crs-l3-tokyo-jgd-crossings

Rerun

cat << 'PYEOF' > /work/find_wards.py
import requests, json, time, sys

# Overpass query to find Tokyo's 23 special wards
# First, find the relation that groups all special wards, and each ward
overpass_url = "https://overpass-api.de/api/interpreter"

# Query for the special wards of Tokyo
query = """
[out:json][timeout:60];
(
  /* Special wards of Tokyo - the 23 ku */
  area["name"="東京都"]["admin_level"="2"]->.tokyo;
  
  /* Get all admin_level=9 relations (special wards) within Tokyo */
  rel[admin_level="9"][boundary="administrative"](area.tokyo);
);
out body; >;
out skel qt;
"""

try:
    r = requests.post(overpass_url, data={"data": query}, timeout=120)
    r.raise_for_status()
    data = r.json()
    print(f"Found {len(data.get('elements',[]))} elements")
    for el in data.get('elements', []):
        tags = el.get('tags', {})
        print(f"  Relation {el['id']}: name={tags.get('name','?')}, name:en={tags.get('name:en','?')}, admin_level={tags.get('admin_level','?')}")
except Exception as e:
    print(f"Error: {e}", file=sys.stderr)
PYEOF
python3 /work/find_wards.py

cat << 'PYEOF' > /work/find_wards2.py
import requests, json, sys

# Try GET with the query parameter
overpass_url = "https://overpass-api.de/api/interpreter"

# Simpler query - find Tokyo wards directly
query = """
[out:json][timeout:60];
(
  rel[name="東京都区部"];
  rel[name="Tokyo special wards"];
  rel[name="Tokyo 23 wards"];
);
out tags;
"""
try:
    r = requests.get(overpass_url, params={"data": query}, timeout=120)
    r.raise_for_status()
    data = r.json()
    print(f"Found {len(data.get('elements',[]))} elements")
    for el in data.get('elements', []):
        tags = el.get('tags', {})
        print(f"  Relation {el['id']}: name={tags.get('name','?')}, name:en={tags.get('name:en','?')}")
except Exception as e:
    print(f"Error: {e}", file=sys.stderr)

# Also try the area approach
print("\n--- Trying area approach ---")
query2 = """
[out:json][timeout:60];
area["name:en"="Tokyo"]["admin_level"="2"];
rel(area)[admin_level="9"][boundary="administrative"];
out tags;
"""
try:
    r = requests.get(overpass_url, params={"data": query2}, timeout=120)
    r.raise_for_status()
    data = r.json()
    print(f"Found {len(data.get('elements',[]))} elements")
    for el in data.get('elements', []):
        tags = el.get('tags', {})
        print(f"  Relation {el['id']}: name={tags.get('name','?')}, name:en={tags.get('name:en','?')}, admin_level={tags.get('admin_level','?')}")
except Exception as e:
    print(f"Error: {e}", file=sys.stderr)

PYEOF
python3 /work/find_wards2.py

cat << 'PYEOF' > /work/find_wards3.py
import requests, json, sys

# Try POST with proper headers
overpass_url = "https://overpass-api.de/api/interpreter"

query = """
[out:json][timeout:120];
area["name:en"="Tokyo"]["admin_level"="2"];
rel(area)[admin_level="9"][boundary="administrative"];
out tags;
"""
try:
    r = requests.post(overpass_url, data={"data": query}, timeout=180,
                     headers={"Accept": "application/json"})
    print(f"Status: {r.status_code}")
    r.raise_for_status()
    data = r.json()
    print(f"Found {len(data.get('elements',[]))} elements")
    for el in data.get('elements', []):
        tags = el.get('tags', {})
        print(f"  Relation {el['id']}: name={tags.get('name','?')}, name:en={tags.get('name:en','?')}")
except Exception as e:
    print(f"Error: {e}", file=sys.stderr)
    
    # Try alternative endpoint
    print("\n--- Trying alternative endpoint ---")
    alt_url = "https://lz4.overpass-api.de/api/interpreter"
    try:
        r = requests.post(alt_url, data={"data": query}, timeout=180)
        r.raise_for_status()
        data = r.json()
        print(f"Found {len(data.get('elements',[]))} elements")
        for el in data.get('elements', []):
            tags = el.get('tags', {})
            print(f"  Relation {el['id']}: name={tags.get('name','?')}, name:en={tags.get('name:en','?')}")
    except Exception as e2:
        print(f"Alt error: {e2}", file=sys.stderr)

PYEOF
python3 /work/find_wards3.py

# Use osmnx which has a robust Overpass interface
python3 << 'PYEOF'
import osmnx as ox
import geopandas as gpd

# Try using osmnx to get Tokyo ward boundaries
# The 23 special wards of Tokyo are admin_level=9
# Let's try getting them by their administrative relation

# First, get the Tokyo area
try:
    # Get all admin boundaries in Tokyo
    gdf = ox.features_from_place("Tokyo, Japan", tags={"boundary": "administrative", "admin_level": "9"})
    print("type:", type(gdf))
    print("columns:", gdf.columns.tolist())
    print("shape:", gdf.shape)
    print(gdf[['name', 'name:en', 'admin_level', 'osm_id']].head(30))
except Exception as e:
    print(f"Error: {e}")
PYEOF

python3 << 'PYEOF'
import osmnx as ox
import geopandas as gpd

# Use ox.geocode_to_gdf with a specific query for Tokyo special wards
try:
    gdf = ox.geocode_to_gdf("Tokyo, Japan")
    print("Tokyo shape:", gdf.shape)
    print(gdf.columns.tolist())
    print(gdf[['name', 'geometry']].head())
except Exception as e:
    print(f"Error: {e}")
PYEOF

Tokyo shape: (1, 17)
['geometry', 'bbox_west', 'bbox_south', 'bbox_east', 'bbox_north', 'place_id', 'osm_type', 'osm_id', 'lat', 'lon', 'class', 'type', 'place_rank', 'importance', 'addresstype', 'name', 'display_name']
    name                                           geometry
0  Tokyo  MULTIPOLYGON (((135.85369 20.42357, 135.8699 2...

python3 << 'PYEOF'
import osmnx as ox
import geopandas as gpd

# Try to get the 23 special wards by name
# The group relation containing all 23 wards
try:
    gdf = ox.geocode_to_gdf("東京都区部, Tokyo, Japan")
    print("shape:", gdf.shape)
    print(gdf.columns.tolist())
    print(gdf[['name', 'osm_type', 'osm_id', 'geometry']])
except Exception as e:
    print(f"Error for 東京都区部: {e}")

try:
    gdf2 = ox.geocode_to_gdf("Tokyo special wards, Japan")
    print("shape2:", gdf2.shape)
    print(gdf2[['name', 'osm_type', 'osm_id']])
except Exception as e:
    print(f"Error for special wards: {e}")
PYEOF

shape: (1, 17)
['geometry', 'bbox_west', 'bbox_south', 'bbox_east', 'bbox_north', 'place_id', 'osm_type', 'osm_id', 'lat', 'lon', 'class', 'type', 'place_rank', 'importance', 'addresstype', 'name', 'display_name']
               name  ...                                           geometry
0  Embassy of China  ...  POLYGON ((139.72712 35.65651, 139.72712 35.656...

[1 rows x 4 columns]
Error for special wards: Nominatim geocoder returned 0 results for query 'Tokyo special wards, Japan'.

python3 << 'PYEOF'
import osmnx as ox
import geopandas as gpd

# Let's try getting the wards individually - they are admin_level 9
# Use the Tokyo area as a filter
tokyo = ox.geocode_to_gdf("Tokyo, Japan")
tokyo_poly = tokyo.geometry.iloc[0]

# Now get all admin_level=9 boundaries within Tokyo
try:
    gdf = ox.features_from_polygon(tokyo_poly, tags={"boundary": "administrative", "admin_level": "9"})
    print("shape:", gdf.shape)
    print("columns:", gdf.columns.tolist())
    # Show relevant columns
    cols = [c for c in gdf.columns if 'name' in c.lower() or 'admin' in c.lower() or 'osm' in c.lower() or 'boundary' in c.lower()]
    print("relevant cols:", cols)
    print(gdf[['name', 'name:en', 'admin_level', 'osm_type', 'osm_id']].head(30))
except Exception as e:
    print(f"Error: {e}")
PYEOF

python3 << 'PYEOF'
import requests
import time

# Try the kumi endpoint
overpass_url = "https://overpass.kumi.systems/api/interpreter"

query = """
[out:json][timeout:120];
area["name:en"="Tokyo"]["admin_level"="2"]->.tokyo;
rel(area.tokyo)[admin_level="9"][boundary="administrative"];
out body center;
"""
try:
    r = requests.post(overpass_url, data={"data": query}, timeout=180)
    r.raise_for_status()
    data = r.json()
    print(f"Found {len(data.get('elements',[]))} elements")
    for el in data.get('elements', []):
        tags = el.get('tags', {})
        print(f"  Relation {el['id']}: name={tags.get('name','?')}, name:en={tags.get('name:en','?')}")
except Exception as e:
    print(f"Error: {e}")
PYEOF

# Try OSM API directly to get the Tokyo special wards relations
curl -s "https://api.openstreetmap.org/api/0.6/relation/1543125" | head -50
echo "---"
# Also try the "Tokyo 23 wards" relation
curl -s "https://api.openstreetmap.org/api/0.6/relation/1543125/full" 2>&1 | head -200

python3 << 'PYEOF'
import requests

# Try OSM API directly for Tokyo special wards relation
# The known relation ID for Tokyo's 23 special wards is 1543125
r = requests.get("https://api.openstreetmap.org/api/0.6/relation/1543125", timeout=30)
print("Status:", r.status_code)
print(r.text[:500])
PYEOF

Status: 200
<?xml version="1.0" encoding="UTF-8"?>
<osm version="0.6" generator="openstreetmap-cgimap 2.1.0 (1327 spike-07.openstreetmap.org)" copyright="OpenStreetMap and contributors" attribution="http://www.openstreetmap.org/copyright" license="http://opendatacommons.org/licenses/odbl/1-0/">
 <relation id="1543125" visible="true" version="382" changeset="180192345" timestamp="2026-03-21T08:07:52Z" user="Gin-an S" uid="22613668">
  <member type="node" ref="265018692" role="admin_centre"/>
  <member type="

python3 << 'PYEOF'
import requests

r = requests.get("https://api.openstreetmap.org/api/0.6/relation/1543125", timeout=30)
import xml.etree.ElementTree as ET
root = ET.fromstring(r.text)
rel = root.find(".//relation")
tags = {t.get('k'): t.get('v') for t in rel.findall('tag')}
print("Tags:", tags)
members = rel.findall('member')
print(f"Members: {len(members)}")
# Show member types
from collections import Counter
member_types = Counter(m.get('type') for m in members)
print("Member types:", member_types)
# Show the roles
role_counts = Counter(m.get('role') for m in members)
print("Role counts:", dict(role_counts))
PYEOF

Tags: {'admin_level': '4', 'alt_name:be': 'Токіё', 'alt_name:hr': 'Tokyo', 'alt_name:la': 'Tocium', 'boundary': 'administrative', 'is_in:country': 'Japan', 'is_in:country_code': 'JP', 'ISO3166-2': 'JP-13', 'name': '東京都', 'name:be': 'Токіа', 'name:br': 'Tokyo', 'name:cnr': 'Tokio', 'name:cs': 'Tokio', 'name:de': 'Tokio', 'name:en': 'Tokyo', 'name:eo': 'Tokio', 'name:es': 'Tokio', 'name:fi': 'Tokio', 'name:fr': 'Tokyo', 'name:hr': 'Tokio', 'name:ia': 'Tokyo', 'name:io': 'Tokyo', 'name:it': 'Tokyo', 'name:ja': '東京都', 'name:ja-Hira': 'とうきょうと', 'name:ja-Latn': 'Tōkyō-to', 'name:ko': '도쿄도', 'name:la': 'Tokium', 'name:mi': 'Tōkio', 'name:nl': 'Tokio', 'name:pl': 'Tokio', 'name:pt': 'Tóquio', 'name:ru': 'Токио', 'name:szl': 'Tokjo', 'name:tr': 'Tokyo', 'name:uk': 'Префектура Токіо', 'name:zh': '东京都/東京都', 'name:zh-Hans': '东京都', 'name:zh-Hant': '東京都', 'official_name:it': 'Metropoli di Tokyo', 'place': 'province', 'ref': '130001', 'type': 'boundary', 'website': 'https://www.metro.tokyo.lg.jp/', 'wikidata': 'Q1490', 'wikipedia': 'ja:東京都'}
Members: 471
Member types: Counter({'way': 416, 'relation': 54, 'node': 1})
Role counts: {'admin_centre': 1, 'outer': 416, 'subarea': 54}

python3 << 'PYEOF'
import requests
import xml.etree.ElementTree as ET

r = requests.get("https://api.openstreetmap.org/api/0.6/relation/1543125/full", timeout=60)
root = ET.fromstring(r.text)

# Find all subarea relations
subarea_rels = []
for rel in root.findall(".//relation"):
    tags = {t.get('k'): t.get('v') for t in rel.findall('tag')}
    tags['id'] = rel.get('id')
    subarea_rels.append(tags)

# The special wards are the ones with admin_level=9 and are in the special ward district
print(f"Total relations: {len(subarea_rels)}")
for t in subarea_rels:
    if t.get('admin_level') == '9':
        print(f"  Relation {t['id']}: name={t.get('name','?')}, name:en={t.get('name:en','?')}, place={t.get('place','?')}")

print("\n--- All subarea names ---")
for t in subarea_rels:
    print(f"  {t['id']}: name={t.get('name','?')}, name:en={t.get('name:en','?')}, admin_level={t.get('admin_level','?')}, place={t.get('place','?')}")
PYEOF

Total relations: 55

--- All subarea names ---
  1543055: name=杉並区, name:en=Suginami, admin_level=7, place=city
  1543056: name=中野区, name:en=Nakano, admin_level=7, place=city
  1543125: name=東京都, name:en=Tokyo, admin_level=4, place=province
  1758858: name=新宿区, name:en=Shinjuku, admin_level=7, place=city
  1758878: name=文京区, name:en=Bunkyō, admin_level=7, place=city
  1758888: name=台東区, name:en=Taito, admin_level=7, place=city
  1758891: name=墨田区, name:en=Sumida, admin_level=7, place=city
  1758897: name=中央区, name:en=Chuo, admin_level=7, place=city
  1758936: name=目黒区, name:en=Meguro, admin_level=7, place=city
  1758947: name=大田区, name:en=Ota, admin_level=7, place=?
  1759474: name=世田谷区, name:en=Setagaya, admin_level=7, place=city
  1759475: name=狛江市, name:en=Komae, admin_level=7, place=city
  1759477: name=渋谷区, name:en=Shibuya, admin_level=7, place=city
  1759506: name=豊島区, name:en=Toshima, admin_level=7, place=city
  1760038: name=北区, name:en=Kita, admin_level=7, place=city
  1760040: name=荒川区, name:en=Arakawa, admin_level=7, place=city
  1760078: name=板橋区, name:en=Itabashi, admin_level=7, place=city
  1760119: name=練馬区, name:en=Nerima, admin_level=7, place=city
  1760124: name=足立区, name:en=Adachi, admin_level=7, place=city
  1761717: name=港区, name:en=Minato, admin_level=7, place=city
  1761718: name=葛飾区, name:en=Katsushika, admin_level=7, place=city
  1761742: name=千代田区, name:en=Chiyoda, admin_level=7, place=city
  1761743: name=江戸川区, name:en=Edogawa, admin_level=7, place=?
  1761771: name=立川市, name:en=Tachikawa, admin_level=7, place=?
  1761804: name=武蔵野市, name:en=Musashino, admin_level=7, place=?
  1761820: name=三鷹市, name:en=Mitaka, admin_level=7, place=?
  1761834: name=青梅市, name:en=Ōme, admin_level=7, place=?
  3554015: name=江東区, name:en=Koto, admin_level=7, place=?
  3554304: name=品川区, name:en=Shinagawa, admin_level=7, place=city
  3605930: name=大島支庁, name:en=Oshima Subprefecture, admin_level=5, place=county
  3605944: name=三宅支庁, name:en=Miyake Subprefecture, admin_level=5, place=county
  3606090: name=八丈支庁, name:en=Hachijō Subprefecture, admin_level=5, place=county
  3606141: name=小笠原支庁, name:en=Ogasawara Subprefecture, admin_level=5, place=district
  3807613: name=稲城市, name:en=Inagi, admin_level=7, place=city
  5100276: name=町田市, name:en=Machida, admin_level=7, place=city
  5299312: name=西多摩郡, name:en=Nishitama District, admin_level=6, place=?
  5301344: name=あきる野市, name:en=Akiruno, admin_level=7, place=?
  5301559: name=羽村市, name:en=Hamura, admin_level=7, place=?
  5301639: name=八王子市, name:en=Hachioji, admin_level=7, place=?
  5301640: name=昭島市, name:en=Akishima, admin_level=7, place=?
  5301641: name=武蔵村山市, name:en=Musashimurayama, admin_level=7, place=?
  5301643: name=福生市, name:en=Fussa, admin_level=7, place=?
  5301789: name=多摩市, name:en=Tama, admin_level=7, place=city
  5301790: name=日野市, name:en=Hino, admin_level=7, place=?
  5302157: name=国分寺市, name:en=Kokubunji, admin_level=7, place=?
  5302158: name=国立市, name:en=Kunitachi, admin_level=7, place=?
  5302159: name=小平市, name:en=Kodaira, admin_level=7, place=?
  5302160: name=東大和市, name:en=Higashiyamato, admin_level=7, place=?
  5302161: name=東村山市, name:en=Higashimurayama, admin_level=7, place=?
  5302326: name=小金井市, name:en=Koganei, admin_level=7, place=?
  5302327: name=府中市, name:en=Fuchu, admin_level=7, place=?
  5302328: name=東久留米市, name:en=Higashikurume, admin_level=7, place=?
  5302329: name=清瀬市, name:en=Kiyose, admin_level=7, place=?
  5302330: name=西東京市, name:en=Nishitokyo, admin_level=7, place=?
  5303898: name=調布市, name:en=Chofu, admin_level=7, place=?

