4d29ecf1d6
# Conflicts: # CHANGELOG.md # transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql
244 lines
8.9 KiB
SQL
244 lines
8.9 KiB
SQL
-- Unified location profile: both scores at (country_code, geoname_id) grain.
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-- Base: dim_locations (ALL GeoNames locations, pop ≥ 1K, ~140K rows).
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-- Enriched with dim_cities (city_slug, city_name, exact venue count) and
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-- venue_pricing_benchmarks (Playtomic pricing/occupancy).
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--
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-- Two scores per location:
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--
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-- Padelnomics Market Score (Marktreife-Score v3, 0–100):
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-- "How mature/established is this padel market?"
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-- Only meaningful for locations matched to a dim_cities row (city_slug IS NOT NULL)
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-- with padel venues. 0 for all other locations.
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--
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-- 40 pts supply development — log-scaled density (LN ceiling 20/100k) × count gate
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-- 25 pts demand evidence — occupancy when available; 40% density proxy otherwise
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-- 15 pts addressable market — log-scaled population, ceiling 1M
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-- 10 pts economic context — income PPS normalised to 200 ceiling
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-- 10 pts data quality — completeness discount
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--
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-- Padelnomics Opportunity Score (Marktpotenzial-Score v3, 0–100):
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-- "Where should I build a padel court?"
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-- Computed for ALL locations — zero-court locations score highest on supply gap.
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-- H3 catchment methodology: addressable market and supply gap use a regional
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-- H3 catchment (res-4 cell + 6 neighbours, ~462km², ~15-18km radius).
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--
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-- 25 pts addressable market — log-scaled catchment population, ceiling 500K
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-- 20 pts economic power — income PPS, normalised to 35,000
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-- 30 pts supply gap — inverted catchment venue density; 0 courts = full marks
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-- 15 pts catchment gap — distance to nearest padel court
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-- 10 pts sports culture — tennis courts within 25km
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--
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-- Consumers query directly with WHERE filters:
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-- cities API: WHERE country_slug = ? AND city_slug IS NOT NULL
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-- opportunity API: WHERE country_slug = ? AND opportunity_score > 0
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-- planner_defaults: WHERE city_slug IS NOT NULL
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-- pseo_*: WHERE city_slug IS NOT NULL AND city_padel_venue_count > 0
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MODEL (
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name serving.location_profiles,
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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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-- All locations from dim_locations (superset)
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base AS (
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SELECT
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l.geoname_id,
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l.country_code,
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l.country_name_en,
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l.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.population_year,
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l.median_income_pps,
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l.income_year,
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l.padel_venue_count,
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l.padel_venues_per_100k,
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l.nearest_padel_court_km,
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l.tennis_courts_within_25km,
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l.h3_cell_res4
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FROM foundation.dim_locations l
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),
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-- Aggregate population and court counts per H3 cell (res 4, ~10km edge).
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-- Grouping by cell first (~30-50K distinct cells vs 140K locations) keeps the
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-- subsequent lateral join small.
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hex_stats AS (
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SELECT
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h3_cell_res4,
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SUM(population) AS hex_population,
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SUM(padel_venue_count) AS hex_padel_courts
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FROM foundation.dim_locations
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GROUP BY h3_cell_res4
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),
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-- For each location, sum hex_stats across the cell + 6 neighbours (k_ring=1).
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-- Effective catchment: ~462km², ~15-18km radius — realistic driving distance.
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catchment AS (
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SELECT
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l.geoname_id,
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SUM(hs.hex_population) AS catchment_population,
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SUM(hs.hex_padel_courts) AS catchment_padel_courts
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FROM base l,
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LATERAL (SELECT UNNEST(h3_grid_disk(l.h3_cell_res4, 1)) AS cell) ring
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JOIN hex_stats hs ON hs.h3_cell_res4 = ring.cell
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GROUP BY l.geoname_id
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),
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-- Match dim_cities via (country_code, geoname_id) to get city_slug + exact venue count.
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-- QUALIFY handles rare multi-city-per-geoname collisions (keep highest venue count).
