo
+-   INLA
+-   gstat
+-   spdep
+-   car
+-   caret
+-   kableExtra
+-   inlabru
+-   feather
+-   raster
 
----
+To install all required packages, run this in your R console:
 
-## 🔄 Processing Flow
+``` r
+install.packages(c("tidyverse", "sf", "tictoc", "terra", "exactextractr", "haven", "nngeo", "INLA", "gstat", "spdep", "car", "caret", "kableExtra", "inlabru", "feather", "raster"))
+```
 
-1. **Data Cleaning & Setup (`00_*.R`)**
-2. **Raster Mosaicking (`01_*.R`)**
-   - Process and harmonize building footprints
-3. **Covariate Preparation (`02_*.R`)**
-4. **Rasterization of Vector Inputs (`04_Rasterize.R`)**
-5. **Covariate Raster Stacking & Cropping (`04_Covs_*.R`)**
+## Data Pre-processing
 
----
+Before running the main scripts, data pre-processing steps are required. Please refer to the [Pre-processing Steps section of the project wiki](https://github.com/datasciencecampus/WorldPop-Malawi-fork/wiki/Pre-processing-Steps) for detailed instructions.
 
-## 🛠️ Requirements
+Run the following scripts in order for a typical workflow:
 
-Make sure the following R packages are installed:
+1.  `01_Raster_Mosaicking_Buildings_2018.R`  - successfully run
+2.  `01_Raster_Mosaicking_Buildings_2024.R` - successfully run
+3.  `01_Raster_Mosaicking_Workflow_2018.R` - successfully run
+4.  `01_Raster_Mosaicking_Workflow_2024.R` - successfully run
+5.  `00_Data_Processing.R` - successfully run
+6.  `00_Data_Processing2.R` - successfully run - caveat "missing files"
+7.  `04_Rasterize.R` - not used in modelling and not run
+8.  `02_Covariates_Extraction.R` - successfully run - with caveat modified to work without data created in `04_Rasterize.R`
+
+## Modelling scripts
+In progress - see [Model code review of the project wiki](https://github.com/datasciencecampus/WorldPop-Malawi-fork/wiki/Model-code-review)
+
+
+## Additional Resources
+
+-   [Full Project Wiki](https://github.com/datasciencecampus/WorldPop-Malawi-fork/wiki)
 
-```r
-install.packages(c("tidyverse", "sf", "terra", "raster", "exactextractr"))

From 3cd0fa911d5d6d3ecfe16b28a5e9f8c356e1f4c8 Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Tue, 21 Apr 2026 11:24:00 +0100
Subject: [PATCH 18/31] extra package needed to run HH modelling

---
 03_HH_Model_Workflow_2024c.R | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/03_HH_Model_Workflow_2024c.R b/03_HH_Model_Workflow_2024c.R
index 7722cc0..48662a1 100644
--- a/03_HH_Model_Workflow_2024c.R
+++ b/03_HH_Model_Workflow_2024c.R
@@ -9,6 +9,7 @@ library(terra)
 library(kableExtra)
 library(inlabru)
 library(feather)
+library(glmnet)
 library(tidyverse)
 
 set.seed(1234) #set seed for reproducibility
@@ -280,9 +281,9 @@ print(final_formula)
 
 #function to drop non-significant variables
 # Start with full model
-current_formula <- as.formula("hh_density ~  x13 + x32 + x36 + x38 + x39 + x40 + 
-    x41 + x44 + x45 + x47 + x49 + x51 + x55 + x56 + x61 + x63 + 
-    x64")
+current_formula <- as.formula("hh_density ~ x19 + x26 + x36 + x37 + x39 + x40 + 
+                                x43 + x46 + x48 + x50 + x52 + x54 + x55 + x56 + x57 + x60 + 
+                                x61 + x62")
 
 # Loop to drop non-significant variables
 repeat {
@@ -327,8 +328,8 @@ covs_selection1 <- covs_selection %>%
 
 #Lasso Regression
 fit1_lasso <- train(
-  hh_density ~ x13 + x32 + x36 + x38 + x39 + x41 + x44 + x45 + 
-    x47 + x49 + x55 + x56 + x61 + x63 + x64,
+  hh_density ~ x19 + x26 + x36 + x40 + x43 + x46 + x48 + x54 + 
+    x55 + x57 + x60 + x61 + x62,
   data = covs_selection1,
   method = "glmnet",
   metric = "RMSE",  # Choose from RMSE, RSquared, AIC, BIC, ...others?