python3 << 'PYEOF'
import requests, time

# Overpass query to get just the 23 special ward relations with their geometries
# These are the 23 special ward relation IDs
ward_ids = [
    1758858, 1758878, 1758888, 1758891, 1758897, 1758936, 1758947, 1759474,
    1759477, 1759506, 1760038, 1760040, 1760078, 1760119, 1760124, 1761717,
    1761718, 1761742, 1761743, 1543055, 1543056, 3554015, 3554304
]

# Build query
rel_ids_str = "(" + "".join(f"rel({rid});" for rid in ward_ids) + ")"
query = f"""
[out:json][timeout:180];
{rel_ids_str}
(._;>;);
out geom;
"""

print(f"Query length: {len(query)} chars")

# Try POST with proper headers
for attempt in range(3):
    for url in [
        "https://overpass-api.de/api/interpreter",
        "https://lz4.overpass-api.de/api/interpreter",
        "https://overpass.kumi.systems/api/interpreter"
    ]:
        try:
            print(f"Trying {url} (attempt {attempt+1})...")
            r = requests.post(url, data={"data": query}, timeout=300,
                            headers={"Content-Type": "application/x-www-form-urlencoded"})
            print(f"Status: {r.status_code}")
            if r.status_code == 200:
                data = r.json()
                print(f"Got {len(data.get('elements',[]))} elements")
                # Save for later use
                import json
                with open('/work/wards_raw.json', 'w') as f:
                    json.dump(data, f)
                print("Saved to /work/wards_raw.json")
                exit(0)
        except Exception as e:
            print(f"Error with {url}: {e}")
    time.sleep(2)

print("All attempts failed")
PYEOF

Query length: 347 chars
Trying https://overpass-api.de/api/interpreter (attempt 1)...
Error with https://overpass-api.de/api/interpreter: HTTPSConnectionPool(host='overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))
Trying https://lz4.overpass-api.de/api/interpreter (attempt 1)...
Error with https://lz4.overpass-api.de/api/interpreter: HTTPSConnectionPool(host='lz4.overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='lz4.overpass-api.de', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))
Trying https://overpass.kumi.systems/api/interpreter (attempt 1)...
Status: 400
Trying https://overpass-api.de/api/interpreter (attempt 2)...
Error with https://overpass-api.de/api/interpreter: HTTPSConnectionPool(host='overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))
Trying https://lz4.overpass-api.de/api/interpreter (attempt 2)...
Error with https://lz4.overpass-api.de/api/interpreter: HTTPSConnectionPool(host='lz4.overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='lz4.overpass-api.de', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))
Trying https://overpass.kumi.systems/api/interpreter (attempt 2)...
Status: 400
Trying https://overpass-api.de/api/interpreter (attempt 3)...
Error with https://overpass-api.de/api/interpreter: HTTPSConnectionPool(host='overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))
Trying https://lz4.overpass-api.de/api/interpreter (attempt 3)...
Error with https://lz4.overpass-api.de/api/interpreter: HTTPSConnectionPool(host='lz4.overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='lz4.overpass-api.de', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))
Trying https://overpass.kumi.systems/api/interpreter (attempt 3)...
Status: 400
All attempts failed

python3 << 'PYEOF'
import requests

query = """
[out:json][timeout:180];
rel(1758858);
(._;>;);
out geom;
"""
r = requests.post("https://overpass.kumi.systems/api/interpreter", data={"data": query}, timeout=180)
print(f"Status: {r.status_code}")
print(r.text[:1000])
PYEOF

Status: 200
{
  "version": 0.6,
  "generator": "Overpass API 0.7.62.11 87bfad18",
  "osm3s": {
    "timestamp_osm_base": "2026-06-08T06:09:44Z",
    "copyright": "The data included in this document is from www.openstreetmap.org. The data is made available under ODbL."
  },
  "elements": [

{
  "type": "node",
  "id": 305437880,
  "lat": 35.6878319,
  "lon": 139.7055409
},
{
  "type": "node",
  "id": 305443168,
  "lat": 35.6868420,
  "lon": 139.7061760
},
{
  "type": "node",
  "id": 305444269,
  "lat": 35.6841918,
  "lon": 139.7092651
},
{
  "type": "node",
  "id": 305446200,
  "lat": 35.6833385,
  "lon": 139.7112711
},
{
  "type": "node",
  "id": 720554562,
  "lat": 35.7145401,
  "lon": 139.7110813
},
{
  "type": "node",
  "id": 885679751,
  "lat": 35.6748986,
  "lon": 139.7148522
},
{
  "type": "node",
  "id": 1986322047,
  "lat": 35.6846161,
  "lon": 139.7075528
},
{
  "type": "node",
  "id": 1986322048,
  "lat": 35.6846398,
  "lon": 139.7072154
},
{
  "type": "node",
  "id": 2389061853,
  "lat"

python3 << 'PYEOF'
import requests, json, time

ward_ids = [
    1758858, 1758878, 1758888, 1758891, 1758897, 1758936, 1758947, 1759474,
    1759477, 1759506, 1760038, 1760040, 1760078, 1760119, 1760124, 1761717,
    1761718, 1761742, 1761743, 1543055, 1543056, 3554015, 3554304
]

# Simpler approach: use recurse properly
# (rel(id); out geom;) gets the relation geometry itself
# (rel(id); >; out geom;) gets all ways and nodes

# Build query that gets each relation's full geometry
parts = []
for wid in ward_ids:
    parts.append(f"rel({wid});")
rel_str = "".join(parts)

query = f"""
[out:json][timeout:300];
{rel_str}
(._;>;);
out geom;
"""

print(f"Query length: {len(query)} chars")

r = requests.post("https://overpass.kumi.systems/api/interpreter", 
                  data={"data": query}, timeout=600)
print(f"Status: {r.status_code}")
if r.status_code == 200:
    data = r.json()
    print(f"Got {len(data.get('elements',[]))} elements")
    # Save
    with open('/work/wards_raw.json', 'w') as f:
        json.dump(data, f)
    print("Saved!")
    
    # Check what type of elements
    from collections import Counter
    types = Counter(el['type'] for el in data['elements'])
    print(f"Element types: {dict(types)}")
else:
    print(f"Error: {r.text[:500]}")
PYEOF

Query length: 345 chars
Status: 200
Got 937 elements
Saved!
Element types: {'node': 856, 'way': 80, 'relation': 1}

python3 << 'PYEOF'
import json

with open('/work/wards_raw.json') as f:
    data = json.load(f)

# Only 1 relation? Let's check
relations = [el for el in data['elements'] if el['type'] == 'relation']
print(f"Relations: {len(relations)}")
for r in relations:
    print(f"  ID: {r['id']}, tags: {r.get('tags', {})}")

# The issue is the implicit union - let me rewrite
PYEOF

python3 << 'PYEOF'
import requests, json, time

ward_ids = [
    1758858, 1758878, 1758888, 1758891, 1758897, 1758936, 1758947, 1759474,
    1759477, 1759506, 1760038, 1760040, 1760078, 1760119, 1760124, 1761717,
    1761718, 1761742, 1761743, 1543055, 1543056, 3554015, 3554304
]

# Use a single union statement with all relations
rel_parts = "; ".join(f"rel({wid})" for wid in ward_ids)

query = f"""
[out:json][timeout:300];
(
  {rel_parts};
);
(._;>;);
out geom;
"""

print(f"Query length: {len(query)} chars")

r = requests.post("https://overpass.kumi.systems/api/interpreter", 
                  data={"data": query}, timeout=600)
print(f"Status: {r.status_code}")
if r.status_code == 200:
    data = r.json()
    print(f"Got {len(data.get('elements',[]))} elements")
    with open('/work/wards_raw.json', 'w') as f:
        json.dump(data, f)
    print("Saved!")
    
    from collections import Counter
    types = Counter(el['type'] for el in data['elements'])
    print(f"Element types: {dict(types)}")
    
    relations = [el for el in data['elements'] if el['type'] == 'relation']
    print(f"Relations: {len(relations)}")
    for r in relations:
        tags = r.get('tags', {})
        print(f"  ID {r['id']}: name={tags.get('name','?')}, name:en={tags.get('name:en','?')}")
else:
    print(f"Error: {r.text[:1000]}")
PYEOF

Query length: 374 chars
Status: 504
Error: <?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
    "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
  <meta http-equiv="content-type" content="text/html; charset=utf-8" lang="en"/>
  <title>OSM3S Response</title>
</head>
<body>

<p>The data included in this document is from www.openstreetmap.org. The data is made available under ODbL.</p>
<p><strong style="color:#FF0000">Error</strong>: runtime error: open64: 0 Success /osm3s_osm_base Dispatcher_Client::request_read_and_idx::timeout. The server is probably too busy to handle your request. </p>

</body>
</html>

python3 << 'PYEOF'
import requests, json, time

ward_ids = [
    1758858, 1758878, 1758888, 1758891, 1758897, 1758936, 1758947, 1759474,
    1759477, 1759506, 1760038, 1760040, 1760078, 1760119, 1760124, 1761717,
    1761718, 1761742, 1761743, 1543055, 1543056, 3554015, 3554304
]

# Fetch each ward individually and collect results
all_elements = []
for wid in ward_ids:
    query = f"""
    [out:json][timeout:120];
    rel({wid});
    (._;>;);
    out geom;
    """
    for attempt in range(3):
        try:
            r = requests.post("https://overpass.kumi.systems/api/interpreter",
                            data={"data": query}, timeout=180)
            if r.status_code == 200:
                data = r.json()
                all_elements.extend(data['elements'])
                print(f"Ward {wid}: {len(data['elements'])} elements")
                break
            else:
                print(f"  Ward {wid} attempt {attempt+1}: status {r.status_code}")
                time.sleep(2)
        except Exception as e:
            print(f"  Ward {wid} attempt {attempt+1}: {e}")
            time.sleep(2)
    time.sleep(0.5)

print(f"\nTotal elements collected: {len(all_elements)}")

# Save combined
combined = {"version": 0.6, "generator": "combined", "elements": all_elements}
with open('/work/wards_raw.json', 'w') as f:
    json.dump(combined, f)

from collections import Counter
types = Counter(el['type'] for el in all_elements)
print(f"Element types: {dict(types)}")

relations = [el for el in all_elements if el['type'] == 'relation']
print(f"Relations: {len(relations)}")
for r in relations:
    tags = r.get('tags', {})
    print(f"  ID {r['id']}: name={tags.get('name','?')}, name:en={tags.get('name:en','?')}")
PYEOF

python3 << 'PYEOF'
import osmnx as ox
import geopandas as gpd
import time

# Use osmnx to fetch features with specific tags
# Tokyo's special wards are tagged as place=city (or suburb) with name ending in 区 
# Let's try a broader query first

# Try to get the "Tokyo special wards" area through Nominatim
try:
    # Get the boundary for the "特別区部" (Special wards area)
    result = ox.geocode_to_gdf("Tokyo, Japan", which_result=1)
    tokyo_poly = result.geometry.iloc[0]
    print(f"Got Tokyo polygon, area approx. {tokyo_poly.area:.6f} deg²")
    
    # Now query for admin_level=9 relations within Tokyo
    gdf = ox.features_from_polygon(tokyo_poly, tags={"boundary": "administrative", "admin_level": "9"})
    print(f"Got {len(gdf)} features")
    print(gdf.columns.tolist())
    
    # Filter to only the ones that are special wards (place=city or name ends with 区)
    mask = gdf['name'].str.endswith('区') if 'name' in gdf.columns else pd.Series(False, index=gdf.index)
    if 'place' in gdf.columns:
        mask = mask | (gdf['place'] == 'city')
    
    wards = gdf[mask].copy()
    print(f"Filtered to {len(wards)} wards")
    print(wards[['name', 'name:en', 'osm_id', 'osm_type']].head(30))
    
    if len(wards) > 0:
        wards.to_file('/work/wards_osmnx.gpkg', layer='wards', driver='GPKG')
        print("Saved!")
        
except Exception as e:
    import traceback
    print(f"Error: {e}")
    traceback.print_exc()
PYEOF

/usr/local/lib/python3.14/site-packages/osmnx/_overpass.py:271: UserWarning: This area is 650 times your configured Overpass max query area size. It will automatically be divided up into multiple sub-queries accordingly. This may take a long time.
  multi_poly_proj = utils_geo._consolidate_subdivide_geometry(poly_proj)
Traceback (most recent call last):
  File "/usr/local/lib/python3.14/site-packages/urllib3/connection.py", line 204, in _new_conn
    sock = connection.create_connection(
        (self._dns_host, self.port),
    ...<2 lines>...
        socket_options=self.socket_options,
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/util/connection.py", line 85, in create_connection
    raise err
  File "/usr/local/lib/python3.14/site-packages/urllib3/util/connection.py", line 73, in create_connection
    sock.connect(sa)
    ~~~~~~~~~~~~^^^^
ConnectionRefusedError: [Errno 111] Connection refused

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 788, in urlopen
    response = self._make_request(
        conn,
    ...<10 lines>...
        **response_kw,
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 488, in _make_request
    raise new_e
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 464, in _make_request
    self._validate_conn(conn)
    ~~~~~~~~~~~~~~~~~~~^^^^^^
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 1106, in _validate_conn
    conn.connect()
    ~~~~~~~~~~~~^^
  File "/usr/local/lib/python3.14/site-packages/urllib3/connection.py", line 759, in connect
    self.sock = sock = self._new_conn()
                       ~~~~~~~~~~~~~~^^
  File "/usr/local/lib/python3.14/site-packages/urllib3/connection.py", line 219, in _new_conn
    raise NewConnectionError(
        self, f"Failed to establish a new connection: {e}"
    ) from e
urllib3.exceptions.NewConnectionError: HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 111] Connection refused

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.14/site-packages/requests/adapters.py", line 696, in send
    resp = conn.urlopen(
        method=request.method,
    ...<9 lines>...
        chunked=chunked,
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 842, in urlopen
    retries = retries.increment(
        method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/util/retry.py", line 543, in increment
    raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 111] Connection refused"))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 17, in <module>
  File "/usr/local/lib/python3.14/site-packages/osmnx/features.py", line 332, in features_from_polygon
    return _create_gdf(response_jsons, polygon, tags)
  File "/usr/local/lib/python3.14/site-packages/osmnx/features.py", line 406, in _create_gdf
    for response_json in response_jsons:
                         ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/osmnx/_overpass.py", line 432, in _download_overpass_features
    yield _overpass_request(OrderedDict(data=query_str))
          ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/osmnx/_overpass.py", line 469, in _overpass_request
    response = requests.post(
        url,
    ...<3 lines>...
        **settings.requests_kwargs,
    )
  File "/usr/local/lib/python3.14/site-packages/requests/api.py", line 134, in post
    return request("post", url, data=data, json=json, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/requests/api.py", line 71, in request
    return session.request(method=method, url=url, **kwargs)
           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/requests/sessions.py", line 651, in request
    resp = self.send(prep, **send_kwargs)
  File "/usr/local/lib/python3.14/site-packages/requests/sessions.py", line 784, in send
    r = adapter.send(request, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/requests/adapters.py", line 729, in send
    raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 111] Connection refused"))

python3 << 'PYEOF'
import osmnx as ox

# Configure OSMnx to use the kumi endpoint
ox.settings.overpass_endpoint = "https://overpass.kumi.systems/api/interpreter"
ox.settings.timeout = 300
ox.settings.memory = 2147483648  # 2GB

