H13 — Public map

The public map translates versioned, section-level evidence into a passenger-facing quieter-journey view. It must make coverage and uncertainty as visible as the acoustic result.

For: passengers, map designers, researchers, journalists, and tunes-web implementers.

Assumptions: map geometries use directed station-to-station sections; crowd data is survey-grade at best; “quieter” is comparative within the released dataset.

Goals

Compare observed acoustic conditions between plausible journey sections.
Show where evidence is dense, sparse, conflicting, or absent.
Keep measured acoustics separate from reported passenger experience.
Let every displayed result be traced to a dataset, schema, and pipeline version.
Remain readable without implying engineering-grade or health-risk authority.

The first map is expected to use L2 section aggregates. Record-level observations and reprocessed metrics remain separate data layers; passenger-facing rankings and styles are interpretations.

flowchart LR
  A[Consented derived observations] --> B[Validation and quality gates]
  B --> C[Section aggregates]
  C --> D[Coverage and confidence]
  C --> E[Objective map layer]
  F[Optional perception reports] --> G[Separate subjective layer]
  D --> E
  D --> G

Aggregation and confidence

Each section summary should expose:

Element	Why it is required
Sample count	Distinguishes repeated evidence from isolated observations
Distribution or spread	Avoids presenting one value as universal
Quality-tier mix	Prevents weak and strong contributions appearing equivalent
Uncertainty dimensions	Separates acoustic, calibration, alignment, and metadata limits
Scope and recency	States what network and release the map represents
Exclusions and caveats	Makes filtering decisions auditable

Objective and subjective layers require independent sample counts. A session-level perception report must not be copied onto every section.

Interpolation

TUNES does not fill unobserved or low-sample sections with confident acoustic values. Empty sections remain empty or insufficient evidence.

Topology snapping is not acoustic interpolation: it assigns a recording interval to a legal network section when GPS is weak. Reprocessing may estimate model-adjusted values only under a documented, versioned method; it creates a new release and does not overwrite observations.

Confidence maps may visualise coverage, tier mix, or a named uncertainty dimension. They must not collapse all uncertainty into an unexplained “accuracy” score.

Future work

Minimum sample gates, aggregate estimators, interval methods, recency rules, and any model-based spatial inference require pilot evidence and an ADR before becoming public-map defaults.

Colour and accessibility

Colour may encode a named acoustic metric or a clearly labelled perception measure, never both at once. The legend must state the metric, weighting, duration/aggregation basis, release, and insufficient-data state.

Do not use colour as the only carrier of meaning.
Do not imply false precision through narrow bands or smooth gradients.
Keep low-confidence and missing-data states visually distinct from “quiet”.
Provide text or pattern equivalents for confidence and coverage.

Future work

The repository does not yet define a canonical palette, breakpoints, or accessibility-tested scale. These belong to tunes-web design work informed by pilot distributions and user testing.

Privacy

The map consumes derived, minimised data. Raw recordings are never exposed because carriage recordings may contain incidental passenger, staff, and announcement speech. Publishing waveform audio would increase re-identification and surveillance risk without being necessary for the default map.

Public layers must also avoid contributor identity, stable cross-release personal identifiers, precise location trails, identifying free text, and combinations of rare route and exact time that enable re-identification. Pseudonymous data is still personal data.

How it works · Privacy · Quality tiers · Legal and governance · Public data model · Calibration and quality tiers · Open data and reproducibility · Subjective experience · Claim language