feat(transform): add dim_locations + dual market scoring models
dim_locations (foundation):
- Seeded from stg_population_geonames (all locations, not venue-dependent)
- Grain: (country_code, geoname_id)
- Enriched with: padel venues within 5km, nearest court distance (ST_Distance_Sphere),
tennis courts within 25km, country income
- Covers zero-court Gemeinden for opportunity scoring
location_opportunity_profile (serving) — Padelnomics Marktpotenzial-Score:
- Answers "Where should I build?" — no padel_venue_count filter
- Formula: population (25) + income (20) + supply gap inverted (30) +
catchment gap (15) + tennis culture (10) = 100pts
- Sorted by opportunity_score DESC
city_market_profile (serving) — Padelnomics Marktreife-Score:
- Add saturation discount (×0.85 when venues_per_100k > 8)
- Update header comment to reference Marktreife-Score branding
- Kept WHERE padel_venue_count > 0 (established markets only)
- column name market_score unchanged (avoids downstream breakage)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Deeman
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-- Location dimension: all known populated places globally (GeoNames cities1000).
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-- This is the opportunity-scoring root — NOT filtered to places with padel courts.
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-- Grain: (country_code, geoname_id) — stable GeoNames numeric ID per location.
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--
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-- Unlike dim_cities (seeded from dim_venues / existing padel markets), dim_locations
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-- covers all locations with population ≥ 1K so zero-court Gemeinden score fully.
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--
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-- Enriched with:
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-- stg_income → country-level median income PPS
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-- stg_padel_courts → padel venue count + nearest court distance (km)
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-- stg_tennis_courts → tennis court count within 25km radius
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--
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-- Distance calculations use ST_Distance_Sphere (DuckDB spatial extension).
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-- A bounding-box pre-filter (~0.5°, ≈55km) reduces the cross-join before the
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-- exact sphere distance is computed.
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MODEL (
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name foundation.dim_locations,
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kind FULL,
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cron '@daily',
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grain (country_code, geoname_id)
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);
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WITH
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-- Base: all GeoNames locations with valid coordinates
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locations AS (
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SELECT
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geoname_id,
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city_name AS location_name,
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-- URL-safe location slug
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LOWER(REGEXP_REPLACE(LOWER(city_name), '[^a-z0-9]+', '-')) AS location_slug,
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country_code,
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lat,
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lon,
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admin1_code,
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admin2_code,
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population,
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population_year AS population_year,
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ref_year
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FROM staging.stg_population_geonames
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WHERE lat IS NOT NULL AND lon IS NOT NULL
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),
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-- Country income (same source and pattern as dim_cities)
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country_income AS (
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SELECT country_code, median_income_pps, ref_year AS income_year
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FROM staging.stg_income
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QUALIFY ROW_NUMBER() OVER (PARTITION BY country_code ORDER BY ref_year DESC) = 1
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),
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-- Padel court lat/lon for distance and density calculations
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padel_courts AS (
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SELECT lat, lon, country_code
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FROM staging.stg_padel_courts
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WHERE lat IS NOT NULL AND lon IS NOT NULL
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),
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-- Nearest padel court distance per location (bbox pre-filter → exact sphere distance)
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nearest_padel AS (
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SELECT
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l.geoname_id,
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MIN(
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ST_Distance_Sphere(
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ST_Point(l.lon, l.lat),
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ST_Point(p.lon, p.lat)
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) / 1000.0
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) AS nearest_padel_court_km
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FROM locations l
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JOIN padel_courts p
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-- ~55km bounding box pre-filter to limit cross-join before sphere calc
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ON ABS(l.lat - p.lat) < 0.5
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AND ABS(l.lon - p.lon) < 0.5
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GROUP BY l.geoname_id
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),
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-- Padel venues within 5km of each location (counts as "local padel supply")
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padel_local AS (
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SELECT
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l.geoname_id,
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COUNT(*) AS padel_venue_count
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FROM locations l
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JOIN padel_courts p
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ON ABS(l.lat - p.lat) < 0.05 -- ~5km bbox pre-filter
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AND ABS(l.lon - p.lon) < 0.05
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WHERE ST_Distance_Sphere(
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ST_Point(l.lon, l.lat),
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ST_Point(p.lon, p.lat)
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) / 1000.0 <= 5.0
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GROUP BY l.geoname_id