# Try again with smaller query area
# Get Tokyo polygon
result = ox.geocode_to_gdf("Tokyo, Japan", which_result=1)
tokyo_poly = result.geometry.iloc[0]
print(f"Got Tokyo polygon")

# Now try features from polygon with smaller area
# First simplify the polygon to reduce complexity
import shapely
tokyo_poly_simple = tokyo_poly.simplify(0.01)
print(f"Simple polygon area: {tokyo_poly_simple.area:.6f} deg²")

# Use the simpler polygon
gdf = ox.features_from_polygon(tokyo_poly_simple, tags={"boundary": "administrative", "admin_level": "9"})
print(f"Got {len(gdf)} features")
print(gdf[['name', 'name:en', 'osm_id', 'osm_type']].head(50))
PYEOF

/usr/local/lib/python3.14/site-packages/osmnx/_overpass.py:271: UserWarning: This area is 650 times your configured Overpass max query area size. It will automatically be divided up into multiple sub-queries accordingly. This may take a long time.
  multi_poly_proj = utils_geo._consolidate_subdivide_geometry(poly_proj)
Traceback (most recent call last):
  File "/usr/local/lib/python3.14/site-packages/urllib3/connection.py", line 204, in _new_conn
    sock = connection.create_connection(
        (self._dns_host, self.port),
    ...<2 lines>...
        socket_options=self.socket_options,
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/util/connection.py", line 85, in create_connection
    raise err
  File "/usr/local/lib/python3.14/site-packages/urllib3/util/connection.py", line 73, in create_connection
    sock.connect(sa)
    ~~~~~~~~~~~~^^^^
ConnectionRefusedError: [Errno 111] Connection refused

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 788, in urlopen
    response = self._make_request(
        conn,
    ...<10 lines>...
        **response_kw,
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 488, in _make_request
    raise new_e
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 464, in _make_request
    self._validate_conn(conn)
    ~~~~~~~~~~~~~~~~~~~^^^^^^
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 1106, in _validate_conn
    conn.connect()
    ~~~~~~~~~~~~^^
  File "/usr/local/lib/python3.14/site-packages/urllib3/connection.py", line 759, in connect
    self.sock = sock = self._new_conn()
                       ~~~~~~~~~~~~~~^^
  File "/usr/local/lib/python3.14/site-packages/urllib3/connection.py", line 219, in _new_conn
    raise NewConnectionError(
        self, f"Failed to establish a new connection: {e}"
    ) from e
urllib3.exceptions.NewConnectionError: HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 111] Connection refused

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.14/site-packages/requests/adapters.py", line 696, in send
    resp = conn.urlopen(
        method=request.method,
    ...<9 lines>...
        chunked=chunked,
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 842, in urlopen
    retries = retries.increment(
        method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/util/retry.py", line 543, in increment
    raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 111] Connection refused"))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 21, in <module>
  File "/usr/local/lib/python3.14/site-packages/osmnx/features.py", line 332, in features_from_polygon
    return _create_gdf(response_jsons, polygon, tags)
  File "/usr/local/lib/python3.14/site-packages/osmnx/features.py", line 406, in _create_gdf
    for response_json in response_jsons:
                         ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/osmnx/_overpass.py", line 432, in _download_overpass_features
    yield _overpass_request(OrderedDict(data=query_str))
          ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/osmnx/_overpass.py", line 469, in _overpass_request
    response = requests.post(
        url,
    ...<3 lines>...
        **settings.requests_kwargs,
    )
  File "/usr/local/lib/python3.14/site-packages/requests/api.py", line 134, in post
    return request("post", url, data=data, json=json, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/requests/api.py", line 71, in request
    return session.request(method=method, url=url, **kwargs)
           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/requests/sessions.py", line 651, in request
    resp = self.send(prep, **send_kwargs)
  File "/usr/local/lib/python3.14/site-packages/requests/sessions.py", line 784, in send
    r = adapter.send(request, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/requests/adapters.py", line 729, in send
    raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 111] Connection refused"))

python3 << 'PYEOF'
import requests, json, time

# Set osmnx to use kumi endpoint via environment
import os
os.environ['OSMNX_OVERPASS_ENDPOINT'] = 'https://overpass.kumi.systems/api/interpreter'

import osmnx as ox
ox.settings.overpass_endpoint = "https://overpass.kumi.systems/api/interpreter"
ox.settings.timeout = 300

# Get Tokyo polygon
result = ox.geocode_to_gdf("Tokyo, Japan", which_result=1)
tokyo_poly = result.geometry.iloc[0]
tokyo_poly_simple = tokyo_poly.simplify(0.01)

# Try query with kumi endpoint
import shapely
gdf = ox.features_from_polygon(tokyo_poly_simple, tags={"boundary": "administrative", "admin_level": "9"})
print(f"Got {len(gdf)} features")
if 'name' in gdf.columns:
    print(gdf[['name', 'name:en', 'osm_id']].head(50))
else:
    print(gdf.columns.tolist())
PYEOF

/usr/local/lib/python3.14/site-packages/osmnx/_overpass.py:271: UserWarning: This area is 650 times your configured Overpass max query area size. It will automatically be divided up into multiple sub-queries accordingly. This may take a long time.
  multi_poly_proj = utils_geo._consolidate_subdivide_geometry(poly_proj)
Traceback (most recent call last):
  File "/usr/local/lib/python3.14/site-packages/urllib3/connection.py", line 204, in _new_conn
    sock = connection.create_connection(
        (self._dns_host, self.port),
    ...<2 lines>...
        socket_options=self.socket_options,
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/util/connection.py", line 85, in create_connection
    raise err
  File "/usr/local/lib/python3.14/site-packages/urllib3/util/connection.py", line 73, in create_connection
    sock.connect(sa)
    ~~~~~~~~~~~~^^^^
ConnectionRefusedError: [Errno 111] Connection refused

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 788, in urlopen
    response = self._make_request(
        conn,
    ...<10 lines>...
        **response_kw,
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 488, in _make_request
    raise new_e
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 464, in _make_request
    self._validate_conn(conn)
    ~~~~~~~~~~~~~~~~~~~^^^^^^
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 1106, in _validate_conn
    conn.connect()
    ~~~~~~~~~~~~^^
  File "/usr/local/lib/python3.14/site-packages/urllib3/connection.py", line 759, in connect
    self.sock = sock = self._new_conn()
                       ~~~~~~~~~~~~~~^^
  File "/usr/local/lib/python3.14/site-packages/urllib3/connection.py", line 219, in _new_conn
    raise NewConnectionError(
        self, f"Failed to establish a new connection: {e}"
    ) from e
urllib3.exceptions.NewConnectionError: HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 111] Connection refused

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.14/site-packages/requests/adapters.py", line 696, in send
    resp = conn.urlopen(
        method=request.method,
    ...<9 lines>...
        chunked=chunked,
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/connectionpool.py", line 842, in urlopen
    retries = retries.increment(
        method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]
    )
  File "/usr/local/lib/python3.14/site-packages/urllib3/util/retry.py", line 543, in increment
    raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 111] Connection refused"))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 18, in <module>
  File "/usr/local/lib/python3.14/site-packages/osmnx/features.py", line 332, in features_from_polygon
    return _create_gdf(response_jsons, polygon, tags)
  File "/usr/local/lib/python3.14/site-packages/osmnx/features.py", line 406, in _create_gdf
    for response_json in response_jsons:
                         ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/osmnx/_overpass.py", line 432, in _download_overpass_features
    yield _overpass_request(OrderedDict(data=query_str))
          ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/osmnx/_overpass.py", line 469, in _overpass_request
    response = requests.post(
        url,
    ...<3 lines>...
        **settings.requests_kwargs,
    )
  File "/usr/local/lib/python3.14/site-packages/requests/api.py", line 134, in post
    return request("post", url, data=data, json=json, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/requests/api.py", line 71, in request
    return session.request(method=method, url=url, **kwargs)
           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/requests/sessions.py", line 651, in request
    resp = self.send(prep, **send_kwargs)
  File "/usr/local/lib/python3.14/site-packages/requests/sessions.py", line 784, in send
    r = adapter.send(request, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/requests/adapters.py", line 729, in send
    raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='overpass-api.de', port=443): Max retries exceeded with url: /api/interpreter (Caused by NewConnectionError("HTTPSConnection(host='overpass-api.de', port=443): Failed to establish a new connection: [Errno 111] Connection refused"))

python3 << 'PYEOF'
import requests, json, time

# Use the kumi endpoint directly - it was working
# Fetch wards one at a time
ward_ids = [
    1758858, 1758878, 1758888, 1758891, 1758897, 1758936, 1758947, 1759474,
    1759477, 1759506, 1760038, 1760040, 1760078, 1760119, 1760124, 1761717,
    1761718, 1761742, 1761743, 1543055, 1543056, 3554015, 3554304
]

all_elements = []
failed = []

for i, wid in enumerate(ward_ids):
    query = f"[out:json][timeout:120];rel({wid});(._;>;);out geom;"
    for attempt in range(3):
        try:
            r = requests.post("https://overpass.kumi.systems/api/interpreter",
                            data={"data": query}, timeout=180)
            if r.status_code == 200:
                data = r.json()
                all_elements.extend(data['elements'])
                tags = data['elements'][0].get('tags', {}) if data['elements'] else {}
                print(f"[{i+1}/23] Ward {wid}: {tags.get('name','?')} ({tags.get('name:en','?')}) - {len(data['elements'])} elems")
                break
            else:
                print(f"  [{i+1}/23] Ward {wid} attempt {attempt+1}: status {r.status_code}")
                time.sleep(2)
        except Exception as e:
            print(f"  [{i+1}/23] Ward {wid} attempt {attempt+1}: {e}")
            time.sleep(2)
    else:
        failed.append(wid)
    time.sleep(0.3)  # Be nice to the API

print(f"\nTotal elements: {len(all_elements)}, failed: {len(failed)} -> {failed}")

# Save
combined = {"version": 0.6, "generator": "combined", "elements": all_elements}
with open('/work/wards_raw.json', 'w') as f:
    json.dump(combined, f)
print("Saved to /work/wards_raw.json")
PYEOF

python3 << 'PYEOF'
import requests, json, time

# From the earlier OSM API call, we saw the ward relations listed under Tokyo relation
# Let's try to get each ward's boundary via the OSM API (fast)
ward_ids = [
    1758858, 1758878, 1758888, 1758891, 1758897, 1758936, 1758947, 1759474,
    1759477, 1759506, 1760038, 1760040, 1760078, 1760119, 1760124, 1761717,
    1761718, 1761742, 1761743, 1543055, 1543056, 3554015, 3554304
]

# First get just the tags for all wards to identify them
all_tags = {}
for wid in ward_ids:
    r = requests.get(f"https://api.openstreetmap.org/api/0.6/relation/{wid}", timeout=30)
    if r.status_code == 200:
        import xml.etree.ElementTree as ET
        root = ET.fromstring(r.text)
        rel = root.find(".//relation")
        tags = {t.get('k'): t.get('v') for t in rel.findall('tag')}
        all_tags[wid] = tags
        print(f"  {wid}: name={tags.get('name','?')}, name:en={tags.get('name:en','?')}")
    time.sleep(0.2)

print(f"\nGot {len(all_tags)} ward tags")
PYEOF

  1758858: name=新宿区, name:en=Shinjuku
  1758878: name=文京区, name:en=Bunkyō
  1758888: name=台東区, name:en=Taito
  1758891: name=墨田区, name:en=Sumida
  1758897: name=中央区, name:en=Chuo
  1758936: name=目黒区, name:en=Meguro
  1758947: name=大田区, name:en=Ota
  1759474: name=世田谷区, name:en=Setagaya
  1759477: name=渋谷区, name:en=Shibuya
  1759506: name=豊島区, name:en=Toshima
  1760038: name=北区, name:en=Kita
  1760040: name=荒川区, name:en=Arakawa
  1760078: name=板橋区, name:en=Itabashi
  1760119: name=練馬区, name:en=Nerima
  1760124: name=足立区, name:en=Adachi
  1761717: name=港区, name:en=Minato
  1761718: name=葛飾区, name:en=Katsushika
  1761742: name=千代田区, name:en=Chiyoda
  1761743: name=江戸川区, name:en=Edogawa
  1543055: name=杉並区, name:en=Suginami
  1543056: name=中野区, name:en=Nakano
  3554015: name=江東区, name:en=Koto
  3554304: name=品川区, name:en=Shinagawa

Got 23 ward tags

python3 << 'PYEOF'
import requests, json, time

# Try batches of 5 wards to speed things up
ward_ids = [
    1758858, 1758878, 1758888, 1758891, 1758897, 1758936, 1758947, 1759474,
    1759477, 1759506, 1760038, 1760040, 1760078, 1760119, 1760124, 1761717,
    1761718, 1761742, 1761743, 1543055, 1543056, 3554015, 3554304
]

all_elements = []
url = "https://overpass.kumi.systems/api/interpreter"

batch_size = 4
for batch_start in range(0, len(ward_ids), batch_size):
    batch = ward_ids[batch_start:batch_start + batch_size]
    
    # Build query: get all 4 relations, recurse, output geom
    rel_str = ";".join(f"rel({wid})" for wid in batch)
    query = f"[out:json][timeout:180];({rel_str};);(._;>;);out geom;"
    
    ok = False
    for attempt in range(3):
        try:
            r = requests.post(url, data={"data": query}, timeout=240)
            if r.status_code == 200:
                data = r.json()
                all_elements.extend(data['elements'])
                # Find which relations we got
                rels = [e for e in data['elements'] if e['type'] == 'relation']
                names = [(r['id'], r.get('tags', {}).get('name','?')) for r in rels]
                print(f"Batch {batch_start//batch_size + 1}: {len(data['elements'])} elems, rels: {names}")
                ok = True
                break
            else:
                print(f"Batch {batch_start//batch_size + 1} attempt {attempt+1}: {r.status_code} - {r.text[:100]}")
                time.sleep(2)
        except Exception as e:
            print(f"Batch {batch_start//batch_size + 1} attempt {attempt+1}: {e}")
            time.sleep(2)
    if not ok:
        print(f"FAILED batch: {batch}")
    time.sleep(1)

print(f"\nTotal elements: {len(all_elements)}")
combined = {"version": 0.6, "generator": "combined", "elements": all_elements}
with open('/work/wards_raw.json', 'w') as f:
    json.dump(combined, f)

from collections import Counter
types = Counter(e['type'] for e in all_elements)
print(f"Types: {dict(types)}")
rels = [e for e in all_elements if e['type'] == 'relation']
print(f"Got {len(rels)} relations (expected 23)")
for r in rels:
    print(f"  {r['id']}: {r.get('tags',{}).get('name','?')} / {r.get('tags',{}).get('name:en','?')}")
PYEOF

timeout 120 python3 << 'PYEOF'
import requests, json, time

ward_ids = [
    1758858, 1758878, 1758888, 1758891, 1758897, 1758936, 1758947, 1759474,
    1759477, 1759506, 1760038, 1760040, 1760078, 1760119, 1760124, 1761717,
    1761718, 1761742, 1761743, 1543055, 1543056, 3554015, 3554304
]

all_elements = []
url = "https://overpass.kumi.systems/api/interpreter"
failed = []

for i, wid in enumerate(ward_ids):
    query = f"[out:json][timeout:60];rel({wid});(._;>;);out geom;"
    ok = False
    for attempt in range(2):
        try:
            r = requests.post(url, data={"data": query}, timeout=120)
            if r.status_code == 200:
                data = r.json()
                all_elements.extend(data['elements'])
                tags = data['elements'][0].get('tags', {}) if data['elements'] else {}
                print(f"[{i+1}/23] {tags.get('name','?')} ({wid}) - {len(data['elements'])} elems")
                ok = True
                break
            else:
                print(f"[{i+1}/23] {wid} attempt {attempt+1}: {r.status_code}")
                time.sleep(2)
        except Exception as e:
            print(f"[{i+1}/23] {wid} attempt {attempt+1}: {e}")
            time.sleep(2)
    if not ok:
        failed.append(wid)
    time.sleep(0.3)

print(f"\nGot {len(all_elements)} elements total, failed: {len(failed)}")
combined = {"version": 0.6, "generator": "combined", "elements": all_elements}
with open('/work/wards_raw.json', 'w') as f:
    json.dump(combined, f)
print("Saved!")
PYEOF

python3 << 'PYEOF'
import requests, xml.etree.ElementTree as ET, time

ward_ids = [
    1758858, 1758878, 1758888, 1758891, 1758897, 1758936, 1758947, 1759474,
    1759477, 1759506, 1760038, 1760040, 1760078, 1760119, 1760124, 1761717,
    1761718, 1761742, 1761743, 1543055, 1543056, 3554015, 3554304
]

import json

all_elements = []
failed = []

