subway-access 0.1.1

Spatial analysis toolkit for evaluating NYC subway accessibility, reliability, and equity gaps.

subway-access

subway-access is a Python toolkit for reproducible NYC subway accessibility analysis.

Authored by Blaise Albis-Burdige.

It is designed to measure neighborhood access to accessible stations with a small, transparent workflow that is easy to inspect, test, and extend.

What ships in the 0.1 line

The current release deliberately implements one narrow, honest slice:

• load a small station dataset plus ADA status data
• generate first-pass Euclidean walk catchments
• join tract-level demographics
• export one tract accessibility-gap table plus catchment GeoJSON
• run the full fixture-backed workflow from the installed CLI

It does not pretend to ship outage-aware reliability scoring, pedestrian network routing, or richer station-level metrics yet. Those surfaces remain explicit typed placeholders that raise NotImplementedError.

Why this exists

Official MTA and NYC data can tell you whether a station is nominally accessible, but the policy question is broader: which neighborhoods have weak access to accessible transit in practice?

This repo aims to grow into a reusable analysis toolkit rather than a notebook dump or trip planner.

Quickstart

Install:

pip install subway-access

Run the packaged demo workflow:

subway-access demo --output-dir demo-output --minutes 10

This writes:

• demo-output/catchments.geojson
• demo-output/accessibility-gaps.csv

Python example

from subway_access import (
    CatchmentRequest,
    analyze_gaps,
    generate_catchments,
    load_accessibility_status,
    load_census_data,
    load_gtfs,
    score_accessibility,
)

stations = load_gtfs().with_accessibility(load_accessibility_status())
demographics = load_census_data()
catchments = generate_catchments(stations, CatchmentRequest(minutes=10))
scores = score_accessibility(stations, catchments, demographics)
gaps = analyze_gaps(scores)
print(len(gaps.records))

Current methodology

The implemented 0.1 workflow is intentionally simple and reproducible:

1. load stations and ADA status
2. create circular walk catchments using a fixed walking speed
3. test each tract centroid against accessible-station catchments
4. compute a tract need score from disability, senior, and poverty rates
5. assign a basic gap score when high-need tracts have no accessible station in catchment

This is intentionally a first-pass Euclidean approximation, not a network isochrone model. Reliability-aware analysis remains future work.

Documentation

• Hosted docs: subway-access.readthedocs.io
• Local preview: make docs
• Strict docs build: make docs-build

Quick links

Docs: Home, Getting Started, CLI Reference, Architecture, Python API, Contributing, Releasing, Changelog

Development

make install-dev
make test
make lint
make docs-build
make ci

License

MIT.