Replace ABS() bbox predicates with BETWEEN in all three spatial CTEs
(nearest_padel, padel_local, tennis_nearby). BETWEEN enables DuckDB's
IEJoin (interval join) which is O((N+M) log M) vs the previous O(N×M)
nested-loop cross-join.
Add country pre-filters to restrict the left side from ~140K global
locations to ~20K rows for padel/tennis CTEs (~8 countries each).
Expected: ~50-200x speedup on the spatial CTE portion of the model.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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>
