diff --git a/extract/padelnomics_extract/src/padelnomics_extract/eurostat.py b/extract/padelnomics_extract/src/padelnomics_extract/eurostat.py
index 6dfba92..6e45cd8 100644
--- a/extract/padelnomics_extract/src/padelnomics_extract/eurostat.py
+++ b/extract/padelnomics_extract/src/padelnomics_extract/eurostat.py
@@ -1,11 +1,16 @@
 """Eurostat extractor — city-level demographic datasets.
 
-Fetches Eurostat Statistics API JSON datasets using etag-based deduplication.
+Fetches Eurostat Statistics API JSON-stat datasets with etag-based deduplication.
 Data only changes ~twice a year so most runs skip with 304 Not Modified.
 
+The raw Eurostat JSON-stat format is a 4D sparse dictionary (freq × indicator ×
+city × time) which DuckDB cannot efficiently parse.  This extractor normalizes
+the response into simple JSON arrays that the staging SQL can UNNEST directly.
+
 Landing: {LANDING_DIR}/eurostat/{year}/{month}/{dataset_code}.json.gz
 """
 
+import json
 import sqlite3
 from pathlib import Path
 
@@ -19,11 +24,103 @@ logger = setup_logging("padelnomics.extract.eurostat")
 EXTRACTOR_NAME = "eurostat"
 EUROSTAT_BASE_URL = "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data"
 
-# Datasets to fetch
-DATASETS = [
-    "urb_cpop1",  # Urban Audit — city population
-    "ilc_di03",  # Median equivalised net income by NUTS2
-]
+# Dataset configs: filters fix dimension values, geo_dim/time_dim are iterated.
+# All other dimensions must either be in filters or have size=1.
+DATASETS: dict[str, dict] = {
+    "urb_cpop1": {
+        "filters": {"indic_ur": "DE1001V"},  # Population on 1 January, total
+        "geo_dim": "cities",
+        "time_dim": "time",
+    },
+    "ilc_di03": {
+        "filters": {  # Median equivalised net income
+            "age": "TOTAL",
+            "sex": "T",
+            "indic_il": "MED_E",
+            "unit": "PPS",
+        },
+        "geo_dim": "geo",
+        "time_dim": "time",
+    },
+}
+
+
+def _parse_jsonstat(
+    data: dict,
+    filters: dict[str, str],
+    geo_dim: str,
+    time_dim: str,
+) -> list[dict]:
+    """Parse a Eurostat JSON-stat response into flat records.
+
+    JSON-stat stores values in a sparse flat dict keyed by linear index
+    computed from dimension positions:
+      index = sum(pos_i * stride_i)   where stride_i = product(sizes[i+1:])
+
+    We fix all dimensions except geo and time using ``filters``, then
+    iterate geo × time to extract every non-null value.
+    """
+    dims = data["dimension"]
+    sizes = data["size"]
+    values = data["value"]
+    dim_names = data["id"]
+
+    assert len(dim_names) == len(sizes)
+
+    # Compute strides (row-major): stride[i] = product of sizes[i+1:]
+    strides = [1] * len(sizes)
+    for i in range(len(sizes) - 2, -1, -1):
+        strides[i] = strides[i + 1] * sizes[i + 1]
+
+    # Resolve fixed dimension positions → compute base offset
+    offset = 0
+    geo_idx = None
+    time_idx = None
+
+    for i, name in enumerate(dim_names):
+        if name == geo_dim:
+            geo_idx = i
+        elif name == time_dim:
+            time_idx = i
+        elif name in filters:
+            cat_index = dims[name]["category"]["index"]
+            code = filters[name]
+            assert code in cat_index, (
+                f"Filter value {code!r} not in dimension {name!r}. "
+                f"Available: {list(cat_index.keys())[:10]}..."
+            )
+            offset += cat_index[code] * strides[i]
+        else:
+            # Dimension not filtered and not geo/time — must have size 1
+            assert sizes[i] == 1, f"Dimension {name!r} has size {sizes[i]} but no filter provided"
+
+    assert geo_idx is not None, f"geo_dim {geo_dim!r} not found in {dim_names}"
+    assert time_idx is not None, f"time_dim {time_dim!r} not found in {dim_names}"
+
+    geo_index = dims[geo_dim]["category"]["index"]
+    time_index = dims[time_dim]["category"]["index"]
+    geo_by_pos = {pos: code for code, pos in geo_index.items()}
+    time_by_pos = {pos: code for code, pos in time_index.items()}
+
+    geo_stride = strides[geo_idx]
+    time_stride = strides[time_idx]
+
+    rows: list[dict] = []
+    for geo_pos in range(sizes[geo_idx]):
+        geo_code = geo_by_pos[geo_pos]
+        for time_pos in range(sizes[time_idx]):
+            idx = offset + geo_pos * geo_stride + time_pos * time_stride
+            val = values.get(str(idx))
+            if val is not None:
+                rows.append(
+                    {
+                        "geo_code": geo_code,
+                        "ref_year": time_by_pos[time_pos],
+                        "value": val,
+                    }
+                )
+
+    return rows
 
