path-global-health / catchment

Weighted Spatial Point Catchment Modelling

catchment

The goal of catchment is to estimate health facility catchment populations using Bayesian gravity models. The primary purpose of these population estimates is to calculate catchment-level incidence rates using routine reporting data from health information systems.

Installation

Before installing this package, first you will need to install the R-INLA and Template Model Builder (TMB) packages. These packages are not on CRAN, and the installation process may depend on the type of computer you are using. I recommend following the installation instructions for INLA first, and then install TMB. Be sure to close out of any active R session before installation!

The catchment package is also not on CRAN (yet!). You can install the development version of catchment from GitHub with:

# install.packages("devtools")
devtools::install_github("PATH-Global-Health/catchment")

Example

We are currently developing documentation and tutorial materials.

# Load libraries
library(raster)
library(catchment)

# 1. Pre-processing --------------------------------------
## Load example dataset
data("example_shp")
data("example_pop")
data("example_locs")

## Get friction surface
fric <- PATHtools::get_friction_surface(example_shp) |>
  raster::raster() |>
  raster::resample(example_pop, fun = "mean")

## Create output folder
f <- tempfile()
fs::dir_create(fs::path(f, "tt"))

# 2. Travel time and intial access surfaces --------------
## Create individual travel time rasters
create_travel_surface(friction_surface = fric, extent_file = example_pop,
  points = example_locs, id_col = "label", x_col = "x", y_col = "y",
  output_dir = fs::path(f, "tt"), individual_surfaces = T, parallel = T, cores = 5)

## Organize travel time matrix
tmat <- travel_mat_from_folder(dir = fs::path(f, "tt"), reference = example_pop)

## Create initial accessbility matrix
pmat <- initial_access_surface(tmat, n_fac_limit = 10, force_threshold = 300, sparse = F)

# 3. Fit catchment model ---------------------------------
## Organize input data
catch_dat <- prepare_data(prob_mat_init = pmat, pop_raster = example_pop,
  location_data = example_locs, mesh.args = list(cutoff = 0.1,max.edge = c(0.1, 4)))

## View INLA mesh
plot(catch_dat$mesh)
plot(example_shp, add = T, border = "red", lwd = 2)
points(example_locs$x, example_locs$y)

## Fit catchment model
mod <- catchment_model(catch_dat)

# 4. Post-processing -------------------------------------
## Estimated catchment populations
catchment_populations(mod)
example_locs$est_pop <- catchment_populations(mod)

catchment's Issues

Require prepare_data() to take population raster

This will allow us to make more generalized post-fit functions. Could also have an optional input for the population vector.