docs: update docs and PROJECT.md for dual score pipeline

Task 8: documentation updates for the dual market score feature.

- CHANGELOG.md: comprehensive [Unreleased] entries for all additions
  (Marktpotenzial-Score, tennis courts, dim_locations, GeoNames expansion,
  DuckDB spatial, SOPS secrets, methodology page updates)
- docs/data-sources-inventory.md: add tennis courts Overpass row, update
  GeoNames entry (cities1000, username=padelnomics, higher score)
- transform/sqlmesh_padelnomics/CLAUDE.md: add dim_locations to conformed
  dimensions table, update source integration map with new pipeline branch,
  document ST_Distance_Sphere bounding-box pattern
- PROJECT.md: add dual score to In Progress, add Gemeinde pSEO + top-50
  ranking page to Next Up, add data backlog items (sports_centre, NUTS-3,
  opportunity map), add Decisions Log entry

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

CHANGELOG.md

@@ -7,6 +7,32 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
 ## [Unreleased]
 
 ### Added
+- **Dual market score system** — split the single market score into two branded scores:
+  - **padelnomics Marktreife-Score™** (market maturity): existing score, refined — only for cities
+    with ≥1 padel venue. Adds ×0.85 saturation discount when `venues_per_100k > 8`.
+  - **padelnomics Marktpotenzial-Score™** (investment opportunity): new score covering ALL
+    GeoNames locations globally (pop ≥1K), including zero-court locations. Rewards supply gaps,
+    underserved catchment areas, and racket sport culture via inverted venue density signal.
+- **Tennis court Overpass extractor** — `extract-overpass-tennis` downloads all OSM
+  `sport=tennis` nodes/ways/relations globally (~150K+ features). Lands at
+  `overpass_tennis/{year}/{month}/courts.json.gz`. Staged in `stg_tennis_courts`.
+- **`foundation.dim_locations`** — new conformed dimension seeded from GeoNames (all locations
+  ≥1K pop), not from padel venues. Grain `(country_code, geoname_id)`. Enriched with:
+  - `nearest_padel_court_km` via `ST_Distance_Sphere` (DuckDB spatial extension)
+  - `padel_venue_count` / `padel_venues_per_100k` (venues within 5km)
+  - `tennis_courts_within_25km` (courts within 25km)
+- **GeoNames expanded** — extractor switched from `cities15000` (50K+ filter, ~24K rows) to
+  `cities1000` (~140K locations, pop ≥1K). Added `lat`, `lon`, `admin1_code`, `admin2_code`
+  to output. Expanded feature codes to include `PPLA3/4/5` (Gemeinden/cantons).
+- **DuckDB spatial extension** — `extensions: [spatial]` added to `config.yaml`. Enables
+  `ST_Distance_Sphere` for great-circle distance and future map features (bounding box
+  queries, geometry columns).
+- **SOPS secrets** — `GEONAMES_USERNAME=padelnomics` and `CENSUS_API_KEY` added to both
+  `.env.dev.sops` and `.env.prod.sops`.
+- **Methodology page updated** — `/en/market-score` now documents both scores with:
+  Two Scores intro section, component cards for each score (4 Marktreife + 5 Marktpotenzial),
+  score band interpretations, expanded FAQ (7 entries). Section headings use the padelnomics
+  wordmark span (Bricolage Grotesque). Bilingual EN + DE (native-quality German, no calques).
 - **Market Score methodology page** — standalone page at `/{lang}/market-score`
   explaining the padelnomics Market Score (Zillow Zestimate-style). Reveals four
   input categories (demographics, economic strength, demand evidence, data

PROJECT.md

@@ -135,7 +135,7 @@
 
 ## In Progress 🔄
 
-_Move here when you start working on it._
+- [ ] **Dual market score system** — Marktreife-Score + Marktpotenzial-Score + expanded data pipeline (merging to master)
 
 ---
 
@@ -155,6 +155,13 @@ _Move here when you start working on it._
 | Submit sitemap to Google Search Console | Set up Google Search Console + Bing Webmaster Tools (SEO hub ready — just add env vars) |
 | Verify Litestream R2 backup running on prod | |
 
