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merge: unified location_profiles serving model + both scores on map tooltips
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# Conflicts:
#	CHANGELOG.md
#	transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql
This commit is contained in:
Deeman
2026-03-06 14:03:55 +01:00
parent 4fbd91b59b a3b4e1fab6
commit 4d29ecf1d6
19 changed files with 320 additions and 315 deletions
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4
transform/sqlmesh_padelnomics/models/foundation/dim_cities.sql
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@@ -2,7 +2,7 @@
-- Built from venue locations (dim_venues) as the primary source — padelnomics
-- tracks cities where padel venues actually exist, not an administrative city list.
--
-- Conformed dimension: used by city_market_profile and all pSEO serving models.
-- Conformed dimension: used by location_profiles and all pSEO serving models.
-- Integrates four sources:
-- dim_venues → city list, venue count, coordinates (Playtomic + OSM)
-- foundation.dim_countries → country_name_en, country_slug, median_income_pps
@@ -128,7 +128,7 @@ SELECT
vc.padel_venue_count,
c.median_income_pps,
c.income_year,
-- GeoNames ID: FK to dim_locations / location_opportunity_profile.
-- GeoNames ID: FK to dim_locations / location_profiles.
-- String match preferred; spatial fallback used when name doesn't match (Milano→Milan, etc.)
COALESCE(gn.geoname_id, gs.spatial_geoname_id) AS geoname_id
FROM venue_cities vc

2
transform/sqlmesh_padelnomics/models/serving/README.md
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@@ -3,4 +3,4 @@
Analytics-ready views consumed by the web app and programmatic SEO.
Query these from `analytics.py` via DuckDB read-only connection.
Naming convention: `serving.<purpose>` (e.g. `serving.city_market_profile`)
Naming convention: `serving.<purpose>` (e.g. `serving.location_profiles`)

117
transform/sqlmesh_padelnomics/models/serving/city_market_profile.sql
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@@ -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

134
transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql
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@@ -1,134 +0,0 @@
-- Per-location padel investment opportunity intelligence.
-- Consumed by: Gemeinde-level pSEO pages, opportunity map, "top markets" lists.
--
-- Padelnomics Marktpotenzial-Score v3 (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).
--
-- H3 catchment methodology (v3):
-- Addressable market and supply gap now use a regional catchment lens rather than
-- the location's own population/court count. Each location is assigned an H3 cell
-- at resolution 4 (~10km center-to-center). Catchment = cell + 6 neighbours (k_ring=1),
-- covering ~462km² — roughly a 15-18km radius, matching realistic driving distance.
-- Population and court counts are first aggregated per H3 cell (hex_stats CTE), then
-- summed across the 7-cell ring (catchment CTE) to avoid scanning all 140K locations
-- per location.
--
-- 25 pts addressable market — log-scaled catchment population, ceiling 500K
-- (opportunity peaks in mid-size catchments; 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 catchment venue density; 0 courts/100K = full marks.
-- Ceiling 8/100K for a gentler gradient and to account for
-- ~87% data undercount vs FIP totals.
-- Linear: GREATEST(0, 1 - catchment_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)
);
WITH
-- Aggregate population and court counts per H3 cell (res 4, ~10km edge).
-- Grouping by cell first (~30-50K distinct cells vs 140K locations) keeps the
-- subsequent lateral join small.
hex_stats AS (
SELECT
h3_cell_res4,
SUM(population) AS hex_population,
SUM(padel_venue_count) AS hex_padel_courts
FROM foundation.dim_locations
GROUP BY h3_cell_res4
),
-- For each location, sum hex_stats across the cell + 6 neighbours (k_ring=1).
-- Effective catchment: ~462km², ~15-18km radius — realistic driving distance.
catchment AS (
SELECT
l.geoname_id,
SUM(hs.hex_population) AS catchment_population,
SUM(hs.hex_padel_courts) AS catchment_padel_courts
FROM foundation.dim_locations l,
LATERAL (SELECT UNNEST(h3_grid_disk(l.h3_cell_res4, 1)) AS cell) ring
JOIN hex_stats hs ON hs.h3_cell_res4 = ring.cell
GROUP BY l.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,
-- Catchment metrics (H3 res-4 cell + 6 neighbours, ~15-18km radius)
COALESCE(c.catchment_population, l.population)::BIGINT AS catchment_population,
COALESCE(c.catchment_padel_courts, l.padel_venue_count)::INTEGER AS catchment_padel_courts,
CASE WHEN COALESCE(c.catchment_population, l.population) > 0
THEN ROUND(
COALESCE(c.catchment_padel_courts, l.padel_venue_count)::DOUBLE
/ COALESCE(c.catchment_population, l.population) * 100000, 2)
ELSE NULL
END AS catchment_venues_per_100k,
ROUND(
-- Addressable market (25 pts): log-scaled catchment population, ceiling 500K.
-- v3: uses H3 catchment population (cell + 6 neighbours, ~15-18km radius) instead
-- of local city population, so mid-size cities surrounded by dense Gemeinden score
-- correctly (e.g. Oldenburg pulls in Ammerland, Wesermarsch, etc.).
25.0 * LEAST(1.0, LN(GREATEST(COALESCE(c.catchment_population, 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 catchment venue density.
-- v3: uses catchment courts / catchment population instead of local 5km count / city pop.
-- 0 courts/100K across the ~15-18km ring = full 30 pts (genuine white space).
-- ≥8/100K = 0 pts (well-served regional market).
+ 30.0 * GREATEST(0.0, 1.0 - COALESCE(
CASE WHEN COALESCE(c.catchment_population, l.population) > 0
THEN COALESCE(c.catchment_padel_courts, l.padel_venue_count)::DOUBLE
/ COALESCE(c.catchment_population, l.population) * 100000
ELSE 0.0
END, 0.0) / 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
LEFT JOIN catchment c ON c.geoname_id = l.geoname_id
ORDER BY opportunity_score DESC

