Add real per-country cost data to ~30 calculator fields so pSEO articles
show country-specific CAPEX/OPEX instead of hardcoded DE defaults.
Extractor:
- eurostat.py: add 8 new datasets (nrg_pc_205, nrg_pc_203, lc_lci_lev,
5×prc_ppp_ind variants); add optional `dataset_code` field so multiple
dict entries can share one Eurostat API endpoint
Staging (4 new models):
- stg_electricity_prices — EUR/kWh by country, semi-annual
- stg_gas_prices — EUR/GJ by country, semi-annual
- stg_labour_costs — EUR/hour by country, annual (future staffed scenario)
- stg_price_levels — PLI indices (EU27=100) for 5 categories, annual
Foundation:
- dim_countries (new) — conformed country dimension; eliminates ~50-line CASE
blocks duplicated in dim_cities/dim_locations; computes ~29 calculator cost
override columns from PLI ratios and energy price ratios vs DE baseline;
NULL for DE so calculator falls through to DEFAULTS unchanged
- dim_cities — replace country_name/slug CASE blocks + country_income CTE
with JOIN dim_countries
- dim_locations — same refactor as dim_cities
Serving:
- pseo_city_costs_de — JOIN dim_countries; add 29 camelCase override columns
auto-applied by calculator (electricity, heating, rentSqm, hallCostSqm, …)
- planner_defaults — JOIN dim_countries; same 29 cost columns flow through
to /api/market-data endpoint
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
