ntl

notebooks/nighttime-lights/analysis-2024/01_process_data/02_download_ntl.R

@@ -11,7 +11,7 @@ roi_sf <- read_sf(file.path(gadm_dir, "rawdata", paste0("gadm41_MMR_",0,".json")
 # Download ---------------------------------------------------------------------
 bm_raster(roi_sf = roi_sf,
           product_id = "VNP46A4",
-          date = 2012:2022,
+          date = 2012:2023,
           bearer = bearer,
           output_location_type = "file",
           file_dir = file.path(ntl_bm_dir, "FinalData", "VNP46A4_rasters"))

notebooks/nighttime-lights/analysis-2024/01_process_data/03_aggregate.R

@@ -10,17 +10,28 @@ if(DELETE_DIR){
 
 # Prep SEZ ---------------------------------------------------------------------
 sez_sf <- read_sf(file.path(data_dir, "SEZ", "RawData", "industrial__special_economic_zones_sept2019.shp"))
-sez_sf <- sez_sf %>% st_buffer(dist = 2.5)
+sez_sf <- sez_sf %>% st_buffer(dist = 2500)
+
+border_df <- read_xlsx(file.path(data_dir, "Border Towns", "RawData", "Myanmar's Border Town Coordinates.xlsx"))
+border_sf <- border_df %>%
+  st_as_sf(coords = c("longitude", "latitude"), crs = 4326)
+
+border_1km_sf <- border_sf %>% st_buffer(dist = 1000)
+border_2_5km_sf <- border_sf %>% st_buffer(dist = 2500)
 
 # Loop through ROIs ------------------------------------------------------------
-for(adm_level in c("bound1", "bound2", "sez", "0", "1", "2", "3")){
+for(adm_level in c("bound1", "bound2", "sez", "0", "1", "2", "3", "border_1km", "border_2_5km")){
 
   if(adm_level == "sez"){
     roi_sf <- sez_sf
   } else if(adm_level == "bound1"){
     roi_sf <- read_sf(file.path(data_dir, "Boundaries", "RawData", "mmr_polbnda_adm1_250k_mimu.shp"))
   } else if(adm_level == "bound2"){
     roi_sf <- read_sf(file.path(data_dir, "Boundaries", "RawData", "mmr_polbnda_adm2_250k_mimu_1.shp"))
+  } else if(adm_level == "border_1km"){
+    roi_sf <- border_1km_sf
+  } else if(adm_level == "border_2_5km"){
+    roi_sf <- border_2_5km_sf
   } else{
     roi_sf <- read_sf(file.path(gadm_dir, "rawdata", paste0("gadm41_MMR_",adm_level,".json")))
   }

notebooks/nighttime-lights/analysis-2024/01_process_data/04_append.R

@@ -3,7 +3,8 @@
 roi = "adm2"
 product <- "VNP46A3"
 
-for(roi in c("adm0", "adm1", "adm2", "adm3", "admsez", "admbound2", "admbound1")){ 
+for(roi in c("adm0", "adm1", "adm2", "adm3", "admsez", "admbound2", "admbound1", 
+             "admborder_1km", "admborder_2_5km")){ 
   for(product in c("VNP46A3", "VNP46A4")){
 
     df <- file.path(ntl_bm_dir, "FinalData", "aggregated", paste0(roi, "_", product)) %>%

notebooks/nighttime-lights/analysis-2024/02_analysis/annual_trends.R

@@ -2,71 +2,85 @@
 
 # Load data --------------------------------------------------------------------
 adm1_df <- readRDS(file.path(ntl_bm_dir, "FinalData", "aggregated",
-                      paste0("adm1", "_", "VNP46A3", ".Rds")))
+                             paste0("adm1", "_", "VNP46A4", ".Rds")))
 
 adm2_df <- readRDS(file.path(ntl_bm_dir, "FinalData", "aggregated",
-                             paste0("adm2", "_", "VNP46A3", ".Rds")))
+                             paste0("adm2", "_", "VNP46A4", ".Rds")))
 
 adm3_df <- readRDS(file.path(ntl_bm_dir, "FinalData", "aggregated",
-                             paste0("adm3", "_", "VNP46A3", ".Rds")))
+                             paste0("adm3", "_", "VNP46A4", ".Rds")))
 
 sez_df <- readRDS(file.path(ntl_bm_dir, "FinalData", "aggregated",
-                             paste0("admsez", "_", "VNP46A3", ".Rds")))
+                            paste0("admsez", "_", "VNP46A4", ".Rds")))
 
