merge: geo headers on city/region hubs (opportunity map pre-select, city highlight, color fix)

Pre-select user's country on opportunity map dropdown (CF-IPCountry),
auto-load the map on page load. Highlight user's city on country
overview maps with a blue ring (CF-IPCity best-effort match). Unify
opportunity score color scale to red/amber/green (was using blue for
low scores). Inject window.__GEO global for client-side geo access.

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

CHANGELOG.md

@@ -6,7 +6,17 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
 
 ## [Unreleased]
 
+### Added
+- **Geo headers on city/region hubs** — Cloudflare geo headers (`CF-IPCountry`, `CF-IPCity`) now used across location-based pages. Opportunity map pre-selects and auto-loads the user's country. Country overview maps highlight the user's city with a blue ring (best-effort CF-IPCity name match). `window.__GEO` JS global injected via `base.html` for client-side map code.
+
+### Fixed
+- **Opportunity map color scale** — low-score bubbles used blue (`#3B82F6`) instead of red (`#DC2626`), inconsistent with the unified `scoreColor()` scale used everywhere else. Fixed in `oppColor()`, legend, and `article-maps.js` tooltip colors. Thresholds aligned to ≥60/30/\<30.
+
 ### Changed
+- **Opportunity Score v5 → v6** — calibrates for saturated markets (Spain avg dropped from ~78 to ~50-60 range). Density ceiling lowered from 8 → 5/100k (Spain at 6-16/100k now hits zero-gap). Supply deficit weight increased from 35 → 40 pts. Addressable market reduced from 25 → 20 pts. Market validation inverted → "market headroom": high country avg maturity now reduces opportunity (saturated market = less room for new entrants).
+- **Markets page map legend** — bubble map now has a visual legend explaining size = venue count, color = Market Score. Opportunity score tooltip color unified to same green/amber/red scale (was using blue for low scores, inconsistent).
+- **Geo-localized article sorting** — `/markets` page sorts articles by user proximity using Cloudflare CF-IPCountry header. User's country first, nearby countries second (DACH, Iberia, Nordics, etc.), rest by published_at. Map bubbles re-ordered so user's country renders on top. Falls back to chronological order when header is absent (local dev).
+- **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)
---   - energy prices, labour costs, PLI indices  (new — from Eurostat datasets)
+--   - median_income_pps               (Eurostat PPS preferred, World Bank GNI PPP fallback)
+--   - 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
-  i.median_income_pps,
-  i.income_year,
+  -- Income: Eurostat PPS preferred, World Bank GNI PPP scaled to PPS as fallback
+  COALESCE(
+    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)
-  p.construction                                                 AS pli_construction,
+  -- PLI construction: Eurostat preferred, World Bank price level ratio scaled to PLI as fallback
+  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/serving/location_profiles.sql

