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Custom geometry: district-level NTL with custom boundaries

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)
#> Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE

states <- get_india_admin("state")
nrow(states)
#> [1] 36
head(states[, c("state_name", "state_code")])
#> Simple feature collection with 6 features and 2 fields
#> Geometry type: GEOMETRY
#> Dimension:     XY
#> Bounding box:  xmin: 76.7052 ymin: 6.7528 xmax: 97.4129 ymax: 30.7941
#> Geodetic CRS:  WGS 84
#>          state_name state_code                       geometry
#> 1     A & N Islands         35 GEOMETRYCOLLECTION (POLYGON...
#> 2    Andhra Pradesh         37 MULTIPOLYGON (((80.7879 15....
#> 3 Arunachal Pradesh         12 POLYGON ((96.1765 29.3452, ...
#> 4             Assam         18 MULTIPOLYGON (((91.5352 25....
#> 5             Bihar         10 POLYGON ((84.1093 27.5208, ...
#> 6        Chandigarh         04 POLYGON ((76.7723 30.7941, ...

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

names(rasters)
#> [1] "2020" "2021" "2022"
rasters[["2022"]]
#> class       : SpatRaster
#> size        : 1024, 1024, 1  (nrow, ncol, nlyr)
#> resolution  : 0.0285501, 0.02962432  (x, y)
#> extent      : 68.1776, 97.4129, 6.7528, 37.0881  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source      : bhuvan_v2_349e9f004c78_2022.tif
#> name        : lyr1
#> min value   :  2.5
#> max value   :  200

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")
#> Warning in st_cast.GEOMETRYCOLLECTION(X[[i]], ...): only first part of
#> geometrycollection is retained
head(panel)
#>         region_id year mean_radiance n_pixels
#> 1   A & N Islands 2020      2.698413       63
#> 37  A & N Islands 2021      2.500000       63
#> 73  A & N Islands 2022      2.698413       63
#> 2  Andhra Pradesh 2020     10.274082    16236
#> 38 Andhra Pradesh 2021     10.506898    16236
#> 74 Andhra Pradesh 2022     14.095067    16236
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)
}

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.