feat(extract): expand GeoNames to cities1000 + add tennis court extractor

GeoNames:
- cities15000 → cities1000 (~140K global locations, pop ≥ 1K)
- Add lat/lon, admin1_code, admin2_code to output (needed for dim_locations)
- Expand feature codes to include PPLA3/4/5 (Gemeinden, cantons, etc.)
- Remove MIN_POPULATION=50K floor — cities1000 already pre-filters to ≥1K
- Update assertions for new scale (~100K+ expected)

Tennis courts:
- New overpass_tennis.py extractor (sport=tennis, 180s Overpass timeout)
- Registered as extract-overpass-tennis, added to EXTRACTORS list
- New stg_tennis_courts.sql staging model (grain: osm_id)

stg_population_geonames: add lat, lon, admin1_code, admin2_code columns

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

transform/sqlmesh_padelnomics/models/staging/stg_population_geonames.sql

@@ -1,5 +1,6 @@
--- GeoNames global city population (cities15000 bulk dataset, filtered to ≥50K).
+-- GeoNames global city/municipality population (cities1000 bulk dataset, pop ≥ 1K).
 -- Global fallback for countries not covered by Eurostat, Census, or ONS.
+-- Broad coverage (140K+ locations) enables Gemeinde-level market intelligence.
 -- One row per geoname_id (GeoNames stable numeric identifier).
 --
 -- Source: data/landing/geonames/{year}/{month}/cities_global.json.gz
@@ -16,6 +17,10 @@ WITH parsed AS (
     TRY_CAST(row ->> 'geoname_id'  AS INTEGER)  AS geoname_id,
     row ->> 'city_name'                          AS city_name,
     row ->> 'country_code'                       AS country_code,
+    TRY_CAST(row ->> 'lat'         AS DOUBLE)    AS lat,
+    TRY_CAST(row ->> 'lon'         AS DOUBLE)    AS lon,
+    row ->> 'admin1_code'                        AS admin1_code,
+    row ->> 'admin2_code'                        AS admin2_code,
     TRY_CAST(row ->> 'population'  AS BIGINT)    AS population,
     TRY_CAST(row ->> 'ref_year'    AS INTEGER)   AS ref_year,
     CURRENT_DATE                                 AS extracted_date
@@ -32,6 +37,10 @@ SELECT
   geoname_id,
   TRIM(city_name)      AS city_name,
   UPPER(country_code)  AS country_code,
+  lat,
+  lon,
+  NULLIF(TRIM(admin1_code), '')  AS admin1_code,
+  NULLIF(TRIM(admin2_code), '')  AS admin2_code,
   population,
   ref_year,
   extracted_date
@@ -40,3 +49,5 @@ WHERE population IS NOT NULL
   AND population > 0
   AND geoname_id IS NOT NULL
   AND city_name IS NOT NULL
+  AND lat IS NOT NULL
+  AND lon IS NOT NULL

transform/sqlmesh_padelnomics/models/staging/stg_tennis_courts.sql

@@ -0,0 +1,72 @@
+-- Tennis court locations from OpenStreetMap via Overpass API (sport=tennis).
+-- Used as a "racket sport culture" signal in the opportunity score:
+-- areas with high tennis court density are prime padel adoption markets.
+--
+-- Source: data/landing/overpass_tennis/{year}/{month}/courts.json.gz
+
+MODEL (
+  name staging.stg_tennis_courts,
+  kind FULL,
+  cron '@daily',
+  grain osm_id
+);
+
+WITH parsed AS (
+  SELECT
+    elem ->> 'type'                     AS osm_type,
+    (elem ->> 'id')::BIGINT             AS osm_id,
+    TRY_CAST(elem ->> 'lat' AS DOUBLE)  AS lat,
+    TRY_CAST(elem ->> 'lon' AS DOUBLE)  AS lon,
+    elem -> 'tags' ->> 'name'           AS name,
+    elem -> 'tags' ->> 'addr:country'   AS country_code,
+    elem -> 'tags' ->> 'addr:city'      AS city_tag,
+    filename                            AS source_file,
+    CURRENT_DATE                        AS extracted_date
+  FROM (
+    SELECT UNNEST(elements) AS elem, filename
+    FROM read_json(
+      @LANDING_DIR || '/overpass_tennis/*/*/courts.json.gz',
+      format = 'auto',
+      filename = true
+    )
+  )
+  WHERE (elem ->> 'type') IS NOT NULL
+),
+deduped AS (
+  SELECT *,
+    ROW_NUMBER() OVER (PARTITION BY osm_id ORDER BY extracted_date DESC) AS rn
+  FROM parsed
+  WHERE osm_type = 'node'
+    AND lat IS NOT NULL AND lon IS NOT NULL
+    AND lat BETWEEN -90  AND 90
+    AND lon BETWEEN -180 AND 180
+),
+with_country AS (
+  SELECT
+    osm_id, lat, lon,
+    COALESCE(NULLIF(TRIM(UPPER(country_code)), ''), CASE
+      WHEN lat BETWEEN 47.27 AND 55.06 AND lon BETWEEN  5.87 AND 15.04 THEN 'DE'
+      WHEN lat BETWEEN 35.95 AND 43.79 AND lon BETWEEN -9.39 AND  4.33 THEN 'ES'
+      WHEN lat BETWEEN 49.90 AND 60.85 AND lon BETWEEN -8.62 AND  1.77 THEN 'GB'
+      WHEN lat BETWEEN 41.36 AND 51.09 AND lon BETWEEN -5.14 AND  9.56 THEN 'FR'
+      WHEN lat BETWEEN 45.46 AND 47.80 AND lon BETWEEN  5.96 AND 10.49 THEN 'CH'
+      WHEN lat BETWEEN 46.37 AND 49.02 AND lon BETWEEN  9.53 AND 17.16 THEN 'AT'
+      WHEN lat BETWEEN 36.35 AND 47.09 AND lon BETWEEN  6.62 AND 18.51 THEN 'IT'
+      WHEN lat BETWEEN 37.00 AND 42.15 AND lon BETWEEN -9.50 AND -6.19 THEN 'PT'
+      ELSE NULL
+    END)                      AS country_code,
+    NULLIF(TRIM(name), '')    AS name,
+    NULLIF(TRIM(city_tag), '') AS city,
+    extracted_date
+  FROM deduped
+  WHERE rn = 1
+)
+SELECT
+  osm_id,
+  lat,
+  lon,
+  country_code,
+  name,
+  city,
+  extracted_date
+FROM with_country