Below is a step-by-step workflow used to estimate the length and
proximity of major roadways as well as nearby greenness for a set of
- Start with your addresses in a .csv file, with the complete address
in one column called “address”. For this example, that file will be
- Use a DeGAUSS Docker command to geocode the addresses using version
3.0.2 of “degauss/geocoder”:
docker run --rm -v $PWD:/tmp degauss/geocoder:3.0.2 sample_addresses.csv
- If you have not previously used this version of this
image, Docker will first download it, which can take several minutes,
depending on the size of the image and internet speeds. Docker will then
create and run a container to geocode the addresses.
- The results file, called “sample_addresses_geocoded_v3.0.2.csv”,
will be written to the same folder where the input CSV file is located.
- This file is the same as the input CSV file, but with
appended columns for matched address components, geocoding score and
precision, latitude, longitude, and a categorical geocoding result. See
geocoding results for more information on the geocoding
- Now that we have geocoded addresses, we can use DeGAUSS to add a
geomarker. In this example we will use the DeGAUSS images for the
proximity to major roadways and greenspace, DeGAUSS/roads version 0.1
and DeGAUSS/greenspace version 0.2. The programs can either be run in
parallel on the geocoded file or they can be run sequentially, creating
one file with both geomarkers. Here, we first added the roadway
geomarker and then add greenspace to that result. This is done using the
following commands while in the directory of the geocoded .csv
docker run --rm -v "$PWD":/tmp degauss/roads:0.1 sample_addresses_geocoded_v3.0.2.csv
docker run --rm -v $PWD:/tmp degauss/greenspace:0.2 sample_addresses_geocoded_v3.0.2_roads_400m_buffer.csv
- These two DeGAUSS containers append new columns to our dataset with
their respective geomarkers, while keeping intact our original dataset.
Now that we have added our geomarkers, we can remove the addresses to
create a geomarker dataset without geographic PHI.