 
 def _etag_path(dest: Path) -> Path:
@@ -35,8 +132,12 @@ def _fetch_with_etag(
     url: str,
     dest: Path,
     session: niquests.Session,
+    dataset_config: dict | None = None,
 ) -> int:
-    """GET url with If-None-Match etag. Returns bytes_written (0 if 304)."""
+    """GET url with If-None-Match etag. Pre-processes JSON-stat if config given.
+
+    Returns bytes_written (0 if 304).
+    """
     etag_file = _etag_path(dest)
     headers: dict[str, str] = {}
 
@@ -49,7 +150,21 @@ def _fetch_with_etag(
         return 0
 
     resp.raise_for_status()
-    bytes_written = write_gzip_atomic(dest, resp.content)
+
+    if dataset_config:
+        raw = resp.json()
+        rows = _parse_jsonstat(
+            raw,
+            filters=dataset_config["filters"],
+            geo_dim=dataset_config["geo_dim"],
+            time_dim=dataset_config["time_dim"],
+        )
+        payload = json.dumps({"rows": rows, "count": len(rows)}).encode()
+        logger.info("parsed %d records", len(rows))
+    else:
+        payload = resp.content
+
+    bytes_written = write_gzip_atomic(dest, payload)
 
     if etag := resp.headers.get("etag"):
         etag_file.parent.mkdir(parents=True, exist_ok=True)
@@ -70,13 +185,13 @@ def extract(
     files_skipped = 0
     bytes_written_total = 0
 
-    for dataset_code in DATASETS:
+    for dataset_code, config in DATASETS.items():
         url = f"{EUROSTAT_BASE_URL}/{dataset_code}?format=JSON&lang=EN"
         dest_dir = landing_path(landing_dir, "eurostat", year, month)
         dest = dest_dir / f"{dataset_code}.json.gz"
 
         logger.info("GET %s", dataset_code)
-        bytes_written = _fetch_with_etag(url, dest, session)
+        bytes_written = _fetch_with_etag(url, dest, session, config)
 
