refactor(serving): replace old models with location_profiles

Delete city_market_profile.sql and location_opportunity_profile.sql.
Update downstream models (planner_defaults, pseo_city_costs_de,
pseo_city_pricing) to read from location_profiles instead.

Subtask 2/5: delete old models + update downstream SQL.

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

transform/sqlmesh_padelnomics/models/serving/city_market_profile.sql

@@ -1,117 +0,0 @@
--- One Big Table: per-city padel market intelligence.
--- Consumed by: SEO article generation, planner city-select pre-fill, API endpoints.
---
--- Padelnomics Marktreife-Score v3 (0–100):
--- Answers "How mature/established is this padel market?"
--- Only computed for cities with ≥1 padel venue (padel_venue_count > 0).
--- For white-space opportunity scoring, see serving.location_opportunity_profile.
---
---   40 pts  supply development — log-scaled density (LN ceiling 20/100k) × count gate
---                                (min(1, count/5) kills small-town inflation)
---   25 pts  demand evidence   — occupancy when available; 40% density proxy otherwise
---   15 pts  addressable market — log-scaled population, ceiling 1M (context only)
---   10 pts  economic context  — income PPS normalised to 200 ceiling
---   10 pts  data quality      — completeness discount
---   No saturation discount: high density = maturity, not a penalty
-
-MODEL (
-  name serving.city_market_profile,
-  kind FULL,
-  cron '@daily',
-  grain (country_code, city_slug)
-);
-
-WITH base AS (
-  SELECT
-    c.country_code,
-    c.country_name_en,
-    c.country_slug,
-    c.city_name,
-    c.city_slug,
-    c.lat,
-    c.lon,
-    c.population,
-    c.population_year,
-    c.padel_venue_count,
-    c.median_income_pps,
-    c.income_year,
-    c.geoname_id,
-    -- Venue density: padel venues per 100K residents
-    CASE WHEN c.population > 0
-      THEN ROUND(c.padel_venue_count::DOUBLE / c.population * 100000, 2)
-      ELSE NULL
-    END                          AS venues_per_100k,
-    -- Data confidence: 1.0 if both population and venues are present
-    CASE
-      WHEN c.population > 0 AND c.padel_venue_count > 0 THEN 1.0
-      WHEN c.population > 0 OR  c.padel_venue_count > 0 THEN 0.5
-      ELSE 0.0
-    END                          AS data_confidence,
-    -- Pricing / occupancy from Playtomic (NULL when no availability data)
-    vpb.median_hourly_rate,
-    vpb.median_peak_rate,
-    vpb.median_offpeak_rate,
-    vpb.median_occupancy_rate,
-    vpb.median_daily_revenue_per_venue,
-    vpb.price_currency
-  FROM foundation.dim_cities c
-  LEFT JOIN serving.venue_pricing_benchmarks vpb
-    ON c.country_code = vpb.country_code
-    AND c.city_slug = vpb.city_slug
-  WHERE c.padel_venue_count > 0
-),
-scored AS (
-  SELECT *,
-    ROUND(
-      -- Supply development (40 pts): THE maturity signal.
-      -- Log-scaled density: LN(density+1)/LN(21) → 20/100k ≈ full marks.
-      -- Count gate: min(1, count/5) — 1 venue=20%, 5+ venues=100%.
-      -- Kills small-town inflation (1 court / 5k pop = 20/100k) without hard cutoffs.
-      40.0 * LEAST(1.0, LN(COALESCE(venues_per_100k, 0) + 1) / LN(21))
-           * LEAST(1.0, padel_venue_count / 5.0)
-      -- Demand evidence (25 pts): occupancy when Playtomic data available.
-      -- Fallback: 40% of density score (avoids double-counting with supply component).
-      + 25.0 * CASE
-          WHEN median_occupancy_rate IS NOT NULL
-            THEN LEAST(1.0, median_occupancy_rate / 0.65)
-          ELSE 0.4 * LEAST(1.0, LN(COALESCE(venues_per_100k, 0) + 1) / LN(21))
-                   * LEAST(1.0, padel_venue_count / 5.0)
-        END
-      -- Addressable market (15 pts): population as context, not maturity signal.
-      -- LN(1) = 0 so zero-pop cities score 0 here.
-      + 15.0 * LEAST(1.0, LN(GREATEST(population, 1)) / LN(1000000))
-      -- Economic context (10 pts): country-level income PPS.
-      -- Flat per country — kept as context modifier, not primary signal.
-      + 10.0 * LEAST(1.0, COALESCE(median_income_pps, 100) / 200.0)
-      -- Data quality (10 pts): completeness discount.
-      + 10.0 * data_confidence
-    , 1)
-                                 AS market_score
-  FROM base
-)
-SELECT
-  s.country_code,
-  s.country_name_en,
-  s.country_slug,
-  s.city_name,
-  s.city_slug,
-  s.lat,
-  s.lon,
-  s.population,
-  s.population_year,
-  s.padel_venue_count,
-  s.venues_per_100k,
-  s.data_confidence,
-  s.market_score,
-  s.median_income_pps,
-  s.income_year,
-  s.median_hourly_rate,
-  s.median_peak_rate,
-  s.median_offpeak_rate,
-  s.median_occupancy_rate,
-  s.median_daily_revenue_per_venue,
-  s.price_currency,
-  s.geoname_id,
-  CURRENT_DATE                   AS refreshed_date
-FROM scored s
-ORDER BY s.market_score DESC

transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql

@@ -1,86 +0,0 @@
--- Per-location padel investment opportunity intelligence.
--- Consumed by: Gemeinde-level pSEO pages, opportunity map, "top markets" lists.
---
--- Padelnomics Marktpotenzial-Score v2 (0–100):
--- Answers "Where should I build a padel court?"
--- Covers ALL GeoNames locations (pop ≥ 1K) — NOT filtered to existing padel markets.
--- Zero-court locations score highest on supply gap component (white space = opportunity).
---
---   25 pts  addressable market — log-scaled population, ceiling 500K
---           (opportunity peaks in mid-size cities; megacities already served)
---   20 pts  economic power     — country income PPS, normalised to 35,000
---                               EU PPS values range 18k-37k; /35k gives real spread.
---                               DE ≈ 13.2pts, ES ≈ 10.7pts, SE ≈ 14.3pts.
---                               Previously /200 caused all countries to saturate at 20/20.
---   30 pts  supply gap         — INVERTED venue density; 0 courts/100K = full marks.
---                               Ceiling raised to 8/100K (was 4) for a gentler gradient
---                               and to account for ~87% data undercount vs FIP totals.
---                               Linear: GREATEST(0, 1 - density/8)
---   15 pts  catchment gap      — distance to nearest padel court.
---                               DuckDB LEAST ignores NULLs: LEAST(1.0, NULL/30) = 1.0,
---                               so NULL nearest_km = full marks (no court in bounding box
---                               = high opportunity). COALESCE fallback is dead code.
---   10 pts  sports culture     — tennis courts within 25km (≥10 = full marks).
---                               NOTE: dim_locations tennis data is empty (all 0 rows).
---                               Component contributes 0 pts everywhere until data lands.
-
-MODEL (
-  name serving.location_opportunity_profile,
-  kind FULL,
-  cron '@daily',
-  grain (country_code, geoname_id)
-);
-
-SELECT
-  l.geoname_id,
-  l.country_code,
-  l.country_name_en,
-  l.country_slug,
-  l.location_name,
-  l.location_slug,
-  l.lat,
-  l.lon,
-  l.admin1_code,
-  l.admin2_code,
-  l.population,
-  l.population_year,
-  l.median_income_pps,
-  l.income_year,
-  l.padel_venue_count,
-  l.padel_venues_per_100k,
-  l.nearest_padel_court_km,
-  l.tennis_courts_within_25km,
-  ROUND(
-    -- Addressable market (25 pts): log-scaled to 500K ceiling.
-    -- Lower ceiling than Marktreife (1M) — opportunity peaks in mid-size cities
-    -- that can support a court but aren't already saturated by large-city operators.
-    25.0 * LEAST(1.0, LN(GREATEST(l.population, 1)) / LN(500000))
-
-    -- Economic power (20 pts): country-level income PPS normalised to 35,000.
-    -- Drives willingness-to-pay for court fees (€20-35/hr target range).
-    -- EU PPS values range 18k-37k; ceiling 35k gives meaningful spread.
-    -- v1 used /200 which caused LEAST(1.0, 115) = 1.0 for ALL countries (flat, no differentiation).
-    -- v2: /35000 → DE 0.66×20=13.2pts, ES 0.53×20=10.7pts, SE 0.71×20=14.3pts.
-    -- Default 15000 for missing data = reasonable developing-market assumption (~0.43).
-    + 20.0 * LEAST(1.0, COALESCE(l.median_income_pps, 15000) / 35000.0)
-
-    -- Supply gap (30 pts): INVERTED venue density.
-    -- 0 courts/100K = full 30 pts (white space); ≥8/100K = 0 pts (served market).
-    -- Ceiling raised from 4→8/100K for a gentler gradient and to account for data
-    -- undercount (~87% of real courts not in our data).
-    -- This is the key signal that separates Marktpotenzial from Marktreife.
-    + 30.0 * GREATEST(0.0, 1.0 - COALESCE(l.padel_venues_per_100k, 0) / 8.0)
-
-    -- Catchment gap (15 pts): distance to nearest existing padel court.
-    -- >30km = full 15 pts (underserved catchment area).
-    -- NULL = no courts found anywhere (rare edge case) → neutral 0.5.
-    + 15.0 * COALESCE(LEAST(1.0, l.nearest_padel_court_km / 30.0), 0.5)
-
-    -- Sports culture proxy (10 pts): tennis courts within 25km.
-    -- ≥10 courts = full 10 pts (proven racket sport market = faster padel adoption).
-    -- 0 courts = 0 pts. Many new padel courts open inside existing tennis clubs.
-    + 10.0 * LEAST(1.0, l.tennis_courts_within_25km / 10.0)
-  , 1)                                                                   AS opportunity_score,
-  CURRENT_DATE                                                           AS refreshed_date
-FROM foundation.dim_locations l
-ORDER BY opportunity_score DESC