@@ -19,22 +19,22 @@
 --   10 pts  economic context  — income PPS normalised to 25,000 ceiling
 --   10 pts  data quality      — completeness discount
 --
--- Padelnomics Opportunity Score (Marktpotenzial-Score v5, 0–100):
+-- Padelnomics Opportunity Score (Marktpotenzial-Score v6, 0–100):
 --   "Where should I build a padel court?"
 --   Computed for ALL locations — zero-court locations score highest on supply deficit.
 --   H3 catchment methodology: addressable market and supply deficit use a regional
 --   H3 catchment (res-5 cell + 6 neighbours, ~24km radius).
 --
---   v5 changes: merge supply gap + catchment gap → single supply deficit (35 pts),
---   add sports culture proxy (10 pts, tennis density), add construction affordability (5 pts),
---   reduce economic power from 20 → 15 pts.
+--   v6 changes: lower density ceiling 8→5/100k (saturated markets hit zero-gap sooner),
+--   increase supply deficit weight 35→40 pts, reduce addressable market 25→20 pts,
+--   invert market validation (high country maturity = LESS opportunity).
 --
---   25 pts  addressable market        — log-scaled catchment population, ceiling 500K
+--   20 pts  addressable market        — log-scaled catchment population, ceiling 500K
 --   15 pts  economic power            — income PPS, normalised to 35,000
---   35 pts  supply deficit            — max(density gap, distance gap); eliminates double-count
+--   40 pts  supply deficit            — max(density gap, distance gap); eliminates double-count
 --   10 pts  sports culture            — tennis court density as racquet-sport adoption proxy
 --    5 pts  construction affordability — income relative to construction costs (PLI)
---   10 pts  market validation         — country-level avg market maturity (from market_scored CTE)
+--   10 pts  market headroom           — inverse country-level avg market maturity
 --
 -- Consumers query directly with WHERE filters:
 --   cities API:       WHERE country_slug = ? AND city_slug IS NOT NULL
@@ -198,9 +198,9 @@ market_scored AS (
     END AS market_score
   FROM with_pricing
 ),
--- Step 2: country-level avg market maturity — used as market validation signal (10 pts).
+-- Step 2: country-level avg market maturity — used as market headroom signal (10 pts).
 -- Filter to market_score > 0 (cities with padel courts only) so zero-court locations
--- don't dilute the country signal. ES proven demand → ~60, SE struggling → ~35.
+-- don't dilute the country signal. Higher avg = more saturated = less headroom.
 country_market AS (
   SELECT
     country_code,
@@ -212,21 +212,21 @@ country_market AS (
 -- Step 3: add opportunity_score using country market validation signal.
 scored AS (
   SELECT ms.*,
-    -- ── Opportunity Score (Marktpotenzial-Score v5, H3 catchment) ──────────
+    -- ── Opportunity Score (Marktpotenzial-Score v6, H3 catchment) ──────────
     ROUND(
-      -- Addressable market (25 pts): log-scaled catchment population, ceiling 500K
-      25.0 * LEAST(1.0, LN(GREATEST(catchment_population, 1)) / LN(500000))
+      -- Addressable market (20 pts): log-scaled catchment population, ceiling 500K
+      20.0 * LEAST(1.0, LN(GREATEST(catchment_population, 1)) / LN(500000))
       -- Economic power (15 pts): income PPS normalised to 35,000
       + 15.0 * LEAST(1.0, COALESCE(median_income_pps, 15000) / 35000.0)
-      -- Supply deficit (35 pts): max of density gap and distance gap.
-      -- Merges old supply gap (30) + catchment gap (15) which were ~80% correlated.
-      + 35.0 * GREATEST(
-          -- density-based gap (H3 catchment): 0 courts = 1.0, 8/100k = 0.0
+      -- Supply deficit (40 pts): max of density gap and distance gap.
+      -- Ceiling 5/100k (down from 8): Spain at 6-16/100k now hits zero-gap.
+      + 40.0 * GREATEST(
+          -- density-based gap (H3 catchment): 0 courts = 1.0, 5/100k = 0.0
           GREATEST(0.0, 1.0 - COALESCE(
             CASE WHEN catchment_population > 0
               THEN GREATEST(catchment_padel_courts, COALESCE(city_padel_venue_count, 0))::DOUBLE / catchment_population * 100000
               ELSE 0.0
-            END, 0.0) / 8.0),
+            END, 0.0) / 5.0),
           -- distance-based gap: 30km+ = 1.0, 0km = 0.0; NULL = 0.5
           COALESCE(LEAST(1.0, nearest_padel_court_km / 30.0), 0.5)
         )
@@ -239,10 +239,11 @@ scored AS (
           COALESCE(median_income_pps, 15000) / 35000.0
           / GREATEST(0.5, COALESCE(pli_construction, 100.0) / 100.0)
         )
-      -- Market validation (10 pts): country-level avg market maturity.
-      -- ES (~70/100): proven demand → ~7 pts. SE (~35/100): emerging → ~3.5 pts.
-      -- NULL (no courts in country yet): 0.5 neutral → 5 pts (untested, not penalised).
-      + 10.0 * COALESCE(cm.country_avg_market_score / 100.0, 0.5)
+      -- Market headroom (10 pts): INVERSE country-level avg market maturity.
+      -- High avg market score = saturated market = LESS opportunity for new entrants.
+      -- ES (~46/100): proven demand, less headroom → ~5.4 pts.
+      -- SE (~40/100): emerging → ~6 pts. NULL: 0.5 neutral → 5 pts.
+      + 10.0 * (1.0 - COALESCE(cm.country_avg_market_score / 100.0, 0.5))
     , 1) AS opportunity_score
   FROM market_scored ms
   LEFT JOIN country_market cm ON ms.country_code = cm.country_code

