merge: Score v6 — World Bank global economic data for non-EU countries

CHANGELOG.md

@@ -7,6 +7,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
 ## [Unreleased]
 
 ### Changed
+- **Score v6: Global economic data** — `dim_countries.median_income_pps` and `pli_construction` now cover all target markets, not just EU. World Bank WDI indicators (GNI per capita PPP + price level ratio) fill gaps for non-EU countries (AR, MX, AE, AU, etc.) with values calibrated to the Eurostat scale using Germany as anchor. EU countries keep exact Eurostat values. New extractor (`worldbank.py`), staging model (`stg_worldbank_income`), and `dim_countries` fallback CTEs. No changes to scoring formulas — the fix is upstream in the data layer.
 - **Market Score v3 → v4** — fixes Spain averaging 54 (should be 65-80). Four calibration changes: count gate threshold lowered from 5 → 3 venues (3 establishes a market pattern), density ceiling lowered from LN(21) → LN(11) (10/100k is reachable for mature markets), demand evidence fallback raised from 0.4 → 0.65 multiplier with 0.3 floor (existence of venues IS evidence of demand), economic context ceiling changed from income/200 → income/25000 (actual discrimination instead of free 10 pts for everyone).
 - **Opportunity Score v4 → v5** — fixes structural flaws: supply gap (30pts) + catchment gap (15pts) merged into single supply deficit (35pts, GREATEST of density gap and distance gap) eliminating ~80% correlated double-count. New sports culture signal (10pts) using tennis court density as racquet-sport adoption proxy. New construction affordability signal (5pts) using income relative to PLI construction costs from `dim_countries`. Economic power reduced from 20 → 15pts. New dependency on `foundation.dim_countries` for `pli_construction`.
 

extract/padelnomics_extract/pyproject.toml

@@ -22,6 +22,7 @@ extract-census-usa-income = "padelnomics_extract.census_usa_income:main"
 extract-ons-uk = "padelnomics_extract.ons_uk:main"
 extract-geonames = "padelnomics_extract.geonames:main"
 extract-gisco = "padelnomics_extract.gisco:main"
+extract-worldbank = "padelnomics_extract.worldbank:main"
 
 [build-system]
 requires = ["hatchling"]

extract/padelnomics_extract/src/padelnomics_extract/all.py

@@ -7,7 +7,7 @@ A graphlib.TopologicalSorter schedules them: tasks with no unmet dependencies
 run immediately in parallel; each completion may unlock new tasks.
 
 Current dependency graph:
-  - All 9 non-availability extractors have no dependencies (run in parallel)
+  - All 10 non-availability extractors have no dependencies (run in parallel)
   - playtomic_availability depends on playtomic_tenants (starts as soon as
     tenants finishes, even if other extractors are still running)
 """
@@ -38,6 +38,8 @@ from .playtomic_availability import EXTRACTOR_NAME as AVAILABILITY_NAME
 from .playtomic_availability import extract as extract_availability
 from .playtomic_tenants import EXTRACTOR_NAME as TENANTS_NAME
 from .playtomic_tenants import extract as extract_tenants
+from .worldbank import EXTRACTOR_NAME as WORLDBANK_NAME
+from .worldbank import extract as extract_worldbank
 
 logger = setup_logging("padelnomics.extract")
 
@@ -54,6 +56,7 @@ EXTRACTORS: dict[str, tuple] = {
     GEONAMES_NAME:             (extract_geonames, []),
     GISCO_NAME:                (extract_gisco, []),
     TENANTS_NAME:              (extract_tenants, []),
+    WORLDBANK_NAME:            (extract_worldbank, []),
     AVAILABILITY_NAME:         (extract_availability, [TENANTS_NAME]),
 }
 