-# Filter -----------------------------------------------------------------------
-clean_data <- function(df){
-  df %>%
-    mutate(year = date %>% year(),
-           month = date %>% month()) %>%
-    filter(month != 12,
-           year >= 2019) 
-}
-
-adm1_df <- adm1_df %>% 
-  clean_data() %>%
-  group_by(year, NAME_1) %>%
-  dplyr::summarise(across(c(ntl_bm_sum, ntl_bm_mean, ntl_bm_median), mean)) %>%
-  ungroup()
-
-adm2_df <- adm2_df %>% 
-  clean_data() %>%
-  group_by(year, NAME_1, NAME_2) %>%
-  dplyr::summarise(across(c(ntl_bm_sum, ntl_bm_mean, ntl_bm_median), mean)) %>%
-  ungroup()
-
-adm3_df <- adm3_df %>% 
-  clean_data() %>%
-  group_by(year, NAME_1, NAME_2, NAME_3) %>%
-  dplyr::summarise(across(c(ntl_bm_sum, ntl_bm_mean, ntl_bm_median), mean)) %>%
-  ungroup()
+border_df <- readRDS(file.path(ntl_bm_dir, "FinalData", "aggregated",
+                               paste0("admborder_1km", "_", "VNP46A4", ".Rds")))
 
-sez_df <- sez_df %>% 
-  clean_data() %>%
-  group_by(year, Name) %>%
-  dplyr::summarise(across(c(ntl_bm_sum, ntl_bm_mean, ntl_bm_median), mean)) %>%
-  ungroup()
+# Filter -----------------------------------------------------------------------
+# clean_data <- function(df){
+#   df %>%
+#     mutate(year = date %>% year(),
+#            month = date %>% month()) %>%
+#     filter(month != 12,
+#            year >= 2019) 
+# }
+# 
+# adm1_df <- adm1_df %>% 
+#   clean_data() %>%
+#   group_by(year, NAME_1) %>%
+#   dplyr::summarise(across(c(ntl_bm_sum, ntl_bm_mean, ntl_bm_median), mean)) %>%
+#   ungroup()
+# 
+# adm2_df <- adm2_df %>% 
+#   clean_data() %>%
+#   group_by(year, NAME_1, NAME_2) %>%
+#   dplyr::summarise(across(c(ntl_bm_sum, ntl_bm_mean, ntl_bm_median), mean)) %>%
+#   ungroup()
+# 
+# adm3_df <- adm3_df %>% 
+#   clean_data() %>%
+#   group_by(year, NAME_1, NAME_2, NAME_3) %>%
+#   dplyr::summarise(across(c(ntl_bm_sum, ntl_bm_mean, ntl_bm_median), mean)) %>%
+#   ungroup()
+# 
+# sez_df <- sez_df %>% 
+#   clean_data() %>%
+#   group_by(year, Name) %>%
+#   dplyr::summarise(across(c(ntl_bm_sum, ntl_bm_mean, ntl_bm_median), mean)) %>%
+#   ungroup()
+# 
+# border_df <- border_df %>% 
+#   clean_data() %>%
+#   group_by(year, Name) %>%
+#   dplyr::summarise(across(c(ntl_bm_sum, ntl_bm_mean, ntl_bm_median), mean)) %>%
+#   ungroup()
 
 # Trends -----------------------------------------------------------------------
 sez_df %>%
-  ggplot(aes(x = year, y = ntl_bm_sum)) +
-  geom_line() +
-  facet_wrap(~Name)
+  ggplot(aes(x = date, y = ntl_bm_sum)) +
+  geom_col() +
+  facet_wrap(~Name, scales = "free_y")
+
+border_df %>%
+  ggplot(aes(x = date, y = ntl_bm_sum)) +
+  geom_col() +
+  facet_wrap(~border_town, scales = "free_y")
 
 adm1_df %>%
   filter(NAME_1 == "Yangon") %>%
-  ggplot(aes(x = year, y = ntl_bm_sum)) +
+  ggplot(aes(x = date, y = ntl_bm_sum)) +
   geom_col() 
 
 adm2_df %>%
   filter(NAME_1 == "Yangon") %>%
-  ggplot(aes(x = year, y = ntl_bm_sum)) +
+  ggplot(aes(x = date, y = ntl_bm_sum)) +
   geom_col() +
   facet_wrap(~NAME_2)
 
 adm3_df %>%
   mutate(NAME_3_clean = paste(NAME_2, NAME_3)) %>%
   filter(NAME_1 == "Yangon") %>%
-  ggplot(aes(x = year, y = ntl_bm_sum)) +
+  ggplot(aes(x = date, y = ntl_bm_sum)) +
   geom_col() +
   facet_wrap(~NAME_3_clean,
              scales = "free_y")