# 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 (download queue, concurrency 1)
  └── download-pbf      → streams OSM PBF from Geofabrik (serialised to avoid
                          redundant parallel downloads; idempotent if file exists)

BullMQ Worker (pipeline queue, concurrency 8)
  ├── refresh-city      → orchestrates full ingest via FlowProducer
  ├── extract-pois      → osmium filter + osm2pgsql flex → raw_pois
  ├── 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) — road-only instance
  └── build-valhalla   → osmium clip + valhalla_build_tiles (road graph only, no transit
                          connections) → manages valhalla_service on :8002
                          Clean tiles ensure cycling/walking/driving routing is never
                          affected by ghost edges from failed transit connections.

BullMQ Worker (valhalla-transit queue, concurrency 1) — transit instance
  ├── download-gtfs-de → downloads & filters GTFS feed for German ÖPNV (bbox-clipped to
  │                       known cities, single-stop trips removed)
  └── build-valhalla   → osmium clip + valhalla_ingest_transit + valhalla_convert_transit
                          + valhalla_build_tiles (road graph with transit connections)
                          → manages valhalla_service on :8002 (separate container/port)

Valhalla road instance (child process of valhalla worker, port 8002)
  ├── sources_to_targets matrix → compute-routing jobs (walking/cycling/driving)
  └── isochrone endpoint        → user click → /api/isochrones (non-transit modes)

Valhalla transit instance (child process of valhalla-transit worker, port 8002)
  ├── isochrone (multimodal) → compute-transit jobs
  └── isochrone endpoint     → user click → /api/isochrones (transit mode)

Protomaps → self-hosted map tiles (PMTiles)
```

## Quick Start

### 1. Configure environment

```bash
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

```bash
docker compose up -d
```

### 3. Add a city

Open [http://localhost:3000/admin](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](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:

```bash
# 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

```bash
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:

```bash
docker compose up postgres valkey -d
```

## Category Definitions

Five categories cover everyday destinations. All categories are scored at the same threshold (5, 10, 15, 20, or 30 minutes — user-selectable). The **universal weight** column shows how strongly a subcategory contributes to its parent category score in the Universal profile (range 0–1); other profiles override specific values — see the Profiles table below.

### Service & Trade

| OSM tag(s) | Subcategory | Universal weight |
|---|---|:---:|
| `shop=supermarket`, `shop=wholesale` | `supermarket` | 1.0 |
| `shop=convenience` | `convenience` | 0.65 |
| `amenity=pharmacy`, `shop=pharmacy` | `pharmacy` | 1.0 |
| `amenity=restaurant`, `amenity=fast_food` | `restaurant` | 0.55 |
| `amenity=cafe`, `shop=bakery` | `cafe` | 0.4 |
| `amenity=bank` | `bank` | 0.35 |
| `amenity=post_office` | `post_office` | 0.4 |
| `shop=greengrocer`, `shop=butcher`, `amenity=marketplace`, `shop=department_store`, `shop=mall` | `market` | 0.4 |
| `shop=laundry`, `shop=dry_cleaning` | `laundry` | 0.3 |
| `amenity=atm` | `atm` | 0.2 |

### Transport

| OSM tag(s) | Subcategory | Universal weight |
|---|---|:---:|
| `railway=station`, `railway=halt` | `train_station` | 1.0 |
| `railway=subway_entrance`, `railway=subway_station` | `metro` | 1.0 |
| `railway=tram_stop` | `tram_stop` | 0.75 |
| `highway=bus_stop`, `amenity=bus_station` | `bus_stop` | 0.55 |
| `amenity=ferry_terminal` | `ferry` | 0.5 |
| `amenity=bicycle_rental` | `bike_share` | 0.5 |
| `amenity=car_sharing` | `car_share` | 0.5 |

**Excluded:** `public_transport=stop_position` and `public_transport=platform` — these duplicate `highway=bus_stop` / `railway=tram_stop` nodes at the exact same location and would double-count stops in scoring.

