padelnomics

deemanone/padelnomics

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Author	SHA1	Message	Date
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
Deeman	55f179ba54	fix(transform): increase geonames object size limit and remove stale column ref - stg_population_geonames: add maximum_object_size=40MB to read_json() call; geonames cities_global.json.gz is ~30MB, exceeding DuckDB's 16MB default - dim_locations: remove stale 'population_year AS population_year' column ref; stg_population_geonames has ref_year, not population_year — caused BinderException Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-02-25 09:56:05 +01:00
Deeman	ebfdc84a94	feat(transform): add dim_locations + dual market scoring models dim_locations (foundation): - Seeded from stg_population_geonames (all locations, not venue-dependent) - Grain: (country_code, geoname_id) - Enriched with: padel venues within 5km, nearest court distance (ST_Distance_Sphere), tennis courts within 25km, country income - Covers zero-court Gemeinden for opportunity scoring location_opportunity_profile (serving) — Padelnomics Marktpotenzial-Score: - Answers "Where should I build?" — no padel_venue_count filter - Formula: population (25) + income (20) + supply gap inverted (30) + catchment gap (15) + tennis culture (10) = 100pts - Sorted by opportunity_score DESC city_market_profile (serving) — Padelnomics Marktreife-Score: - Add saturation discount (×0.85 when venues_per_100k > 8) - Update header comment to reference Marktreife-Score branding - Kept WHERE padel_venue_count > 0 (established markets only) - column name market_score unchanged (avoids downstream breakage) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-02-24 16:28:16 +01:00

Author

SHA1

Message

Date

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

Deeman

55f179ba54

fix(transform): increase geonames object size limit and remove stale column ref

- stg_population_geonames: add maximum_object_size=40MB to read_json() call;
  geonames cities_global.json.gz is ~30MB, exceeding DuckDB's 16MB default
- dim_locations: remove stale 'population_year AS population_year' column ref;
  stg_population_geonames has ref_year, not population_year — caused BinderException

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

2026-02-25 09:56:05 +01:00

Deeman

ebfdc84a94

feat(transform): add dim_locations + dual market scoring models

dim_locations (foundation):
- Seeded from stg_population_geonames (all locations, not venue-dependent)
- Grain: (country_code, geoname_id)
- Enriched with: padel venues within 5km, nearest court distance (ST_Distance_Sphere),
  tennis courts within 25km, country income
- Covers zero-court Gemeinden for opportunity scoring

location_opportunity_profile (serving) — Padelnomics Marktpotenzial-Score:
- Answers "Where should I build?" — no padel_venue_count filter
- Formula: population (25) + income (20) + supply gap inverted (30) +
           catchment gap (15) + tennis culture (10) = 100pts
- Sorted by opportunity_score DESC

city_market_profile (serving) — Padelnomics Marktreife-Score:
- Add saturation discount (×0.85 when venues_per_100k > 8)
- Update header comment to reference Marktreife-Score branding
- Kept WHERE padel_venue_count > 0 (established markets only)
- column name market_score unchanged (avoids downstream breakage)

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

2026-02-24 16:28:16 +01:00

3 Commits