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fix(content): slugify transliteration + article links + country overview ranking

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- Add @slugify SQLMesh macro (STRIP_ACCENTS + ß→ss) replacing broken
  inline REGEXP_REPLACE that dropped non-ASCII chars (Düsseldorf → d-sseldorf)
- Apply @slugify to dim_venues, dim_cities, dim_locations
- Fix Python slugify() to pre-replace ß→ss before NFKD normalization
- Add language prefix to B2B article market links (/markets/germany → /de/markets/germany)
- Change country overview top-5 ranking: venue count (not raw market_score)
  for top cities, population for top opportunity cities

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Deeman
2026-03-03 10:46:30 +01:00
parent 0fc0ca66b1
commit a00c8727d7
8 changed files with 33 additions and 14 deletions
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3
transform/sqlmesh_padelnomics/models/foundation/dim_cities.sql
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@@ -33,8 +33,7 @@ venue_cities AS (
SELECT
country_code,
city AS city_name,
-- Lowercase before regex so uppercase letters aren't stripped to '-'
LOWER(REGEXP_REPLACE(LOWER(city), '[^a-z0-9]+', '-')) AS city_slug,
@slugify(city) AS city_slug,
COUNT(*) AS padel_venue_count,
AVG(lat) AS centroid_lat,
AVG(lon) AS centroid_lon

2
transform/sqlmesh_padelnomics/models/foundation/dim_locations.sql
View File

@@ -38,7 +38,7 @@ locations AS (
geoname_id,
city_name AS location_name,
-- URL-safe location slug
LOWER(REGEXP_REPLACE(LOWER(city_name), '[^a-z0-9]+', '-')) AS location_slug,
@slugify(city_name) AS location_slug,
country_code,
lat,
lon,

2
transform/sqlmesh_padelnomics/models/foundation/dim_venues.sql
View File

@@ -99,7 +99,7 @@ SELECT
indoor_court_count,
outdoor_court_count,
-- Conformed city key: enables deterministic joins to dim_cities / venue_pricing_benchmarks
LOWER(REGEXP_REPLACE(LOWER(COALESCE(city, '')), '[^a-z0-9]+', '-')) AS city_slug,
@slugify(COALESCE(city, '')) AS city_slug,
extracted_date
FROM ranked
QUALIFY ROW_NUMBER() OVER (

12
transform/sqlmesh_padelnomics/models/serving/pseo_country_overview.sql
View File

@@ -20,15 +20,15 @@ SELECT
SUM(padel_venue_count) AS total_venues,
ROUND(AVG(market_score), 1) AS avg_market_score,
MAX(market_score) AS top_city_market_score,
-- Top 5 cities by market score for internal linking (DuckDB list slice syntax)
LIST(city_slug ORDER BY market_score DESC NULLS LAST)[1:5] AS top_city_slugs,
LIST(city_name ORDER BY market_score DESC NULLS LAST)[1:5] AS top_city_names,
-- Top 5 cities by venue count (prominence), then score for internal linking
LIST(city_slug ORDER BY padel_venue_count DESC, market_score DESC NULLS LAST)[1:5] AS top_city_slugs,
LIST(city_name ORDER BY padel_venue_count DESC, market_score DESC NULLS LAST)[1:5] AS top_city_names,
-- Opportunity score aggregates (NULL-safe: cities without geoname_id match excluded from AVG)
ROUND(AVG(opportunity_score), 1) AS avg_opportunity_score,
MAX(opportunity_score) AS top_opportunity_score,
-- Top 5 cities by opportunity score (may differ from top market score cities)
LIST(city_slug ORDER BY opportunity_score DESC NULLS LAST)[1:5] AS top_opportunity_slugs,
LIST(city_name ORDER BY opportunity_score DESC NULLS LAST)[1:5] AS top_opportunity_names,
-- Top 5 opportunity cities by population (prominence), then opportunity score
LIST(city_slug ORDER BY population DESC, opportunity_score DESC NULLS LAST)[1:5] AS top_opportunity_slugs,
LIST(city_name ORDER BY population DESC, opportunity_score DESC NULLS LAST)[1:5] AS top_opportunity_names,
-- Pricing medians across cities (NULL when no Playtomic coverage in country)
ROUND(MEDIAN(median_hourly_rate), 0) AS median_hourly_rate,
ROUND(MEDIAN(median_peak_rate), 0) AS median_peak_rate,