transform/sqlmesh_padelnomics/models/serving/planner_defaults.sql

@@ -76,11 +76,12 @@ city_profiles AS (
     city_slug,
     country_code,
     city_name,
-    padel_venue_count,
+    city_padel_venue_count AS padel_venue_count,
     population,
     market_score,
-    venues_per_100k
+    city_venues_per_100k AS venues_per_100k
-  FROM serving.city_market_profile
+  FROM serving.location_profiles
+  WHERE city_slug IS NOT NULL
 )
 SELECT
   cp.city_slug,

transform/sqlmesh_padelnomics/models/serving/pseo_city_costs_de.sql

@@ -31,10 +31,10 @@ SELECT
   c.lon,
   -- Market metrics
   c.population,
-  c.padel_venue_count,
+  c.city_padel_venue_count AS padel_venue_count,
-  c.venues_per_100k,
+  c.city_venues_per_100k AS venues_per_100k,
   c.market_score,
-  lop.opportunity_score,
+  c.opportunity_score,
   c.data_confidence,
   -- Pricing (from Playtomic, NULL when no coverage)
   c.median_hourly_rate,
@@ -85,15 +85,13 @@ SELECT
   cc.working_capital                AS "workingCapital",
   cc.permits_compliance             AS "permitsCompliance",
   CURRENT_DATE                      AS refreshed_date
-FROM serving.city_market_profile c
+FROM serving.location_profiles c
 LEFT JOIN serving.planner_defaults p
   ON c.country_code = p.country_code
   AND c.city_slug = p.city_slug
-LEFT JOIN serving.location_opportunity_profile lop
-  ON c.country_code = lop.country_code
-  AND c.geoname_id = lop.geoname_id
 LEFT JOIN foundation.dim_countries cc
   ON c.country_code = cc.country_code
 -- Only cities with actual padel presence and at least some rate data
-WHERE c.padel_venue_count > 0
+WHERE c.city_slug IS NOT NULL
+  AND c.city_padel_venue_count > 0
   AND (p.rate_peak IS NOT NULL OR c.median_peak_rate IS NOT NULL)

transform/sqlmesh_padelnomics/models/serving/pseo_city_pricing.sql

@@ -1,6 +1,6 @@
 -- pSEO article data: per-city padel court pricing.
 -- One row per city — consumed by the city-pricing.md.jinja template.
--- Joins venue_pricing_benchmarks (real Playtomic data) with city_market_profile
+-- Joins venue_pricing_benchmarks (real Playtomic data) with location_profiles
 -- (population, venue count, country metadata).
 --
 -- Stricter filter than pseo_city_costs_de: requires >= 2 venues with real
@@ -16,7 +16,7 @@ MODEL (
 SELECT
   -- Composite natural key: country_slug + city_slug ensures uniqueness across countries
   c.country_slug || '-' || c.city_slug  AS city_key,
-  -- City identity (from city_market_profile, which has the canonical city_slug)
+  -- City identity (from location_profiles, which has the canonical city_slug)
   c.city_slug,
   c.city_name,
   c.country_code,
@@ -24,8 +24,8 @@ SELECT
   c.country_slug,
   -- Market context
   c.population,
-  c.padel_venue_count,
+  c.city_padel_venue_count AS padel_venue_count,
-  c.venues_per_100k,
+  c.city_venues_per_100k AS venues_per_100k,
   c.market_score,
   -- Pricing benchmarks (from Playtomic availability data)
   vpb.median_hourly_rate,
@@ -38,9 +38,10 @@ SELECT
   vpb.price_currency,
   CURRENT_DATE AS refreshed_date
 FROM serving.venue_pricing_benchmarks vpb
--- Join city_market_profile to get the canonical city_slug and country metadata
+-- Join location_profiles to get canonical city metadata
-INNER JOIN serving.city_market_profile c
+INNER JOIN serving.location_profiles c
   ON  vpb.country_code = c.country_code
   AND vpb.city_slug = c.city_slug
+  AND c.city_slug IS NOT NULL
 -- Only cities with enough venues for meaningful pricing statistics
 WHERE vpb.venue_count >= 2