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

web/src/padelnomics/app.py

@@ -148,6 +148,18 @@ def create_app() -> Quart:
     # Per-request hooks
     # -------------------------------------------------------------------------
 
+    @app.before_request
+    async def set_user_geo():
+        """Stash Cloudflare geo headers in g for proximity sorting.
+
+        Requires nginx: proxy_set_header CF-IPCountry $http_cf_ipcountry;
+                        proxy_set_header CF-RegionCode $http_cf_regioncode;
+                        proxy_set_header CF-IPCity $http_cf_ipcity;
+        """
+        g.user_country = request.headers.get("CF-IPCountry", "").upper() or ""
+        g.user_region = request.headers.get("CF-RegionCode", "") or ""
+        g.user_city = request.headers.get("CF-IPCity", "") or ""
+
     @app.before_request
     async def validate_lang():
         """404 unsupported language prefixes (e.g. /fr/terms)."""
@@ -234,6 +246,8 @@ def create_app() -> Quart:
             "csrf_token": get_csrf_token,
             "ab_variant": getattr(g, "ab_variant", None),
             "ab_tag": getattr(g, "ab_tag", None),
+            "user_country": g.get("user_country", ""),
+            "user_city": g.get("user_city", ""),
             "lang": effective_lang,
             "t": get_translations(effective_lang),
             "v": _ASSET_VERSION,

web/src/padelnomics/content/routes.py

@@ -212,6 +212,13 @@ async def markets():
         FROM serving.pseo_country_overview
         ORDER BY total_venues DESC
     """)
+    # Sort so user's country renders last (on top in Leaflet z-order)
+    user_country = g.get("user_country", "")
+    if user_country and map_countries:
+        map_countries = sorted(
+            map_countries,
+            key=lambda c: 1 if c["country_code"] == user_country else 0,
+        )
 
     return await render_template(
         "markets.html",
@@ -237,9 +244,46 @@ async def market_results():
     return await render_template("partials/market_results.html", articles=articles)
 
 
+_NEARBY_COUNTRIES: dict[str, tuple[str, ...]] = {
+    "DE": ("AT", "CH"), "AT": ("DE", "CH"), "CH": ("DE", "AT"),
+    "ES": ("PT",), "PT": ("ES",),
+    "GB": ("IE",), "IE": ("GB",),
+    "US": ("CA",), "CA": ("US",),
+    "IT": ("CH",), "FR": ("BE", "CH"), "BE": ("FR", "NL", "DE"),
+    "NL": ("BE", "DE"), "SE": ("NO", "DK", "FI"), "NO": ("SE", "DK"),
+    "DK": ("SE", "NO", "DE"), "FI": ("SE",),
+    "MX": ("US",), "BR": ("AR",), "AR": ("BR",),
+}
+
+
+def _geo_order_clause(user_country: str) -> tuple[str, list]:
+    """Build ORDER BY clause that sorts user's country first, nearby second.
+
+    Returns (order_sql, params) where order_sql starts with the geo CASE
+    followed by published_at DESC.  Caller prepends 'ORDER BY'.
+    """
+    if not user_country:
+        return "published_at DESC", []
+
+    nearby = _NEARBY_COUNTRIES.get(user_country, ())
+    if nearby:
+        placeholders = ",".join("?" * len(nearby))
+        geo_case = f"""CASE WHEN country = ? THEN 0
+             WHEN country IN ({placeholders}) THEN 1
+             ELSE 2 END,
+        published_at DESC"""
+        return geo_case, [user_country, *nearby]
+
+    return """CASE WHEN country = ? THEN 0 ELSE 1 END,
+        published_at DESC""", [user_country]
+
+
 async def _filter_articles(q: str, country: str, region: str) -> list[dict]:
-    """Query published articles for the current language."""
+    """Query published articles for the current language, geo-sorted."""
     lang = g.get("lang", "en")
+    user_country = g.get("user_country", "")
+    geo_order, geo_params = _geo_order_clause(user_country)
+
     if q:
         # FTS query
         wheres = ["articles_fts MATCH ?"]
@@ -253,14 +297,16 @@ async def _filter_articles(q: str, country: str, region: str) -> list[dict]:
             wheres.append("a.region = ?")
             params.append(region)
         where = " AND ".join(wheres)
+        # Geo-sort references a.country
+        order = geo_order.replace("country", "a.country")
         return await fetch_all(
             f"""SELECT a.* FROM articles a
                 JOIN articles_fts ON articles_fts.rowid = a.id
                 WHERE {where}
                 AND a.status = 'published' AND a.published_at <= datetime('now')
-                ORDER BY a.published_at DESC
+                ORDER BY {order}
                 LIMIT 100""",
-            tuple(params),
+            tuple(params + geo_params),
         )
     else:
         wheres = ["status = 'published'", "published_at <= datetime('now')", "language = ?"]
@@ -274,8 +320,8 @@ async def _filter_articles(q: str, country: str, region: str) -> list[dict]:
         where = " AND ".join(wheres)
         return await fetch_all(
             f"""SELECT * FROM articles WHERE {where}
-                ORDER BY published_at DESC LIMIT 100""",
-            tuple(params),
+                ORDER BY {geo_order} LIMIT 100""",
+            tuple(params + geo_params),
         )
 