From c2b7b51bf66a54f0438f97b5c0d5ae6bcdbdd71c Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Tue, 21 Apr 2026 11:24:21 +0100
Subject: [PATCH 19/31] Improved readme

---
 README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 832c3f5..1d282c9 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ Before running the main scripts, data pre-processing steps are required. Please
 
 Run the following scripts in order for a typical workflow:
 
-1.  `01_Raster_Mosaicking_Buildings_2018.R`  - successfully run
+1.  `01_Raster_Mosaicking_Buildings_2018.R` - successfully run
 2.  `01_Raster_Mosaicking_Buildings_2024.R` - successfully run
 3.  `01_Raster_Mosaicking_Workflow_2018.R` - successfully run
 4.  `01_Raster_Mosaicking_Workflow_2024.R` - successfully run
@@ -51,10 +51,11 @@ Run the following scripts in order for a typical workflow:
 8.  `02_Covariates_Extraction.R` - successfully run - with caveat modified to work without data created in `04_Rasterize.R`
 
 ## Modelling scripts
+
 In progress - see [Model code review of the project wiki](https://github.com/datasciencecampus/WorldPop-Malawi-fork/wiki/Model-code-review)
 
+The important script in this section is `03_HH_Model_Workflow_2024.R`
 
 ## Additional Resources
 
 -   [Full Project Wiki](https://github.com/datasciencecampus/WorldPop-Malawi-fork/wiki)
-

From 0bbbb7b242bcfdb575583f392efc69b10c4bf950 Mon Sep 17 00:00:00 2001
From: TSGreen <Tim.Green@ons.gov.uk>
Date: Tue, 21 Apr 2026 15:08:51 +0100
Subject: [PATCH 20/31] refactor: Update names of covars in rast object to
 match file naming

---
 02_Covariates_Extraction.R | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/02_Covariates_Extraction.R b/02_Covariates_Extraction.R
index 39ceb90..5fae453 100644
--- a/02_Covariates_Extraction.R
+++ b/02_Covariates_Extraction.R
@@ -90,12 +90,10 @@ raster_list
 #Stack all unique covariates
 raster_2024_covariates <- rast(paste0(covs_path_2024, c(raster_list)))
 
-# Find duplicate layer names
-dup_names <- names(raster_2024_covariates)[duplicated(names(raster_2024_covariates))]
+names(raster_2024_covariates) <- sources(raster_2024_covariates) %>% 
+  lapply(str_split_i, "/", -1) %>%
+  lapply(str_split_i, ".tif", 1)
 
-# Remove layers with duplicate names (keep only the first occurrence)
-unique_layers <- !duplicated(names(raster_2024_covariates))
-raster_2024_covariates <- raster_2024_covariates[[unique_layers]]
 
 #Extract rasters using their mean values
 tic()

From 9e65e7c8962decd16f1fe584d34fee6d95132ee3 Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Mon, 4 May 2026 10:17:23 +0100
Subject: [PATCH 21/31] Added "glmnet" as one of the packages to install

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1d282c9..ed11571 100644
--- a/README.md
+++ b/README.md
@@ -28,11 +28,12 @@ This project requires R and the following R packages:
 -   inlabru
 -   feather
 -   raster
+-   glmnet
 
 To install all required packages, run this in your R console:
 
 ``` r
-install.packages(c("tidyverse", "sf", "tictoc", "terra", "exactextractr", "haven", "nngeo", "INLA", "gstat", "spdep", "car", "caret", "kableExtra", "inlabru", "feather", "raster"))
+install.packages(c("tidyverse", "sf", "tictoc", "terra", "exactextractr", "haven", "nngeo", "INLA", "gstat", "spdep", "car", "caret", "kableExtra", "inlabru", "feather", "raster", "glmnet"))
 ```
 
 ## Data Pre-processing

From ae7b4456208efc66cff4ba71128b910ce1716a53 Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Mon, 4 May 2026 13:54:12 +0100
Subject: [PATCH 22/31] zomba data processing function in utils and added
 processing step to  00_Data_Processing2 script

