From 924b11a630a29f82d1e7c87c700285ea6c8b5cbf Mon Sep 17 00:00:00 2001 From: Rob Marty Date: Tue, 25 Jun 2024 13:16:11 -0400 Subject: [PATCH] ntl fixes --- .../01_clean_data/02_extract_to_polygons.R | 12 +++- .../ntl-analysis/02_analysis/annual_trends.R | 6 +- .../02_analysis/compare_viirs_sources.R | 4 +- .../ntl-analysis/02_analysis/monthly_trends.R | 10 ++-- .../02_analysis/percent_change_adm.R | 52 +++++++++++------- .../figures/annual_trends_adm0.png | Bin 127963 -> 128297 bytes .../figures/annual_trends_adm1.png | Bin 96179 -> 97495 bytes .../figures/monthly_trends_adm0.png | Bin 38283 -> 39493 bytes .../figures/monthly_trends_adm1.png | Bin 118093 -> 105757 bytes notebooks/ntl-analysis/figures/ntl_19_22.png | Bin 327615 -> 327633 bytes .../ntl-analysis/figures/pc_map_lbn_adm1.png | Bin 89011 -> 118957 bytes .../ntl-analysis/figures/pc_map_lbn_adm2.png | Bin 108931 -> 147296 bytes .../ntl-analysis/figures/pc_map_lbn_adm3.png | Bin 296222 -> 414121 bytes .../ntl-analysis/figures/pc_map_lbn_adm4.png | Bin 102774 -> 200942 bytes .../figures/pc_table_lbn_adm1.png | Bin 134361 -> 158252 bytes .../figures/pc_table_lbn_adm2.png | Bin 388762 -> 466817 bytes .../figures/pc_table_lbn_adm3.png | Bin 534392 -> 615293 bytes .../figures/pc_table_lbn_adm4.png | Bin 505721 -> 588601 bytes .../figures/viirs_diff_sources.png | Bin 74539 -> 73882 bytes .../ntl-analysis/figures/viirs_dmsp_adm0.png | Bin 77532 -> 78446 bytes 20 files changed, 51 insertions(+), 33 deletions(-) diff --git a/notebooks/ntl-analysis/01_clean_data/02_extract_to_polygons.R b/notebooks/ntl-analysis/01_clean_data/02_extract_to_polygons.R index a0c2518..1cf820e 100644 --- a/notebooks/ntl-analysis/01_clean_data/02_extract_to_polygons.R +++ b/notebooks/ntl-analysis/01_clean_data/02_extract_to_polygons.R @@ -43,7 +43,7 @@ for(roi_name in c("cadaster", "lbn_adm0", "lbn_adm1", "lbn_adm2", "lbn_adm3", "l roi_sf$adm_id <- 1:nrow(roi_sf) # Aggregate annual ------------------------------------------------------------- - for(year in 1992:2022){ + for(year in 1992:2023){ ## Only process if file already hasn't been created OUT_FILE <- file.path(ntl_dir, "aggregated-to-polygons", roi_name, "individual-annual", paste0("ntl_", year, ".Rds")) @@ -52,13 +52,19 @@ for(roi_name in c("cadaster", "lbn_adm0", "lbn_adm1", "lbn_adm2", "lbn_adm3", "l if(year >= 2012){ bm_r <- raster(file.path(ntl_dir, "ntl-rasters", "blackmarble", "annual", paste0("VNP46A4_NearNadir_Composite_Snow_Free_qflag255_1_t",year,".tif"))) - viirs_r_r <- raster(file.path(ntl_dir, "ntl-rasters", "viirs", "annual", paste0("LBN_viirs_mean_",year,".tif"))) + + if(year <= 2022){ + viirs_r_r <- raster(file.path(ntl_dir, "ntl-rasters", "viirs", "annual", paste0("LBN_viirs_mean_",year,".tif"))) + } else{ + viirs_r_r <- raster() + } + } else{ bm_r <- raster() viirs_r_r <- raster() } - if(year >= 2014){ + if(year %in% 2014:2022){ viirs_c_r <- raster(file.path(ntl_dir, "ntl-rasters", "viirs", "annual", paste0("LBN_viirs_corrected_mean_",year,".tif"))) } else{ viirs_c_r <- raster() diff --git a/notebooks/ntl-analysis/02_analysis/annual_trends.R b/notebooks/ntl-analysis/02_analysis/annual_trends.R index 2ba7b40..f2d2995 100644 --- a/notebooks/ntl-analysis/02_analysis/annual_trends.R +++ b/notebooks/ntl-analysis/02_analysis/annual_trends.R @@ -4,7 +4,7 @@ gdp_df <- WDI(country = "LBN", indicator = c("NY.GDP.MKTP.KD", "NY.GDP.PCAP.KD"), start = 1992, - end = 2022, + end = 2023, extra = TRUE) gdp_long_df <- gdp_df %>% @@ -33,7 +33,7 @@ ntl_gdp_df %>% labs(x = NULL, y = "Nighttime Lights", title = "Annual Nighttime Lights and GDP") + - scale_x_continuous(breaks = seq(1995, 2022, 4)) + + scale_x_continuous(breaks = seq(1995, 2023, 4)) + theme_classic2() + facet_wrap(~name, scales = "free_y", ncol = 1) + theme(strip.background = element_blank(), @@ -55,7 +55,7 @@ ntl_df %>% labs(x = NULL, y = "Nighttime Lights", title = "Annual Nighttime Lights: Governorate Level") + - scale_x_continuous(breaks = seq(1995, 2022, 8)) + + scale_x_continuous(breaks = seq(1995, 2023, 8)) + theme_classic2() + theme(strip.background = element_blank(), strip.text = element_text(face = "bold")) + diff --git a/notebooks/ntl-analysis/02_analysis/compare_viirs_sources.R b/notebooks/ntl-analysis/02_analysis/compare_viirs_sources.R index b65a06a..d339aab 100644 --- a/notebooks/ntl-analysis/02_analysis/compare_viirs_sources.R +++ b/notebooks/ntl-analysis/02_analysis/compare_viirs_sources.R @@ -16,8 +16,8 @@ ntl0_df %>% ggplot() + geom_line(aes(x = year, y = value, color = name), linewidth = 1) + - scale_x_continuous(breaks = seq(2012,2022,2), - labels = seq(2012,2022,2)) + + scale_x_continuous(breaks = seq(2012,2023,2), + labels = seq(2012,2023,2)) + labs(title = "VIIRS Nighttime Lights: Different Sources", color = "", x = "Year", diff --git a/notebooks/ntl-analysis/02_analysis/monthly_trends.R b/notebooks/ntl-analysis/02_analysis/monthly_trends.R index bf79a7d..b4db337 100644 --- a/notebooks/ntl-analysis/02_analysis/monthly_trends.R +++ b/notebooks/ntl-analysis/02_analysis/monthly_trends.R @@ -12,8 +12,9 @@ ntl_df %>% labs(x = NULL, y = "Nighttime Lights", title = "Monthly Nighttime Lights: Country Level") + - scale_x_date(date_breaks = "24 months", date_labels = "%Y") + # Set breaks to every 6 months - theme_classic2() + scale_x_date(date_breaks = "12 months", date_labels = "%Y") + # Set breaks to every 6 months + theme_classic2() + + theme(axis.text = element_text(size = 8)) ggsave(filename = file.path(figures_dir, "monthly_trends_adm0.png"), height = 2, width = 4.5) @@ -30,10 +31,11 @@ ntl_df %>% labs(x = NULL, y = "Nighttime Lights", title = "Monthly Nighttime Lights: ADM 1") + - scale_x_date(date_breaks = "48 months", date_labels = "%Y") + # Set breaks to every 6 months + scale_x_date(date_breaks = "24 months", date_labels = "%Y") + # Set breaks to every 6 months theme_classic2() + theme(strip.background = element_blank(), - strip.text = element_text(face = "bold")) + + strip.text = element_text(face = "bold"), + axis.text = element_text(size = 6)) + facet_wrap(~admin1Name, scales = "free_y") diff --git a/notebooks/ntl-analysis/02_analysis/percent_change_adm.R b/notebooks/ntl-analysis/02_analysis/percent_change_adm.R index fdfa89c..26e82c9 100644 --- a/notebooks/ntl-analysis/02_analysis/percent_change_adm.R +++ b/notebooks/ntl-analysis/02_analysis/percent_change_adm.R @@ -31,36 +31,41 @@ for(roi_name in c("lbn_adm1", "lbn_adm2", "lbn_adm3", "lbn_adm4")){ # Prep data ------------------------------------------------------------------- ntl_pc_df <- ntl_df %>% - dplyr::filter(year %in% c(2019, 2020, 2021, 2022)) %>% + dplyr::filter(year %in% c(2019, 2020, 2021, 