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