+### Gemeinde-level pSEO (follow-up from dual score work)
+
+| 🛠 Tech |
+|--------|
+| Gemeinde-level pSEO article template — consumes `location_opportunity_profile` data, targets "Padel in [Ort]" + "Padel bauen in [Ort]" queries (zero SERP competition confirmed) |
+| "Top 50 underserved locations" ranking page — high-value SEO content, fully programmatic from `location_opportunity_profile` ORDER BY opportunity_score DESC |
+
 ### Week 1–2 — First Revenue
 
 | 🛠 Tech | 📣 Business |
@@ -196,6 +203,9 @@ _Move here when you start working on it._
 - [ ] Padel Hall Accelerator (€999 — report + call + supplier intros)
 
 ### Data & Intelligence
+- [ ] Sports centre Overpass extract (`leisure=sports_centre`) — additional market signal for `dim_locations`
+- [ ] City-level income enrichment (Eurostat NUTS-3 regional income — replaces country-level PPS proxy, higher granularity)
+- [ ] Interactive opportunity map / explorer in web app (map UI over `location_opportunity_profile` — bounding box queries via ST_Distance_Sphere)
 - [ ] Multi-source data aggregation (add booking platforms beyond Playtomic)
 - [ ] Google Maps signals (reviews, ratings)
 - [ ] Weather + demographic overlays
@@ -246,3 +256,4 @@ _Move here when you start working on it._
 | 2026-02-22 | Credit system over pay-per-lead blast | Suppliers self-select → higher quality perception; scales without manual intervention |
 | 2026-02-22 | No soft email gate on planner | Planner already captures emails at natural points (scenario save → login, quote wizard step 9). Gate would add friction without meaningful list value. Revisit if data shows a gap. |
 | 2026-02-22 | Wipe test suppliers before launch | 5 `example.com` entries from seed_dev_data.py — empty directory with "Be the first" CTA is better than obviously fake data |
+| 2026-02-24 | Split market score into two branded scores | Marktreife-Score (existing market maturity, cities with ≥1 venue) vs Marktpotenzial-Score (greenfield opportunity, all GeoNames locations globally). SERP analysis confirmed zero competition for hyperlocal Gemeinde-level market intelligence pages. |

docs/data-sources-inventory.md

@@ -13,7 +13,8 @@ Purpose: Identify and track data sources feeding the Padelnomics DuckDB analytic
 
 | Source | Category | Status | Score | Credentials | Pipeline refs |
 |--------|----------|--------|-------|-------------|---------------|
-| OpenStreetMap / Overpass | Court locations | ✅ Ingested | 5 | None | `extract-overpass` → `stg_padel_courts` |
+| OpenStreetMap / Overpass (padel) | Court locations | ✅ Ingested | 5 | None | `extract-overpass` → `stg_padel_courts` |
+| OpenStreetMap / Overpass (tennis) | Court locations | ✅ Ingested | 4 | None | `extract-overpass-tennis` → `stg_tennis_courts` |
 | Playtomic — tenants | Court locations | ✅ Ingested | 5 | None | `extract-playtomic-tenants` → `stg_playtomic_venues/resources/opening_hours` |
 | Playtomic — availability | Pricing / utilisation | ✅ Ingested | 5 | None | `extract-playtomic-availability` → `stg_playtomic_availability` |
 | Eurostat `urb_cpop1` | Demographics — EU city population | ✅ Ingested | 5 | None | `extract-eurostat` → `stg_population` |
@@ -21,7 +22,7 @@ Purpose: Identify and track data sources feeding the Padelnomics DuckDB analytic
 | Eurostat SDMX city labels | Demographics — EU city lookup | ✅ Ingested | 4 | None | `extract-eurostat-city-labels` → `stg_city_labels` |
 | ONS UK mid-year estimates | Demographics — UK population | ✅ Ingested | 4 | None | `extract-ons-uk` → `stg_population_uk` |
 | US Census ACS 5-year | Demographics — US population | ✅ Ingested† | 3 | `CENSUS_API_KEY` (free) | `extract-census-usa` → `stg_population_usa` |
-| GeoNames cities15000 | Demographics — global fallback | ✅ Ingested† | 3 | `GEONAMES_USERNAME` (free) | `extract-geonames` → `stg_population_geonames` |
+| GeoNames cities1000 | Demographics — global locations ≥1K pop | ✅ Ingested† | 4 | `GEONAMES_USERNAME=padelnomics` (free) | `extract-geonames` → `stg_population_geonames` → `dim_locations` |
 | ECB / Frankfurter.app | FX rates | 🔲 Planned | 4 | None | `extract-fx` → `stg_fx_rates` (proposed) |
 | FIP World Padel Report | Market reports | 🔲 Planned | 4 | None (PDF) | Annual seed table |
 | PadelAPI.org | Tournament data | 🔲 Planned | 3 | Free-tier token | 50k req/mo |