### Work & School

| OSM tag(s) | Subcategory | Universal weight |
|---|---|:---:|
| `amenity=kindergarten`, `amenity=childcare` | `kindergarten` | 0.75 |
| `amenity=school` | `school` | 0.7 |
| `office=coworking` | `coworking` | 0.55 |
| `amenity=university`, `amenity=college` | `university` | 0.55 |
| `amenity=driving_school` | `driving_school` | 0.2 |

**Excluded:** `office=company`, `office=government`, `landuse=commercial`, `landuse=office` — ubiquitous in every urban block; they always win the "nearest POI" race in the detail view, masking meaningful destinations. Government buildings are captured via `amenity=townhall` / `amenity=police` in Culture & Community instead.

### Culture & Community

| OSM tag(s) | Subcategory | Universal weight |
|---|---|:---:|
| `amenity=hospital` | `hospital` | 1.0 |
| `amenity=clinic`, `amenity=doctors` | `clinic` | 0.8 |
| `amenity=library` | `library` | 0.7 |
| `amenity=community_centre`, `leisure=arts_centre` | `community_center` | 0.6 |
| `amenity=social_facility` | `social_services` | 0.6 |
| `amenity=theatre`, `amenity=cinema` | `theatre` | 0.5 |
| `tourism=museum` | `museum` | 0.4 |
| `amenity=townhall`, `amenity=police` | `government` | 0.4 |
| `amenity=place_of_worship` | `place_of_worship` | 0.25 |

### Recreation

| OSM tag(s) | Subcategory | Universal weight |
|---|---|:---:|
| `leisure=park`, `leisure=garden` | `park` | 1.0 |
| `leisure=playground` | `playground` | 0.85 |
| `leisure=sports_centre`, `leisure=pitch` | `sports_facility` | 0.65 |
| `leisure=fitness_centre` | `gym` | 0.65 |
| `leisure=nature_reserve`, `leisure=golf_course`, `landuse=recreation_ground`, `landuse=grass`, `landuse=meadow`, `landuse=forest` | `green_space` | 0.6 |
| `leisure=swimming_pool`, `amenity=swimming_pool` | `swimming_pool` | 0.55 |

## 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 weights | Notable subcategory overrides (vs. Universal) |
|---------|-------|------------------|------------------------------------------------|
| Universal | ⚖️ | All 1.0 | Baseline — see tables above for all weights |
| Young Family | 👨‍👩‍👧 | Work & School 1.5, Recreation 1.4, Service 1.2, Culture 0.9, Transport 1.0 | school → 1.0, kindergarten → 1.0, playground → 1.0, clinic → 1.0, park → 1.0; gym → 0.5, university → 0.2, driving\_school → (inherits 0.2) |
| Senior | 🧓 | Culture & Community 1.5, Service 1.4, Transport 1.1, Recreation 1.0, Work & School 0.3 | hospital → 1.0, clinic → 1.0, pharmacy → 1.0, social\_services → 0.9, bus\_stop → 0.75, tram\_stop → 0.8, metro → 0.8; school → 0.05, kindergarten → 0.05, university → 0.15 |
| Young Professional | 💼 | Transport 1.5, Recreation 1.1, Service 1.0, Culture 0.9, Work & School 0.7 | metro → 1.0, train\_station → 1.0, tram\_stop → 0.85, bike\_share → 0.7; gym → 0.9, restaurant → 0.75, coworking → 0.85; school → 0.1, kindergarten → 0.05 |
| Student | 🎓 | Work & School 1.5, Transport 1.4, Culture & Community 1.2, Service 0.9, Recreation 0.8 | university → 1.0, library → 1.0, coworking → 0.9, bike\_share → 0.85, cafe → 0.9, metro → 1.0, train\_station → 0.9; school → 0.05, kindergarten → 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. For `transit` mode the multimodal isochrone comes from the dedicated transit Valhalla instance. For `fifteen` (Best mode), cycling is used as the representative display isochrone 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)