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city_match AS (
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SELECT
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c.country_code,
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c.geoname_id,
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c.city_slug,
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c.city_name,
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c.padel_venue_count AS city_padel_venue_count
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FROM foundation.dim_cities c
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WHERE c.geoname_id IS NOT NULL
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QUALIFY ROW_NUMBER() OVER (
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PARTITION BY c.country_code, c.geoname_id
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ORDER BY c.padel_venue_count DESC
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) = 1
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),
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-- Pricing / occupancy from Playtomic (via city_slug) + H3 catchment
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with_pricing AS (
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SELECT
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b.*,
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cm.city_slug,
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cm.city_name,
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cm.city_padel_venue_count,
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vpb.median_hourly_rate,
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vpb.median_peak_rate,
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vpb.median_offpeak_rate,
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vpb.median_occupancy_rate,
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vpb.median_daily_revenue_per_venue,
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vpb.price_currency,
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COALESCE(ct.catchment_population, b.population)::BIGINT AS catchment_population,
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COALESCE(ct.catchment_padel_courts, b.padel_venue_count)::INTEGER AS catchment_padel_courts
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FROM base b
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LEFT JOIN city_match cm
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ON b.country_code = cm.country_code
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AND b.geoname_id = cm.geoname_id
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LEFT JOIN serving.venue_pricing_benchmarks vpb
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ON cm.country_code = vpb.country_code
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AND cm.city_slug = vpb.city_slug
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LEFT JOIN catchment ct
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ON b.geoname_id = ct.geoname_id
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),
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-- Both scores computed from the enriched base
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scored AS (
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SELECT *,
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-- City-level venue density (from dim_cities exact count, not dim_locations spatial 5km)
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CASE WHEN population > 0
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THEN ROUND(COALESCE(city_padel_venue_count, 0)::DOUBLE / population * 100000, 2)
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ELSE NULL
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END AS city_venues_per_100k,
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-- Data confidence (for market_score)
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CASE
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WHEN population > 0 AND COALESCE(city_padel_venue_count, 0) > 0 THEN 1.0
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WHEN population > 0 OR COALESCE(city_padel_venue_count, 0) > 0 THEN 0.5
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ELSE 0.0
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END AS data_confidence,
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-- ── Market Score (Marktreife-Score v3) ──────────────────────────────────
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-- 0 when no city match or no venues (city_padel_venue_count NULL or 0)
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CASE WHEN COALESCE(city_padel_venue_count, 0) > 0 THEN
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ROUND(
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-- Supply development (40 pts)
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40.0 * LEAST(1.0, LN(
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COALESCE(
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CASE WHEN population > 0
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THEN COALESCE(city_padel_venue_count, 0)::DOUBLE / population * 100000
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ELSE 0 END
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, 0) + 1) / LN(21))
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* LEAST(1.0, COALESCE(city_padel_venue_count, 0) / 5.0)
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-- Demand evidence (25 pts)
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+ 25.0 * CASE
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WHEN median_occupancy_rate IS NOT NULL
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THEN LEAST(1.0, median_occupancy_rate / 0.65)
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ELSE 0.4 * LEAST(1.0, LN(
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COALESCE(
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CASE WHEN population > 0
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THEN COALESCE(city_padel_venue_count, 0)::DOUBLE / population * 100000
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ELSE 0 END
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, 0) + 1) / LN(21))
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* LEAST(1.0, COALESCE(city_padel_venue_count, 0) / 5.0)
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END
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-- Addressable market (15 pts)
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+ 15.0 * LEAST(1.0, LN(GREATEST(population, 1)) / LN(1000000))
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-- Economic context (10 pts)
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+ 10.0 * LEAST(1.0, COALESCE(median_income_pps, 100) / 200.0)
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-- Data quality (10 pts)
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+ 10.0 * CASE
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WHEN population > 0 AND COALESCE(city_padel_venue_count, 0) > 0 THEN 1.0
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WHEN population > 0 OR COALESCE(city_padel_venue_count, 0) > 0 THEN 0.5
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ELSE 0.0
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END
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, 1)
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ELSE 0
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END AS market_score,
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-- ── Opportunity Score (Marktpotenzial-Score v3, H3 catchment) ──────────
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ROUND(
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-- Addressable market (25 pts): log-scaled catchment population, ceiling 500K
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25.0 * LEAST(1.0, LN(GREATEST(catchment_population, 1)) / LN(500000))
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-- Economic power (20 pts): income PPS normalised to 35,000
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+ 20.0 * LEAST(1.0, COALESCE(median_income_pps, 15000) / 35000.0)
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-- Supply gap (30 pts): inverted catchment venue density
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+ 30.0 * GREATEST(0.0, 1.0 - COALESCE(
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CASE WHEN catchment_population > 0
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THEN catchment_padel_courts::DOUBLE / catchment_population * 100000
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ELSE 0.0
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END, 0.0) / 8.0)
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-- Catchment gap (15 pts): distance to nearest court
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+ 15.0 * COALESCE(LEAST(1.0, nearest_padel_court_km / 30.0), 0.5)
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-- Sports culture (10 pts): tennis courts within 25km
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+ 10.0 * LEAST(1.0, tennis_courts_within_25km / 10.0)
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, 1) AS opportunity_score
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FROM with_pricing
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)
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SELECT
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s.geoname_id,
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s.country_code,
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s.country_name_en,
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s.country_slug,
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s.location_name,
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s.location_slug,
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s.city_slug,
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s.city_name,
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s.lat,
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s.lon,
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s.admin1_code,
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s.admin2_code,
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s.population,
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s.population_year,
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s.median_income_pps,
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s.income_year,
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s.padel_venue_count,
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s.padel_venues_per_100k,
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s.nearest_padel_court_km,
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s.tennis_courts_within_25km,
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s.city_padel_venue_count,
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s.city_venues_per_100k,
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s.data_confidence,
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s.catchment_population,
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s.catchment_padel_courts,
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CASE WHEN s.catchment_population > 0
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THEN ROUND(s.catchment_padel_courts::DOUBLE / s.catchment_population * 100000, 2)
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ELSE NULL
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END AS catchment_venues_per_100k,
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s.market_score,
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s.opportunity_score,
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s.median_hourly_rate,
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s.median_peak_rate,
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s.median_offpeak_rate,
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s.median_occupancy_rate,
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s.median_daily_revenue_per_venue,
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s.price_currency,
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CURRENT_DATE AS refreshed_date
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FROM scored s
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ORDER BY s.market_score DESC, s.opportunity_score DESC
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