9f8ca82505
Adds sqlmesh_padelnomics UV workspace member at transform/sqlmesh_padelnomics/.
DuckDB gateway, LANDING_DIR variable, @daily cron on all models.
Raw layer (reads landing zone gzip JSON):
raw_overpass_courts — OSM padel court elements (nodes with lat/lon/tags)
raw_playtomic_tenants — Playtomic venue records (tenant_id, location, name)
raw_eurostat_population — Eurostat urb_cpop1 city population (unpivoted)
Staging layer (typed, deduped, country-resolved):
stg_padel_courts — OSM nodes only, ~100m bbox country approximation
stg_playtomic_venues — deduplicated Playtomic venues
stg_population — city population by year with integer types
Foundation layer:
dim_venues — deduped union of OSM + Playtomic (~100m grid)
dim_cities — Eurostat cities with population + venue counts
Serving layer (consumed by web app and SEO generation):
city_market_profile — OBT: market score, venue density, population per city
planner_defaults — per-city calculator pre-fill values with country median
fallbacks and competitive-pressure rate adjustments
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
73 lines
2.6 KiB
SQL
73 lines
2.6 KiB
SQL
-- Per-city planner defaults for the financial calculator.
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-- When a user selects a city in the planner, these values pre-fill the inputs.
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-- Consumed by: padelnomics.planner.routes — city_defaults(city_slug) lookup.
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--
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-- Values are derived from market data where available, otherwise fall back to
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-- country-level medians, then to global fallbacks from market research report.
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--
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-- Units are explicit in column names (EUR, %, h). All monetary values in EUR.
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MODEL (
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name padelnomics.planner_defaults,
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kind FULL,
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cron '@daily',
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grain city_slug
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);
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WITH country_medians AS (
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-- Country-level fallback values from market research (hardcoded until we
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-- have richer pricing data from Playtomic or direct scraping).
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SELECT * FROM (VALUES
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-- (country_code, hourly_rate_peak_eur, monthly_rent_eur_sqm, capex_court_eur,
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-- avg_utilisation_pct, courts_typical)
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('DE', 22.0, 14.0, 42000.0, 0.55, 4),
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('ES', 16.0, 9.0, 32000.0, 0.62, 6),
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('GB', 24.0, 18.0, 48000.0, 0.52, 4),
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('FR', 18.0, 12.0, 36000.0, 0.58, 5),
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('IT', 15.0, 10.0, 30000.0, 0.60, 6),
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('PT', 12.0, 8.0, 28000.0, 0.65, 6),
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('AT', 20.0, 13.0, 40000.0, 0.54, 4),
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('CH', 28.0, 22.0, 55000.0, 0.50, 4),
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('NL', 20.0, 15.0, 40000.0, 0.56, 4),
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('BE', 18.0, 13.0, 36000.0, 0.57, 4),
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('SE', 22.0, 14.0, 42000.0, 0.50, 4),
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('US', 20.0, 12.0, 38000.0, 0.58, 6)
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) AS t(country_code, hourly_rate_peak_eur, monthly_rent_eur_sqm, capex_court_eur,
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avg_utilisation_pct, courts_typical)
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),
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city_venue_density AS (
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SELECT
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city_slug,
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country_code,
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padel_venue_count,
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population,
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venues_per_100k,
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market_score
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FROM padelnomics.city_market_profile
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)
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SELECT
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cvd.city_slug,
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cvd.country_code,
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cvd.padel_venue_count,
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cvd.population,
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cvd.market_score,
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-- Hourly rate: adjust country median by market maturity
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-- (high-density markets → slightly lower rates from competition)
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ROUND(
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cm.hourly_rate_peak_eur
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* CASE
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WHEN cvd.venues_per_100k > 4 THEN 0.90 -- very competitive
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WHEN cvd.venues_per_100k > 2 THEN 0.95 -- competitive
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WHEN cvd.venues_per_100k < 0.5 THEN 1.10 -- underserved premium
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ELSE 1.0
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END
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, 2) AS hourly_rate_peak_eur,
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ROUND(cm.hourly_rate_peak_eur * 0.75, 2) AS hourly_rate_offpeak_eur,
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cm.monthly_rent_eur_sqm,
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cm.capex_court_eur,
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cm.avg_utilisation_pct,
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cm.courts_typical,
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CURRENT_DATE AS refreshed_date
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FROM city_venue_density cvd
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LEFT JOIN country_medians cm ON cvd.country_code = cm.country_code
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