extract/padelnomics_extract/src/padelnomics_extract/worldbank.py

@@ -0,0 +1,153 @@
+"""World Bank WDI extractor — GNI per capita PPP and price level ratio.
+
+Fetches two indicators (one API call each, no key required):
+  - NY.GNP.PCAP.PP.CD  — GNI per capita, PPP (international $)
+  - PA.NUS.PPPC.RF      — Price level ratio (PPP conversion factor / exchange rate)
+
+These provide global fallbacks behind Eurostat for dim_countries.median_income_pps
+and dim_countries.pli_construction (see dim_countries.sql for calibration logic).
+
+API: World Bank API v2 — https://api.worldbank.org/v2/
+No API key required. No env vars.
+
+Landing: {LANDING_DIR}/worldbank/{year}/{month}/wdi_indicators.json.gz
+Output:  {"rows": [{"country_code": "DE", "indicator": "NY.GNP.PCAP.PP.CD",
+                    "ref_year": 2023, "value": 74200.0}, ...], "count": N}
+"""
+
+import json
+import sqlite3
+from pathlib import Path
+
+import niquests
+
+from ._shared import HTTP_TIMEOUT_SECONDS, run_extractor, setup_logging
+from .utils import get_last_cursor, landing_path, write_gzip_atomic
+
+logger = setup_logging("padelnomics.extract.worldbank")
+
+EXTRACTOR_NAME = "worldbank"
+
+INDICATORS = ["NY.GNP.PCAP.PP.CD", "PA.NUS.PPPC.RF"]
+# 6 years of data — we take the latest non-null per country in staging
+DATE_RANGE = "2019:2025"
+MAX_PER_PAGE = 5000
+MAX_PAGES = 3
+
+WDI_BASE_URL = "https://api.worldbank.org/v2/country/all/indicator"
+
+# WB aggregate codes that look like real 2-letter country codes.
+# These are regional/income-group aggregates, not actual countries.
+_WB_AGGREGATE_CODES = frozenset({
+    "EU", "OE",
+    "XC", "XD", "XE", "XF", "XG", "XH", "XI", "XJ", "XL", "XM",
+    "XN", "XO", "XP", "XQ", "XR", "XS", "XT", "XU", "XV", "XY",
+    "ZF", "ZG", "ZH", "ZI", "ZJ", "ZQ", "ZT",
+    "V1", "V2", "V3", "V4",
+})
+
+
+def _normalize_country_code(wb_code: str) -> str | None:
+    """Normalize WB country code to ISO alpha-2. Returns None for aggregates."""
+    code = wb_code.strip().upper()
+    if len(code) != 2:
+        return None
+    # Reject codes starting with a digit (e.g. "1W" for World)
+    if code[0].isdigit():
+        return None
+    if code in _WB_AGGREGATE_CODES:
+        return None
+    return code
+
+
+def _fetch_indicator(
+    session: niquests.Session,
+    indicator: str,
+) -> list[dict]:
+    """Fetch all records for one indicator. Returns list of row dicts."""
+    rows: list[dict] = []
+    page = 1
+
+    while page <= MAX_PAGES:
+        url = (
+            f"{WDI_BASE_URL}/{indicator}"
+            f"?format=json&date={DATE_RANGE}&per_page={MAX_PER_PAGE}&page={page}"
+        )
+        logger.info("GET %s page %d", indicator, page)
+        resp = session.get(url, timeout=HTTP_TIMEOUT_SECONDS * 2)
+        resp.raise_for_status()
+
+        data = resp.json()
+        assert isinstance(data, list) and len(data) == 2, (
+            f"unexpected WB response shape for {indicator}: {type(data)}, len={len(data)}"
+        )
+        meta, records = data
+        total_pages = meta.get("pages", 1)
+
+        if records is None:
+            logger.warning("WB returned null data for %s page %d", indicator, page)
+            break
+
+        for record in records:
+            value = record.get("value")
+            if value is None:
+                continue
+            country_code = _normalize_country_code(record["country"]["id"])
+            if country_code is None:
+                continue
+            rows.append({
+                "country_code": country_code,
+                "indicator": indicator,
+                "ref_year": int(record["date"]),
+                "value": float(value),
+            })
+
+        if page >= total_pages:
+            break
+        page += 1
+
+    return rows
+
+
+def extract(
+    landing_dir: Path,
+    year_month: str,
+    conn: sqlite3.Connection,
+    session: niquests.Session,
+) -> dict:
+    """Fetch WDI indicators. Skips if already run this month."""
+    last_cursor = get_last_cursor(conn, EXTRACTOR_NAME)
+    if last_cursor == year_month:
+        logger.info("already have data for %s — skipping", year_month)
+        return {"files_written": 0, "files_skipped": 1, "bytes_written": 0}
+
+    rows: list[dict] = []
+    for indicator in INDICATORS:
+        indicator_rows = _fetch_indicator(session, indicator)
+        logger.info("%s: %d records", indicator, len(indicator_rows))
+        rows.extend(indicator_rows)
+
+    assert len(rows) >= 200, f"expected ≥200 WB records, got {len(rows)} — API may have changed"
+    logger.info("total: %d WDI records", len(rows))
+
+    year, month = year_month.split("/")
+    dest_dir = landing_path(landing_dir, "worldbank", year, month)
+    dest = dest_dir / "wdi_indicators.json.gz"
+    payload = json.dumps({"rows": rows, "count": len(rows)}).encode()
+    bytes_written = write_gzip_atomic(dest, payload)
+    logger.info("written %s bytes compressed", f"{bytes_written:,}")
+
+    return {
+        "files_written": 1,
+        "files_skipped": 0,
+        "bytes_written": bytes_written,
+        "cursor_value": year_month,
+    }
+
+
+def main() -> None:
+    run_extractor(EXTRACTOR_NAME, extract)
+
+
+if __name__ == "__main__":
+    main()

