feat(data): Sprint 1-5 population pipeline — city labels, US/UK/Global extractors

Part A: Data Layer — Sprints 1-5

Sprint 1 — Eurostat SDMX city labels (unblocks EU population):
- New extractor: eurostat_city_labels.py — fetches ESTAT/CITIES codelist
  (city_code → city_name mapping) with ETag dedup
- New staging model: stg_city_labels.sql — grain city_code
- Updated dim_cities.sql — joins Eurostat population via city code lookup;
  replaces hardcoded 0::BIGINT population

Sprint 2 — Market score formula v2:
- city_market_profile.sql: 30pt population (LN/1M), 25pt income PPS (/200),
  30pt demand (occupancy or density), 15pt data confidence
- Moved venue_pricing_benchmarks join into base CTE so median_occupancy_rate
  is available to the scoring formula

Sprint 3 — US Census ACS extractor:
- New extractor: census_usa.py — ACS 5-year place population (vintage 2023)
- New staging model: stg_population_usa.sql — grain (place_fips, ref_year)

Sprint 4 — ONS UK extractor:
- New extractor: ons_uk.py — 2021 Census LAD population via ONS beta API
- New staging model: stg_population_uk.sql — grain (lad_code, ref_year)

Sprint 5 — GeoNames global extractor:
- New extractor: geonames.py — cities15000.zip bulk download, filtered to ≥50K pop
- New staging model: stg_population_geonames.sql — grain geoname_id
- dim_cities: 5-source population cascade (Eurostat > Census > ONS > GeoNames > 0)
  with case/whitespace-insensitive city name matching

Registered all 4 new CLI entrypoints in pyproject.toml and all.py.

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

transform/sqlmesh_padelnomics/models/foundation/dim_cities.sql

@@ -3,14 +3,17 @@
 -- tracks cities where padel venues actually exist, not an administrative city list.
 --
 -- Conformed dimension: used by city_market_profile and all pSEO serving models.
--- Integrates two sources:
---   dim_venues    → city list, venue count, coordinates (Playtomic + OSM)
---   stg_income    → country-level median income (Eurostat)
+-- Integrates four sources:
+--   dim_venues           → city list, venue count, coordinates (Playtomic + OSM)
+--   stg_income           → country-level median income (Eurostat)
+--   stg_city_labels      → Eurostat city_code → city_name mapping (EU cities)
+--   stg_population       → Eurostat city-level population (EU, joined via city code)
+--   stg_population_usa   → US Census ACS place population
+--   stg_population_uk    → ONS LAD population
+--   stg_population_geonames → GeoNames global fallback
 --
--- Population note: Eurostat uses coded identifiers (e.g. DE001C = Berlin) with no
--- city name column in the dataset we extract. City-level population requires a
--- separate code→name lookup extract (future improvement). Population is set to 0
--- until that source is available; market_score degrades gracefully.
+-- Population cascade: Eurostat EU > US Census > ONS UK > GeoNames > 0.
+-- City name matching is case/whitespace-insensitive within each country.
 --
 -- Grain: (country_code, city_slug) — two cities in different countries can share a
 -- city name. QUALIFY enforces no duplicate (country_code, city_slug) pairs.
@@ -42,6 +45,39 @@ country_income AS (
   SELECT country_code, median_income_pps, ref_year AS income_year
   FROM staging.stg_income
   QUALIFY ROW_NUMBER() OVER (PARTITION BY country_code ORDER BY ref_year DESC) = 1
+),
+-- Eurostat EU population: join city labels (code→name) with population values.
+-- QUALIFY keeps only the most recent year per (country, city name).
+eurostat_pop AS (
+  SELECT
+    cl.country_code,
+    cl.city_name,
+    p.population,
+    p.ref_year
+  FROM staging.stg_city_labels cl
+  JOIN staging.stg_population p ON cl.city_code = p.city_code
+  QUALIFY ROW_NUMBER() OVER (
+    PARTITION BY cl.country_code, cl.city_name
+    ORDER BY p.ref_year DESC
+  ) = 1
+),
+-- US Census ACS population (place-level, filtered to ≥50K)
+us_pop AS (
+  SELECT city_name, country_code, population, ref_year
+  FROM staging.stg_population_usa
+  QUALIFY ROW_NUMBER() OVER (PARTITION BY place_fips ORDER BY ref_year DESC) = 1
+),
+-- ONS UK Local Authority District population
+uk_pop AS (
+  SELECT lad_name AS city_name, country_code, population, ref_year
+  FROM staging.stg_population_uk
+  QUALIFY ROW_NUMBER() OVER (PARTITION BY lad_code ORDER BY ref_year DESC) = 1
+),
+-- GeoNames global fallback (all cities ≥50K)
+geonames_pop AS (
+  SELECT city_name, country_code, population, ref_year
+  FROM staging.stg_population_geonames
+  QUALIFY ROW_NUMBER() OVER (PARTITION BY geoname_id ORDER BY ref_year DESC) = 1
 )
 SELECT
   vc.country_code,
@@ -99,15 +135,43 @@ SELECT
   ))                                                         AS country_slug,
   vc.centroid_lat                                            AS lat,
   vc.centroid_lon                                            AS lon,
-  -- Population: requires code→name Eurostat lookup (not yet extracted); defaults to 0.
-  -- market_score uses LOG(GREATEST(population, 1)) so 0 degrades score gracefully.
-  0::BIGINT                                                  AS population,
-  0::INTEGER                                                 AS population_year,
+  -- Population cascade: Eurostat EU > US Census > ONS UK > GeoNames > 0.
+  -- City name match is case/whitespace-insensitive within each country.
+  COALESCE(
+    ep.population,
+    usa.population,
+    uk.population,
+    gn.population,
+    0
+  )::BIGINT                                                  AS population,
+  COALESCE(
+    ep.ref_year,
+    usa.ref_year,
+    uk.ref_year,
+    gn.ref_year,
+    0
+  )::INTEGER                                                 AS population_year,
   vc.padel_venue_count,
   ci.median_income_pps,
   ci.income_year
 FROM venue_cities vc
 LEFT JOIN country_income ci ON vc.country_code = ci.country_code
+-- Eurostat EU population (via city code→name lookup)
+LEFT JOIN eurostat_pop ep
+  ON vc.country_code = ep.country_code
+  AND LOWER(TRIM(vc.city_name)) = LOWER(TRIM(ep.city_name))
+-- US Census population
+LEFT JOIN us_pop usa
+  ON vc.country_code = usa.country_code
+  AND LOWER(TRIM(vc.city_name)) = LOWER(TRIM(usa.city_name))
+-- ONS UK population
+LEFT JOIN uk_pop uk
+  ON vc.country_code = uk.country_code
+  AND LOWER(TRIM(vc.city_name)) = LOWER(TRIM(uk.city_name))
+-- GeoNames global fallback
+LEFT JOIN geonames_pop gn
+  ON vc.country_code = gn.country_code
+  AND LOWER(TRIM(vc.city_name)) = LOWER(TRIM(gn.city_name))
 -- Enforce grain: if two cities in the same country have the same slug
 -- (e.g. 'São Paulo' and 'Sao Paulo'), keep the one with more venues
 QUALIFY ROW_NUMBER() OVER (