 

web/src/padelnomics/content/templates/markets.html

@@ -16,7 +16,22 @@
     <p class="text-slate">{{ t.mkt_subheading }}</p>
   </header>
 
-  <div id="markets-map" style="height:420px; border-radius:12px;" class="mb-6"></div>
+  <div id="markets-map" style="height:420px; border-radius:12px;" class="mb-4"></div>
+
+  <!-- Map legend -->
+  <div class="mb-6" style="display:flex; gap:1.5rem; align-items:center; font-size:0.82rem; color:#64748B;">
+    <span style="display:flex; align-items:center; gap:0.35rem;">
+      <span style="display:inline-block; width:12px; height:12px; border-radius:50%; background:#16A34A; border:2px solid white; box-shadow:0 1px 3px rgba(0,0,0,0.2);"></span>
+      <span style="display:inline-block; width:18px; height:18px; border-radius:50%; background:#16A34A; border:2px solid white; box-shadow:0 1px 3px rgba(0,0,0,0.2);"></span>
+      {{ t.mkt_legend_size }}
+    </span>
+    <span style="display:flex; align-items:center; gap:0.35rem;">
+      <span style="display:inline-block; width:14px; height:14px; border-radius:50%; background:#16A34A; border:2px solid white; box-shadow:0 1px 3px rgba(0,0,0,0.2);"></span>
+      <span style="display:inline-block; width:14px; height:14px; border-radius:50%; background:#D97706; border:2px solid white; box-shadow:0 1px 3px rgba(0,0,0,0.2);"></span>
+      <span style="display:inline-block; width:14px; height:14px; border-radius:50%; background:#DC2626; border:2px solid white; box-shadow:0 1px 3px rgba(0,0,0,0.2);"></span>
+      {{ t.mkt_legend_color }}
+    </span>
+  </div>
 
   <!-- Filters -->
   <div class="card mb-8">
@@ -100,7 +115,7 @@
       if (!c.lat || !c.lon) return;
       var size = 12 + 44 * Math.sqrt(c.total_venues / maxV);
       var color = scoreColor(c.avg_market_score);
-      var oppColor = c.avg_opportunity_score >= 60 ? '#16A34A' : (c.avg_opportunity_score >= 30 ? '#D97706' : '#3B82F6');
+      var oppColor = scoreColor(c.avg_opportunity_score || 0);
       var tip = '<strong>' + c.country_name_en + '</strong><br>'
         + c.total_venues + ' venues · ' + c.city_count + ' cities<br>'
         + '<span style="color:' + color + ';font-weight:600;">Padelnomics Market Score: ' + c.avg_market_score + '/100</span><br>'

