Frequency is Freedom

This project draws maps to show that more frequent transit service, especially especially bus service, gives people more access to their city. The interactive article can be found here. While the motivation for the project and the focus of the article is my experience in Chicago, the code has been set up so that it could be recreated for any city. It relies upon OSMNX, which is a mash up of Open Street Maps and NetworkX, and publicly available GTFS data. If you would like to recreate this project for your city, follow the steps below.

How this Works

OSMNX allows you to download a NetworkX graph of all walking routes in a city. You can also set a travel time for each edge, taken from a constant walking speed. From the GTFS data we can infer travel times between transit stops, as well as the average time someone would spend waiting for a bus or train at a transit stop. We can then add new edges to the graph, directly connecting transit stops. The travel times for the edges are taken from the sum of how long someone would be stuck waiting, on average, for the bus or train plus the average travel time once riding.

The purpose of this project is to test the effect on the frequency of service, and so the transit travel times are updated each time an isochrone is built based on the freq_multipliers specified. The isochrone modules can be found in src/isochrones.py. See instructions below for generating your own maps.

Development

1. This project uses Poetry to manage dependencies. If you don't yet have Poetry, the latest installation instructions can be found here. Install all needed packages with:

   poetry install

2. You can find GTFS data from any transit agency that makes it available in The Mobility Database. Once you've found and downloaded your data, add you raw GTFS tables to data/gtfs_raw and update the GTFS_PATH in src/filepaths.py accordingly with whatever subdirectories you may use.. For example, it's been set here to GTFS_PATH = DATA_DIR / "gtfs_raw/chicago"

3. To download the citywide network graph and then add transit travel times, update create_transit_graph.py with the name of your city. For example, you'll see that within the if __name__ == "__main__": loop it's currently set to city="Chicago, Illinois". The city name must be consistent throughout your code, because the city name determines the name of the pickle file where the graph is saved.

4. You may then build the transit graph with:

   poetry run python create_transit_graph.py

   The script will prompt you to specify which date you want to pull schedules for. You can also specify a date from the command line. For example, for this article, I specified Monday, August 22nd with:

   poetry run python create_transit_graph.py -m 20220822

5. After creating the graph, you are ready to make some isochrones! A walking isochrone can me made like so:

   import osmnx as ox
   
   from src.filepaths import DATA_DIR
   from src.isochrones import WalkingIsochrone
   import src.graphs as graphs
   
   address = "626 W Jackson Blvd, Chicago IL"
   lat_lng = ox.geocoder.geocode(address)
   
   city = "Chicago, Illinois"
   graph = graphs.load_citywide_graph(city)
   walking_isochrone = WalkingIsochrone(graph)
   
   filepath = "plots/walking_isochrone.png"
   walking_isochrone.make_isochrone(lat_lng, filepath=filepath)

   And a transit isochrone can be made like so:

   import osmnx as ox
   
   from src.filepaths import DATA_DIR
   from src.isochrones import TransitIsochrone
   import src.graphs as graphs
   
   address = "626 W Jackson Blvd, Chicago IL"
   lat_lng = ox.geocoder.geocode(address)
   
   city = "Chicago, Illinois"
   transit_isochrone = TransitIsochrone(DATA_DIR, city)
   
   trip_times = [15, 30, 45, 60]
   freq_multipliers = [1]
   filepath = "plots/transit_isochrone_from_my_apartment.png"
   transit_isochrone.make_isochrone(my_lat_lon, 
      trip_times=trip_times, 
      freq_multipliers=freq_multipliers,
      filepath=filepath)

6. To recreate the charts from the article, run:

   poetry run python create_maps_for_article.py

   You can edit that file to change the starting location and city name. Use the same city name as above, because the city name determines the name of the pickle file where the graph is saved.

7. Optionally, if you would like to work with jupyter notebooks while using poetry, after running poetry install, run:

   poetry run python -m ipykernel install --user --name frequency-is-freedom
   poetry run jupyter lab

   And then select the newly created kernel, frequency-is-freedom.

Docker

1. Download and run Docker if you don't already have it.

2. Build the docker image, and name it freq-is-freedom-app, with:

   docker build . -t freq-is-freedom-app

   That command uses the Dockerfile and .dockerignore to self-contained image that can be run from within a container. You can reference those files as templates if you are deploying a python app that uses pipenv.

3. To test that it words, run the image from within a locally hosted container like so:

   docker run -p 8080:8080 --name app-container freq-is-freedom-app

If you need to take a few tries at those step (I sure did), the following command will delete all containers that are no longer running: bash docker container prune

Container Registry

1. Install the Google Could CLI.

2. Authenticate your CLI installation to this project with:

   gcloud init

   You will be prompted to specify this app's Google Cloud project, which is named ideo-social-listenting. Slack Joe Gambino if you need access.

3. Build the image you created above within a container on the registry with the following command:

   gcloud builds submit --tag gcr.io/mobility-373002/freq-is-freedom-app

   The format of that url is gcr.io/GOOGLE_CONSOLE_PROJECT_NAME/IMAGE_NAME.

   The first time you run the above command, it may fail if you don't first enable the container registry service. You shouldn't have to do this, as I've already done it, but including this here for completeness.

   gcloud services enable containerregistry.googleapis.com

Ideas for follow up projects

• Calcualte the coverage area of the isochrones and then calculate statistics for what percentage of the city you can access.
• Use OSMNX's tie to Open Street Maps to count how many of the city's restaruants, office buildings, greenspace, etc. transit makes accessible to you.
• Bring in census population data to calculate the labor pool that is available to commute to any one location.
• Bring in real estate data to show that property value is tied to transit accessibility.