Co-authored-by: Copilot <copilot@github.com>
---
 00_Data_Processing2.R | 11 ++++++-
 utils.R               | 67 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 77 insertions(+), 1 deletion(-)

diff --git a/00_Data_Processing2.R b/00_Data_Processing2.R
index d7b5aa5..dccf666 100644
--- a/00_Data_Processing2.R
+++ b/00_Data_Processing2.R
@@ -784,9 +784,16 @@ dhs_hh_size<-  dhs_centroids_sf %>%
 ####################################################################################
 ######### PROCESS ZOMBA DISTRICT DATA ################################################ 
 
+# NSO has given 14 csvs that need to be rbound - function in utils.R
+rbind_zomba_csvs(
+    csv_dir = file.path(input_path, "zomba_csv"),
+    output_file = file.path(output_path,"zomba_rbind_data.csv")
+)
+
 zomba_data_path <- file.path(output_path, "zomba_rbind_data.csv")
+
 if (file.exists(zomba_data_path)){
-  zomba_data <- read.csv(zomba_data_path) ## FILE MISSING!
+  zomba_data <- read.csv(zomba_data_path)
   
   #Add a new column to data called hh_count
   zomba_data <- zomba_data %>%  
@@ -828,6 +835,8 @@ if (file.exists(zomba_data_path)){
     summarise(zomba_hh_count = sum(hh_count, na.rm = T),
               zomba_pop = sum(household_size, na.rm = T)) %>%  
     ungroup() 
+  
+  print("Zomba data processed")
 } else {
   print("The Zomba data cannot be found. Skipping processing of this data for now.")
 }
diff --git a/utils.R b/utils.R
index 18fb336..d55cbf6 100644
--- a/utils.R
+++ b/utils.R
@@ -14,3 +14,70 @@ generate_buffered_country_boundary <- function(
   st_write(country_buffer, file.path(shape_path, file_name))
   return(country_buffer)
 }
+
+rbind_zomba_csvs <- function(csv_dir, output_file){
+  #' Produce rbind csv file for zomba from 14 files provided by NSO in "zomba_csv"
+  #' folder. 
+  #' Headers are normalised, NA rows are removed, and then rbound together to
+  #' make "zomba_rbind_data.csv" file.
+  if (!dir.exists(csv_dir)) {
+    stop("Directory does not exist: ", csv_dir)
+  }
+
+  output_path <- file.path(output_file)
+  csv_files <- list.files(
+    csv_dir,
+    pattern = "\\.csv$",
+    full.names = TRUE,
+    ignore.case = TRUE
+  )
+  csv_files <- sort(csv_files)
+
+  if (length(csv_files) == 0) {
+    stop("No CSV files found in: ", csv_dir)
+  }
+
+  zomba_data_list <- lapply(
+    csv_files,
+    function(csv_file) {
+      zomba_data <- read.csv(csv_file, stringsAsFactors = FALSE, check.names = FALSE)
+      # change headers to lowercase and replace spaces to _ to normalise
+      names(zomba_data) <- gsub("\\s+", "_", tolower(trimws(names(zomba_data))))
+      # "Nkapita.csv" had "household_name" instead of "household_number" like
+      # the rest
+      names(zomba_data)[names(zomba_data) == "household_name"] <- "household_number"
+      # remove rows where every field is blank or missing
+      empty_rows <- apply(
+        zomba_data,
+        1,
+        function(row) all(is.na(row) | trimws(as.character(row)) == "")
+      )
+      zomba_data <- zomba_data[!empty_rows, , drop = FALSE]
+      zomba_data
+    }
+  )
+  
+  # Look at the first file and determine what columns are expected
+  expected_columns <- names(zomba_data_list[[1]])
+  
+  matching_columns <- vapply(
+    zomba_data_list,
+    function(zomba_data) identical(names(zomba_data), expected_columns),
+    logical(1)
+  )
+
+  # Check that every CSV has the same normalized columns as the first file and
+  # stop with the filenames of any CSVs that still do not match.
+  if (!all(matching_columns)) {
+    mismatched_files <- basename(csv_files[!matching_columns])
+    stop(
+      "CSV files do not share identical columns and cannot be row-bound with rbind: ",
+      paste(mismatched_files, collapse = ", ")
+    )
+  }
+
+  zomba_rbind_data <- do.call(rbind, zomba_data_list)
+  write.csv(zomba_rbind_data, output_path, row.names = FALSE)
+
+  return()
+}

