- Merge supply gap (30pts) + catchment gap (15pts) → supply deficit (35pts, GREATEST)
Eliminates ~80% correlated double-count on a single signal.
- Add sports culture signal (10pts): tennis court density as racquet-sport adoption proxy.
Ceiling 50 courts/25km. Harmless when tennis data is zero (contributes 0).
- Add construction affordability (5pts): income relative to PLI construction costs.
Joins dim_countries.pli_construction. High income + low build cost = high score.
- Reduce economic power from 20 → 15pts to make room.
New weights: addressable market 25, economic power 15, supply deficit 35,
sports culture 10, construction affordability 5, market validation 10.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
A. location_profiles.sql: supply gap now uses GREATEST(catchment_padel_courts,
COALESCE(city_padel_venue_count, 0)) so Playtomic venues prevent cities like
Murcia/Cordoba/Gijon from receiving a full 30-pt supply gap bonus when their
OSM catchment count is zero. Expected ~10-15 pt drop for affected ES cities.
B. pseo_country_overview.sql: add population-weighted lat/lon centroid columns
so the markets map can use accurate country positions from this table.
C/D. content/routes.py + markets.html: query pseo_country_overview in the route
and pass as map_countries to the template, replacing the fetch('/api/...') call
with inline JSON. Map scores now match pseo_country_overview (pop-weighted),
and the page loads without an extra round-trip.
E. api.py: add @login_required to all 4 endpoints. Unauthenticated callers get
a 302 redirect to login instead of data.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two targeted fixes for inflated country scores (ES 83, SE 77):
1. pseo_country_overview: replace AVG() with population-weighted averages
for avg_opportunity_score and avg_market_score. Madrid/Barcelona now
dominate Spain's average instead of hundreds of 30K-town white-space
towns. Expected ES drop from ~83 to ~55-65.
2. location_profiles: replace dead sports culture component (10 pts,
tennis data all zeros) with market validation signal.
Split scored CTE into: market_scored → country_market → scored.
country_market aggregates AVG(market_score) per country from cities
with padel courts (market_score > 0), so zero-court locations don't
dilute the signal. ES (~60/100) → ~6 pts. SE (~35/100) → ~3.5 pts.
NULL → 0.5 neutral → 5 pts (untested market, not penalised).
Score budget unchanged: 25+20+30+15+10 = 100 pts.
No new models, no new data sources, no cycles.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Merges worktree-h3-catchment-index. dim_locations now computes h3_cell_res5
(res 5, ~8.5km edge). location_profiles and dim_locations updated;
old location_opportunity_profile.sql already removed on master.
Conflict: location_opportunity_profile.sql deleted on master, kept deletion
and applied h3_cell_res4→res5 rename to location_profiles instead.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Combines city_market_profile and location_opportunity_profile into a
single serving model at (country_code, geoname_id) grain. Both Market
Score and Opportunity Score computed per location. City data enriched
via LEFT JOIN dim_cities on geoname_id.
Subtask 1/5: create new model (old models not yet removed).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