243
transform/sqlmesh_padelnomics/models/serving/location_profiles.sql Normal file
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@@ -0,0 +1,243 @@
-- Unified location profile: both scores at (country_code, geoname_id) grain.
-- Base: dim_locations (ALL GeoNames locations, pop ≥ 1K, ~140K rows).
-- Enriched with dim_cities (city_slug, city_name, exact venue count) and
-- venue_pricing_benchmarks (Playtomic pricing/occupancy).
--
-- Two scores per location:
--
-- Padelnomics Market Score (Marktreife-Score v3, 0–100):
-- "How mature/established is this padel market?"
-- Only meaningful for locations matched to a dim_cities row (city_slug IS NOT NULL)
-- with padel venues. 0 for all other locations.
--
-- 40 pts supply development — log-scaled density (LN ceiling 20/100k) × count gate
-- 25 pts demand evidence — occupancy when available; 40% density proxy otherwise
-- 15 pts addressable market — log-scaled population, ceiling 1M
-- 10 pts economic context — income PPS normalised to 200 ceiling
-- 10 pts data quality — completeness discount
--
-- Padelnomics Opportunity Score (Marktpotenzial-Score v3, 0–100):
-- "Where should I build a padel court?"
-- Computed for ALL locations — zero-court locations score highest on supply gap.
-- H3 catchment methodology: addressable market and supply gap use a regional
-- H3 catchment (res-4 cell + 6 neighbours, ~462km², ~15-18km radius).
--
-- 25 pts addressable market — log-scaled catchment population, ceiling 500K
-- 20 pts economic power — income PPS, normalised to 35,000
-- 30 pts supply gap — inverted catchment venue density; 0 courts = full marks
-- 15 pts catchment gap — distance to nearest padel court
-- 10 pts sports culture — tennis courts within 25km
--
-- Consumers query directly with WHERE filters:
-- cities API: WHERE country_slug = ? AND city_slug IS NOT NULL
-- opportunity API: WHERE country_slug = ? AND opportunity_score > 0
-- planner_defaults: WHERE city_slug IS NOT NULL
-- pseo_*: WHERE city_slug IS NOT NULL AND city_padel_venue_count > 0
MODEL (
name serving.location_profiles,
kind FULL,
cron '@daily',
grain (country_code, geoname_id)
);
WITH
-- All locations from dim_locations (superset)
base AS (
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,
l.h3_cell_res4
FROM foundation.dim_locations l
),
-- Aggregate population and court counts per H3 cell (res 4, ~10km edge).
-- Grouping by cell first (~30-50K distinct cells vs 140K locations) keeps the
-- subsequent lateral join small.
hex_stats AS (
SELECT
h3_cell_res4,
SUM(population) AS hex_population,
SUM(padel_venue_count) AS hex_padel_courts
FROM foundation.dim_locations
GROUP BY h3_cell_res4
),
-- For each location, sum hex_stats across the cell + 6 neighbours (k_ring=1).
-- Effective catchment: ~462km², ~15-18km radius — realistic driving distance.
catchment AS (
SELECT
l.geoname_id,