infra/supervisor/workflows.toml

@@ -72,3 +72,8 @@ description = "UK local authority population estimates from ONS"
 module = "padelnomics_extract.gisco"
 schedule = "0 0 1 1 *"
 description = "EU geographic boundaries (NUTS2 polygons) from Eurostat GISCO"
+
+[worldbank]
+module = "padelnomics_extract.worldbank"
+schedule = "monthly"
+description = "GNI per capita PPP + price level ratio from World Bank WDI"

transform/sqlmesh_padelnomics/models/foundation/dim_countries.sql

@@ -2,10 +2,14 @@
 --
 -- Consolidates data previously duplicated across dim_cities and dim_locations:
 --   - country_name_en / country_slug  (was: ~50-line CASE blocks in both models)
---   - median_income_pps               (was: country_income CTE in both models)
+--   - median_income_pps               (Eurostat PPS preferred, World Bank GNI PPP fallback)
---   - energy prices, labour costs, PLI indices  (new — from Eurostat datasets)
+--   - energy prices, labour costs, PLI indices  (Eurostat, WB price level ratio fallback)
 --   - cost override columns for the financial calculator
 --
+-- World Bank fallback: for non-EU countries (AR, MX, AE, AU, etc.), income and PLI
+-- are derived from WB WDI indicators calibrated to the Eurostat scale using Germany
+-- as anchor. See de_calibration CTE. EU countries keep exact Eurostat values.
+--
 -- Used by: dim_cities, dim_locations, pseo_city_costs_de, planner_defaults.
 -- Grain: country_code (one row per ISO 3166-1 alpha-2 country code).
 -- Kind: FULL — small table (~40 rows), full refresh daily.
@@ -82,6 +86,26 @@ de_elec AS (
 de_gas AS (
   SELECT gas_eur_gj FROM latest_gas WHERE country_code = 'DE'
 ),
+-- Latest World Bank WDI per country (GNI PPP + price level ratio)
+latest_wb AS (
+  SELECT country_code, gni_ppp, price_level_ratio, ref_year AS wb_year
+  FROM staging.stg_worldbank_income
+  WHERE gni_ppp IS NOT NULL OR price_level_ratio IS NOT NULL
+  QUALIFY ROW_NUMBER() OVER (PARTITION BY country_code ORDER BY ref_year DESC) = 1
+),
+-- Germany calibration anchor: Eurostat PPS + WB GNI PPP + WB price ratio + Eurostat PLI construction.
+-- Used to scale World Bank values into Eurostat-comparable ranges.
+-- Single row; if DE is missing from any source, that ratio produces NULL (safe fallthrough).
+de_calibration AS (
+  SELECT
+    i.median_income_pps   AS de_eurostat_pps,
+    wb.gni_ppp            AS de_gni_ppp,
+    wb.price_level_ratio  AS de_price_level_ratio,
+    p.construction        AS de_pli_construction
+  FROM (SELECT median_income_pps FROM latest_income WHERE country_code = 'DE') i
+  CROSS JOIN (SELECT gni_ppp, price_level_ratio FROM latest_wb WHERE country_code = 'DE') wb
+  CROSS JOIN (SELECT construction FROM pli_pivoted WHERE country_code = 'DE') p
+),
 -- All distinct country codes from any source
 all_countries AS (
   SELECT country_code FROM latest_income
@@ -93,6 +117,8 @@ all_countries AS (
   SELECT country_code FROM latest_labour
   UNION
   SELECT country_code FROM pli_pivoted
+  UNION
+  SELECT country_code FROM latest_wb
   -- Ensure known padel markets appear even if Eurostat doesn't cover them yet
   UNION ALL
   SELECT unnest(['DE','ES','GB','FR','IT','PT','AT','CH','NL','BE','SE','NO','DK','FI',
@@ -149,15 +175,21 @@ SELECT
       ELSE ac.country_code
     END, '[^a-zA-Z0-9]+', '-'
   ))                                                             AS country_slug,
-  -- Income data
+  -- Income: Eurostat PPS preferred, World Bank GNI PPP scaled to PPS as fallback
-  i.median_income_pps,
+  COALESCE(
-  i.income_year,
+    i.median_income_pps,
+    ROUND(wb.gni_ppp * (de_cal.de_eurostat_pps / NULLIF(de_cal.de_gni_ppp, 0)), 0)
+  )                                                            AS median_income_pps,
+  COALESCE(i.income_year, wb.wb_year)                          AS income_year,
   -- Raw energy and labour data (for reference / future staffed-scenario use)
   e.electricity_eur_kwh,
   g.gas_eur_gj,
   la.labour_cost_eur_hour,
-  -- PLI indices per category (EU27=100)
+  -- PLI construction: Eurostat preferred, World Bank price level ratio scaled to PLI as fallback
-  p.construction                                                 AS pli_construction,
+  COALESCE(
+    p.construction,
+    ROUND(wb.price_level_ratio / NULLIF(de_cal.de_price_level_ratio, 0) * de_cal.de_pli_construction, 1)
+  )                                                              AS pli_construction,
   p.housing                                                      AS pli_housing,
   p.services                                                     AS pli_services,
   p.misc                                                         AS pli_misc,
@@ -278,8 +310,10 @@ LEFT JOIN latest_electricity e  ON ac.country_code = e.country_code
 LEFT JOIN latest_gas g          ON ac.country_code = g.country_code
 LEFT JOIN latest_labour la      ON ac.country_code = la.country_code
 LEFT JOIN pli_pivoted p         ON ac.country_code = p.country_code
+LEFT JOIN latest_wb wb         ON ac.country_code = wb.country_code
 CROSS JOIN de_pli de_p
 CROSS JOIN de_elec de_e
 CROSS JOIN de_gas de_g
+CROSS JOIN de_calibration de_cal
 -- Enforce grain
 QUALIFY ROW_NUMBER() OVER (PARTITION BY ac.country_code ORDER BY ac.country_code) = 1

