GeoHealthAccess

• Description
• Installation
• Usage
  • Data acquisition
  • Preprocessing
  • Modeling
• Example
• Docker
• Methodology
• Authors

Description

Modeling population accessibility to health facilities has always been tedious and time-consuming. From the selection of relevant data sources to the modeling in itself, a wide range of skills and software solutions are required. GeoHealthAccess is a tool that aims to automate the process using a set of high resolution, global and open datasets -- in order to enable fast and automated country-scaled analysis. To that end, input datasets are automatically pulled from various sources:

• Geofabrik (OpenStreetMap) for the transport network ;
• Copernicus Global Land Cover for land cover ;
• Global Surface Water for surface water ;
• Shuttle Radar Topography Mission for topography ;
• and WorldPop for population maps.

Installation

GeoHealthAccess have three system dependencies: gdal is used to process raster data, osmium-tool to process OpenStreetMap data and grass to perform a cost distance analysis. Alternatively, a docker image is also available (see below).

# Ubuntu 20.04
apt-get install gdal-bin osmium-tool grass-core

The python package can then be installed using pip:

# Download source code
git clone https://github.com/BLSQ/geohealthaccess
cd geohealthaccess
pip install -e .

# To install devevelopment dependencies such as pytest, use:
pip install -e .[dev]

Usage

The geohealthaccess program is divided into three commands:

• download for automatic data acquisition
• preprocess for preprocessing of input data
• access to compute travel times

geohealthaccess --help

Usage: geohealthaccess [OPTIONS] COMMAND [ARGS]...

  Map accessibility to health services.

Options:
  --help  Show this message and exit.

Commands:
  access      Map travel times to the provided health facilities.
  download    Download input datasets.
  preprocess  Preprocess and co-register input datasets.

Data acquisition

NASA EarthData credentials are required to download SRTM tiles. An account can be created here.

geohealthaccess download --help

Usage: geohealthaccess download [OPTIONS]

  Download input datasets.

Options:
  -c, --country TEXT         ISO A3 country code  [required]
  -o, --output-dir PATH      Output directory
  -u, --earthdata-user TEXT  NASA EarthData username  [required]
  -p, --earthdata-pass TEXT  NASA EarthData password  [required]
  -f, --overwrite            Overwrite existing files
  --help                     Show this message and exit.

If output-dir is not provided, files will be written to ./data/raw.

NASA EarthData credentials can also be set using environment variables:

export EARTHDATA_USERNAME=<your_username>
export EARTHDATA_PASSWORD=<your_password>

Preprocessing

geohealthaccess preprocess --help

Usage: geohealthaccess preprocess [OPTIONS]

  Preprocess and co-register input datasets.

Options:
  -c, --country TEXT      ISO A3 country code  [required]
  -s, --crs TEXT          CRS as a PROJ4 string  [required]
  -r, --resolution FLOAT  Pixel size in `crs` units
  -i, --input-dir PATH    Input data directory
  -o, --output-dir PATH   Output data directory
  -f, --overwrite         Overwrite existing files
  --help                  Show this message and exit.

If not specified, input-dir will be set to ./data/raw and output-dir to ./data/input.

Modeling

geohealthaccess access --help

Usage: geohealthaccess access [OPTIONS]

  Map travel times to the provided health facilities.

Options:
  -i, --input-dir PATH      Input data directory
  -o, --output-dir PATH     Output data directory
  --car / --no-car          Enable/disable car scenario
  --walk / --no-walk        Enable/disable walk scenario
  --bike / --no-bike        Enable/disable bike scenario
  -s, --travel-speeds PATH  JSON file with custom travel speeds
  -d, --destinations PATH   Destination points (GeoJSON or Geopackage)
  -f, --overwrite           Overwrite existing files
  --help                    Show this message and exit.

If not specified, input-dir is set to ./data/input, interm-dir to ./data/intermediary and output-dir to ./data/output. By default, only the car scenario is enabled and if no destinations are provided, health facilities extracted from OpenStreetMap will be used as target points for the cost distance analysis. Likewise, default values for travel speeds are used if the --travel-speeds option is not set.

