Custom geometry: district-level NTL with custom boundaries

Live data were not downloaded when this vignette was built, so its examples were not run. Run this vignette locally to see the output.

lightson accepts any sf polygon layer. The workflow below starts with the bundled LGD state boundaries, then swaps in district-level GeoJSONs from bharatviz.

State-level workflow (bundled boundaries)

library(sf)

states <- get_india_admin("state")
nrow(states)
head(states[, c("state_name", "state_code")])

Download Bhuvan WMS rasters for a subset of years. bhuvan_raster() derives the bounding box from the geometry:

names(rasters)
rasters[["2022"]]

Bhuvan WMS returns a classed map with four colours (< 5, 5-25, 26-200 and > 200 nW/cm^2/sr). bhuvan_raster() converts each class to a representative radiance (2.5, 15, 112.5 and 200), so values are banded rather than continuous, and the brightest class is a floor.

panel <- extract_panel(rasters, states, id_col = "state_name")
head(panel)

library(ggplot2)
sel <- c("Bihar", "Maharashtra", "Tamil Nadu", "Uttar Pradesh")
plot_ntl_trend(
  panel[panel$region_id %in% sel, ],
  region   = sel,
  caption  = "Source: ISRO Bhuvan NTL portal"
)

plot_ntl_map(
  rasters[["2022"]],
  polygons = states,
  title    = "India nighttime lights, 2022"
)

District-level workflow

BHARATVIZ <- "https://bharatviz.org"

districts <- sf::read_sf(
  file.path(BHARATVIZ, "India-bhuvan-districts.geojson")
)
nrow(districts)

rasters_dist <- bhuvan_raster(districts, years = 2018:2023)
names(rasters_dist)

panel_dist <- extract_panel(rasters_dist, districts, id_col = "district_name")
head(panel_dist)

plot_ntl_trend(panel_dist, region = c("Mumbai", "Delhi", "Chennai", "Bengaluru"))

plot_ntl_map(rasters_dist[["2022"]], polygons = districts)

LGD boundaries

lgd <- sf::read_sf(file.path(BHARATVIZ, "India_LGD_districts.geojson"))
panel_lgd <- extract_panel(rasters_dist, lgd, id_col = "district_name")
head(panel_lgd)

Historical census boundaries

dist_2011 <- sf::read_sf(file.path(BHARATVIZ, "India-2011-districts.geojson"))
panel_2011 <- extract_panel(rasters_dist, dist_2011)
head(panel_2011)

extract_panel() picks the first character column as region_id automatically, or pass id_col explicitly.

VIIRS path

To use NASA VIIRS rasters, supply an Earthdata token:

token <- earthdata_token()
rasters_viirs <- ntl_download(region = districts, years = 2018:2023, token = token)
if (length(rasters_viirs) > 0) {
  panel_viirs <- extract_panel(rasters_viirs, districts, id_col = "district_name")
  head(panel_viirs)
}