web/src/padelnomics/locales/de.json

@@ -606,6 +606,8 @@
   "mkt_all_countries": "Alle Länder",
   "mkt_all_regions": "Alle Regionen",
   "mkt_no_results": "Keine Märkte gefunden. Passe Deine Filter an.",
+  "mkt_legend_size": "Kreisgröße = Anzahl Anlagen",
+  "mkt_legend_color": "Farbe = Market Score",
   "waitlist_markets_title": "Marktdaten — Demnächst verfügbar",
   "waitlist_markets_sub": "Detaillierte Marktberichte für Padel-Investoren: Baukosten, Umsatz-Benchmarks, Auslastungsdaten und ROI-Analysen nach Stadt und Region.",
   "waitlist_markets_feature1": "Echte Kostendaten aus laufenden Anlagen in über 30 Ländern",

web/src/padelnomics/locales/en.json

@@ -606,6 +606,8 @@
   "mkt_all_countries": "All Countries",
   "mkt_all_regions": "All Regions",
   "mkt_no_results": "No markets found. Try adjusting your filters.",
+  "mkt_legend_size": "Bubble size = venue count",
+  "mkt_legend_color": "Color = Market Score",
   "waitlist_markets_title": "Markets Intelligence — Coming Soon",
   "waitlist_markets_sub": "Deep-dive market reports for padel investors: construction costs, revenue benchmarks, occupancy data, and ROI analysis by city and region.",
   "waitlist_markets_feature1": "Real cost data from operating venues across 30+ countries",

web/src/padelnomics/public/routes.py

@@ -79,12 +79,23 @@ async def opportunity_map():
     if not await is_flag_enabled("maps", default=True):
         abort(404)
     countries = await fetch_analytics("""
-        SELECT DISTINCT country_slug, country_name_en
+        SELECT DISTINCT country_slug, country_name_en, country_code
         FROM serving.location_profiles
         WHERE city_slug IS NOT NULL
         ORDER BY country_name_en
     """)
-    return await render_template("opportunity_map.html", countries=countries)
+    user_cc = g.get("user_country", "")
+    selected_slug = ""
+    if user_cc:
+        for c in countries:
+            if c["country_code"] == user_cc:
+                selected_slug = c["country_slug"]
+                break
+        countries = sorted(
+            countries,
+            key=lambda c: (0 if c["country_code"] == user_cc else 1, c["country_name_en"]),
+        )
+    return await render_template("opportunity_map.html", countries=countries, selected_slug=selected_slug)
 
 
 @bp.route("/opportunity-map/data")

web/src/padelnomics/public/templates/opportunity_map.html

@@ -30,7 +30,7 @@
             hx-trigger="change">
       <option value="">— choose country —</option>
       {% for c in countries %}
-      <option value="{{ c.country_slug }}">{{ c.country_name_en }}</option>
+      <option value="{{ c.country_slug }}" {% if c.country_slug == selected_slug %}selected{% endif %}>{{ c.country_name_en }}</option>
       {% endfor %}
     </select>
   </div>
@@ -41,9 +41,9 @@
   <div class="mt-4 text-sm text-slate">
     <strong>Circle size:</strong> population &nbsp;|&nbsp;
     <strong>Color:</strong>
-    <span style="display:inline-block;width:10px;height:10px;border-radius:50%;background:#16A34A;vertical-align:middle;margin:0 4px"></span>High (≥70) &nbsp;
-    <span style="display:inline-block;width:10px;height:10px;border-radius:50%;background:#D97706;vertical-align:middle;margin:0 4px"></span>Mid (40–70) &nbsp;
-    <span style="display:inline-block;width:10px;height:10px;border-radius:50%;background:#3B82F6;vertical-align:middle;margin:0 4px"></span>Low (&lt;40)
+    <span style="display:inline-block;width:10px;height:10px;border-radius:50%;background:#16A34A;vertical-align:middle;margin:0 4px"></span>High (≥60) &nbsp;
+    <span style="display:inline-block;width:10px;height:10px;border-radius:50%;background:#D97706;vertical-align:middle;margin:0 4px"></span>Mid (30–60) &nbsp;
+    <span style="display:inline-block;width:10px;height:10px;border-radius:50%;background:#DC2626;vertical-align:middle;margin:0 4px"></span>Low (&lt;30)
   </div>
 </main>
 {% endblock %}
@@ -62,9 +62,9 @@
   var refLayer = L.layerGroup().addTo(map);
 