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),
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-- Tennis courts within 25km of each location (sports culture proxy)
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tennis_nearby AS (
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SELECT
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l.geoname_id,
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COUNT(*) AS tennis_courts_within_25km
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FROM locations l
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JOIN staging.stg_tennis_courts t
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ON ABS(l.lat - t.lat) < 0.23 -- ~25km bbox pre-filter
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AND ABS(l.lon - t.lon) < 0.23
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WHERE ST_Distance_Sphere(
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ST_Point(l.lon, l.lat),
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ST_Point(t.lon, t.lat)
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) / 1000.0 <= 25.0
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GROUP BY l.geoname_id
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)
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SELECT
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l.geoname_id,
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l.country_code,
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-- Human-readable country name (consistent with dim_cities)
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CASE l.country_code
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WHEN 'DE' THEN 'Germany'
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WHEN 'ES' THEN 'Spain'
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WHEN 'GB' THEN 'United Kingdom'
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WHEN 'FR' THEN 'France'
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WHEN 'IT' THEN 'Italy'
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WHEN 'PT' THEN 'Portugal'
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WHEN 'AT' THEN 'Austria'
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WHEN 'CH' THEN 'Switzerland'
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WHEN 'NL' THEN 'Netherlands'
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WHEN 'BE' THEN 'Belgium'
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WHEN 'SE' THEN 'Sweden'
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WHEN 'NO' THEN 'Norway'
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WHEN 'DK' THEN 'Denmark'
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WHEN 'FI' THEN 'Finland'
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WHEN 'US' THEN 'United States'
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WHEN 'AR' THEN 'Argentina'
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WHEN 'MX' THEN 'Mexico'
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WHEN 'AE' THEN 'UAE'
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WHEN 'AU' THEN 'Australia'
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WHEN 'IE' THEN 'Ireland'
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ELSE l.country_code
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END AS country_name_en,
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-- URL-safe country slug
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LOWER(REGEXP_REPLACE(
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CASE l.country_code
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WHEN 'DE' THEN 'Germany'
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WHEN 'ES' THEN 'Spain'
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WHEN 'GB' THEN 'United Kingdom'
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WHEN 'FR' THEN 'France'
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WHEN 'IT' THEN 'Italy'
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WHEN 'PT' THEN 'Portugal'
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WHEN 'AT' THEN 'Austria'
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WHEN 'CH' THEN 'Switzerland'
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WHEN 'NL' THEN 'Netherlands'
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WHEN 'BE' THEN 'Belgium'
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WHEN 'SE' THEN 'Sweden'
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WHEN 'NO' THEN 'Norway'
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WHEN 'DK' THEN 'Denmark'
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WHEN 'FI' THEN 'Finland'
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WHEN 'US' THEN 'United States'
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WHEN 'AR' THEN 'Argentina'
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WHEN 'MX' THEN 'Mexico'
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WHEN 'AE' THEN 'UAE'
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WHEN 'AU' THEN 'Australia'
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WHEN 'IE' THEN 'Ireland'
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ELSE l.country_code
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END, '[^a-zA-Z0-9]+', '-'
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)) AS country_slug,
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l.location_name,
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l.location_slug,
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l.lat,
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l.lon,
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l.admin1_code,
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l.admin2_code,
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l.population,
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l.ref_year AS population_year,
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ci.median_income_pps,
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ci.income_year,
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COALESCE(pl.padel_venue_count, 0)::INTEGER AS padel_venue_count,
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-- Venues per 100K residents (NULL if population = 0)
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CASE WHEN l.population > 0
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THEN ROUND(COALESCE(pl.padel_venue_count, 0)::DOUBLE / l.population * 100000, 2)
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ELSE NULL
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END AS padel_venues_per_100k,
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np.nearest_padel_court_km,
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COALESCE(tn.tennis_courts_within_25km, 0)::INTEGER AS tennis_courts_within_25km,
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CURRENT_DATE AS refreshed_date
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FROM locations l
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LEFT JOIN country_income ci ON l.country_code = ci.country_code
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LEFT JOIN nearest_padel np ON l.geoname_id = np.geoname_id
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LEFT JOIN padel_local pl ON l.geoname_id = pl.geoname_id
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LEFT JOIN tennis_nearby tn ON l.geoname_id = tn.geoname_id
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-- Enforce grain: deduplicate if city slug collides within same country
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QUALIFY ROW_NUMBER() OVER (
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PARTITION BY l.country_code, l.geoname_id
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ORDER BY l.population DESC NULLS LAST
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) = 1
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