# First, download from OSM API (just for Shinjuku to see structure)
wid = 1758858
r = requests.get(f"https://api.openstreetmap.org/api/0.6/relation/{wid}/full", timeout=30)
print(f"Status: {r.status_code}")
root = ET.fromstring(r.text)
print(f"Root: {root.tag}")
ways = root.findall(".//way")
nodes = root.findall(".//node")
rels = root.findall(".//relation")
print(f"Ways: {len(ways)}, Nodes: {len(nodes)}, Rels: {len(rels)}")

# Check a way
way0 = ways[0]
print(f"Way {way0.get('id')}: {len(way0.findall('nd'))} nodes, tags: {[(t.get('k'), t.get('v')) for t in way0.findall('tag')]}")
PYEOF

Status: 200
Root: osm
Ways: 106, Nodes: 1194, Rels: 95
Way 264490583: 3 nodes, tags: [('admin_level', '7'), ('boundary', 'administrative')]

python3 << 'PYEOF'
import requests, xml.etree.ElementTree as ET, json, time

ward_ids = [
    1758858, 1758878, 1758888, 1758891, 1758897, 1758936, 1758947, 1759474,
    1759477, 1759506, 1760038, 1760040, 1760078, 1760119, 1760124, 1761717,
    1761718, 1761742, 1761743, 1543055, 1543056, 3554015, 3554304
]

# Map names
ward_names = {
    1758858: ("新宿区", "Shinjuku"),
    1758878: ("文京区", "Bunkyō"),
    1758888: ("台東区", "Taito"),
    1758891: ("墨田区", "Sumida"),
    1758897: ("中央区", "Chuo"),
    1758936: ("目黒区", "Meguro"),
    1758947: ("大田区", "Ota"),
    1759474: ("世田谷区", "Setagaya"),
    1759477: ("渋谷区", "Shibuya"),
    1759506: ("豊島区", "Toshima"),
    1760038: ("北区", "Kita"),
    1760040: ("荒川区", "Arakawa"),
    1760078: ("板橋区", "Itabashi"),
    1760119: ("練馬区", "Nerima"),
    1760124: ("足立区", "Adachi"),
    1761717: ("港区", "Minato"),
    1761718: ("葛飾区", "Katsushika"),
    1761742: ("千代田区", "Chiyoda"),
    1761743: ("江戸川区", "Edogawa"),
    1543055: ("杉並区", "Suginami"),
    1543056: ("中野区", "Nakano"),
    3554015: ("江東区", "Koto"),
    3554304: ("品川区", "Shinagawa"),
}

all_elements = []

for i, wid in enumerate(ward_ids):
    jp, en = ward_names[wid]
    for attempt in range(2):
        try:
            r = requests.get(f"https://api.openstreetmap.org/api/0.6/relation/{wid}/full", timeout=30)
            if r.status_code == 200:
                root = ET.fromstring(r.text)
                # Convert XML elements to dicts similar to Overpass JSON
                for el_type in ['node', 'way', 'relation']:
                    for el in root.findall(el_type):
                        elem = {"type": el_type, "id": int(el.get('id'))}
                        if el_type == 'node':
                            elem["lat"] = float(el.get('lat'))
                            elem["lon"] = float(el.get('lon'))
                        elif el_type == 'way':
                            elem["nodes"] = [int(nd.get('ref')) for nd in el.findall('nd')]
                        elif el_type == 'relation':
                            elem["members"] = []
                            for m in el.findall('member'):
                                elem["members"].append({
                                    "type": m.get('type'),
                                    "ref": int(m.get('ref')),
                                    "role": m.get('role')
                                })
                        
                        tags = {t.get('k'): t.get('v') for t in el.findall('tag')}
                        if tags:
                            elem["tags"] = tags
                        all_elements.append(elem)
                print(f"[{i+1}/23] {jp} ({en}) - OK")
                break
            else:
                print(f"[{i+1}/23] {wid} attempt {attempt+1}: {r.status_code}")
                time.sleep(2)
        except Exception as e:
            print(f"[{i+1}/23] {wid} attempt {attempt+1}: {e}")
            time.sleep(2)
    time.sleep(0.1)

print(f"\nTotal elements: {len(all_elements)}")

# Save
combined = {"version": 0.6, "generator": "combined", "elements": all_elements}
with open('/work/wards_raw.json', 'w') as f:
    json.dump(combined, f)
print("Saved to /work/wards_raw.json")

# Count types
from collections import Counter
types = Counter(e['type'] for e in all_elements)
print(f"Types: {dict(types)}")
rels = [e for e in all_elements if e['type'] == 'relation']
print(f"Relations: {len(rels)}")
for r in rels:
    print(f"  {r['id']} -> tags: {r.get('tags', {}).get('name', '?')}")
PYEOF

[1/23] 新宿区 (Shinjuku) - OK
[2/23] 文京区 (Bunkyō) - OK
[3/23] 台東区 (Taito) - OK
[4/23] 墨田区 (Sumida) - OK
[5/23] 中央区 (Chuo) - OK
[6/23] 目黒区 (Meguro) - OK
[7/23] 大田区 (Ota) - OK
[8/23] 世田谷区 (Setagaya) - OK
[9/23] 渋谷区 (Shibuya) - OK
[10/23] 豊島区 (Toshima) - OK
[11/23] 北区 (Kita) - OK
[12/23] 荒川区 (Arakawa) - OK
[13/23] 板橋区 (Itabashi) - OK
[14/23] 練馬区 (Nerima) - OK
[15/23] 足立区 (Adachi) - OK
[16/23] 港区 (Minato) - OK
[17/23] 葛飾区 (Katsushika) - OK
[18/23] 千代田区 (Chiyoda) - OK
[19/23] 江戸川区 (Edogawa) - OK
[20/23] 杉並区 (Suginami) - OK
[21/23] 中野区 (Nakano) - OK
[22/23] 江東区 (Koto) - OK
[23/23] 品川区 (Shinagawa) - OK

Total elements: 25320
Saved to /work/wards_raw.json
Types: {'node': 22282, 'way': 2086, 'relation': 952}
Relations: 952
  1758858 -> tags: 新宿区
  16550782 -> tags: 馬場下町
  17069519 -> tags: 霞ヶ丘町
  17069520 -> tags: 信濃町
  17069521 -> tags: 南元町
  17069525 -> tags: 若葉
  17069526 -> tags: 須賀町
  17069527 -> tags: 左門町
  17069528 -> tags: 大京町
  17069529 -> tags: 内藤町
  17081654 -> tags: 新宿
  17081657 -> tags: 歌舞伎町
  17081666 -> tags: 西新宿
  17081778 -> tags: 北新宿
  17081783 -> tags: 百人町
  17081787 -> tags: 大久保
  17081792 -> tags: 高田馬場
  17082281 -> tags: 上落合
  17082284 -> tags: 中井
  17082289 -> tags: 中落合
  17082294 -> tags: 下落合
  17082299 -> tags: 西落合
  17086963 -> tags: 四谷
  17086964 -> tags: 愛住町
  17086965 -> tags: 舟町
  17086966 -> tags: 荒木町
  17090430 -> tags: 四谷三栄町
  17090431 -> tags: 四谷坂町
  17090432 -> tags: 四谷本塩町
  17090433 -> tags: 富久町
  17090434 -> tags: 余丁町
  17090435 -> tags: 市谷台町
  17090436 -> tags: 住吉町
  17090437 -> tags: 片町
  17090438 -> tags: 河田町
  17090439 -> tags: 市谷仲之町
  17090440 -> tags: 市谷本村町
  17090441 -> tags: 市谷八幡町
  17090442 -> tags: 若松町
  17090446 -> tags: 戸山
  17090741 -> tags: 市谷薬王寺町
  17091431 -> tags: 市谷加賀町
  17091432 -> tags: 市谷左内町
  17091433 -> tags: 市谷鷹匠町
  17091434 -> tags: 市谷長延寺町
  17091438 -> tags: 市谷田町
  17091643 -> tags: 市谷砂土原町
  17091644 -> tags: 市谷船河原町
  17091645 -> tags: 市谷柳町
  17091646 -> tags: 市谷甲良町
  17091647 -> tags: 市谷山伏町
  17091648 -> tags: 払方町
  17097441 -> tags: 納戸町
  17097442 -> tags: 二十騎町
  17097443 -> tags: 南山伏町
  17127692 -> tags: 北山伏町
  17127693 -> tags: 細工町
  17127694 -> tags: 南町
  17127695 -> tags: 中町
  17127696 -> tags: 北町
  17127697 -> tags: 箪笥町
  17143195 -> tags: 若宮町
  17143196 -> tags: 袋町
  17143197 -> tags: 岩戸町
  17143198 -> tags: 横寺町
  17143205 -> tags: 神楽坂
  17143206 -> tags: 神楽河岸
  17143207 -> tags: 揚場町
  17143208 -> tags: 津久戸町
  17143209 -> tags: 下宮比町
  17143210 -> tags: 新小川町
  17143211 -> tags: 筑土八幡町
  17143212 -> tags: 白銀町
  17143838 -> tags: 喜久井町
  17143842 -> tags: 原町
  17143843 -> tags: 弁天町
  17143844 -> tags: 南榎町
  17143845 -> tags: 榎町
  17143846 -> tags: 東榎町
  17143847 -> tags: 矢来町
  17143848 -> tags: 東五軒町
  17143849 -> tags: 西五軒町
  17143850 -> tags: 赤城元町
  17143851 -> tags: 赤城下町
  17143852 -> tags: 築地町
  17143853 -> tags: 水道町
  17143854 -> tags: 天神町
  17143855 -> tags: 中里町
  17143856 -> tags: 改代町
  17144181 -> tags: 山吹町
  17144182 -> tags: 早稲田南町
  17144183 -> tags: 早稲田町
  17144184 -> tags: 早稲田鶴巻町
  17144186 -> tags: 戸塚町
  17144190 -> tags: 西早稲田
  1758878 -> tags: 文京区
  18115096 -> tags: 湯島
  18122363 -> tags: 本郷
  18124931 -> tags: 後楽
  18124969 -> tags: 春日
  18125027 -> tags: 弥生
  18130797 -> tags: 根津
  18130836 -> tags: 千駄木
  18130863 -> tags: 向丘
  18138934 -> tags: 西片
  18139075 -> tags: 小石川
  18142855 -> tags: 水道
  18146998 -> tags: 関口
  18147123 -> tags: 白山
  18147525 -> tags: 本駒込
  18152273 -> tags: 千石
  18152342 -> tags: 小日向
  18152456 -> tags: 目白台
  18155747 -> tags: 音羽
  18155915 -> tags: 大塚
  1758888 -> tags: 台東区
  9046136 -> tags: 浅草
  15902968 -> tags: 鳥越
  15904168 -> tags: 浅草橋
  15905168 -> tags: 小島
  15905173 -> tags: 三筋
  16003588 -> tags: 元浅草
  16012890 -> tags: 松が谷
  16012978 -> tags: 北上野
  16013050 -> tags: 東上野
  16013296 -> tags: 入谷
  16400401 -> tags: 花川戸
  18158033 -> tags: 柳橋
  18158230 -> tags: 蔵前
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  16675719 -> tags: 千住東
  16675720 -> tags: 柳原
  16675721 -> tags: 千住旭町
  16675722 -> tags: 日ノ出町
  1761717 -> tags: 港区
  3562039 -> tags: 麻布台
  3562067 -> tags: 麻布十番
  3562087 -> tags: 麻布永坂町
  3562096 -> tags: 麻布狸穴町
  3782444 -> tags: 台場
  8421156 -> tags: 六本木
  8421157 -> tags: 新橋
  8421158 -> tags: 赤坂
  18165520 -> tags: 東新橋
  18173485 -> tags: 浜松町
  18173536 -> tags: 海岸
  18177254 -> tags: 芝浦
  18177351 -> tags: 芝
  18177439 -> tags: 西新橋
  18180725 -> tags: 芝大門
  18180831 -> tags: 芝公園
  18181013 -> tags: 虎ノ門
  18181014 -> tags: 愛宕
  18184164 -> tags: 三田
  18184214 -> tags: 東麻布
  18189557 -> tags: 南麻布
  18190520 -> tags: 元麻布
  18212415 -> tags: 西麻布
  18220721 -> tags: 南青山
  18225930 -> tags: 北青山
  18747161 -> tags: 港南
  18764773 -> tags: 元赤坂
  18773145 -> tags: 高輪
  18773152 -> tags: 白金
  18773158 -> tags: 白金台
  1761718 -> tags: 葛飾区
  18072416 -> tags: 新小岩
  18072422 -> tags: 西新小岩
  18072431 -> tags: 東新小岩
  18072545 -> tags: 奥戸
  18076913 -> tags: 細田
  18076918 -> tags: 鎌倉
  18076927 -> tags: 高砂
  18081635 -> tags: 柴又
  18088194 -> tags: 新宿
  18088195 -> tags: 金町浄水場
  18088202 -> tags: 金町
  18097530 -> tags: 東金町
  18097543 -> tags: 水元公園
  18098638 -> tags: 東水元
  18098643 -> tags: 南水元
  18098649 -> tags: 水元
  18098656 -> tags: 西水元
  18123639 -> tags: 東四つ木
  18123645 -> tags: 四つ木
  18129679 -> tags: 東立石
  18129688 -> tags: 立石
  18129734 -> tags: 青戸
  18129742 -> tags: 白鳥
  18153896 -> tags: 亀有
  18154002 -> tags: 西亀有
  18154006 -> tags: お花茶屋
  18154009 -> tags: 宝町
  18154262 -> tags: 東堀切
  18154271 -> tags: 堀切
  18154799 -> tags: 小菅
  1761742 -> tags: 千代田区
  3406698 -> tags: 神田駿河台
  3407418 -> tags: 神田三崎町
  3518461 -> tags: 一ツ橋
  3544394 -> tags: 丸の内
  3545196 -> tags: 大手町
  3545325 -> tags: 神田神保町
  3545343 -> tags: 西神田
  3545396 -> tags: 神田猿楽町
  3546176 -> tags: 神田小川町
  3546241 -> tags: 神田錦町
  3546461 -> tags: 神田淡路町
  3546503 -> tags: 内神田
  3547343 -> tags: 神田美土代町
  3547347 -> tags: 神田司町
  3547376 -> tags: 神田多町
  3547422 -> tags: 神田須田町
  3548065 -> tags: 神田鍛冶町
  3548073 -> tags: 鍛冶町
  3548434 -> tags: 神田美倉町
  3548437 -> tags: 神田西福田町
  3548466 -> tags: 神田北乗物町
  3548468 -> tags: 神田紺屋町
  3548480 -> tags: 神田富山町
  3548496 -> tags: 神田東松下町
  3548512 -> tags: 神田岩本町
  3548542 -> tags: 神田東紺屋町
  3548627 -> tags: 千代田
  3551852 -> tags: 皇居外苑
  3551876 -> tags: 北の丸公園
  3553641 -> tags: 岩本町
  3553663 -> tags: 東神田
  3562289 -> tags: 有楽町
  3562298 -> tags: 神田佐久間河岸
  3564036 -> tags: 神田佐久間町
  3564037 -> tags: 神田平河町
  3564815 -> tags: 神田和泉町
  3564853 -> tags: 神田松永町
  3787140 -> tags: 内幸町
  3787155 -> tags: 日比谷公園
  3788300 -> tags: 霞が関
  3788327 -> tags: 永田町
  4243735 -> tags: 平河町
  4243743 -> tags: 隼町
  5444723 -> tags: 紀尾井町
  5821839 -> tags: 麹町
  16310455 -> tags: 外神田
  16929945 -> tags: 飯田橋
  16929946 -> tags: 富士見
  17705903 -> tags: 九段北
  18147013 -> tags: 九段南
  18737549 -> tags: 神田練塀町
  18737550 -> tags: 神田相生町
  18737551 -> tags: 神田花岡町
  18746134 -> tags: 一番町
  18746135 -> tags: 二番町
  18746136 -> tags: 三番町
  18746137 -> tags: 四番町
  18746138 -> tags: 五番町
  18746139 -> tags: 六番町
  1761743 -> tags: 江戸川区
  17947092 -> tags: 小松川
  17947816 -> tags: 平井
  17948190 -> tags: 松島
  17961732 -> tags: 中央
  17965966 -> tags: 大杉
  17965969 -> tags: 松本
  17967645 -> tags: 本一色
  17967646 -> tags: 興宮町
  17967650 -> tags: 上一色
  17968141 -> tags: 北小岩
  17968156 -> tags: 西小岩
  17968548 -> tags: 東小岩
  17968867 -> tags: 南小岩
  17971333 -> tags: 東松本
  17971334 -> tags: 鹿骨町
  17971341 -> tags: 鹿骨
  17971399 -> tags: 新堀
  17971402 -> tags: 谷河内
  17971406 -> tags: 春江町
  17971449 -> tags: 北篠崎
  17971452 -> tags: 西篠崎
  17971457 -> tags: 上篠崎
  17971551 -> tags: 篠崎町
  17972533 -> tags: 下篠崎町
  17972534 -> tags: 東篠崎町
  17972537 -> tags: 東篠崎
  17972543 -> tags: 南篠崎町
  17990726 -> tags: 瑞江
  17990730 -> tags: 東瑞江
  17990734 -> tags: 西瑞江
  17990741 -> tags: 江戸川
  17990759 -> tags: 西小松川町
  17993274 -> tags: 東小松川
  17993417 -> tags: 松江
  17993422 -> tags: 西一之江
  17993431 -> tags: 一之江
  17993433 -> tags: 一之江町
  17993434 -> tags: 二之江町
  17993570 -> tags: 船堀
  17995049 -> tags: 宇喜田町
  18016369 -> tags: 東葛西
  18016377 -> tags: 南葛西
  18016464 -> tags: 中葛西
  18016470 -> tags: 北葛西
  18016761 -> tags: 西葛西
  18016764 -> tags: 清新町
  18016765 -> tags: 堀江町
  18016815 -> tags: 臨海町
  1543055 -> tags: 杉並区
  17146537 -> tags: 方南
  17264723 -> tags: 和田
  17264728 -> tags: 和泉
  17264733 -> tags: 永福
  17264737 -> tags: 堀ノ内
  17264837 -> tags: 梅里
  17264840 -> tags: 大宮
  17265258 -> tags: 松ノ木
  17294917 -> tags: 成田東
  17294922 -> tags: 成田西
  17294927 -> tags: 浜田山
  17295267 -> tags: 下高井戸
  17299394 -> tags: 上高井戸
  17299399 -> tags: 高井戸東
  17299403 -> tags: 高井戸西
  17299409 -> tags: 久我山
  17299510 -> tags: 宮前
  17299514 -> tags: 松庵
  17323138 -> tags: 荻窪
  17323143 -> tags: 南荻窪
  17323281 -> tags: 西荻南
  17324147 -> tags: 西荻北
  17324152 -> tags: 上荻
  17324157 -> tags: 善福寺
  17326884 -> tags: 高円寺南
  17326889 -> tags: 高円寺北
  17326983 -> tags: 阿佐谷南
  17326990 -> tags: 阿佐谷北
  17327877 -> tags: 天沼
  17327881 -> tags: 本天沼
  17327885 -> tags: 清水
  17349409 -> tags: 桃井
  17351018 -> tags: 今川
  17351208 -> tags: 下井草
  17351213 -> tags: 上井草
  17351219 -> tags: 井草
  1543056 -> tags: 中野区
  18585052 -> tags: 南台
  18585059 -> tags: 弥生町
  18609477 -> tags: 本町
  18609613 -> tags: 中央
  18609619 -> tags: 東中野
  18612993 -> tags: 中野
  18613037 -> tags: 上高田
  18613268 -> tags: 新井
  18613271 -> tags: 松が丘
  18635699 -> tags: 沼袋
  18636847 -> tags: 江古田
  18636851 -> tags: 江原町
  18639506 -> tags: 野方
  18639509 -> tags: 丸山
  18639616 -> tags: 大和町
  18639620 -> tags: 若宮
  18639624 -> tags: 白鷺
  18639878 -> tags: 鷺宮
  18639884 -> tags: 上鷺宮
  3554015 -> tags: 江東区
  3785830 -> tags: 青海
  3788693 -> tags: 有明
  3789147 -> tags: 豊洲
  12752669 -> tags: 東雲
  12752689 -> tags: 辰巳
  12752795 -> tags: 新木場
  12752807 -> tags: 若洲
  12752844 -> tags: 夢の島
  16928977 -> tags: 枝川
  16928978 -> tags: 潮見
  16928979 -> tags: 塩浜
  16929043 -> tags: 新大橋
  16929044 -> tags: 常盤
  16929045 -> tags: 高橋
  16929046 -> tags: 森下
  16929047 -> tags: 亀戸
  16929395 -> tags: 毛利
  16929396 -> tags: 住吉
  16929397 -> tags: 猿江
  16929398 -> tags: 大島
  16951961 -> tags: 清澄
  16953109 -> tags: 白河
  16953113 -> tags: 扇橋
  16953114 -> tags: 石島
  16953115 -> tags: 千田
  16953116 -> tags: 海辺
  16990950 -> tags: 新砂
  17004199 -> tags: 千石
  17004280 -> tags: 三好
  17004285 -> tags: 平野
  17004553 -> tags: 佐賀
  17004708 -> tags: 福住
  17004711 -> tags: 深川
  17004712 -> tags: 冬木
  17004725 -> tags: 永代
  17007589 -> tags: 門前仲町
  17007592 -> tags: 富岡
  17007596 -> tags: 牡丹
  17007600 -> tags: 古石場
  17007604 -> tags: 越中島
  17007725 -> tags: 木場
  17007733 -> tags: 東陽
  17007869 -> tags: 南砂
  17007877 -> tags: 北砂
  17007886 -> tags: 東砂
  3554304 -> tags: 品川区
  3785823 -> tags: 東八潮
  12765982 -> tags: 八潮
  16885638 -> tags: 東品川
  16885639 -> tags: 東大井
  16885640 -> tags: 南大井
  16885645 -> tags: 勝島
  16885773 -> tags: 南品川
  16885774 -> tags: 広町
  16885808 -> tags: 北品川
  17011744 -> tags: 西品川
  17015455 -> tags: 大井
  17015462 -> tags: 西大井
  17015467 -> tags: 二葉
  17015576 -> tags: 豊町
  17015583 -> tags: 戸越
  17016053 -> tags: 上大崎
  17019961 -> tags: 大崎
  17083327 -> tags: 東五反田
  17083336 -> tags: 西五反田
  17083403 -> tags: 小山台
  17086405 -> tags: 平塚
  17086408 -> tags: 東中延
  17086415 -> tags: 中延
  17086419 -> tags: 西中延
  17086493 -> tags: 旗の台
  17086501 -> tags: 荏原
  17086509 -> tags: 小山