SUM(hs.hex_population) AS catchment_population,
SUM(hs.hex_padel_courts) AS catchment_padel_courts
FROM base l,
LATERAL (SELECT UNNEST(h3_grid_disk(l.h3_cell_res4, 1)) AS cell) ring
JOIN hex_stats hs ON hs.h3_cell_res4 = ring.cell
GROUP BY l.geoname_id
),
-- Match dim_cities via (country_code, geoname_id) to get city_slug + exact venue count.
-- QUALIFY handles rare multi-city-per-geoname collisions (keep highest venue count).
city_match AS (
SELECT
c.country_code,
c.geoname_id,
c.city_slug,
c.city_name,
c.padel_venue_count AS city_padel_venue_count
FROM foundation.dim_cities c
WHERE c.geoname_id IS NOT NULL
QUALIFY ROW_NUMBER() OVER (
PARTITION BY c.country_code, c.geoname_id
ORDER BY c.padel_venue_count DESC
) = 1
),
-- Pricing / occupancy from Playtomic (via city_slug) + H3 catchment
with_pricing AS (
SELECT
b.*,
cm.city_slug,
cm.city_name,
cm.city_padel_venue_count,
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,
COALESCE(ct.catchment_population, b.population)::BIGINT AS catchment_population,
COALESCE(ct.catchment_padel_courts, b.padel_venue_count)::INTEGER AS catchment_padel_courts
FROM base b
LEFT JOIN city_match cm
ON b.country_code = cm.country_code
AND b.geoname_id = cm.geoname_id
LEFT JOIN serving.venue_pricing_benchmarks vpb
ON cm.country_code = vpb.country_code
AND cm.city_slug = vpb.city_slug
LEFT JOIN catchment ct
ON b.geoname_id = ct.geoname_id
),
-- Both scores computed from the enriched base
scored AS (
SELECT *,
-- City-level venue density (from dim_cities exact count, not dim_locations spatial 5km)
CASE WHEN population > 0
THEN ROUND(COALESCE(city_padel_venue_count, 0)::DOUBLE / population * 100000, 2)
ELSE NULL
END AS city_venues_per_100k,
-- Data confidence (for market_score)
CASE
WHEN population > 0 AND COALESCE(city_padel_venue_count, 0) > 0 THEN 1.0
WHEN population > 0 OR COALESCE(city_padel_venue_count, 0) > 0 THEN 0.5
ELSE 0.0
END AS data_confidence,
-- ── Market Score (Marktreife-Score v3) ──────────────────────────────────
-- 0 when no city match or no venues (city_padel_venue_count NULL or 0)
CASE WHEN COALESCE(city_padel_venue_count, 0) > 0 THEN
ROUND(
-- Supply development (40 pts)
40.0 * LEAST(1.0, LN(
COALESCE(
CASE WHEN population > 0
THEN COALESCE(city_padel_venue_count, 0)::DOUBLE / population * 100000
ELSE 0 END
, 0) + 1) / LN(21))
* LEAST(1.0, COALESCE(city_padel_venue_count, 0) / 5.0)
-- Demand evidence (25 pts)
+ 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(
CASE WHEN population > 0
THEN COALESCE(city_padel_venue_count, 0)::DOUBLE / population * 100000
ELSE 0 END
, 0) + 1) / LN(21))
* LEAST(1.0, COALESCE(city_padel_venue_count, 0) / 5.0)
END
-- Addressable market (15 pts)
+ 15.0 * LEAST(1.0, LN(GREATEST(population, 1)) / LN(1000000))
-- Economic context (10 pts)
+ 10.0 * LEAST(1.0, COALESCE(median_income_pps, 100) / 200.0)
-- Data quality (10 pts)
+ 10.0 * CASE
