10266c3a24
Raises supply gap ceiling from 4/100k to 8/100k in location_opportunity_profile.sql. The original 4/100k hard cliff truncated opportunity scores to 0 for any city with ≥4 courts/100k, but our data undercounts ~87% of real courts (FIP: 17,300 Spanish courts vs 2,239 in our DB). Raising to 8/100k gives a gentler gradient and fairer partial credit when density data is incomplete. Documents existing formula behaviour discovered during analysis: - Income PPS: country-level constants (18k-37k range) saturate the /200 ceiling — all EU countries get flat 20/20 pts until city-level income data lands. - Catchment NULL: DuckDB LEAST(1.0, NULL) = 1.0 (ignores nulls), so NULL nearest_padel_court_km already yields full 15 pts. COALESCE fallback is dead code but harmless. - Tennis courts within 25km: dim_locations data is empty (all 0 rows) — 10-court threshold is correct for when data arrives, contributes 0 pts everywhere for now. Effective score impact: minimal (99% of locations have 0 courts/100k, so supply gap was already at max). Only ~1,050 dense-court cities see a score increase (from 0 gap pts to partial gap pts). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
serving
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)