Extracted 23 valid ward polygons
   ward_id  ...                                           geometry
0  1758858  ...  MULTIPOLYGON (((139.68569 35.6841, 139.68744 3...
1  1758878  ...  MULTIPOLYGON (((139.75744 35.7016, 139.75722 3...
2  1758888  ...  MULTIPOLYGON (((139.76996 35.70523, 139.772 35...
3  1758891  ...  MULTIPOLYGON (((139.7997 35.68971, 139.79983 3...
4  1758897  ...  MULTIPOLYGON (((139.76507 35.65225, 139.7655 3...

[5 rows x 4 columns]
Bounds: [139.5628986  35.5233559 139.9188878  35.8174937]
Saved intermediate wards
Wards in GDF: 23
    ward_id ward_name_en
0   1758858     Shinjuku
1   1758878       Bunkyō
2   1758888        Taito
3   1758891       Sumida
4   1758897         Chuo
5   1758936       Meguro
6   1758947          Ota
7   1759474     Setagaya
8   1759477      Shibuya
9   1759506      Toshima
10  1760038         Kita
11  1760040      Arakawa
12  1760078     Itabashi
13  1760119       Nerima
14  1760124       Adachi
15  1761717       Minato
16  1761718   Katsushika
17  1761742      Chiyoda
18  1761743      Edogawa
19  1543055     Suginami
20  1543056       Nakano
21  3554015         Koto
22  3554304    Shinagawa

BBox: 139.5628986, 35.5233559, 139.9188878, 35.8174937
Query length: 247 chars
BBox query: (35.5233559,139.5628986,35.8174937,139.9188878)
Status: 504
Error: <?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
    "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
  <meta http-equiv="content-type" content="text/html; charset=utf-8" lang="en"/>
  <title>OSM3S Response</title>
</head>
<body>

<p>The data included in this document is from www.openstreetmap.org. The data is made available under ODbL.</p>
<p><strong style="color:#FF0000

python3 << 'PYEOF'
import overturemaps
# Check what's available
print(dir(overturemaps))
help(overturemaps.core)
PYEOF

['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'core', 'geodataframe', 'get_all_overture_types', 'models', 'record_batch_reader']
Help on module overturemaps.core in overturemaps:

NAME
    overturemaps.core

FUNCTIONS
    count_rows(
        overture_type,
        bbox=None,
        release=None,
        connect_timeout=None,
        request_timeout=None,
        stac=False
    ) -> int
        Return the number of rows matching the given parameters.

    geoarrow_schema_adapter(schema: pa.Schema) -> pa.Schema
        Convert a geoarrow-compatible schema to a proper geoarrow schema

        This assumes there is a single "geometry" column with WKB formatting

        Parameters
        ----------
        schema: pa.Schema

        Returns
        -------
        pa.Schema
        A copy of the input schema with the geometry field replaced with
        a new one with the proper geoarrow ARROW:extension metadata

    geodataframe(
        overture_type: str,
        bbox: BBox | tuple[float, float, float, float] | list[float] | None = None,
        release: str = None,
        connect_timeout: int = None,
        request_timeout: int = None,
        stac: bool = False
    ) -> GeoDataFrame
        Loads geoparquet for specified type into a geopandas dataframe

        Parameters
        ----------
        overture_type: type to load
        bbox: optional bounding box for data fetch (xmin, ymin, xmax, ymax)
        connect_timeout: optional connection timeout in seconds
        request_timeout: optional request timeout in seconds

        Returns
        -------
        GeoDataFrame with the optionally filtered theme data

    get_all_overture_types() -> List[str]

    get_available_releases() -> Tuple[List[str], str]
        Fetch available releases from the STAC catalog.

        Returns
        -------
        Tuple of (all_releases, latest_release) where:
            - all_releases is a list of release version strings
            - latest_release is the latest release version string

    get_latest_release() -> str
        Get the latest release version.

        Returns
        -------
        str: The latest release version

    query_gers_registry(gers_id: str) -> Optional[Tuple[str, BBox | None]]
        Query the GERS registry to get the filepath and bbox for a given GERS ID.

        The registry always uses the latest release.

        Parameters
        ----------
        gers_id: The GERS ID to look up

        Returns
        -------
        Tuple of (filepath, bbox) where bbox is a BBox, or None if not found

    record_batch_reader(
        overture_type,
        bbox=None,
        release=None,
        connect_timeout=None,
        request_timeout=None,
        stac=False
    ) -> Optional[pa.RecordBatchReader]
        Return a pyarrow RecordBatchReader for the desired bounding box and s3 path, or None on error.

    record_batch_reader_from_gers(
        gers_id: str,
        connect_timeout: int = None,
        request_timeout: int = None,
        registry_result: Optional[Tuple[str, BBox | None]] = None
    ) -> Optional[pa.RecordBatchReader]
        Return a pyarrow RecordBatchReader for a specific GERS ID.

        The registry always uses the latest release.

        Parameters
        ----------
        gers_id: The GERS ID to look up
        connect_timeout: Optional connection timeout in seconds
        request_timeout: Optional request timeout in seconds
        registry_result: Optional pre-fetched registry result (filepath, bbox)
                        to avoid duplicate registry queries

        Returns
        -------
        RecordBatchReader with the feature data, or None if not found

DATA
    ALL_RELEASES = ['2026-05-20.0', '2026-04-15.0']
    HAS_GEOPANDAS = True
    List = typing.List
        Deprecated alias to list.

    Optional = typing.Optional
        Optional[X] is equivalent to Union[X, None].

    STAC_CATALOG_URL = 'https://stac.overturemaps.org/catalog.json'
    Tuple = typing.Tuple
        Deprecated alias to builtins.tuple.

        Tuple[X, Y] is the cross-product type of X and Y.

        Example: Tuple[T1, T2] is a tuple of two elements corresponding
        to type variables T1 and T2.  Tuple[int, float, str] is a tuple
        of an int, a float and a string.

        To specify a variable-length tuple of homogeneous type, use Tuple[T, ...].

    type_theme_map = {'address': 'addresses', 'bathymetry': 'base', 'build...

FILE
    /usr/local/lib/python3.14/site-packages/overturemaps/core.py

python3 << 'PYEOF'
import overturemaps
types = overturemaps.get_all_overture_types()
print("\n".join(sorted(types)))
PYEOF

address
bathymetry
building
building_part
connector
division
division_area
division_boundary
infrastructure
land
land_cover
land_use
place
segment
water

python3 << 'PYEOF'
import overturemaps

# Get a small sample of segment data to understand the schema
# Tokyo 23 wards bbox
bbox = (139.56, 35.52, 139.92, 35.82)

# Get a few records to understand the schema
reader = overturemaps.record_batch_reader("segment", bbox=bbox)
if reader:
    schema = reader.schema
    print("Schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
    
    # Read a few rows
    import pyarrow as pa
    batches = []
    for i, batch in enumerate(reader):
        batches.append(batch)
        if i >= 2:
            break
    if batches:
        tbl = pa.concat_tables(batches)
        df = tbl.to_pandas()
        print(f"\nSample ({len(df)} rows):")
        print(df.columns.tolist())
        print(df.head(3))
        # Check subType and road related columns
        for col in df.columns:
            if any(x in col.lower() for x in ['road', 'highway', 'class', 'type', 'subtype', 'sub_type']):
                print(f"\n{col} values (first 10):")
                vals = df[col].dropna().unique()[:20]
                print(vals)
else:
    print("No reader returned")
PYEOF

Schema:
  id: string
  names: struct<primary: string, common: map<string, string ('common')>, rules: list<element: struct<variant: string, language: string, perspectives: struct<mode: string, countries: list<element: string>>, value: string, between: list<element: double>, side: string>>>
  subtype: string
  class: string
  subclass: string
  subclass_rules: list<element: struct<value: string, between: list<element: double>>>
  connectors: list<element: struct<connector_id: string, at: double>>
  road_surface: list<element: struct<value: string, between: list<element: double>>>
  road_flags: list<element: struct<values: list<element: string>, between: list<element: double>>>
  rail_flags: list<element: struct<values: list<element: string>, between: list<element: double>>>
  width_rules: list<element: struct<value: double, between: list<element: double>>>
  level_rules: list<element: struct<value: int32, between: list<element: double>>>
  access_restrictions: list<element: struct<access_type: string, when: struct<during: string, heading: string, using: list<element: string>, recognized: list<element: string>, mode: list<element: string>, vehicle: list<element: struct<dimension: string, comparison: string, value: double, unit: string>>>, between: list<element: double>>>
  speed_limits: list<element: struct<min_speed: struct<value: int32, unit: string>, max_speed: struct<value: int32, unit: string>, is_max_speed_variable: bool, when: struct<during: string, heading: string, using: list<element: string>, recognized: list<element: string>, mode: list<element: string>, vehicle: list<element: struct<dimension: string, comparison: string, value: double, unit: string>>>, between: list<element: double>>>
  prohibited_transitions: list<element: struct<sequence: list<element: struct<connector_id: string, segment_id: string>>, final_heading: string, when: struct<heading: string, during: string, using: list<element: string>, recognized: list<element: string>, mode: list<element: string>, vehicle: list<element: struct<dimension: string, comparison: string, value: double, unit: string>>>, between: list<element: double>>>
  routes: list<element: struct<name: string, network: string, ref: string, symbol: string, wikidata: string, between: list<element: double>>>
  destinations: list<element: struct<labels: list<element: struct<value: string, type: string>>, symbols: list<element: string>, from_connector_id: string, to_segment_id: string, to_connector_id: string, when: struct<heading: string>, final_heading: string>>
  sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: list<element: double>>>
  geometry: binary
  version: int32
  bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>