Three output rasters are created for each enabled scenario and provided destination points:

• cost_<scenario>_<destinations>.tif : cumulated cost (or travel time, in minutes) to reach the nearest destinations feature.
• nearest_<scenario>_<destinations>.tif : ID of the nearest destinations feature.
• and backlink_<scenario>_<destinations>.tif : backlink raster.

S3 and Google Cloud Storage

Directories and files provided as option to the geohalthaccess CLIs can be located on S3 and GCS buckets. Paths must be prefixed with s3:// or gcs://, for instance:

geohealthaccess download \
  --country BDI \
  --output-dir "s3://<bucket_name>/bdi/data/raw" \
  --logs-dir "s3://<bucket_name>/bdi/logs"

geohealthaccess preprocess \
  --country BDI \
  --crs "EPSG:3857" \
  --resolution 100 \
  --input-dir "s3://<bucket_name>/bdi/data/raw" \
  --output-dir "s3://<bucket_name>/bdi/data/input" \
  --logs-dir "s3://<bucket_name>/bdi/logs"

geohealthaccess acces --car --no-walk --no-bike \
  --input-dir "s3://<bucket_name>/bdi/data/input" \
  --interm-dir "s3://<bucket_name>/bdi/data/intermediary" \
  --output-dir "s3://<bucket_name>/bdi/data/output" \
  --logs-dir "s3://<bucket_name>/bdi/data/logs"

The following environment variables are required to allow S3 and/or GCS access:

• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY
• AWS_REGION (defaults to us-east-1)
• S3_ENDPOINT_URL
• GOOGLE_APPLICATION_CREDENTIALS (path to JSON file containing credentials)

Example

Creating a map of travel times to the nearest health facility for Burundi:

# Download input datasets
geohealthaccess download \
	--country BDI \
	--earthdata-user <your_username> \
	--earthdata-pass <your_password>

# Preprocess input datasets to a common raster grid
geohealthaccess preprocess \
	--country BDI \
	--crs "EPSG:3857" \
	--resolution 100

# Compute travel times to nearest health facility for the `car` and `walk`
# scenarios.
geohealthaccess access --car --walk

# Or use your own data for destinations:
geohealthaccess access --car --walk -d hospitals.geojson -d clinics.geojson

Using, building and publishing the Docker image

A docker image is available on Docker Hub. To launch the geohealthaccess app locally:

cd <path_to_geohealthaccess>
docker run -v $(pwd):/project:rw blsq/geohealthaccess:latest

We also provide a docker-compose.yml file to facilitate local development using Docker:

cd <path_to_geohealthaccess>
docker-compose build
docker-compose run app

Images are automatically rebuilt and published on Docker Hub through a GitHub workflow triggered each time a new release is published in this repo. Alternatively, you can also trigger the workflow manually from the Actions section.

Running the tests

We use pytest for our test suite. Make sure that the development dependencies are installed, and simply launch the test command using the CLI (or using Docker: docker-compose run app test).

Deploying on Airflow

The whole flow (download, preprocess and access) can be orchestrated using Apache Airflow. The DAG can be found in the airflow directory and is ready to used.

It relies on a few Airflow variables to run:

• gha_earthdata_username and gha_earthdata_password for the EarthData credentials
• gha_aws_access_key_id, gha_aws_secret_access_key and gha_aws_region for AWS S3 storage if appropriate
• gha_google_application_credentials for GCP GCS storage if appropriate

Methodology

: Processing chain (red=input, yellow=intermediary, green=output).

Authors

The GeoHealthAccess project is funded by Innoviris and led by a partnership between Bluesquare and the Spatial Epidemiology Lab (Université Libre de Bruxelles). The project is based on a previous work from IGEAT-ANAGEO (Université Libre de Bruxelles) and the Department of Geography (University of Namur) in the She Decides project.

• Grégoire Lurton (Bluesquare)
• Yann Forget (Spatial Epidemiology Lab, Université Libre de Bruxelles)
• Moritz Lennert (IGEAT-ANAGEO, Université Libre de Bruxelles)
• Sabine Vanhuysse (IGEAT-ANAGEO, Université Libre de Bruxelles)
• Taïs Grippa (IGEAT-ANAGEO, Université Libre de Bruxelles)
• Catherine Linard (Department of Geography, University of Namur)
• Pierre Vanliefland (Bluesquare)