From 8a3a37f69618a56d87b7f7fdbd1bd2e86067f803 Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Mon, 4 May 2026 13:55:01 +0100
Subject: [PATCH 23/31] Added message to say that malemia data is available

---
 00_Data_Processing2.R | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/00_Data_Processing2.R b/00_Data_Processing2.R
index dccf666..34d2808 100644
--- a/00_Data_Processing2.R
+++ b/00_Data_Processing2.R
@@ -9,6 +9,8 @@ library(haven)
 library(tidyverse)
 #library(foreign)
 
+source("utils.R")
+
 options(scipen = 999) # turn off scientific notation for all variables
 
 #Specify Drive Path
@@ -845,10 +847,10 @@ if (file.exists(zomba_data_path)){
 ####################################################################################
 ######### PROCESS MALEMA DISTRICT DATA ################################################ 
 
-malemia_data_path <- file.path(input_path, "malemia_hh_without_IDs.csv") ## FILE MISSING!
+malemia_data_path <- file.path(input_path, "malemia_hh_without_IDs.csv")
 
 if (file.exists(malemia_data_path)) {
-  malemia_data <- read.csv(malemia_data_path) ## FILE MISSING!
+  malemia_data <- read.csv(malemia_data_path)
   
   #Add a new column to data called hh_count
   malemia_data <- malemia_data %>%  
@@ -890,6 +892,7 @@ if (file.exists(malemia_data_path)) {
     summarise(malemia_hh_count = sum(hh_count, na.rm = T)) %>% 
     #zomba_pop = sum(household_size, na.rm = T)) %>%  
     ungroup() 
+  print("Malemia data processed")
 } else {
   print("Malemia data cannot be found. Skipping processing of this for now.")
 }

From 1ea5407c58f09f479d1c5a3e19be29e9a6e6a005 Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Mon, 4 May 2026 14:01:02 +0100
Subject: [PATCH 24/31] Updated readme to remove "missing files" flag

---
 README.md | 22 +---------------------
 1 file changed, 1 insertion(+), 21 deletions(-)

diff --git a/README.md b/README.md
index ed11571..77f4a8a 100644
--- a/README.md
+++ b/README.md
@@ -10,26 +10,6 @@ This is the `ons-compatability-updates` branch, which includes updates for compa
 