Traceback (most recent call last):
  File "<stdin>", line 23, in <module>
  File "pyarrow/table.pxi", line 6259, in pyarrow.lib.concat_tables
TypeError: Cannot convert pyarrow.lib.RecordBatch to pyarrow.lib.Table

python3 << 'PYEOF'
import overturemaps
import geopandas as gpd

# Tokyo 23 wards bbox
bbox = (139.56, 35.52, 139.92, 35.82)

# Load segment data into GeoDataFrame
gdf_segments = overturemaps.geodataframe("segment", bbox=bbox)
print(f"Total segments: {len(gdf_segments)}")
print(f"Columns: {gdf_segments.columns.tolist()}")
print(f"\nSubtype values: {gdf_segments['subtype'].value_counts()}")

print(f"\nClass values: {gdf_segments['class'].value_counts()}")

# Filter to road segments only (not rail, etc.)
road = gdf_segments[gdf_segments['subtype'] == 'road'].copy()
print(f"\nRoad segments: {len(road)}")
print(f"Road class values:")
print(road['class'].value_counts())

# Check road_flags for service roads
print(f"\nSample road flags:")
print(road['road_flags'].head(10))
PYEOF

Total segments: 470180
Columns: ['id', 'names', 'subtype', 'class', 'subclass', 'subclass_rules', 'connectors', 'road_surface', 'road_flags', 'rail_flags', 'width_rules', 'level_rules', 'access_restrictions', 'speed_limits', 'prohibited_transitions', 'routes', 'destinations', 'sources', 'geometry', 'version', 'bbox']

Subtype values: subtype
road     465852
rail       4265
water        63
Name: count, dtype: int64

Class values: class
residential       112307
service            99809
footway            83076
unclassified       72785
tertiary           39396
steps              14645
primary            12975
secondary           9932
path                9731
trunk               5553
standard_gauge      3092
pedestrian          2447
motorway            1153
cycleway            1143
subway               851
track                657
unknown              335
light_rail            98
tram                  38
monorail              34
living_street         28
narrow_gauge          20
bridleway             11
funicular              1
Name: count, dtype: int64

Road segments: 465852
Road class values:
class
residential      112307
service           99809
footway           83076
unclassified      72785
tertiary          39396
steps             14645
primary           12975
secondary          9932
path               9731
trunk              5553
pedestrian         2447
motorway           1153
cycleway           1143
track               657
unknown             204
living_street        28
bridleway            11
Name: count, dtype: int64

Sample road flags:
6     None
7     None
8     None
9     None
10    None
11    None
12    None
13    None
14    None
15    None
Name: road_flags, dtype: object

Loading ward boundaries...
  23 wards loaded
Loading road network from Overture Maps...
  BBox: (np.float64(139.5628986), np.float64(35.5233559), np.float64(139.9188878), np.float64(35.8174937))
  Total road segments: 454942
  Drivable segments: 247298
  Subclass distribution: subclass
link    3452
Name: count, dtype: int64
  Intersecting roads with ward boundaries...
  Roads near boundaries: 12869

Reprojecting to EPSG:6675...

Traceback (most recent call last):
  File "/work/solve.py", line 76, in <module>
    gdf_roads_metric = gdf_roads_near.to_crs(METRIC_CRS)
  File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1842, in to_crs
    geom = df.geometry.to_crs(crs=crs, epsg=epsg)
  File "/usr/local/lib/python3.14/site-packages/geopandas/geoseries.py", line 1251, in to_crs
    self.values.to_crs(crs=crs, epsg=epsg), index=self.index, name=self.name
    ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/geopandas/_compat.py", line 88, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/geopandas/array.py", line 1114, in to_crs
    raise ValueError(
    ...<2 lines>...
    )
ValueError: Cannot transform naive geometries.  Please set a crs on the object first.

Loading ward boundaries...
  23 wards loaded
Loading road network from Overture Maps...
  BBox: (np.float64(139.5628986), np.float64(35.5233559), np.float64(139.9188878), np.float64(35.8174937))
  Total road segments: 454942
  Drivable segments: 247298
  Subclass distribution: subclass
link    3452
Name: count, dtype: int64
  Intersecting roads with ward boundaries...
  Roads near boundaries: 12869

Reprojecting to EPSG:6675...
  Wards bounds (metric): [216700.16576346 -49700.22274089 249096.12191291 -17122.51818113]

Finding crossing points...
  Total crossing points: 4401

Creating 50m buffers...
  4401 buffers created

Clipping buffers to ward boundaries...
  4401 clipped buffer polygons

Computing crossing densities...
  Density computed for 23 wards
    ward_id ward_name_en  crossing_count  ward_area_km2  crossings_per_km2
0   1758858     Shinjuku             222         0.2798           793.3463
1   1758878       Bunkyō             128         0.2059           621.6040
2   1758888        Taito              93         0.0968           961.1569
3   1758891       Sumida              65         0.1834           354.3979
4   1758897         Chuo             130         0.0979          1327.4320
5   1758936       Meguro             200         0.1403          1425.3842
6   1758947          Ota             180         2.0080            89.6404
7   1759474     Setagaya             384         0.6800           564.6809
8   1759477      Shibuya             179         0.1610          1111.9087
9   1759506      Toshima             236         0.3267           722.3358
10  1760038         Kita             219         0.5006           437.4343
11  1760040      Arakawa             145         0.2075           698.6412
12  1760078     Itabashi             262         0.6177           424.1807
13  1760119       Nerima             342         0.5993           570.6947
14  1760124       Adachi             306         1.0224           299.2902
15  1761717       Minato             155         0.2057           753.3584
16  1761718   Katsushika             209         0.7982           261.8546
17  1761742      Chiyoda             173         0.0781          2214.5323
18  1761743      Edogawa             145         5.2544            27.5960
19  1543055     Suginami             166         0.3421           485.2473
20  1543056       Nakano             197         0.1809          1089.1137
21  3554015         Koto             118         5.9942            19.6857
22  3554304    Shinagawa             147         0.6178           237.9595

Saving to tokyo_crossings.gpkg...
  Layer 1 - wards_jgd: 23 polygons
  Layer 2 - crossing_points: 4401 points
  Layer 3 - crossing_buffers_50m: 4401 polygons
  Layer 4 - buffer_ward_intersection: 4401 polygons
  Layer 5 - ward_crossing_density_wgs84: 23 rows

✅ Done! File saved to /work/tokyo_crossings.gpkg

python3 << 'PYEOF'
import geopandas as gpd
import pyproj

# Check the CRS
crs = pyproj.CRS.from_epsg(6675)
print("EPSG:6675:", crs)
print("Unit:", crs.axis_info[0].unit_name)
print()

# Check areas in different CRS
gdf = gpd.read_file('/work/wards_intermediate.gpkg', layer='wards')

# WGS84 area (should be approximate in sq deg)
gdf['area_wgs84_deg2'] = gdf.geometry.area
print("WGS84 areas (sq deg):")
print(gdf[['ward_name_en', 'area_wgs84_deg2']].to_string())

# Convert to an equal-area CRS for Japan
# JGD2011 / UTM zone 54N (EPSG:6676) - covers Tokyo
gdf_utm = gdf.to_crs("EPSG:6676")
gdf_utm['area_km2'] = gdf_utm.geometry.area / 1_000_000
print("\n\nUTM 54N areas (km²):")
print(gdf_utm[['ward_name_en', 'area_km2']].to_string())
print(f"\nChiyoda: {gdf_utm[gdf_utm['ward_name_en']=='Chiyoda']['area_km2'].values[0]:.4f} km²")

# Also try EPSG:6675
gdf_6675 = gdf.to_crs("EPSG:6675")
gdf_6675['area_km2_6675'] = gdf_6675.geometry.area / 1_000_000
print("\n\nEPSG:6675 areas (km²):")
print(gdf_6675[['ward_name_en', 'area_km2_6675']].to_string())
PYEOF

EPSG:6675: EPSG:6675
Unit: metre

WGS84 areas (sq deg):
   ward_name_en  area_wgs84_deg2
0      Shinjuku         0.000028
1        Bunkyō         0.000020
2         Taito         0.000010
3        Sumida         0.000018
4          Chuo         0.000010
5        Meguro         0.000014
6           Ota         0.000199
7      Setagaya         0.000068
8       Shibuya         0.000016
9       Toshima         0.000033
10         Kita         0.000050
11      Arakawa         0.000021
12     Itabashi         0.000062
13       Nerima         0.000060
14       Adachi         0.000102
15       Minato         0.000020
16   Katsushika         0.000079
17      Chiyoda         0.000008
18      Edogawa         0.000522
19     Suginami         0.000034
20       Nakano         0.000018
21         Koto         0.000595
22    Shinagawa         0.000061


UTM 54N areas (km²):
   ward_name_en  area_km2
0      Shinjuku  0.279546
1        Bunkyō  0.205707
2         Taito  0.096657
3        Sumida  0.183213
4          Chuo  0.097831
5        Meguro  0.140173
6           Ota  2.005978
7      Setagaya  0.679377
8       Shibuya  0.160824
9       Toshima  0.326388
10         Kita  0.500137
11      Arakawa  0.207327
12     Itabashi  0.617049
13       Nerima  0.598699
14       Adachi  1.021335
15       Minato  0.205534
16   Katsushika  0.797285
17      Chiyoda  0.078039
18      Edogawa  5.248752
19     Suginami  0.341769
20       Nakano  0.180703
21         Koto  5.987740
22    Shinagawa  0.617113

Chiyoda: 0.0780 km²


EPSG:6675 areas (km²):
   ward_name_en  area_km2_6675
0      Shinjuku       0.279827
1        Bunkyō       0.205919
2         Taito       0.096758
3        Sumida       0.183410
4          Chuo       0.097933
5        Meguro       0.140313
6           Ota       2.008023
7      Setagaya       0.680030
8       Shibuya       0.160984
9       Toshima       0.326718
10         Kita       0.500647
11      Arakawa       0.207546
12     Itabashi       0.617661
13       Nerima       0.599270
14       Adachi       1.022419
15       Minato       0.205745
16   Katsushika       0.798153
17      Chiyoda       0.078120
18      Edogawa       5.254387
19     Suginami       0.342094
20       Nakano       0.180881
21         Koto       5.994200
22    Shinagawa       0.617752

python3 << 'PYEOF'
import geopandas as gpd
gdf = gpd.read_file('/work/wards_intermediate.gpkg', layer='wards')
print(gdf.geometry.geom_type)
print(gdf.iloc[17]['ward_name_en'], ":", gdf.iloc[17].geometry.geom_type)
print(gdf.iloc[17].geometry)
PYEOF

