fifteen/README.md

Transportationer — 15-Minute City Analyzer

A web application for analyzing urban accessibility through the lens of the 15-minute city concept. Shows a heatmap indicating distance to locations of interest across 5 categories: Service & Trade, Transport, Work & School, Culture & Community, and Recreation.

Architecture

Browser (Next.js / React)
  ├── MapLibre GL JS (map + canvas heatmap / isochrone overlay)
  └── API calls → Next.js API routes

Next.js App Server
  ├── Public API: /api/cities /api/tiles /api/stats /api/location-score /api/isochrones
  ├── Admin API: /api/admin/** (auth-protected)
  ├── PostgreSQL + PostGIS (POIs, grid points, precomputed scores)
  └── Valkey (API response cache, BullMQ queues)

BullMQ Worker (pipeline queue, concurrency 8)
  ├── refresh-city      → orchestrates full ingest via FlowProducer
  ├── download-pbf      → streams OSM PBF from Geofabrik
  ├── extract-pois      → osmium filter + osm2pgsql flex → raw_pois
  ├── build-valhalla    → clips PBF, builds Valhalla routing tiles + transit tiles
  ├── download-gtfs-de  → downloads & extracts GTFS feed for German ÖPNV
  ├── generate-grid     → PostGIS 200 m hex grid → grid_points
  ├── compute-scores    → two-phase orchestrator (see Scoring below)
  ├── compute-routing   → Valhalla matrix → grid_poi_details
  │                        (15 parallel jobs: 3 modes × 5 categories)
  └── compute-transit   → Valhalla isochrones → grid_poi_details (travel_mode='transit')
                           (1 job per city, covers all categories via PostGIS spatial join)

BullMQ Worker (valhalla queue, concurrency 1)
  └── build-valhalla   → valhalla_ingest_transit + valhalla_convert_transit (GTFS → tiles),
                          valhalla_build_tiles (road graph + transit connection),
                          manages valhalla_service

Valhalla (child process of valhalla worker)
  ├── sources_to_targets matrix → compute-routing jobs (walking/cycling/driving)
  ├── isochrone (multimodal)    → compute-transit jobs
  └── isochrone endpoint        → user click → /api/isochrones

Protomaps → self-hosted map tiles (PMTiles)

Quick Start

1. Configure environment

cp .env.example .env
# Edit .env with strong passwords

# Generate admin password hash
node -e "require('bcryptjs').hash('yourpassword', 12).then(console.log)"
# Paste result as ADMIN_PASSWORD_HASH in .env

2. Start services

docker compose up -d

3. Add a city

Open http://localhost:3000/admin, log in, click Add City, browse Geofabrik regions (e.g. europe/germany/berlin), and start ingestion. Progress is shown live.

Processing time:

• Small city (< 100k pop): ~5–15 minutes
• Large city (1M+ pop): ~30–90 minutes

4. Explore

Open http://localhost:3000 and select your city.

Map Tiles

By default the app uses CartoDB Positron (CDN). For fully offline operation, download a PMTiles file for your region:

# Example: download Berlin region tiles
wget https://maps.protomaps.com/builds/berlin.pmtiles -O apps/web/public/tiles/region.pmtiles
# Then switch to the PMTiles style:
cp apps/web/public/tiles/style.pmtiles.json apps/web/public/tiles/style.json

Development

npm install
npm run dev               # Next.js dev server on :3000
npm run worker:dev        # BullMQ worker with hot reload

Required local services: PostgreSQL+PostGIS, Valkey. Easiest via:

docker compose up postgres valkey -d

Category Definitions

Category	OSM Sources
Service & Trade	supermarkets, shops, restaurants, pharmacies, banks, cafés
Transport	bus stops, metro, tram, train stations, bike share, car share
Work & School	offices, coworking, schools, kindergartens, universities
Culture & Community	libraries, hospitals, clinics, museums, theatres, community centres
Recreation	parks, playgrounds, sports centres, gyms, green spaces

Scoring

Data pipeline

For each city the pipeline runs in two phases:

Phase 1 — Routing (parallel child jobs)

Walking, cycling, driving — 15 jobs (3 modes × 5 categories): A PostGIS KNN lateral join (<-> operator) finds the 6 nearest POIs in the category for each grid point (200 m hexagonal spacing). Those POI coordinates are sent in batches of 20 to Valhalla's sources_to_targets matrix API to obtain exact real-network travel times. The nearest POI per subcategory is persisted to grid_poi_details.

Transit — 1 job per city (compute-transit): Valhalla's matrix API does not support transit. Instead, for each grid point a multimodal isochrone is fetched from Valhalla at contour intervals of 5, 10, 15, 20, and 30 minutes (fixed departure: Tuesday 08:00 to ensure reproducible GTFS results). PostGIS ST_Within then classifies all POIs in the city into the smallest contour they fall within, giving estimated travel times of 300 s / 600 s / 900 s / 1 200 s / 1 800 s respectively. Grid points outside the transit network are silently skipped — transit contributes nothing to their score and the other modes compensate.