 ## Dependencies
 
-This project requires R and the following R packages:
-
--   tidyverse
--   sf
--   tictoc
--   terra
--   exactextractr
--   haven
--   nngeo
--   INLA
--   gstat
--   spdep
--   car
--   caret
--   kableExtra
--   inlabru
--   feather
--   raster
--   glmnet
-
 To install all required packages, run this in your R console:
 
 ``` r
@@ -47,7 +27,7 @@ Run the following scripts in order for a typical workflow:
 3.  `01_Raster_Mosaicking_Workflow_2018.R` - successfully run
 4.  `01_Raster_Mosaicking_Workflow_2024.R` - successfully run
 5.  `00_Data_Processing.R` - successfully run
-6.  `00_Data_Processing2.R` - successfully run - caveat "missing files"
+6.  `00_Data_Processing2.R` - successfully run
 7.  `04_Rasterize.R` - not used in modelling and not run
 8.  `02_Covariates_Extraction.R` - successfully run - with caveat modified to work without data created in `04_Rasterize.R`
 

From adbd1d0b5afb8cf86c7544f26862e6711274169b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Do=C4=9Fu=C5=9F=20=C3=96ks=C3=BCz?= <dogus.oksuz@ons.gov.uk>
Date: Mon, 11 May 2026 12:33:58 +0100
Subject: [PATCH 25/31] Standardize household identifier and row-bind all TA
 CSVs into a single Zomba dataset

---
 data_merge_zomba.R | 97 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 97 insertions(+)
 create mode 100644 data_merge_zomba.R

diff --git a/data_merge_zomba.R b/data_merge_zomba.R
new file mode 100644
index 0000000..ae8b0eb
--- /dev/null
+++ b/data_merge_zomba.R
@@ -0,0 +1,97 @@
+library(tidyverse)
+
+#Loading data zomba_csv
+Chikowi <- read_csv("Chikowi.csv")
+glimpse(Chikowi)
+chimbalanga <- read_csv("Chimbalanga.csv")
+glimpse(chimbalanga)
+idana <- read_csv("Idana.csv")
+glimpse(idana)
+kumtumanji <- read_csv("Kumtumanji.csv")
+glimpse(kumtumanji)
+Malemia <- read_csv("Malemia.csv")
+glimpse(Malemia)
+Mbiza <- read_csv("Mbiza.csv")
+glimpse(Mbiza)
+Mkumbira <- read_csv("Mkumbira.csv")
+glimpse(Mkumbira)
+Mlumbe <- read_csv("Mlumbe.csv")
+glimpse(Mlumbe)
+Mwambo <- read_csv("Mwambo.csv")
+glimpse(Mwambo)
+Mwembere <- read_csv("Mwembere.csv")
+glimpse(Mwembere)
+Ngwelero <- read_csv("Ngwelero.csv")
+glimpse(Ngwelero)
+Nkagula <- read_csv("Nkagula.csv")
+glimpse(Nkagula)
+Nkapita <- read_csv("Nkapita.csv")
+glimpse(Nkapita)
+Ntholowa <- read_csv("Ntholowa.csv")
+glimpse(Ntholowa)
+
+#loading Malemia hh data
+Malemia_hh <- read_csv("malemia_hh_without_IDs.csv")
+
+#standardize column labels
+
+
+standardise_names <- function(df) {
+  df %>%
+    rename_with(~ "household_number",
+                .cols = any_of(c("HOUSEHOLD NUMBER", "household_name")))
+}
+
+
+Chikowi      <- standardise_names(Chikowi)
+chimbalanga <- standardise_names(chimbalanga)
+idana       <- standardise_names(idana)
+kumtumanji  <- standardise_names(kumtumanji)
+Malemia     <- standardise_names(Malemia)
+Mbiza       <- standardise_names(Mbiza)
+Mkumbira    <- standardise_names(Mkumbira)
+Mlumbe      <- standardise_names(Mlumbe)
+Mwembere    <- standardise_names(Mwembere)
+Ngwelero    <- standardise_names(Ngwelero)
+Nkagula     <- standardise_names(Nkagula)
+Nkapita     <- standardise_names(Nkapita)
+Ntholowa    <- standardise_names(Ntholowa)
+
+#validate 
+colnames(Chikowi)
+
+#Merge files
+
+zomba_rbind_data <- bind_rows(
+  Chikowi,
+  chimbalanga,
+  idana,
+  kumtumanji,
+  Malemia,
+  Mbiza,
+  Mkumbira,
+  Mlumbe,
+  Mwembere,
+  Ngwelero,
+  Nkagula,
+  Nkapita,
+  Ntholowa
+)
+
+glimpse(zomba_rbind_data)
+
+#remove the fully empty rows 
+zomba_rbind_data <- zomba_rbind_data %>%
+  filter(!if_all(everything(), is.na))
+
+#to confirm all TAs are included 
+count(zomba_rbind_data, traditional_authority_name)
+
+#to write it as a CSV
+
+write.csv(
+  zomba_rbind_data,
+  "zomba_households_merged.csv",
+  row.names = FALSE
+)
+

