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>

scripts/init_landing_seeds.py

@@ -85,6 +85,8 @@ def main() -> None:
             json.dumps({"rows": [], "count": 0}).encode(),
         "eurostat/1970/01/ilc_di03.json.gz":
             json.dumps({"rows": [], "count": 0}).encode(),
+        "eurostat/1970/01/nama_10r_2hhinc.json.gz":
+            json.dumps({"rows": [], "count": 0}).encode(),
         "eurostat_city_labels/1970/01/cities_codelist.json.gz":
             json.dumps({"rows": [], "count": 0}).encode(),