WHEN population > 0 AND COALESCE(city_padel_venue_count, 0) > 0 THEN 1.0
WHEN population > 0 OR COALESCE(city_padel_venue_count, 0) > 0 THEN 0.5
ELSE 0.0
END
, 1)
ELSE 0
END AS market_score,
-- ── Opportunity Score (Marktpotenzial-Score v3, H3 catchment) ──────────
ROUND(
-- Addressable market (25 pts): log-scaled catchment population, ceiling 500K
25.0 * LEAST(1.0, LN(GREATEST(catchment_population, 1)) / LN(500000))
-- Economic power (20 pts): income PPS normalised to 35,000
+ 20.0 * LEAST(1.0, COALESCE(median_income_pps, 15000) / 35000.0)
-- Supply gap (30 pts): inverted catchment venue density
+ 30.0 * GREATEST(0.0, 1.0 - COALESCE(
CASE WHEN catchment_population > 0
THEN catchment_padel_courts::DOUBLE / catchment_population * 100000
ELSE 0.0
END, 0.0) / 8.0)
-- Catchment gap (15 pts): distance to nearest court
+ 15.0 * COALESCE(LEAST(1.0, nearest_padel_court_km / 30.0), 0.5)
-- Sports culture (10 pts): tennis courts within 25km
+ 10.0 * LEAST(1.0, tennis_courts_within_25km / 10.0)
, 1) AS opportunity_score
FROM with_pricing
)
SELECT
s.geoname_id,
s.country_code,
s.country_name_en,
s.country_slug,
s.location_name,
s.location_slug,
s.city_slug,
s.city_name,
s.lat,
s.lon,
s.admin1_code,
s.admin2_code,
s.population,
s.population_year,
s.median_income_pps,
s.income_year,
s.padel_venue_count,
s.padel_venues_per_100k,
s.nearest_padel_court_km,
s.tennis_courts_within_25km,
s.city_padel_venue_count,
s.city_venues_per_100k,
s.data_confidence,
s.catchment_population,
s.catchment_padel_courts,
CASE WHEN s.catchment_population > 0
THEN ROUND(s.catchment_padel_courts::DOUBLE / s.catchment_population * 100000, 2)
ELSE NULL
END AS catchment_venues_per_100k,
s.market_score,
s.opportunity_score,
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,
CURRENT_DATE AS refreshed_date
FROM scored s
ORDER BY s.market_score DESC, s.opportunity_score DESC

7
transform/sqlmesh_padelnomics/models/serving/planner_defaults.sql
View File

@@ -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
FROM serving.city_market_profile
city_venues_per_100k AS venues_per_100k
FROM serving.location_profiles
WHERE city_slug IS NOT NULL
)
SELECT
cp.city_slug,

14
transform/sqlmesh_padelnomics/models/serving/pseo_city_costs_de.sql
View File

@@ -31,10 +31,10 @@ SELECT
c.lon,
-- Market metrics
c.population,
c.padel_venue_count,
c.venues_per_100k,
c.city_padel_venue_count AS padel_venue_count,
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)

13
transform/sqlmesh_padelnomics/models/serving/pseo_city_pricing.sql
View File

@@ -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.venues_per_100k,
c.city_padel_venue_count AS padel_venue_count,
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
INNER JOIN serving.city_market_profile c
-- Join location_profiles to get canonical city metadata
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