2022, 2023)) %>% dplyr::select(year, adm_id, ntl_bm_mean) %>% mutate(year = paste0("yr", year)) %>% pivot_wider(id_cols = adm_id, names_from = year, values_from = "ntl_bm_mean") %>% - mutate(pc19 = (yr2022 - yr2019) / yr2019 * 100, - pc20 = (yr2022 - yr2020) / yr2020 * 100, - pc21 = (yr2022 - yr2021) / yr2021 * 100) + mutate(pc19 = (yr2023 - yr2019) / yr2019 * 100, + pc20 = (yr2023 - yr2020) / yr2020 * 100, + pc21 = (yr2023 - yr2021) / yr2021 * 100, + pc22 = (yr2023 - yr2022) / yr2022 * 100) roi_sf <- roi_sf %>% left_join(ntl_pc_df, by = "adm_id") - roi_sf$pc19[roi_sf$pc19 > 0] <- NA + roi_sf$pc19[roi_sf$pc19 > 100] <- 100 roi_sf$pc19[is.na(roi_sf$pc19)] <- 0 - roi_sf$pc20[roi_sf$pc20 > 0] <- NA + roi_sf$pc20[roi_sf$pc20 > 100] <- 100 roi_sf$pc20[is.na(roi_sf$pc20)] <- 0 - roi_sf$pc21[roi_sf$pc21 > 0] <- NA + roi_sf$pc21[roi_sf$pc21 > 100] <- 100 roi_sf$pc21[is.na(roi_sf$pc21)] <- 0 + roi_sf$pc22[roi_sf$pc22 > 100] <- 100 + roi_sf$pc22[is.na(roi_sf$pc22)] <- 0 + # Map -------------------------------------------------------------------------- city_df <- data.frame(lat = 33.898316, lon = 35.505696, name = "Beirut") roi_long_sf <- roi_sf %>% - dplyr::select(adm_id, pc19, pc20, pc21) %>% + dplyr::select(adm_id, pc19, pc20, pc21, pc22) %>% pivot_longer(cols = -c(adm_id, geometry)) %>% dplyr::mutate(name_clean = case_when( - name == "pc19" ~ "2019 to 2022", - name == "pc20" ~ "2020 to 2022", - name == "pc21" ~ "2021 to 2022" + name == "pc19" ~ "2019 to 2023", + name == "pc20" ~ "2020 to 2023", + name == "pc21" ~ "2021 to 2023", + name == "pc22" ~ "2022 to 2023" )) p <- ggplot() + @@ -71,8 +76,8 @@ for(roi_name in c("lbn_adm1", "lbn_adm2", "lbn_adm3", "lbn_adm4")){ color = "% Change\nin NTL") + coord_sf() + theme_void() + - scale_fill_distiller(palette = "YlOrRd", direction = -1) + - scale_color_distiller(palette = "YlOrRd", direction = -1) + + scale_fill_gradient2(low = "red", high = "forestgreen") + + scale_color_gradient2(low = "red", high = "forestgreen") + facet_wrap(~name_clean) if(roi_name != "lbn_adm4"){ @@ -99,7 +104,7 @@ for(roi_name in c("lbn_adm1", "lbn_adm2", "lbn_adm3", "lbn_adm4")){ roi_sf %>% st_drop_geometry() %>% - dplyr::select(name, yr2019, yr2020, yr2021, yr2022, pc19, pc20, pc21) %>% + dplyr::select(name, yr2019, yr2020, yr2021, yr2022, yr2023, pc19, pc20, pc21, pc22) %>% #dplyr::filter(pc < 0) %>% arrange(-yr2019) %>% head(30) %>% @@ -107,23 +112,28 @@ for(roi_name in c("lbn_adm1", "lbn_adm2", "lbn_adm3", "lbn_adm4")){ yr2020 = round(yr2020, 2), yr2021 = round(yr2021, 2), yr2022 = round(yr2022, 2), + yr2023 = round(yr2023, 2), pc19 = round(pc19,1), pc20 = round(pc20,1), - pc21 = round(pc21,1)) %>% + pc21 = round(pc21,1), + pc22 = round(pc22,1)) %>% dplyr::rename("Name" = name, "NTL: 2019" = yr2019, "NTL: 2020" = yr2020, "NTL: 2021" = yr2021, "NTL: 2022" = yr2022, - "PC: 19 to 22" = pc19, - "PC: 20 to 22" = pc20, - "PC: 21 to 22" = pc21) %>% + "NTL: 2023" = yr2023, + "19 to 22" = pc19, + "20 to 22" = pc20, + "21 to 22" = pc21, + "22 to 23" = pc22) %>% gt() %>% #gt_hulk_col_numeric(`NTL: 2019`, trim = TRUE) %>% #gt_hulk_col_numeric(`NTL: 2022`, trim = TRUE) %>% - gt_color_rows(c(`PC: 19 to 22`, - `PC: 20 to 22`, - `PC: 21 to 22`), + gt_color_rows(c(`19 to 22`, + `20 to 22`, + `21 to 22`, + `22 to 23`), 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