0     MultiPolygon
1     MultiPolygon
2     MultiPolygon
3     MultiPolygon
4     MultiPolygon
5     MultiPolygon
6     MultiPolygon
7     MultiPolygon
8     MultiPolygon
9     MultiPolygon
10    MultiPolygon
11    MultiPolygon
12    MultiPolygon
13    MultiPolygon
14    MultiPolygon
15    MultiPolygon
16    MultiPolygon
17    MultiPolygon
18    MultiPolygon
19    MultiPolygon
20    MultiPolygon
21    MultiPolygon
22    MultiPolygon
dtype: str
Chiyoda : MultiPolygon
MULTIPOLYGON (((139.7486141 35.6704039, 139.7486917 35.6704612, 139.7494764 35.6709329, 139.7476059 35.6696021, 139.7478709 35.6698129, 139.7481349 35.6700241, 139.7483891 35.670228, 139.7486141 35.6704039)), ((139.7510025 35.6704209, 139.7513346 35.6703072, 139.7515927 35.6702196, 139.7517969 35.6701505, 139.7494764 35.6709329, 139.7496664 35.6708786, 139.749937 35.6707864, 139.7503584 35.6706419, 139.7506471 35.6705429, 139.7510025 35.6704209)), ((139.7450303 35.6709556, 139.7450047 35.6707758, 139.7449729 35.6706801, 139.7450298 35.6710063, 139.7450303 35.6709556)), ((139.7448636 35.6710069, 139.7433302 35.6713028, 139.7432957 35.6713185, 139.7431964 35.6713515, 139.7450298 35.6710063, 139.7448636 35.6710069)), ((139.733632 35.6790141, 139.7335734 35.6790577, 139.7362549 35.6787873, 139.7361995 35.6787833, 139.7354699 35.6787397, 139.7349657 35.678731, 139.7345526 35.6787833, 139.7336728 35.6789837, 139.733632 35.6790141)), ((139.7306814 35.6835905, 139.73036 35.6842285, 139.7301347 35.6850171, 139.7303824 35.6859677, 139.7304856 35.6863457, 139.7306814 35.6835905)), ((139.7323985 35.6900059, 139.7337211 35.6907525, 139.7342351 35.691043, 139.7316172 35.689112, 139.7323985 35.6900059)), ((139.7374279 35.6930834, 139.7357468 35.6919391, 139.7357143 35.6919781, 139.7375817 35.6932288, 139.7374279 35.6930834)), ((139.7390212 35.6949829, 139.7378195 35.6934537, 139.7375817 35.6932288, 139.7391957 35.6952325, 139.7390212 35.6949829)), ((139.7434683 35.7001195, 139.7440051 35.7014675, 139.7440352 35.7015018, 139.7443078 35.7013586, 139.7441385 35.7011369, 139.7439561 35.700856, 139.7434683 35.7001195)), ((139.7525822 35.7024954, 139.7527138 35.7024839, 139.7528459 35.7024723, 139.752943 35.7024639, 139.7530554 35.7024538, 139.7532465 35.7024368, 139.7533053 35.7024316, 139.7533973 35.7024196, 139.7535125 35.7024042, 139.7536394 35.7023875, 139.7537628 35.7023713, 139.7539074 35.7023521, 139.7540099 35.7023385, 139.7540629 35.7023315, 139.7541424 35.7023142, 139.7542631 35.7022883, 139.754425 35.7022535, 139.7545318 35.7022305, 139.7546832 35.7021979, 139.7548164 35.7021691, 139.7549805 35.7021336, 139.7551417 35.702099, 139.7553272 35.7020591, 139.7518637 35.7025552, 139.7518815 35.702554, 139.7520582 35.7025405, 139.7522534 35.7025243, 139.7524413 35.7025079, 139.7525822 35.7024954)), ((139.7449497 35.7024438, 139.7448905 35.7024109, 139.7447806 35.7023497, 139.7447418 35.7023285, 139.7444309 35.7024742, 139.7449497 35.7024438)), ((139.7459394 35.7027787, 139.7460516 35.7028138, 139.7461993 35.7028596, 139.7462508 35.7028681, 139.7451394 35.7025289, 139.7453427 35.7025933, 139.7455677 35.7026639, 139.7457768 35.702728, 139.7459394 35.7027787)), ((139.7474137 35.7029498, 139.7478559 35.7029441, 139.7481281 35.7029408, 139.7483547 35.7029373, 139.7486083 35.702934, 139.7488613 35.702931, 139.7491111 35.7029277, 139.7491665 35.7029179, 139.7493549 35.7028848, 139.749601 35.7028413, 139.7498193 35.7028026, 139.7499463 35.7027798, 139.7500505 35.7027617, 139.7502136 35.7027328, 139.7503262 35.7027132, 139.7504446 35.7026928, 139.75054 35.7026766, 139.7506557 35.702657, 139.7507556 35.7026399, 139.7508232 35.7026285, 139.7508864 35.7026176, 139.7462508 35.7028681, 139.7463472 35.7028842, 139.7466414 35.702932, 139.7467974 35.7029576, 139.74698 35.7029549, 139.7474137 35.7029498)), ((139.7566491 35.669176, 139.7563862 35.6692587, 139.7560659 35.669359, 139.7555382 35.6695231, 139.7554563 35.6695573, 139.758555 35.6686296, 139.7580242 35.6687506, 139.7578549 35.6688027, 139.7574585 35.6689249, 139.7570502 35.6690511, 139.7568743 35.6691059, 139.7566491 35.669176)), ((139.7531618 35.6703446, 139.753489 35.6702479, 139.7536015 35.6702113, 139.753915 35.6701075, 139.7540988 35.6700424, 139.7544045 35.6699355, 139.7547353 35.6698193, 139.7549979 35.6697265, 139.7552476 35.6696403, 139.7553509 35.6696154, 139.7554563 35.6695573, 139.7520512 35.670609, 139.7521 35.6706265, 139.7524363 35.670545, 139.7527104 35.6704774, 139.7531618 35.6703446)), ((139.7611866 35.6726058, 139.7615956 35.672706, 139.7623183 35.6728919, 139.7625945 35.6729503, 139.760429 35.67207, 139.7605227 35.6721907, 139.760642 35.6723283, 139.7607989 35.6724438, 139.7609881 35.6725382, 139.7611866 35.6726058)), ((139.7638888 35.6733513, 139.7636341 35.6732322, 139.7630775 35.6730622, 139.7625945 35.6729503, 139.7639845 35.6734227, 139.7638888 35.6733513)), ((139.7646514 35.6743801, 139.7644454 35.674021, 139.7641987 35.673632, 139.7640914 35.6735024, 139.7639845 35.6734227, 139.7646514 35.6743801)), ((139.764723 35.6745036, 139.7650679 35.674873, 139.7653844 35.6752229, 139.7646514 35.6743801, 139.764723 35.6745036)), ((139.7696248 35.6871375, 139.7696334 35.6871284, 139.7679348 35.6885909, 139.7680039 35.688545, 139.7687954 35.6880095, 139.7689738 35.6878218, 139.7691934 35.6875904, 139.7693426 35.6874336, 139.7696248 35.6871375)), ((139.7652205 35.6998471, 139.7650018 35.6999063, 139.7649946 35.6999082, 139.7664336 35.6995314, 139.7662661 35.6995711, 139.7660303 35.6996308, 139.7657437 35.6997069, 139.765455 35.6997842, 139.7652205 35.6998471)), ((139.7631893 35.7003758, 139.7629791 35.7005182, 139.7629034 35.7005944, 139.7627906 35.7007096, 139.7626886 35.7008344, 139.7625714 35.7009779, 139.762506 35.7010383, 139.762368 35.7011659, 139.7623008 35.7012143, 139.7622701 35.7012367, 139.7649946 35.6999082, 139.7647471 35.699973, 139.7643333 35.7000657, 139.7637304 35.7001999, 139.7634565 35.700261, 139.7631893 35.7003758)), ((139.7617909 35.7014682, 139.761801 35.7014655, 139.7618941 35.7014402, 139.762168 35.7013113, 139.7622701 35.7012367, 139.7617909 35.7014682)), ((139.7603199 35.7015931, 139.7607034 35.7015905, 139.7608722 35.7015898, 139.7612975 35.7015623, 139.7615994 35.7015213, 139.7617909 35.7014682, 139.7585915 35.7015825, 139.7586411 35.7015831, 139.7589331 35.7015867, 139.7592477 35.701589, 139.7595337 35.7015905, 139.7598727 35.7015921, 139.7603199 35.7015931)), ((139.7572197 35.7016473, 139.756894 35.7017178, 139.7566654 35.7017687, 139.7585915 35.7015825, 139.7585096 35.7015815, 139.7582298 35.7015778, 139.7578752 35.701573, 139.7577799 35.7015792, 139.7574411 35.7015997, 139.7572197 35.7016473)), ((139.755891 35.7019374, 139.7560472 35.7019039, 139.756206 35.7018696, 139.756365 35.7018354, 139.756615 35.7017816, 139.7566654 35.7017687, 139.7553766 35.7020484, 139.7554192 35.7020392, 139.7555255 35.7020164, 139.7556467 35.7019901, 139.7557241 35.7019734, 139.755891 35.7019374)), ((139.7679564 35.7026126, 139.7679483 35.7027422, 139.7675903 35.7027204, 139.7676023 35.702556, 139.7675661 35.7025527, 139.7674535 35.7025237, 139.7666583 35.702471, 139.7666611 35.7024887, 139.7666907 35.7026793, 139.7665618 35.7027011, 139.7680898 35.7028809, 139.7681012 35.7027945, 139.7681334 35.7027948, 139.7681468 35.7026246, 139.7679564 35.7026126)), ((139.7707101 35.6834691, 139.7707168 35.6834295, 139.770655 35.6833074, 139.7705118 35.6830169, 139.7702598 35.6824998, 139.7701916 35.6823963, 139.7699902 35.6820916, 139.7697915 35.6817909, 139.7697273 35.6817127, 139.7696337 35.6815978, 139.7696184 35.6815329, 139.7695449 35.6814337, 139.7694567 35.6813012, 139.7693988 35.6812063, 139.7693317 35.6810959, 139.7692711 35.681, 139.769004 35.6805829, 139.7689181 35.6804451, 139.7706529 35.6836897, 139.770614 35.6835781, 139.7706497 35.6835676, 139.7706872 35.6835234, 139.7707002 35.6835026, 139.7707101 35.6834691)), ((139.7708478 35.68376, 139.7708089 35.6837658, 139.77069 35.6837938, 139.7706529 35.6836897, 139.7709768 35.6846493, 139.7708934 35.684394, 139.7711603 35.6843366, 139.7709377 35.6838047, 139.7709108 35.6837774, 139.7708813 35.6837654, 139.7708478 35.68376)), ((139.7708215 35.6852389, 139.7709661 35.6849955, 139.771045 35.6848578, 139.7709768 35.6846493, 139.7707855 35.6853225, 139.7708215 35.6852389)), ((139.7704329 35.6861388, 139.770441 35.6861197, 139.7696334 35.6871284, 139.7697121 35.6870451, 139.7700499 35.6866901, 139.770196 35.6864966, 139.7703853 35.6862442, 139.7704329 35.6861388)), ((139.7710696 35.689612, 139.7697083 35.689169, 139.7700744 35.6892899, 139.7703588 35.6893846, 139.7708043 35.6895281, 139.7710696 35.689612)), ((139.7710771 35.6896144, 139.7710696 35.689612, 139.7723895 35.690038, 139.7723338 35.6900178, 139.771921 35.6898852, 139.7717447 35.6898284, 139.7713438 35.6897003, 139.7710771 35.6896144)), ((139.7730897 35.6902752, 139.7733667 35.6903672, 139.773642 35.6904583, 139.7738118 35.6905136, 139.7723895 35.690038, 139.7724341 35.6900563, 139.7725089 35.690084, 139.7726048 35.6901153, 139.7729148 35.6902179, 139.7730897 35.6902752)), ((139.7738118 35.6905136, 139.7751828 35.6909688, 139.7749531 35.6908922, 139.7744924 35.690739, 139.7742095 35.6906452, 139.7739822 35.6905697, 139.7738571 35.6905283, 139.7738118 35.6905136)), ((139.7783942 35.6921928, 139.7780529 35.692059, 139.7775452 35.6918586, 139.7774301 35.6918099, 139.777078 35.6916605, 139.7766635 35.6914825, 139.776609 35.6914596, 139.7799412 35.6931966, 139.779778 35.6930845, 139.7792753 35.692715, 139.7788096 35.6923738, 139.7787557 35.6923352, 139.7783942 35.6921928)), ((139.7800706 35.693085, 139.7802409 35.6928966, 139.7802868 35.6928387, 139.7799412 35.6931966, 139.7800706 35.693085)), ((139.7815063 35.6937564, 139.7812817 35.6935941, 139.781193 35.6935296, 139.7807912 35.6932241, 139.7804349 35.6929514, 139.7803371 35.6928769, 139.7802868 35.6928387, 139.7828455 35.6951307, 139.7827572 35.6946639, 139.7826236 35.6945668, 139.7822051 35.6942614, 139.7817271 35.6939157, 139.7815063 35.6937564)), ((139.7828455 35.6951307, 139.7816047 35.6952473, 139.7820053 35.6952208, 139.7825446 35.6951647, 139.7826847 35.695149, 139.7828455 35.6951307)), ((139.7720701 35.7050933, 139.7721556 35.7050997, 139.7732677 35.7050056, 139.7730094 35.7028553, 139.7720003 35.7050955, 139.7720701 35.7050933)), ((139.7715833 35.7051204, 139.7708976 35.7051694, 139.7703908 35.705205, 139.7699582 35.7052349, 139.7720003 35.7050955, 139.7715833 35.7051204)))

python3 << 'PYEOF'
import requests, xml.etree.ElementTree as ET

# Check the actual members of Shinjuku ward relation
r = requests.get("https://api.openstreetmap.org/api/0.6/relation/1758858", timeout=30)
root = ET.fromstring(r.text)
rel = root.find(".//relation")
members = rel.findall('member')

roles = {}
for m in members:
    role = m.get('role')
    mtype = m.get('type')
    ref = m.get('ref')
    if role not in roles:
        roles[role] = []
    roles[role].append((mtype, ref))

for role, items in roles.items():
    print(f"\nRole '{role}': {len(items)} members")
    # Show first few
    for mtype, ref in items[:5]:
        print(f"  {mtype} {ref}")
    if len(items) > 5:
        print(f"  ... and {len(items)-5} more")
PYEOF

Role 'label': 1 members
  node 2389061853

Role 'outer': 106 members
  way 1245777293
  way 286087173
  way 1245777305
  way 1245777304
  way 1245795660
  ... and 101 more

Role 'subarea': 94 members
  relation 17069519
  relation 17069520
  relation 17069521
  relation 17069525
  relation 17069526
  ... and 89 more

python3 << 'PYEOF'
import requests, xml.etree.ElementTree as ET

# Check Chiyoda ward
r = requests.get("https://api.openstreetmap.org/api/0.6/relation/1761742", timeout=30)
root = ET.fromstring(r.text)
rel = root.find(".//relation")
members = rel.findall('member')

roles = {}
for m in members:
    role = m.get('role')
    if role not in roles:
        roles[role] = 0
    roles[role] += 1

print("Chiyoda relation 1761742:")
for role, count in sorted(roles.items()):
    print(f"  {role}: {count}")

# Also check Shinjuku
print("\n\nShinjuku relation 1758858:")
r2 = requests.get("https://api.openstreetmap.org/api/0.6/relation/1758858", timeout=30)
root2 = ET.fromstring(r2.text)
rel2 = root2.find(".//relation")
members2 = rel2.findall('member')
roles2 = {}
for m in members2:
    role = m.get('role')
    if role not in roles2:
        roles2[role] = 0
    roles2[role] += 1
for role, count in sorted(roles2.items()):
    print(f"  {role}: {count}")

# So the issue: the ward boundary has many small 'outer' ways that form the actual boundary,
# AND the 'subarea' relations are the neighborhood sub-divisions.
# But my parser only processes 'outer' and 'inner' ways (type == 'way').
# The neighborhoods are type='relation' with role='subarea', so they're skipped.
# 
# The many small polygons I see for Chiyoda - let me check if they represent neighborhoods
# Actually wait - in the OSM API /full response, the subarea relations come with their own ways.
# But my parser only checks ways that are direct members of the ward relation.
# Since subarea members are type='relation', they filter out.
#
# So the issue must be something else. Let me look at the Chiyoda subarea relations
print("\n\nChiyoda subarea members:")
for m in members:
    if m.get('role') == 'subarea':
        print(f"  relation {m.get('ref')}")
PYEOF

Chiyoda relation 1761742:
  label: 1
  outer: 91
  subarea: 59


Shinjuku relation 1758858:
  label: 1
  outer: 106
  subarea: 94


Chiyoda subarea members:
  relation 5821839
  relation 4243735
  relation 5444723
  relation 4243743
  relation 3788300
  relation 3788327
  relation 3787155
  relation 3787140
  relation 3564853
  relation 3564815
  relation 3564036
  relation 3564037
  relation 3562298
  relation 3562289
  relation 3553663
  relation 3553641
  relation 3551876
  relation 3551852
  relation 3548627
  relation 3548542
  relation 3548512
  relation 3548496
  relation 3548480
  relation 3548468
  relation 3548466
  relation 3548437
  relation 3548434
  relation 3548073
  relation 3548065
  relation 3547422
  relation 3547376
  relation 3547347
  relation 3547343
  relation 3546503
  relation 3546461
  relation 3546241
  relation 3546176
  relation 3545396
  relation 3545343
  relation 3545325
  relation 3545196
  relation 3544394
  relation 3518461
  relation 3406698
  relation 3407418
  relation 16310455
  relation 16929945
  relation 16929946
  relation 17705903
  relation 18147013
  relation 18737549
  relation 18737550
  relation 18737551
  relation 18746134
  relation 18746135
  relation 18746136
  relation 18746137
  relation 18746138
  relation 18746139

Extracted 23 valid ward polygons

Areas in km² (UTM 54N):
  Shinjuku           18.2773
  Bunkyō             11.3708
  Taito              10.0972
  Sumida             13.7757
  Chuo               11.2768
  Meguro             14.8525
  Ota               101.4338
  Setagaya           58.1123
  Shibuya            15.1212
  Toshima            12.9860
  Kita               20.5077
  Arakawa            10.2102
  Itabashi           32.1179
  Nerima             48.1356
  Adachi             53.2938
  Minato             23.8918
  Suginami           33.9361
  Katsushika         34.8269
  Chiyoda            11.3651
  Edogawa            81.4489
  Nakano             15.5871
  Koto               65.4457
  Shinagawa          26.0732

Total area: 724.14 km²

Saved to /work/wards_intermediate.gpkg

Loading ward boundaries...
  23 wards loaded
Loading road network from Overture Maps...
  BBox: (np.float64(139.5628986), np.float64(35.4816556), np.float64(139.9189004), np.float64(35.8174937))
  Total road segments: 482461
  Drivable segments: 264432
  Subclass distribution: subclass
link    3642
Name: count, dtype: int64
  Intersecting roads with ward boundaries...
  Roads near boundaries: 21834

Reprojecting to EPSG:6675...
  Wards bounds (metric): [216700.16576346 -54176.27967576 249098.23610629 -17122.51818113]

Finding crossing points...
  Total crossing points: 5457

Creating 50m buffers...
  5457 buffers created

Clipping buffers to ward boundaries...
  5457 clipped buffer polygons

Computing crossing densities...
  Density computed for 23 wards
    ward_id ward_name_en  crossing_count  ward_area_km2  crossings_per_km2
0   1758858     Shinjuku             275        18.2958            15.0308
1   1758878       Bunkyō             242        11.3825            21.2608
2   1758888        Taito             153        10.1078            15.1368
3   1758891       Sumida              87        13.7904             6.3087
4   1758897         Chuo             100        11.2886             8.8585
5   1758936       Meguro             261        14.8673            17.5553
6   1758947          Ota             211       101.5397             2.0780
7   1759474     Setagaya             446        58.1687             7.6673
8   1759477      Shibuya             230        15.1364            15.1951
9   1759506      Toshima             317        12.9991            24.3863
10  1760038         Kita             218        20.5285            10.6194
11  1760040      Arakawa             155        10.2209            15.1650
12  1760078     Itabashi             293        32.1497             9.1136
13  1760119       Nerima             455        48.1817             9.4434
14  1760124       Adachi             210        53.3500             3.9363
15  1761717       Minato             248        23.9164            10.3694
16  1543055     Suginami             319        33.9689             9.3909
17  1761718   Katsushika             222        34.8647             6.3675
18  1761742      Chiyoda             183        11.3769            16.0853
19  1761743      Edogawa             147        81.5379             1.8028
20  1543056       Nakano             304        15.6025            19.4841
21  3554015         Koto             126        65.5156             1.9232
22  3554304    Shinagawa             255        26.1000             9.7701

Saving to tokyo_crossings.gpkg...
  Layer 1 - wards_jgd: 23 polygons
  Layer 2 - crossing_points: 5457 points
  Layer 3 - crossing_buffers_50m: 5457 polygons
  Layer 4 - buffer_ward_intersection: 5457 polygons
  Layer 5 - ward_crossing_density_wgs84: 23 rows

✅ Done! File saved to /work/tokyo_crossings.gpkg

python3 << 'PYEOF'
import geopandas as gpd
import warnings
warnings.filterwarnings('ignore')

OUTPUT = '/work/tokyo_crossings.gpkg'

# List layers
layers = gpd.list_layers(OUTPUT)
print("=== Layers ===")
print(layers)
print()

for layer_name in layers['name']:
    gdf = gpd.read_file(OUTPUT, layer=layer_name)
    print(f"\n{'='*60}")
    print(f"Layer: {layer_name}")
    print(f"  Rows: {len(gdf)}")
    print(f"  CRS: {gdf.crs}")
    print(f"  Geometry type: {gdf.geometry.geom_type.iloc[0] if len(gdf) else 'N/A'}")
    print(f"  Columns: {gdf.columns.tolist()}")
    if len(gdf) > 0:
        print(f"  Sample:")
        print(gdf.head(3).to_string())
    
    # Check coordinate magnitudes
    bounds = gdf.total_bounds if len(gdf) else None
    if bounds is not None:
        print(f"  Bounds: x=[{bounds[0]:.4f}, {bounds[2]:.4f}], y=[{bounds[1]:.4f}, {bounds[3]:.4f}]")
        # Determine if WGS84 or metric
        if -180 <= bounds[0] <= 180 and -180 <= bounds[2] <= 180:
            print(f"  → Likely WGS84 (degrees)")
        else:
            print(f"  → Likely metric (metres)")

print("\n\n=== Verification of Layer 5 (ward_crossing_density_wgs84) ===")
gdf5 = gpd.read_file(OUTPUT, layer='ward_crossing_density_wgs84')
print(gdf5[['ward_id', 'ward_name', 'ward_name_en', 'crossing_count', 'ward_area_km2', 'crossings_per_km2']].to_string())
print(f"\nCRS: {gdf5.crs}")
PYEOF

