Splits the single market score into two branded scores backed by a new
global data pipeline covering all GeoNames locations (pop ≥1K):
Data pipeline:
- GeoNames expanded: cities1000 (~140K locations) vs old cities15000
(~24K). Added lat/lon/admin1/admin2. Feature codes include PPLA3/4/5.
- Tennis court Overpass extractor (extract-overpass-tennis → stg_tennis_courts)
- foundation.dim_locations: new conformed dim seeded from GeoNames,
enriched with nearest_padel_court_km (ST_Distance_Sphere), padel venue
count within 5km, tennis courts within 25km
- DuckDB spatial extension enabled (extensions: [spatial] in config.yaml)
- GEONAMES_USERNAME + CENSUS_API_KEY added to .env.dev.sops + .env.prod.sops
Scoring models:
- city_market_profile.sql (Marktreife-Score): adds x0.85 saturation
discount when venues_per_100k > 8
- location_opportunity_profile.sql (Marktpotenzial-Score): new model,
no filter on padel_venue_count, rewards supply gaps + catchment gaps
Methodology page:
- market_score.html: Two Scores intro, 5 Marktpotenzial component cards,
score bands for both scores, FAQ 5-7, padelnomics wordmark spans on h2s
- en.json + de.json: 30+ new keys, native German (no calques), TM on chips
Docs: CHANGELOG, data-sources-inventory, SQLMesh CLAUDE.md, PROJECT.md
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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