- 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>
Creates minimal .jsonl.gz and .json.gz seed files so all SQLMesh staging
models can compile and run before real extraction data arrives.
Each seed has a single null record filtered by the staging model's WHERE
clause (tenant_id IS NOT NULL, geoname_id IS NOT NULL, type IS NOT NULL, etc).
Covers both formats (JSONL + blob) for the UNION ALL transition CTEs:
playtomic/1970/01/: tenants.{jsonl,json}.gz, availability seeds (morning + recheck)
geonames/1970/01/: cities_global.{jsonl,json}.gz
overpass_tennis/1970/01/: courts.{jsonl,json}.gz
overpass/1970/01/: courts.json.gz (padel, unchanged format)
eurostat/1970/01/: urb_cpop1.json.gz, ilc_di03.json.gz
eurostat_city_labels/1970/01/: cities_codelist.json.gz
ons_uk/1970/01/: lad_population.json.gz
census_usa/1970/01/: acs5_places.json.gz
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
