exposure — compute_exposure • greenSD

Computes population-weighted greenspace fraction or human exposure to greenspace based on a population-weighted exposure model (Chen et al., 2022), using population data from the Global Human Settlement Layer (GHSL; Pesaresi et al., 2024). See Details for the underlying method and assumptions.

Usage

compute_exposure(
  r = NULL,
  res = c(10, 10),
  pop_year = 2020,
  radius = 500,
  grid_size = NULL,
  height = FALSE,
  pop_out = FALSE,
  quiet = TRUE
)

Arguments

r: A SpatRaster with single/multiple greenspace layer(s), either fractional or binary (where non-green = 0 and green = 1), typically the output from get_gsdc(), get_esa_wc(), or get_tile_green().
res: numeric vector of length 2. The actual spatial resolution (in meters). Default is c(10, 10).
pop_year: numeric. Year of the GHSL dataset to use. Must be one of: 2015, 2020, 2025, or 2030. Default is 2020.
radius: numeric. Buffer radius (in meters) used for local averaging. Default is 500.
grid_size: numeric. Optional. If provided, output is aggregated to grid cells of this size (in meters) and returned as an sf object.
height: logical. Whether to compute greenspace volume for population-weighted greenspace fraction or human exposure to greenspace using Meta's global canopy height map (Tolan et al., 2024). (The default is FALSE)
pop_out: logical. Whether return population layer.
quiet: logical. Whether show progress bars for some process.

Value

SpatRaster or sf. A SpatRaster (if grid_size is NULL) with layers pwgf_*, or an sf object with columns pwge_* representing population-weighted greenspace exposure values aggregated to each grid polygon.

Details

This function implements the population-weighted greenspace exposure (PWGE) model:

Start with a population raster. Each pixel $ i $ has a population value $ P_i $.
Create a circular buffer of radius $ d $ around each pixel center.
For each buffer, calculate greenspace fraction: $$G_i^d = \frac{\text{Area of greenspace within buffer}}{\text{Total buffer area}}$$
Repeat for all $ i = 1, 2, ..., N $ grid cells.
Compute overall exposure: $$GE^d = \frac{\sum_i P_i \cdot G_i^d}{\sum_i P_i}$$

References

Chen, B., Wu, S., Song, Y. et al. Contrasting inequality in human exposure to greenspace between cities of Global North and Global South. Nat Commun 13, 4636 (2022). https://doi.org/10.1038/s41467-022-32258-4

Pesaresi, M., Schiavina, M., Politis, P., Freire, S., Krasnodębska, K., Uhl, J. H., … Kemper, T. (2024). Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data. International Journal of Digital Earth, 17(1). https://doi.org/10.1080/17538947.2024.2390454

Tolan, J., Yang, H. I., Nosarzewski, B., Couairon, G., Vo, H. V., Brandt, J., ... & Couprie, C. (2024). Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Remote Sensing of Environment, 300, 113888.

Examples

sample_data <- terra::rast(system.file("extdata", "detroit_gs.tif", package = "greenSD"))
pwgf <- compute_exposure(
  # r = sample_data,
  pop_year = 2020,
  radius = 1500
)