Potential Spatial Accessibility Software Service (PASS)

The CDO Data Science team developed a geographic information system, Potential Accessibility Software Service (PASS). PASS offers an advanced quantitative approach to measure how spatially accessible population demand is to a given service.

Spatial accessibility is the consideration of how physical and social space and place affect how a population can traverse through it to access given service locations (i.e., points of interest, POIs). Though abstract in nature, it can be measured through considerations like where potential population demand is located, the geographic distance to get from the population location to the service locations offered, the supply at the service locations, as well as the probability of a population going to one service location over another based on the capacity. PASS uses the enhanced 3-Step Floating Catchment Area (3SFCA) methodology to accomplish this, which is further explained in a report, which should be downloaded and viewed in your web browser.

PASS lets you select a geographic area of interest by panning and zooming on the interactive map, and lets you define the parameters to model spatial accessibility to better reflect Canada's diverse society. For example, individuals living in urban areas versus rural areas, have different assumptions (e.g., willingness to commute further distances) and considerations (e.g., transportation) for how to access a service.

Installation

Dependencies

• Python > 3.6
• PostgreSQL > 11
• PostGIS (PostgreSQL extension) > 2.5
• Docker

Steps to Install

1. Make sure you have the above dependencies installed.
2. Clone repo.
3. Set up a Python environment in the terminal, you can use the requirements.txt file to clone the environment, but there might be some issues installing some of the Python packages, especially if you have a Windows OS. In this case, it is recommended to create a new environment and then pip install the individual dependencies listed in requirements.txt. If you have a Windows OS, it is suggested to review this Stack Overflow conversation to assure the dependencies are installed correctly.
4. Create a data folder within the local repo's root directory. In that folder you should have the following types of data:

• DEMAND_GEO: Geographic data file (*.shp) representing your demand locations (e.g., shapefile of postal code polygons).

• DEMAND_POP: *.csv file that at least has the demand geographic data file IDs and a variable to represent the population counts for the demand geographic data file.

• SUPPLY: *.csv file that stores your POIs, this file should at least have a variable to represent the POI IDs and two variables for longitude and latitude.

• DEMAND_GEO_WEIGHT (optional): a more granular geographic data file (*.shp) + a population attribute/variable for calculating mean-weighted centroids to represent the demand locations more precisely.

  Requirements for these data files are further detailed in the Database Initialization and Population document.

5. Copy and rename config_template.json to config.json. Enter the necessary values in where it specifies "ENTER ...". There is also a config_example.json to better demonstrate the values that should be presented in config.json. Please refer to the Database Initialization and Population document for more information on how to properly prepare config.json.
6. Initialize a new database and add the PostGIS database extension to store the geographic data. Make sure to add the PostgreSQL database connection information to config.json.
7. Now you have to populate the database based on your data, which can be accomplished by running the following command in the terminal: python modules/InitSchema.py. However, before running this Python script, you will need to initialize a local API for calculating a distance matrix for car commute time or distance. To learn more about how to set up the API for calculating a distance matrix, refer to the Distance Matrix Calculation Set Up Instructions document.
8. Run python modules/InitSchema.py to initialize your database, this could take a while depending on your computer specs and the total data size (e.g., all of Canada could take over 5 hours).
9. Confirm your database has three populated data tables: demand, poi, and distance_matrix_car.

Run

Once you completed the 'Steps to Install', which you only need to complete once, to run the actual web app from now on, you must complete the following:

1. Activate your Python environment, e.g., source activate pass
2. Run app.py in terminal: python app.py
3. After running the command, go to your browser, and you can open the app with your provided HOST:PORT

Development

Below is an image of PASS's architecture.

To learn about how to calculate the distance matrix, refer to the pass_distance_matrix_api markdown.

To learn more about the data configuration and database initialization and storage, refer to the pass_config markdown.

The following are areas that could be further developed:

• Allowing for either web or local containerized version of the ORS API.
• Incorporating a distance_matrix_transit database table that stores a distance matrix calculated by General Transit Feed Specification (GTFS) data to account for public transportation. Some work has been started for this. The modules/gtfs.py script collects all GTFS feeds and data in Canada and then can be used with the Open Transit Planner API that handles calculating distance matrix with GTFS and OpenStreeetMap (*.osm.pbf) road network data.

Contribute

• Assign yourself to an issue, or create an issue
• Create new branch related to that issue
• Pull from master branch (this might get updated eventually to dev once a first version is stable)
• Work on your branch, when you have completed the code and tested it, you can then push the branch to the remote repository
• Make a merge/pull request, admin will review and merge into whichever branch