Phase 2 — Score aggregation

Scores are precomputed for every combination of:

• 5 thresholds: 5, 10, 15, 20, 30 minutes
• 5 travel modes (see below)
• 5 profiles: Universal, Young Family, Senior, Young Professional, Student

Travel modes

Mode	Internal key	How travel time is obtained
Best mode	fifteen	Synthetic — minimum travel time across walking, cycling, and transit per subcategory. A destination reachable by any of these modes within the threshold counts as accessible. Driving excluded intentionally.
Walking	walking	Valhalla pedestrian matrix, exact seconds
Cycling	cycling	Valhalla bicycle matrix, exact seconds
Transit	transit	Valhalla multimodal isochrone, quantised to 5-min bands (requires GTFS feed)
Driving	driving	Valhalla auto matrix, exact seconds

The fifteen mode is computed entirely in memory during Phase 2: for each (grid point, category, subcategory) the minimum travel time across the three active modes is used, then scored normally. No extra routing jobs are needed.

Scoring formula

Each subcategory i within a category contributes a sigmoid score based on the real travel time t and the selected threshold T (both in seconds):

sigmoid(t, T) = 1 / (1 + exp(4 × (t − T) / T))

The sigmoid equals 0.5 exactly at the threshold and approaches 1 for very short times. It is continuous, so a 14-minute trip to a park still contributes nearly as much as a 10-minute trip under a 15-minute threshold.

The category score combines all subcategories via a complement product, weighted by per-profile subcategory importance weights w_i ∈ [0, 1]:

category_score = 1 − ∏ (1 − w_i × sigmoid(t_i, T))

This captures diversity of coverage: one nearby supermarket already yields a high score, but also having a pharmacy and a bakery pushes it higher. Missing subcategories (no POI found) are simply omitted from the product and do not penalise the score.

Profiles

Each profile carries two sets of weights:

• Category weights (used as slider presets in the UI, range 0–2): how much relative importance each of the 5 categories receives in the composite score.
• Subcategory weights (baked into precomputed scores, range 0–1): how strongly a specific subcategory contributes to its parent category score.

Profile	Emoji	Category emphasis	Notable subcategory boosts
Universal	⚖️	All equal (1.0)	Balanced baseline
Young Family	👨‍👩‍👧	Work & School 1.5×, Recreation 1.4×, Service 1.2×	school, kindergarten, playground, clinic all → 1.0
Senior	🧓	Culture & Community 1.5×, Service 1.4×, Transport 1.1×	hospital, clinic, pharmacy, social services → 1.0; school → 0.05
Young Professional	💼	Transport 1.5×, Recreation 1.1×	metro, train → 1.0; gym 0.9; coworking 0.85; school → 0.1
Student	🎓	Work & School 1.5×, Transport 1.4×, Culture 1.2×	university, library → 1.0; bike share 0.85; school → 0.05

Composite score

The composite shown on the heatmap is a weighted average of the 5 category scores. Category weights come from the selected profile but can be adjusted freely in the UI. All scores are precomputed — changing the profile, threshold, or travel mode only queries the database; adjusting the category weight sliders re-blends entirely client-side with no round-trip.

Per-location score (pin)

When a user places a pin on the map:

1. The nearest grid point is found via a PostGIS <-> KNN query.
2. Precomputed grid_scores rows for that grid point, travel mode, threshold, and profile are returned — one row per category.
3. Per-subcategory detail rows from grid_poi_details are also fetched, showing the name, straight-line distance, and travel time to the nearest POI in each subcategory for the requested mode.
4. An isochrone overlay is fetched live from Valhalla and shown on the map (walking is used as the representative mode for fifteen and transit since Valhalla's interactive isochrone only supports single-mode costing).

The pin panel also shows estate value data (land price in €/m² from the BORIS NI cadastre) for cities in Lower Saxony, including a percentile rank among all zones in the city and a "peer percentile" rank among zones with similar accessibility scores.

Hidden gem score

For cities with BORIS NI estate value data, a hidden gem score is precomputed per grid point at the end of Phase 2:

hidden_gem_score = composite_accessibility × (1 − price_rank_within_decile)

• composite_accessibility — average of all category scores for that grid point (walking / 15 min / universal profile)
• price_rank_within_decile — PERCENT_RANK() of the nearest zone's land price among all zones in the same accessibility decile (0 = cheapest, 1 = most expensive relative to equally accessible peers)

The result is in [0, 1]: high only when a location is both accessible and priced below its peers. Stored in grid_points.hidden_gem_score and served as a separate MVT overlay at /api/tiles/hidden-gems/.

The map offers three mutually exclusive base overlays (switchable in the control panel):

• Accessibility — default grid heatmap coloured by composite score
• Land value — BORIS NI zones coloured by €/m² (Lower Saxony cities only)
• Hidden gems — grid points coloured by hidden gem score (Lower Saxony cities only)