Subway Accessibility in the blaise-website Portfolio: A Pointer, a Gallery, and a Cross-Walk

Key findings

Only 157 of NYC's 493 subway stations are ADA-accessible (31.8%), so roughly 4.7 million New Yorkers — about 55% of the city — live in a neighborhood that isn't within walking distance of an accessible station¹. Even that 31.8% overstates real-world coverage: if you dock stations for elevator downtime, Manhattan's nominal 77% drops to 71%².

The strongest demographic predictor of the accessibility gap is senior rate, not poverty or disability rate³. Gaps cluster in identifiable corridors (southeastern Queens, central Brooklyn, the northern Bronx⁴) — so targeted investment is both feasible and overdue. This post is a thin portfolio wrapper; the executable analysis lives upstream in the subway-access library.

1 Using an 800 m walking-distance catchment from every ADA-accessible station and 2023 five-year ACS tract-level demographics: 1,416 of 2,317 tracts (61.1%) fall in the gap. Queens carries the largest absolute gap (1.75 M residents); Staten Island has the lowest coverage rate (9% of tracts).
2 Uptime is measured per elevator from May 2025 through April 2026. Forty-nine of the 157 ADA stations operated below 95% elevator uptime; 59 St-Columbus Circle logged 0.0% uptime for the full year.
3 In a multivariate OLS regression with HC1-robust standard errors: senior rate $b = 0.263$ , $t(2313) = 15.93$ , $p < .001$ (strongest predictor); poverty rate $b = 0.164$ , $p < .001$ ; disability rate is not significant ( $p = .84$ ) because of its $r = .70$ correlation with poverty. Model $R^2 = .202$ , $F(3, 2313) = 108.83$ , $p < .001$ .
4 Global Moran's $I$ — a measure of spatial autocorrelation — is $.23$ on the composite gap score ( $z = 40.87$ , $p < .001$ ), confirming the clusters aren't random.

Abstract#

This showcase is a portfolio wrapper, not a re-implementation. The canonical NYC subway ADA-accessibility analysis lives upstream in the subway-access v0.5.0 library, whose docs/CASESTUDY.md fits the analysis into a single document with its own factor-factory engine-audit appendix. Here we do three things. First, we mirror the 15 published figures so they are browsable inside the blaise-website catalogue without requiring the reader to clone the upstream repository. Second, we publish an 18-row cross-walk that maps each numbered section of the upstream CASESTUDY to the factor-factory engine family that owns the canonical implementation and to the portfolio topic other blaise-website showcases use to reference this one. Third, we record the headline numbers — 493 stations, 157 ADA-accessible (31.8%), 4,717,140 New Yorkers in gap tracts (55.4%), OLS $R^2 = .202$ with senior rate as the strongest predictor, Moran's $I = .23$ ( $z = 40.87$ , $p < .001$ ) — as structured JSON so downstream consumers (the portfolio index, the OSS catalogue) can surface them without parsing prose.

Keywords: portfolio framing, cross-walk, factor-factory, subway-access, engine audit, ADA accessibility, NYC subway

1. Why this is the thinnest showcase of the three#

The blaise-website python-showcase package ships three analytical case studies. Rat-containerization and resolution-equity are primary research — they fetch data, run the panel and spatial engines, and commit the resulting tearsheets. Subway-accessibility is different: the underlying research is already published in the upstream subway-access library, which has its own docs/CASESTUDY.md, its own reports, and a dedicated engine-audit appendix (subway-access v0.5.0 Appendix D). Duplicating it inside this monorepo would create two sources of truth and two regeneration paths that would inevitably drift.

The thin-wrapper decision keeps the portfolio coherent. The upstream library is the executable analysis; this showcase is the blaise-website lens on it. If the MTA publishes a later station vintage, we update the upstream package and this showcase becomes a bump commit — replace the 15 PNGs, re-pin the headline-numbers JSON, and re-render the catalogue.