From 153917c2bb92adefd645832faf3f2c7c86103689 Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Thu, 21 May 2026 14:43:34 +0100
Subject: [PATCH 26/31] change the generate buffered country boundry function
 in utils to have a variable called buffer

---
 utils.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/utils.R b/utils.R
index d55cbf6..8f9f77b 100644
--- a/utils.R
+++ b/utils.R
@@ -2,7 +2,8 @@ library(sf)
 
 generate_buffered_country_boundary <- function(
     shape_path,
-    file_name = "Country_Shapefile_Buffer_10km.shp"
+    file_name,
+    buffer
 ){
   #' Produce buffered country boundary shapefile by dissolving EA geometries and
   #' adding 10km buffer
@@ -10,7 +11,7 @@ generate_buffered_country_boundary <- function(
   ea_geoms <- st_read(file.path(shape_path, "2018_MPHC_EAs_Final_for_Use.shp"))
   country_buffer <- ea_geoms %>% 
     st_union() %>% 
-    st_buffer(10E3)
+    st_buffer(buffer)
   st_write(country_buffer, file.path(shape_path, file_name))
   return(country_buffer)
 }

From 51a3305b6f16b1c6a0a8f1b3204f335134aded0a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Do=C4=9Fu=C5=9F=20=C3=96ks=C3=BCz?= <dogus.oksuz@ons.gov.uk>
Date: Fri, 5 Jun 2026 11:36:55 +0100
Subject: [PATCH 27/31] updated zomba data merge

---
 data_merge_zomba.R | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/data_merge_zomba.R b/data_merge_zomba.R
index ae8b0eb..ede6bb6 100644
--- a/data_merge_zomba.R
+++ b/data_merge_zomba.R
@@ -34,8 +34,6 @@ glimpse(Ntholowa)
 Malemia_hh <- read_csv("malemia_hh_without_IDs.csv")
 
 #standardize column labels
-
-
 standardise_names <- function(df) {
   df %>%
     rename_with(~ "household_number",
@@ -51,6 +49,7 @@ Malemia     <- standardise_names(Malemia)
 Mbiza       <- standardise_names(Mbiza)
 Mkumbira    <- standardise_names(Mkumbira)
 Mlumbe      <- standardise_names(Mlumbe)
+Mwambo      <- standardise_names(Mwambo)
 Mwembere    <- standardise_names(Mwembere)
 Ngwelero    <- standardise_names(Ngwelero)
 Nkagula     <- standardise_names(Nkagula)
@@ -61,7 +60,6 @@ Ntholowa    <- standardise_names(Ntholowa)
 colnames(Chikowi)
 
 #Merge files
-
 zomba_rbind_data <- bind_rows(
   Chikowi,
   chimbalanga,
@@ -70,6 +68,7 @@ zomba_rbind_data <- bind_rows(
   Malemia,
   Mbiza,
   Mkumbira,
+  Mwambo,
   Mlumbe,
   Mwembere,
   Ngwelero,

From 18ee4a11270a4a870fab9f3adb63f533cd4259d6 Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Wed, 10 Jun 2026 15:32:19 +0100
Subject: [PATCH 28/31] Spacified 10km buffer in raster  mosaicking scripts

---
 01_Raster_Mosaicking_Buildings_2018.R | 2 +-
 01_Raster_Mosaicking_Buildings_2024.R | 2 +-
 01_Raster_Mosaicking_Workflow_2018.R  | 2 +-
 01_Raster_Mosaicking_Workflow_2024.R  | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/01_Raster_Mosaicking_Buildings_2018.R b/01_Raster_Mosaicking_Buildings_2018.R
index 733b75a..47f2828 100644
--- a/01_Raster_Mosaicking_Buildings_2018.R
+++ b/01_Raster_Mosaicking_Buildings_2018.R
@@ -18,7 +18,7 @@ boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
 if(file.exists(file.path(shp_path, boundary_data_filename))) {
   boundary <- st_read(file.path(shp_path, boundary_data_filename))
 } else {
-  boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
+  boundary <- generate_buffered_country_boundary(shape_path = shp_path, file_name = boundary_data_filename, buffer = 10E3)
 }
 
 r1 <- rast(file.path(building_path, "mwi_buildings_count_2018_glv2_5_t0_5_C_100m_v1.tif")) 
diff --git a/01_Raster_Mosaicking_Buildings_2024.R b/01_Raster_Mosaicking_Buildings_2024.R
index d810846..36a07ff 100644
--- a/01_Raster_Mosaicking_Buildings_2024.R
+++ b/01_Raster_Mosaicking_Buildings_2024.R
@@ -16,7 +16,7 @@ boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
 if(file.exists(file.path(shp_path, boundary_data_filename))) {