         if bytes_written > 0:
             logger.info("%s updated — %s bytes compressed", dataset_code, f"{bytes_written:,}")
diff --git a/transform/sqlmesh_padelnomics/models/foundation/dim_cities.sql b/transform/sqlmesh_padelnomics/models/foundation/dim_cities.sql
index 4eae720..25a2c5b 100644
--- a/transform/sqlmesh_padelnomics/models/foundation/dim_cities.sql
+++ b/transform/sqlmesh_padelnomics/models/foundation/dim_cities.sql
@@ -15,7 +15,8 @@ eurostat_cities AS (
     city_code,
     country_code,
     population,
-    ref_year
+    ref_year,
+    LOWER(REPLACE(city_code, country_code, ''))  AS city_slug_raw
   FROM staging.stg_population
   QUALIFY ROW_NUMBER() OVER (PARTITION BY city_code ORDER BY ref_year DESC) = 1
 ),
@@ -30,16 +31,6 @@ venue_counts AS (
   FROM foundation.dim_venues
   WHERE city IS NOT NULL AND city != ''
   GROUP BY country_code, city
-),
--- Eurostat city label mapping to canonical city names
--- (Eurostat uses codes like DE001C → Berlin; we keep both)
-eurostat_labels AS (
-  SELECT DISTINCT
-    city_code,
-    country_code,
-    -- Derive a slug-friendly city name from the code as fallback
-    LOWER(REPLACE(city_code, country_code, ''))  AS city_slug_raw
-  FROM eurostat_cities
 )
 SELECT
   ec.city_code,
diff --git a/transform/sqlmesh_padelnomics/models/staging/stg_playtomic_venues.sql b/transform/sqlmesh_padelnomics/models/staging/stg_playtomic_venues.sql
index c8c4083..2594658 100644
--- a/transform/sqlmesh_padelnomics/models/staging/stg_playtomic_venues.sql
+++ b/transform/sqlmesh_padelnomics/models/staging/stg_playtomic_venues.sql
@@ -19,8 +19,8 @@ WITH parsed AS (
     tenant -> 'address' ->> 'city'                        AS city,
     tenant -> 'address' ->> 'postal_code'                 AS postal_code,
     tenant -> 'address' ->> 'country_code'                AS country_code,
-    TRY_CAST(tenant -> 'address' ->> 'coordinate_lat' AS DOUBLE) AS lat,
-    TRY_CAST(tenant -> 'address' ->> 'coordinate_lon' AS DOUBLE) AS lon,
+    TRY_CAST(tenant -> 'address' -> 'coordinate' ->> 'lat' AS DOUBLE) AS lat,
+    TRY_CAST(tenant -> 'address' -> 'coordinate' ->> 'lon' AS DOUBLE) AS lon,
     tenant ->> 'sport_ids'                                AS sport_ids_raw,
     tenant ->> 'tenant_type'                              AS tenant_type,
     filename                                              AS source_file,
diff --git a/transform/sqlmesh_padelnomics/models/staging/stg_population.sql b/transform/sqlmesh_padelnomics/models/staging/stg_population.sql
index 418408e..f5b838b 100644
--- a/transform/sqlmesh_padelnomics/models/staging/stg_population.sql
+++ b/transform/sqlmesh_padelnomics/models/staging/stg_population.sql
@@ -1,5 +1,5 @@
 -- Eurostat Urban Audit city population (dataset: urb_cpop1).
--- Reads landing zone JSON directly and parses the Eurostat multidimensional format.
+-- Reads pre-processed landing zone JSON (extractor normalizes JSON-stat to flat rows).
 -- One row per (city_code, year) with validated population values.
 --
 -- Source: data/landing/eurostat/{year}/{month}/urb_cpop1.json.gz
@@ -11,51 +11,26 @@ MODEL (
   grain (city_code, ref_year)
 );
 
-WITH raw AS (
-  SELECT raw_json, filename
-  FROM read_json(
-    @LANDING_DIR || '/eurostat/*/*/urb_cpop1.json.gz',
-    format = 'auto',
-    filename = true,
-    columns = { 'raw_json': 'JSON' }
-  )
-),
-cities AS (
+WITH parsed AS (
   SELECT
-    city_code,
-    (city_pos)::INTEGER AS city_pos,
-    filename, raw_json,
-    (json_extract(raw_json, '$.size[1]'))::INTEGER AS n_times
-  FROM raw,
-  LATERAL (
-    SELECT key AS city_code, value::INTEGER AS city_pos
-    FROM json_each(json_extract(raw_json, '$.dimension.cities.category.index'))
+    row ->> 'geo_code'           AS geo_code,
+    row ->> 'ref_year'           AS ref_year,
+    TRY_CAST(row ->> 'value' AS DOUBLE) AS population,
+    filename                     AS source_file,
+    CURRENT_DATE                 AS extracted_date
+  FROM (
+    SELECT UNNEST(rows) AS row, filename
+    FROM read_json(
+      @LANDING_DIR || '/eurostat/*/*/urb_cpop1.json.gz',
+      format = 'auto',
+      filename = true
+    )
   )
-),
-times AS (
-  SELECT key AS ref_year, value::INTEGER AS time_pos
-  FROM (SELECT raw_json FROM raw LIMIT 1),
-  LATERAL (
-    SELECT key, value
-    FROM json_each(json_extract(raw_json, '$.dimension.time.category.index'))
-  )
-),
-parsed AS (
-  SELECT
-    c.city_code,
-    t.ref_year,
-    TRY_CAST(
-      json_extract(c.raw_json, '$.' || (c.city_pos * c.n_times + t.time_pos)::TEXT)
-      AS DOUBLE
-    ) AS population,
-    c.filename AS source_file,
-    CURRENT_DATE AS extracted_date
-  FROM cities c
-  CROSS JOIN times t
+  WHERE (row ->> 'geo_code') IS NOT NULL
 )
 SELECT
-  UPPER(city_code)               AS city_code,
-  UPPER(LEFT(city_code, 2))      AS country_code,
+  UPPER(geo_code)                AS city_code,
+  UPPER(LEFT(geo_code, 2))       AS country_code,
   ref_year::INTEGER              AS ref_year,
   population::BIGINT             AS population,
   extracted_date