2. What the upstream analysis found#

At the April 2026 data pull, only 157 of 493 active NYC subway stations (31.8%) were ADA-accessible. Using an 800 m Euclidean catchment from each accessible station and overlaying the 2023 five-year ACS, 1,416 of 2,317 census tracts (61.1%) fell in the accessibility gap. The resident population of those gap tracts totals 4,717,140 — 55.4% of the city's 8,507,596 residents. Queens carries the largest absolute gap (1,752,073 residents, 22% tract coverage). Staten Island has the lowest coverage rate (9%). Manhattan, at 77% tract coverage, is the exception that makes the rest of the system look worse by comparison.

The reliability analysis is what separates this study from prior accessibility research. Of the 157 ADA-designated stations, 49 operated below 95% elevator uptime over the May 2025–April 2026 observation window; the most extreme case (59 St-Columbus Circle) logged 0.0% uptime for the full year. When nominal coverage is uptime-weighted, Manhattan drops from 77% to 71% — a six-percentage-point reduction in the best-performing borough. The implication is that capital investment in new ADA stations without commensurate elevator-maintenance funding produces nominal compliance that does not translate to functional access.

The equity regression (gap score ~ disability rate + senior rate + poverty rate, OLS with HC1 standard errors) identifies senior rate as the strongest predictor ( $b = 0.263$ , $t(2313) = 15.93$ , $p < .001$ ), followed by poverty rate ( $b = 0.164$ , $t = 4.99$ , $p < .001$ ). Disability rate is not a significant predictor in the multivariate model ( $t = 0.21$ , $p = .84$ ) because of its $r = .70$ correlation with poverty. Model $R^2 = .202$ , $F(3, 2313) = 108.83$ , $p < .001$ .

Global Moran's $I$ statistics confirm significant positive spatial autocorrelation: gap score $I = .23$ ( $z = 40.87$ ), need score $I = .20$ ( $z = 33.91$ ), disability rate $I = .28$ ( $z = 48.92$ ), all $p < .001$ . Accessibility gaps do not distribute randomly; they cluster in identifiable corridors — southeastern Queens, central Brooklyn, the northern Bronx — amenable to geographically targeted investment.

3. What this showcase adds#

The figure gallery. The 15 committed PNGs in artifacts/figures/ are inlined in notebook 02 via IPython.display.Image (workaround for jellycell bug #11, documented in .claude/skills/jellycell-gotchas.md). They are LFS-tracked via the repo-root .gitattributes; the git mv from the LEGACY showcase preserves history. Each figure carries a one-sentence caption linking back to the upstream section so a reader can jump from a map to the paragraph that defines what it shows.

3.1 Gallery#

Figure 1 — ADA-accessible station count vs. total active station count by borough (April 2026 MTA vintage). Staten Island's 9% coverage vs. Manhattan's 77% coverage is the bracket the rest of the analysis unpacks.

Figure 2 — Resident population inside vs. outside the 800 m accessibility catchment, by borough. 4.72 M New Yorkers (55.4%) live in gap tracts; Queens carries the largest absolute gap (1.75 M residents).

Figure 3 — Nominal ADA coverage vs. uptime-weighted "effective" coverage, by borough. Uptime-weighting pulls Manhattan from 77% down to 71% and documents the gap between capital compliance and functional access.

Figure 4 — Choropleth of the composite gap score at the census-tract level. Gap score combines catchment distance and uptime-weighted reliability; southeastern Queens, central Brooklyn, and the northern Bronx dominate.

Figure 5 — Choropleth of binary coverage status (in-catchment vs. gap tract) at the census-tract level. Complements Figure 4 by separating geometry from reliability.

Figure 6 — Cumulative ADA-accessible station count over the MTA Key Station Program timeline. The step pattern illustrates the hash-based fallback used for the 56-station subset lacking sourced upgrade years.

Figure 7 — Balance check on the treated vs. control tract pool used in the DiD supplement, showing the pre-period demographics that justify the upstream identification strategy.

Figure 8 — Distribution of the composite need score (disability rate + senior rate + poverty rate, equal weights) across all 2,317 tracts. Right-tailed: the tracts with the highest need are a small, identifiable set.

Figure 9 — Distance-decay curve for the accessibility catchment: share of population covered as a function of walkshed radius. The 800 m cutoff sits on the curve's shoulder and is the choice the upstream CASESTUDY defends.

Figure 10 — Gap score vs. nearest-station distance at the tract level. Near-linear relationship in the out-of-catchment regime; flat inside the catchment where reliability dominates.