transform/sqlmesh_padelnomics/CLAUDE.md

@@ -55,15 +55,16 @@ Grain must match reality — use `QUALIFY ROW_NUMBER()` to enforce it.
 | Dimension | Grain | Used by |
 |-----------|-------|---------|
 | `foundation.dim_venues` | `venue_id` | `dim_cities`, `dim_venue_capacity`, `fct_daily_availability` (via capacity join) |
-| `foundation.dim_cities` | `city_slug` | `serving.city_market_profile` → all pSEO serving models |
+| `foundation.dim_cities` | `(country_code, city_slug)` | `serving.city_market_profile` → all pSEO serving models |
+| `foundation.dim_locations` | `(country_code, geoname_id)` | `serving.location_opportunity_profile` — all GeoNames locations (pop ≥1K), incl. zero-court locations |
 | `foundation.dim_venue_capacity` | `tenant_id` | `foundation.fct_daily_availability` |
 
 ## Source integration map
 
 ```
 stg_playtomic_venues  ─┐
-stg_playtomic_resources─┤→ dim_venues ─┬→ dim_cities ─→ city_market_profile
+stg_playtomic_resources─┤→ dim_venues ─┬→ dim_cities ──────────────→ city_market_profile
-stg_padel_courts      ─┘               └→ dim_venue_capacity
+stg_padel_courts      ─┘               └→ dim_venue_capacity              (Marktreife-Score)
                                                             ↓
 stg_playtomic_availability ──→ fct_availability_slot ──→ fct_daily_availability
                                                             ↓
@@ -71,8 +72,33 @@ stg_playtomic_availability ──→ fct_availability_slot ──→ fct_daily_a
                                                             ↓
 stg_population ──→ dim_cities ─────────────────────────────┘
 stg_income     ──→ dim_cities
+
+stg_population_geonames ─┐
+stg_padel_courts         ─┤→ dim_locations ──→ location_opportunity_profile
+stg_tennis_courts        ─┤                         (Marktpotenzial-Score)
+stg_income               ─┘
 ```
 
+## Distance calculation pattern (ST_Distance_Sphere)
+
+Use a bounding-box pre-filter before calling `ST_Distance_Sphere` to avoid full cross-joins:
+
+```sql
+-- Nearest padel court (km) per location
+SELECT l.geoname_id,
+  MIN(ST_Distance_Sphere(
+    ST_Point(l.lon, l.lat), ST_Point(p.lon, p.lat)
+  ) / 1000.0) AS nearest_km
+FROM locations l
+JOIN padel_courts p
+  ON ABS(l.lat - p.lat) < 0.5   -- ~55km pre-filter
+ AND ABS(l.lon - p.lon) < 0.5
+GROUP BY l.geoname_id
+```
+
+Requires `extensions: [spatial]` in `config.yaml` (already set). DuckDB spatial must
+`INSTALL spatial; LOAD spatial;` before `ST_Distance_Sphere` / `ST_Point` are available.
+
 ## Common pitfalls
 
 - **Don't add business logic to staging.** Even a CASE statement renaming values = business