fix(transform): tighten H3 catchment to res 5 (~24km radius)

Res 4 + k_ring(1) gave ~50-60km effective radius, causing Oldenburg to
absorb Bremen (40km away) and destroying score differentiation.

Res 5 + k_ring(1) gives ~24km — captures adjacent Gemeinden (Delmenhorst
at 15km) without bleeding into unrelated cities at 40km+.

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

transform/sqlmesh_padelnomics/models/foundation/dim_locations.sql

@@ -215,7 +215,7 @@ SELECT
   l.location_slug,
   l.lat,
   l.lon,
-  h3_latlng_to_cell(l.lat, l.lon, 4)                                    AS h3_cell_res4,
+  h3_latlng_to_cell(l.lat, l.lon, 5)                                    AS h3_cell_res5,
   l.admin1_code,
   l.admin2_code,
   l.population,

transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql

@@ -9,8 +9,9 @@
 -- H3 catchment methodology (v3):
 --   Addressable market and supply gap now use a regional catchment lens rather than
 --   the location's own population/court count. Each location is assigned an H3 cell
---   at resolution 4 (~10km center-to-center). Catchment = cell + 6 neighbours (k_ring=1),
---   covering ~462km² — roughly a 15-18km radius, matching realistic driving distance.
+--   at resolution 5 (~8.5km edge). Catchment = cell + 6 neighbours (k_ring=1),
+--   covering ~24km effective radius — realistic driving distance without absorbing
+--   unrelated cities (e.g. Oldenburg stays separate from Bremen at ~40km).
 --   Population and court counts are first aggregated per H3 cell (hex_stats CTE), then
 --   summed across the 7-cell ring (catchment CTE) to avoid scanning all 140K locations
 --   per location.
@@ -41,27 +42,27 @@ MODEL (
 );
 
 WITH
--- Aggregate population and court counts per H3 cell (res 4, ~10km edge).
--- Grouping by cell first (~30-50K distinct cells vs 140K locations) keeps the
+-- Aggregate population and court counts per H3 cell (res 5, ~8.5km edge).
+-- Grouping by cell first (~50-80K distinct cells vs 140K locations) keeps the
 -- subsequent lateral join small.
 hex_stats AS (
   SELECT
-    h3_cell_res4,
+    h3_cell_res5,
     SUM(population)        AS hex_population,
     SUM(padel_venue_count) AS hex_padel_courts
   FROM foundation.dim_locations
-  GROUP BY h3_cell_res4
+  GROUP BY h3_cell_res5
 ),
 -- For each location, sum hex_stats across the cell + 6 neighbours (k_ring=1).
--- Effective catchment: ~462km², ~15-18km radius — realistic driving distance.
+-- Effective catchment: ~24km radius — realistic driving distance.
 catchment AS (
   SELECT
     l.geoname_id,
     SUM(hs.hex_population)   AS catchment_population,
     SUM(hs.hex_padel_courts) AS catchment_padel_courts
   FROM foundation.dim_locations l,
-  LATERAL (SELECT UNNEST(h3_grid_disk(l.h3_cell_res4, 1)) AS cell) ring
-  JOIN hex_stats hs ON hs.h3_cell_res4 = ring.cell
+  LATERAL (SELECT UNNEST(h3_grid_disk(l.h3_cell_res5, 1)) AS cell) ring
+  JOIN hex_stats hs ON hs.h3_cell_res5 = ring.cell
   GROUP BY l.geoname_id
 )
 SELECT
@@ -83,7 +84,7 @@ SELECT
   l.padel_venues_per_100k,
   l.nearest_padel_court_km,
   l.tennis_courts_within_25km,
-  -- Catchment metrics (H3 res-4 cell + 6 neighbours, ~15-18km radius)
+  -- Catchment metrics (H3 res-5 cell + 6 neighbours, ~24km radius)
   COALESCE(c.catchment_population, l.population)::BIGINT               AS catchment_population,
   COALESCE(c.catchment_padel_courts, l.padel_venue_count)::INTEGER     AS catchment_padel_courts,
   CASE WHEN COALESCE(c.catchment_population, l.population) > 0
@@ -94,7 +95,7 @@ SELECT
   END                                                                    AS catchment_venues_per_100k,
   ROUND(
     -- Addressable market (25 pts): log-scaled catchment population, ceiling 500K.
-    -- v3: uses H3 catchment population (cell + 6 neighbours, ~15-18km radius) instead
+    -- v3: uses H3 catchment population (cell + 6 neighbours, ~24km radius) instead
     -- of local city population, so mid-size cities surrounded by dense Gemeinden score
     -- correctly (e.g. Oldenburg pulls in Ammerland, Wesermarsch, etc.).
     25.0 * LEAST(1.0, LN(GREATEST(COALESCE(c.catchment_population, l.population), 1)) / LN(500000))
@@ -109,7 +110,7 @@ SELECT
 
     -- Supply gap (30 pts): INVERTED catchment venue density.
     -- v3: uses catchment courts / catchment population instead of local 5km count / city pop.
-    -- 0 courts/100K across the ~15-18km ring = full 30 pts (genuine white space).
+    -- 0 courts/100K across the ~24km ring = full 30 pts (genuine white space).
     -- ≥8/100K = 0 pts (well-served regional market).
     + 30.0 * GREATEST(0.0, 1.0 - COALESCE(
         CASE WHEN COALESCE(c.catchment_population, l.population) > 0