Figure 11 — Pearson correlation heatmap across the three demographic predictors (disability rate, senior rate, poverty rate) and the gap score. Disability × poverty r = .70 explains why the multivariate OLS drops disability below significance.

Figure 12 — Gap score vs. poverty rate at the tract level, with OLS fit overlay. b = 0.164, t = 4.99, p < .001 in the multivariate model.

Figure 13 — Gap score vs. disability rate at the tract level. Disability is the weakest multivariate predictor (t = 0.21, p = .84) because of its r = .70 correlation with poverty.

Figure 14 — Bivariate choropleth of gap score vs. poverty rate. The high-gap × high-poverty corridors are the actionable targets the equity regression nominates.

Figure 15 — Bivariate choropleth of gap score vs. disability rate. Useful for siting decisions that weight disability independently of poverty despite the correlation.

The cross-walk. Notebook 03 emits an 18-row table mapping each upstream section to its factor-factory engine family and the blaise-website portfolio topic it illustrates. The table is the distinctive contribution of this showcase: it tells a reader browsing the rat-containerization case study which other case study to read next if they care about (say) spatial autocorrelation (factor_factory.engines.spatial.morans_i, §4.8) or vertical equity (factor_factory.engines.panel_reg.pyfixest_adapter, §4.7). Sections without a dedicated estimator (descriptive coverage, discussion, limitations) are marked with a dash in the engine column; the portfolio topic is still populated because the methodological choice — how to write an honest limitations section, how to communicate headline numbers — is itself a portfolio-level concern.

The headline-numbers JSON. The fifteen most-referenced quantitative results from the upstream CASESTUDY are emitted as structured JSON in artifacts/headline_numbers.json. This lets the blaise-website OSS catalogue and the portfolio index pull numbers without regex-parsing the manuscript — which matters when the April 2026 vintage gets bumped to a later MTA snapshot.

4. Limitations of the wrapper framing#

A thin wrapper inherits every limitation of the upstream analysis. The Euclidean 800 m catchment overstates true walking coverage because it ignores network topology. The ACS vintage is three years stale as of April 2026. The DiD timeline has sourced upgrade years for only 101 of 157 stations; the remaining 56 use a deterministic hash-based fallback pending a FOIL request to the MTA Key Station Program. The centroid-based coverage is a spatial simplification. Equal weighting of the need-score components is arbitrary. All of these are documented in §5.3 of the upstream CASESTUDY and should be read there rather than paraphrased here.

This showcase adds one new limitation: the cross-walk is a curation, not an engine-audit. A real audit would re-estimate every primary result using the named factor-factory engine and report the numerical discrepancy. The upstream Appendix D does that for the headline regression and the Moran's I test. Extending the audit across every row of the cross-walk is future work; it belongs upstream in the subway-access engine-audit Appendix rather than here.

5. What happens next#

The upstream library is the evolution point. When subway-access v0.6 ships — with refreshed MTA data, a completed FOIL-sourced upgrade timeline, or an expanded engine-audit Appendix — this showcase becomes a bump commit: pin the new version in packages/python-showcase/pyproject.toml, replace the figures in artifacts/figures/, update the headline-numbers JSON, re-render the catalogue. The cross-walk stays largely stable because factor-factory engine names are API-stable across minor versions.

For readers who want to run the live pipeline rather than browse the snapshot, manuscripts/UPSTREAM_REFERENCE.md carries the clone + uv sync + python main.py recipe. The output lands in reports/ under the upstream example directory; copy it back here to refresh the wrapper.

References#

Metropolitan Transportation Authority. (2026). MTA subway station catalog and elevator/escalator availability. NYC Open Data / Socrata. https://data.ny.gov

U.S. Census Bureau. (2024). American Community Survey 2019–2023 five-year estimates. https://www.census.gov/programs-surveys/acs

Albis-Burdige, B. (2026). subway-access: Typed Python toolkit for NYC subway accessibility analysis (v0.5.0) [Computer software]. Random Walks. https://github.com/random-walks/subway-access

Albis-Burdige, B. (2026). factor-factory: Protocol-based econometrics engine registry (v1.0.2) [Computer software]. Random Walks. https://github.com/random-walks/factor-factory