   boundary <- st_read(file.path(shp_path, boundary_data_filename))
 } else {
-  boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
+  boundary <- generate_buffered_country_boundary(shape_path = shp_path, file_name = boundary_data_filename, buffer = 10E3)
 }
 r1 <- rast(file.path(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
 
diff --git a/01_Raster_Mosaicking_Workflow_2018.R b/01_Raster_Mosaicking_Workflow_2018.R
index 4362ab7..9225eb9 100644
--- a/01_Raster_Mosaicking_Workflow_2018.R
+++ b/01_Raster_Mosaicking_Workflow_2018.R
@@ -16,7 +16,7 @@ boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
 if(file.exists(file.path(shp_path, boundary_data_filename))) {
   boundary <- st_read(file.path(shp_path, boundary_data_filename))
 } else {
-  boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
+  boundary <- generate_buffered_country_boundary(shape_path = shp_path, file_name = boundary_data_filename, buffer = 10E3)
 }
 
 r1 <- rast(file.path(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
diff --git a/01_Raster_Mosaicking_Workflow_2024.R b/01_Raster_Mosaicking_Workflow_2024.R
index e798833..6576b85 100644
--- a/01_Raster_Mosaicking_Workflow_2024.R
+++ b/01_Raster_Mosaicking_Workflow_2024.R
@@ -16,7 +16,7 @@ boundary_data_filename <- "Country_Shapefile_Buffer_10km.shp"
 if(file.exists(file.path(shp_path, boundary_data_filename))) {
   boundary <- st_read(file.path(shp_path, boundary_data_filename))
 } else {
-  boundary <- generate_buffered_country_boundary(shp_path, boundary_data_filename)
+  boundary <- generate_buffered_country_boundary(shape_path = shp_path, file_name = boundary_data_filename, buffer = 10E3)
 }
 
 r1 <- rast(file.path(building_path, "mwi_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))

From a31c9f1db5d4e051a9bb76e3c68e43a7e1de21e8 Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Wed, 10 Jun 2026 16:40:27 +0100
Subject: [PATCH 29/31] Added country file with no buffer to rasterize.R script

---
 04_Rasterize.R | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/04_Rasterize.R b/04_Rasterize.R
index 4bf0673..310f9d9 100644
--- a/04_Rasterize.R
+++ b/04_Rasterize.R
@@ -16,7 +16,6 @@ bcount_path_2024 <- paste0(drive_path, "Input_Data/Mosaic_Buildings_2024/")
 #Load datasets
 ea <- st_read(file.path(shapefile_path, "2018_MPHC_EAs_Final_for_Use.shp")) # replaces "EA_Shapefile.shp"
 bcount <- rast(file.path(bcount_path_2024, "MOS_MLW_buildings_count_2023_glv2_5_t0_5_C_100m_v1.tif"))
-country <- st_read(file.path(shapefile_path, "Country_Shapefile.shp"))
 hh_size <- read.csv(paste0(output_path, "summarized_survey_data.csv"))
 mphc_structures_2018 <- st_read(paste0(output_path, "mphc_structures_points.gpkg"))
 mphc_2018_sf <- st_read(paste0(output_path, "mphc_2018_sf_ea.gpkg"))
@@ -45,10 +44,25 @@ hh_size <- hh_size |>
 hh_ea <-full_join(ea, hh_size, by = "EA_CODE")
 
 # Rasterize Country ------------------------------------------------------
+country_data_filename <- "Country_Shapefile.shp"
+if(file.exists(file.path(shapefile_path, country_data_filename))) {
+  country <- st_read(file.path(shapefile_path, country_data_filename))
+} else {
+  country <- generate_buffered_country_boundary(shape_path = shapefile_path,
+                                                file_name = country_data_filename,
+                                                buffer = 0)
+}
 
 country <- st_transform(country, crs = st_crs(bcount))
 
-country_raster <- rasterize(country, bcount, field = "Country_ID")
+# Convert to sf object, set original CRS (same as EA data), then transform
+country <- st_as_sf(country) %>%
+  st_set_crs(st_crs(ea)) %>%  # Set to EA's original CRS
+  st_transform(crs = st_crs(bcount)) %>%  # Transform to WGS84
+  st_make_valid() %>%  # Fix invalid geometry
+  mutate(Country_ID = 1)
+