transform/sqlmesh_padelnomics/models/staging/stg_worldbank_income.sql

@@ -0,0 +1,41 @@
+-- World Bank WDI indicators: GNI per capita PPP and price level ratio.
+-- Pivoted to one row per (country_code, ref_year) with both indicators as columns.
+--
+-- Source: data/landing/worldbank/{year}/{month}/wdi_indicators.json.gz
+-- Extracted by: worldbank.py
+-- Used by: dim_countries (fallback behind Eurostat for non-EU countries)
+
+MODEL (
+  name staging.stg_worldbank_income,
+  kind FULL,
+  cron '@daily',
+  grain (country_code, ref_year)
+);
+
+WITH parsed AS (
+  SELECT
+    row ->> 'country_code'                          AS country_code,
+    TRY_CAST(row ->> 'ref_year' AS INTEGER)         AS ref_year,
+    row ->> 'indicator'                             AS indicator,
+    TRY_CAST(row ->> 'value' AS DOUBLE)             AS value,
+    CURRENT_DATE                                    AS extracted_date
+  FROM (
+    SELECT UNNEST(rows) AS row
+    FROM read_json(
+      @LANDING_DIR || '/worldbank/*/*/wdi_indicators.json.gz',
+      auto_detect = true
+    )
+  )
+  WHERE (row ->> 'country_code') IS NOT NULL
+)
+SELECT
+  country_code,
+  ref_year,
+  MAX(value) FILTER (WHERE indicator = 'NY.GNP.PCAP.PP.CD') AS gni_ppp,
+  MAX(value) FILTER (WHERE indicator = 'PA.NUS.PPPC.RF')     AS price_level_ratio,
+  MAX(extracted_date)                                         AS extracted_date
+FROM parsed
+WHERE value IS NOT NULL
+  AND value > 0
+  AND LENGTH(country_code) = 2
+GROUP BY country_code, ref_year