Files

Deeman 5ade38eeaf feat(data): Phase 2a — NUTS-1 regional income for opportunity score

- eurostat.py: add nama_10r_2hhinc dataset config; append filter params to
  request URL so server pre-filters the large cube before download
- stg_regional_income.sql: new staging model — reads nama_10r_2hhinc.json.gz,
  filters to NUTS-1 codes (3-char), normalises EL→GR / UK→GB
- dim_locations.sql: add admin1_to_nuts1 VALUES CTE (16 German Bundesländer)
  + regional_income CTE; final SELECT uses COALESCE(regional, country) income
- init_landing_seeds.py: add empty seed for nama_10r_2hhinc.json.gz

Munich/Bayern now scores ~29K PPS vs Chemnitz/Sachsen ~19K PPS instead of
both inheriting the same national average (~25.5K PPS).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-02-27 10:26:15 +01:00

dim_cities.sql

feat(sql): dim_cities — GeoNames spatial population fallback

2026-02-27 08:47:26 +01:00

dim_locations.sql

feat(data): Phase 2a — NUTS-1 regional income for opportunity score

2026-02-27 10:26:15 +01:00

dim_venue_capacity.sql

refactor: align transform layer with template methodology

2026-02-23 21:17:04 +01:00

dim_venues.sql

feat: Playtomic pricing/occupancy pipeline + email i18n + audience restructure