+country_raster <- terra::rasterize(country, bcount, field = "Country_ID")
 plot(country_raster)
 
 #stack rasters

From 921219ae9c2fe0a5224089b20fd5e698d4190c1a Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Wed, 10 Jun 2026 16:41:28 +0100
Subject: [PATCH 30/31] Added documentation on new data and running

---
 00_Data_Processing2.R | 3 ++-
 README.md             | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/00_Data_Processing2.R b/00_Data_Processing2.R
index 34d2808..67b2b20 100644
--- a/00_Data_Processing2.R
+++ b/00_Data_Processing2.R
@@ -594,7 +594,8 @@ sum(Naca_data$hh_count)
 #####################################################################################
 ####################################################################################
 ######### PROCESS DHS Listing DATA ################################################ 
-
+# Data sent by NSO was "MDHS 2024 HH_Listing_EA.xlxs" which is in "MNSO-Data". This
+# file was converted to csv, called "DHS_Segmented_File.csv" and added to Output_data
 
 dhs_file_path <- file.path(input_path, "DHS_Segmented_File.csv")
 if (!file.exists(dhs_file_path)) {
diff --git a/README.md b/README.md
index 77f4a8a..b1cdfab 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@ Run the following scripts in order for a typical workflow:
 4.  `01_Raster_Mosaicking_Workflow_2024.R` - successfully run
 5.  `00_Data_Processing.R` - successfully run
 6.  `00_Data_Processing2.R` - successfully run
-7.  `04_Rasterize.R` - not used in modelling and not run
+7.  `04_Rasterize.R` - mostly not used except for "# Rasterize Country" (line 47)
 8.  `02_Covariates_Extraction.R` - successfully run - with caveat modified to work without data created in `04_Rasterize.R`
 
 ## Modelling scripts

From 7bb5a42a4b12f1180e0aed1cb917be2864a83d8b Mon Sep 17 00:00:00 2001
From: Amanda Baizan Edge <amanda.baizan.edge@ons.gov.uk>
Date: Wed, 17 Jun 2026 12:05:32 +0100
Subject: [PATCH 31/31] Found an issue between loading the country file and
 creating it - which was creating NAs by forcing CRS change which was not
 needed. Changed the colours to make the plot more visible.

---
 04_Rasterize.R | 28 +++++++++++++++++++---------
 1 file changed, 19 insertions(+), 9 deletions(-)

diff --git a/04_Rasterize.R b/04_Rasterize.R
index 310f9d9..0384057 100644
--- a/04_Rasterize.R
+++ b/04_Rasterize.R
@@ -53,17 +53,27 @@ if(file.exists(file.path(shapefile_path, country_data_filename))) {
                                                 buffer = 0)
 }
 
-country <- st_transform(country, crs = st_crs(bcount))
+# Handle both outputs from generate_buffered_country_boundary(): sf or bare sfc geometry.
+country <- if (inherits(country, "sf")) {
+  country
+} else {
+  st_as_sf(country)
+}
+
+# If geometry came back without CRS, assume EA CRS because country was derived from EA union.
+if (is.na(st_crs(country))) {
+  country <- st_set_crs(country, st_crs(ea))
+}
 
-# Convert to sf object, set original CRS (same as EA data), then transform
-country <- st_as_sf(country) %>%
-  st_set_crs(st_crs(ea)) %>%  # Set to EA's original CRS
-  st_transform(crs = st_crs(bcount)) %>%  # Transform to WGS84
-  st_make_valid() %>%  # Fix invalid geometry
-  mutate(Country_ID = 1)
+country <- country %>%
+  st_transform(crs = st_crs(bcount)) %>%
+  st_make_valid() %>%
+  mutate(Country_ID = 1L)
 
-country_raster <- terra::rasterize(country, bcount, field = "Country_ID")
-plot(country_raster)
+# Rasterize from terra vector for consistent behavior.
+country_raster <- terra::rasterize(terra::vect(country), bcount, field = "Country_ID", touches = TRUE)
+plot(country_raster, col = "#e63946", plg = list(title = "country_id"))
+plot(terra::as.polygons(country_raster, dissolve = TRUE), add = TRUE, border = "black", lwd = 0.6)
 
 #stack rasters
 stack_raster <- c(bcount, country_raster)