Custom geometry: district-level NTL with custom boundaries
In lightson: Download and Analyse Nighttime Lights Satellite Data

library(lightson)
# Live downloads write to the user cache, so skip them on CRAN.
live_ok <- identical(Sys.getenv("NOT_CRAN"), "true")
if (live_ok) {
  rasters <- tryCatch(bhuvan_raster("IND", years = 2020:2022), error = function(e) list())
  live_ok <- length(rasters) > 0
}

knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  dev.args = list(bg = "transparent"),
  eval     = live_ok
)

cat("_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)
}