Python module to facilitate the acquisition and analysis of Indego bikeshare data

1. Download all CSV files with: indego scrape
2. Move these files from your download folder to a better spot
3. Import the CSV files to PostgreSQL with: indego import-to-sql

TODO:

4. Aggregate trips based upon an input polygon shapefile

Create a virtual environment using conda:

conda env create -f environment.yml

Activate the environment:

conda activate bikeshare-data

Create a .env file to store configuration parameters:

DEFAULT_DB_URI = postgresql://postgres:password@localhost:5432/indego
DEFAULT_DATA_FOLDER = /path/to/data/dir
STUDY_AREA = "ST_SetSRID(ST_GeomFromText('POLYGON((-75.179070999792 39.9561219999082, ...etc...))'), 4326)"

Make sure that the database defined by DEFAULT_DB_URI already exists and that the postgis extension can be (or has been) enabled.

indego scrape-trips

This will download all CSV files to your computer's Downloads folder. Cut and paste these files into the DEFAULT_DATA_FOLDER before continuing to the next step.

indego import-to-sql

This imports all CSVs into a single table named raw_trips with 4.3 million rows (as of 8/2021), and then generates a cleaned up version named trips. This will take approximately 8 minutes to run.

indego scrape-stations

This creates a spatial table in the database named station_shapes

indego analyze

The study area boundary is defined using WKT to facilitate future integration into a backend API that will communicate with a webmap frontend where users point/click to define a custom study area.

<script src="https://cdn.rawgit.com/mapbox/wellknown/master/wellknown.js"></script>


let my_wkt = wellknown.stringify(data.features[0].geometry);

This returns a well-known-text description of the shape:

POLYGON ((-75.179070999792 39.9561219999082,-75.179121999973 39.9559839999298,-75.1793000003484 39.9559849996663))

To work with spatial queries, wrap the WKT within:

ST_SetSRID(
    ST_GeomFromText('my_wkt'),
    4326
)