   function oppColor(score) {
-    if (score >= 70) return '#16A34A';
-    if (score >= 40) return '#D97706';
-    return '#3B82F6';
+    if (score >= 60) return '#16A34A';
+    if (score >= 30) return '#D97706';
+    return '#DC2626';
   }
 
   function makeIcon(size, color) {
@@ -133,6 +133,10 @@
   document.body.addEventListener('htmx:afterSwap', function(e) {
     if (e.detail.target.id === 'map-data') renderMap();
   });
+
+  // Auto-load if country pre-selected via geo header
+  var sel = document.getElementById('opp-country-select');
+  if (sel.value) htmx.trigger(sel, 'change');
 })();
 </script>
 {% endblock %}

web/src/padelnomics/static/css/input.css

@@ -892,6 +892,12 @@
   transform: scale(1.1);
 }
 
+/* User's city highlight — blue ring on top of score-colored bubble */
+.pn-marker--highlight {
+  border: 3px solid #3B82F6;
+  box-shadow: 0 0 0 2px rgba(59, 130, 246, 0.3), 0 2px 8px rgba(0,0,0,0.28);
+}
+
 /* Non-article city markers: faded + dashed border, no click affordance */
 .pn-marker--muted {
   opacity: 0.45;

web/src/padelnomics/static/js/article-maps.js

@@ -49,7 +49,7 @@
           var pop = c.population >= 1000000
             ? (c.population / 1000000).toFixed(1) + 'M'
             : (c.population >= 1000 ? Math.round(c.population / 1000) + 'K' : (c.population || ''));
-          var oppColor = c.opportunity_score >= 60 ? '#16A34A' : (c.opportunity_score >= 30 ? '#D97706' : '#3B82F6');
+          var oppColor = c.opportunity_score >= 60 ? '#16A34A' : (c.opportunity_score >= 30 ? '#D97706' : '#DC2626');
           var tip = '<strong>' + c.city_name + '</strong><br>'
             + (c.padel_venue_count || 0) + ' venues'
             + (pop ? ' · ' + pop : '')
@@ -69,6 +69,26 @@
           bounds.push([c.lat, c.lon]);
         });
         if (bounds.length) map.fitBounds(bounds, { padding: [24, 24] });
+
+        // Highlight user's city (best-effort name match via CF-IPCity)
+        var uc = (window.__GEO || {}).city || '';
+        if (uc) {
+          var match = data.find(function(c) {
+            return c.city_name && c.city_name.toLowerCase() === uc.toLowerCase();
+          });
+          if (match && match.lat && match.lon) {
+            var hSize = 10 + 36 * Math.sqrt((match.padel_venue_count || 1) / maxV);
+            var hs = Math.round(hSize);
+            var hColor = scoreColor(match.market_score);
+            var hIcon = L.divIcon({
+              className: '',
+              html: '<div class="pn-marker pn-marker--highlight" style="width:' + hs + 'px;height:' + hs + 'px;background:' + hColor + ';"></div>',
+              iconSize: [hs, hs],
+              iconAnchor: [hs / 2, hs / 2],
+            });
+            L.marker([match.lat, match.lon], { icon: hIcon }).addTo(map);
+          }
+        }
       })
       .catch(function(err) { console.error('Country map fetch failed:', err); });
   }

web/src/padelnomics/templates/base.html

@@ -36,6 +36,7 @@
   <meta property="og:image" content="{{ url_for('static', filename='images/logo.png', _external=True) }}">
   <meta name="twitter:card" content="summary_large_image">
 
+  <script>window.__GEO = {country: "{{ user_country }}", city: "{{ user_city }}"};</script>
   {% block head %}{% endblock %}
 </head>
 <body>