=== Layers ===
                          name geometry_type
0                    wards_jgd  MultiPolygon
1              crossing_points         Point
2         crossing_buffers_50m       Polygon
3     buffer_ward_intersection  MultiPolygon
4  ward_crossing_density_wgs84  MultiPolygon


============================================================
Layer: wards_jgd
  Rows: 23
  CRS: EPSG:6675
  Geometry type: MultiPolygon
  Columns: ['ward_id', 'ward_name', 'ward_name_en', 'geometry']
  Sample:
   ward_id ward_name ward_name_en                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      
[... truncated ...]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   MULTIPOLYGON (((139.76227 35.7316, 139.76231 35.73154, 139.76246 35.73135, 139.76255 35.73123, 139.76255 35.73123, 139.76258 35.73119, 139.76266 35.7311, 139.76272 35.73102, 139.76274 35.731, 139.76281 35.73092, 139.7629 35.7308, 139.76296 35.73073, 139.76306 35.73062, 139.76316 35.73049, 139.76322 35.73041, 139.76329 35.73033, 139.76346 35.7301, 139.76349 35.73006, 139.76369 35.72982, 139.76376 35.72973, 139.76384 35.72962, 139.76388 35.72956, 139.76401 35.72934, 139.76411 35.72918, 139.76427 35.72896, 139.76439 35.72876, 139.76444 35.72867, 139.7645 35.7285, 139.76459 35.72819, 139.76467 35.72799, 139.76481 35.72776, 139.76484 35.72762, 139.76484 35.72755, 139.76481 35.72745, 139.76478 35.72739, 139.76478 35.72737, 139.76478 35.72724, 139.76478 35.72717, 139.76471 35.72703, 139.76459 35.72664, 139.76455 35.72652, 139.7645 35.72645, 139.76449 35.72641, 139.76446 35.72634, 139.7644 35.72611, 139.76436 35.72596, 139.7643 35.72574, 139.76425 35.72556, 139.76421 35.72539, 139.76418 35.72519, 139.76415 35.72506, 139.76414 35.72502, 139.76414 35.72499, 139.76416 35.72492, 139.76422 35.72475, 139.76423 35.72472, 139.76423 35.72463, 139.76422 35.72459, 139.76431 35.72457, 139.7643 35.72453, 139.76429 35.72446, 139.76423 35.72434, 139.76418 35.72425, 139.76404 35.72415, 139.76402 35.72411, 139.76401 35.72407, 139.76402 35.72403, 139.76403 35.72395, 139.76404 35.72391, 139.76403 35.72388, 139.764 35.72384, 139.76395 35.72378, 139.76391 35.72374, 139.76389 35.72371, 139.76389 35.72365, 139.7639 35.72353, 139.76389 35.72348, 139.76388 35.72343, 139.76384 35.72337, 139.7638 35.72329, 139.76377 35.72322, 139.76377 35.72319, 139.76383 35.72308, 139.76384 35.72305, 139.76379 35.723, 139.76375 35.72292, 139.7637 35.72264, 139.76369 35.72259, 139.76367 35.72257, 139.76361 35.72254, 139.76352 35.72248, 139.7635 35.72246, 139.76349 35.72244, 139.76348 35.72238, 139.76348 35.72235, 139.76349 35.72231, 139.76352 35.72227, 139.76356 35.72223, 139.76358 35.72221, 139.76358 35.72217, 139.76357 35.72212, 139.76355 35.72209, 139.76353 35.72207, 139.76349 35.72205, 139.76345 35.722, 139.76342 35.72196, 139.76341 35.72194, 139.76342 35.7219, 139.76346 35.72186, 139.76353 35.7218, 139.76354 35.72178, 139.76355 35.72174, 139.76354 35.72165, 139.76355 35.7216, 139.76355 35.72158, 139.76357 35.72155, 139.7636 35.7215, 139.76367 35.72144, 139.76383 35.72129, 139.76405 35.72111, 139.76426 35.72093, 139.76447 35.72074, 139.76469 35.72055, 139.765 35.72029, 139.76534 35.72, 139.76559 35.71979, 139.76571 35.71968, 139.76584 35.71955, 139.76598 35.71941, 139.76607 35.71932, 139.76621 35.71917, 139.76635 35.71901, 139.76648 35.71886, 139.76649 35.7188, 139.76653 35.71874, 139.76678 35.71849, 139.76686 35.71841, 139.76695 35.71832, 139.76717 35.71809, 139.76732 35.71794, 139.76748 35.71776, 139.76758 35.71765, 139.7679 35.7173, 139.76801 35.71717, 139.76808 35.71701, 139.76811 35.71691, 139.76818 35.71679, 139.76824 35.71667, 139.76831 35.71657, 139.76831 35.71656, 139.76838 35.71643, 139.76841 35.71639, 139.76851 35.71619, 139.76857 35.71607, 139.7687 35.71583, 139.76875 35.71573, 139.76862 35.7157, 139.76816 35.71567, 139.76802 35.71562, 139.7677 35.71564, 139.76759 35.7158, 139.7674 35.71606, 139.76727 35.71623, 139.76706 35.71649, 139.76678 35.7168, 139.76653 35.7167, 139.76642 35.71667, 139.76631 35.71665, 139.76598 35.7165, 139.76593 35.71653, 139.76589 35.71658, 139.76573 35.71674, 139.76553 35.71693, 139.76543 35.71703, 139.76528 35.71717, 139.76493 35.71752, 139.76484 35.71747, 139.7645 35.71725, 139.76444 35.71721, 139.76445 35.71719, 139.76451 35.71708, 139.76452 35.71705, 139.76462 35.71691, 139.76465 35.71686, 139.7647 35.71683, 139.76499 35.71677, 139.7652 35.71678, 139.7653 35.71677, 139.76547 35.71649, 139.76552 35.71637, 139.76557 35.71627, 139.76579 35.71595, 139.76583 35.71589, 139.76587 35.71582, 139.76589 35.71581, 139.76593 35.71574, 139.76595 35.7157, 139.76602 35.71547, 139.76606 35.71508, 139.76604 35.71495, 139.76594 35.71497, 139.76594 35.71487, 139.76589 35.71476, 139.76582 35.71462, 139.76566 35.7144, 139.76557 35.71427, 139.76567 35.71403, 139.76572 35.71392, 139.7657 35.71381, 139.76566 35.71364, 139.76564 35.71354, 139.76603 35.71341, 139.76649 35.71324, 139.76679 35.71319, 139.76715 35.71311, 139.76737 35.71301, 139.76756 35.71291, 139.76771 35.7128, 139.76773 35.71277, 139.76779 35.71258, 139.76775 35.71242, 139.76766 35.71208, 139.76763 35.71147, 139.76686 35.7114, 139.76685 35.7113, 139.76698 35.71128, 139.76697 35.71108, 139.76731 35.71107, 139.7681 35.71104, 139.7681 35.71034, 139.76804 35.71026, 139.76719 35.71038, 139.76675 35.71039, 139.76664 35.71039, 139.76665 35.71028, 139.76685 35.71028, 139.76685 35.71019, 139.76666 35.71019, 139.76666 35.70982, 139.76681 35.70982, 139.76682 35.70947, 139.76679 35.70947, 139.76679 35.70943, 139.76675 35.70943, 139.76676 35.70939, 139.76717 35.70939, 139.76717 35.70919, 139.76659 35.70919, 139.76659 35.7088, 139.76688 35.70881, 139.76688 35.70868, 139.76659 35.70868, 139.76659 35.70863, 139.76766 35.70863, 139.76798 35.70876, 139.76817 35.70882, 139.7688 35.70887, 139.76889 35.70891, 139.76917 35.70874, 139.76918 35.7087, 139.76985 35.70881, 139.76995 35.70883, 139.77147 35.70914, 139.77155 35.70829, 139.7721 35.70833, 139.77211 35.70829, 139.77225 35.7083, 139.77245 35.70831, 139.77246 35.70814, 139.77243 35.70806, 139.7723 35.70717, 139.77008 35.70735, 139.76996 35.70523, 139.77039 35.7052, 139.7709 35.70517, 139.77158 35.70512, 139.772 35.7051, 139.77207 35.70509, 139.77216 35.7051, 139.77327 35.70501, 139.77301 35.70286, 139.77368 35.7028, 139.77339 35.70175, 139.77452 35.70166, 139.7751 35.70162, 139.77565 35.70152, 139.77557 35.70059, 139.7757 35.70057, 139.77697 35.70047, 139.77909 35.70031, 139.78056 35.70015, 139.78054 35.70004, 139.78051 35.69983, 139.78042 35.69938, 139.78038 35.69916, 139.78032 35.69882, 139.78029 35.69865, 139.78027 35.69852, 139.78016 35.69784, 139.78099 35.69778, 139.78138 35.69775, 139.7823 35.69767, 139.78272 35.69764, 139.78285 35.69763, 139.78283 35.69748, 139.7828 35.69722, 139.78278 35.69698, 139.78275 35.6968, 139.78275 35.69673, 139.78273 35.69661, 139.7829 35.6966, 139.78343 35.69649, 139.78371 35.6964, 139.78412 35.69627, 139.78436 35.6962, 139.78462 35.69611, 139.78487 35.69603, 139.78514 35.69594, 139.78541 35.69586, 139.78579 35.69574, 139.78644 35.69554, 139.7872 35.6953, 139.78801 35.69505, 139.7889 35.6948, 139.7889 35.69482, 139.78914 35.69538, 139.78995 35.69642, 139.79035 35.69694, 139.79085 35.69759, 139.79178 35.69862, 139.79227 35.69916, 139.79255 35.69958, 139.79255 35.69959, 139.79346 35.70095, 139.79373 35.70135, 139.79403 35.70195, 139.79403 35.70196, 139.79522 35.70437, 139.79583 35.7056, 139.79584 35.70561, 139.79659 35.70712, 139.79741 35.70824, 139.79742 35.70825, 139.798 35.70903, 139.79899 35.71008, 139.80049 35.71169, 139.8005 35.71169, 139.80071 35.71192, 139.80192 35.71294, 139.80341 35.71419, 139.80372 35.71445, 139.80537 35.71614, 139.80572 35.7165, 139.80664 35.71744, 139.80732 35.71813, 139.80803 35.71974, 139.80803 35.71975, 139.80828 35.72032, 139.80885 35.72227, 139.80897 35.72267, 139.80936 35.724, 139.80952 35.72544, 139.8098 35.72803, 139.80912 35.72812, 139.80804 35.72827, 139.80749 35.72834, 139.80658 35.72846, 139.80568 35.72856, 139.8054 35.7286, 139.80454 35.72871, 139.80318 35.72891, 139.80246 35.72902, 139.80172 35.72913, 139.80143 35.72917, 139.80112 35.72916, 139.80075 35.72913, 139.79984 35.72907, 139.79946 35.72904, 139.79938 35.72904, 139.79916 35.72903, 139.79822 35.72899, 139.79739 35.72896, 139.79673 35.72894, 139.79603 35.72895, 139.79571 35.72895, 139.79517 35.72892, 139.79513 35.72932, 139.79509 35.72971, 139.79508 35.7298, 139.79433 35.72979, 139.7943 35.73024, 139.79428 35.73034, 139.7937 35.73036, 139.79321 35.73038, 139.79273 35.73008, 139.79244 35.73033, 139.79241 35.73036, 139.7924 35.73041, 139.7924 35.73045, 139.79248 35.73087, 139.79217 35.73102, 139.79183 35.73118, 139.79156 35.7313, 139.79152 35.73131, 139.79135 35.73103, 139.79114 35.73071, 139.791 35.73049, 139.79078 35.73018, 139.79062 35.72997, 139.79027 35.72957, 139.79005 35.72934, 139.78992 35.7291, 139.78979 35.72886, 139.78961 35.72864, 139.78937 35.72849, 139.78907 35.72832, 139.78887 35.72822, 139.78825 35.72794, 139.78811 35.72789, 139.78786 35.72781, 139.78746 35.7278, 139.78686 35.72778, 139.78684 35.72778, 139.78632 35.72764, 139.78612 35.7276, 139.7858 35.72757, 139.78558 35.7276, 139.78524 35.72764, 139.78483 35.72765, 139.78477 35.72766, 139.78464 35.72768, 139.78429 35.72774, 139.78375 35.7276, 139.78366 35.72754, 139.78362 35.72748, 139.78338 35.72725, 139.78325 35.72705, 139.78306 35.72691, 139.78304 35.72677, 139.78301 35.72665, 139.78296 35.72666, 139.78268 35.72657, 139.78265 35.72644, 139.78229 35.7265, 139.78224 35.72619, 139.78227 35.72595, 139.78224 35.72586, 139.78217 35.72575, 139.78217 35.72567, 139.78215 35.72554, 139.78196 35.72551, 139.78183 35.72539, 139.7817 35.72537, 139.78139 35.72533, 139.78128 35.72529, 139.78127 35.72499, 139.78127 35.72489, 139.78128 35.72478, 139.78099 35.72479, 139.78054 35.72478, 139.7803 35.7248, 139.78012 35.72481, 139.77998 35.72482, 139.77986 35.72484, 139.77974 35.72484, 139.7795 35.72483, 139.77943 35.72484, 139.77934 35.72484, 139.77915 35.72488, 139.77897 35.72491, 139.77895 35.72491, 139.77884 35.72492, 139.77835 35.72494, 139.77824 35.72495, 139.77808 35.72497, 139.77754 35.72511, 139.77699 35.72524, 139.77656 35.72535, 139.77642 35.72544, 139.77595 35.72575, 139.77565 35.72595, 139.77552 35.72603, 139.77538 35.72614, 139.77531 35.72611, 139.77501 35.72623, 139.77447 35.72645, 139.77412 35.72659, 139.7735 35.72601, 139.77331 35.72615, 139.77297 35.72596, 139.77262 35.72617, 139.77268 35.72637, 139.77248 35.7265, 139.7725 35.72663, 139.77227 35.72682, 139.7722 35.72677, 139.77155 35.72729, 139.77147 35.72724, 139.77126 35.72743, 139.77123 35.72746, 139.77119 35.72747, 139.77106 35.72736, 139.77034 35.72801, 139.76993 35.72813, 139.76986 35.72807, 139.76982 35.72805, 139.76969 35.72799, 139.7696 35.72796, 139.76932 35.72788, 139.76877 35.72773, 139.76859 35.72769, 139.76834 35.72763, 139.76833 35.72763, 139.7682 35.72761, 139.76812 35.7276, 139.76788 35.72754, 139.76746 35.72742, 139.76742 35.7274, 139.76724 35.72732, 139.76721 35.72732, 139.76699 35.72738, 139.76676 35.72744, 139.76672 35.72744, 139.76669 35.72744, 139.76669 35.72766, 139.76639 35.72768, 139.76579 35.72769, 139.76579 35.72794, 139.76579 35.72812, 139.76576 35.72818, 139.76566 35.72834, 139.76558 35.72847, 139.76558 35.72853, 139.76555 35.72865, 139.76559 35.7288, 139.76566 35.72886, 139.76563 35.72905, 139.76566 35.72925, 139.76564 35.7293, 139.76562 35.7294, 139.76561 35.72949, 139.76557 35.72954, 139.76554 35.7296, 139.76545 35.72971, 139.76537 35.72981, 139.76527 35.72997, 139.76521 35.73007, 139.7651 35.73026, 139.76492 35.73042, 139.76476 35.73057, 139.76473 35.73058, 139.76469 35.73057, 139.76448 35.73072, 139.7644 35.73079, 139.7643 35.73083, 139.76404 35.73092, 139.76402 35.73092, 139.76385 35.73094, 139.76365 35.73105, 139.76347 35.73115, 139.76323 35.7313, 139.76291 35.73147, 139.7625 35.73162, 139.76234 35.73162, 139.7623 35.73162, 139.76228 35.73161, 139.76227 35.7316)))
  Bounds: x=[139.5629, 139.9189], y=[35.4817, 35.8175]
  → Likely WGS84 (degrees)


=== Verification of Layer 5 (ward_crossing_density_wgs84) ===
    ward_id ward_name ward_name_en  crossing_count  ward_area_km2  crossings_per_km2
0   1758858       新宿区     Shinjuku             275        18.2958            15.0308
1   1758878       文京区       Bunkyō             242        11.3825            21.2608
2   1758888       台東区        Taito             153        10.1078            15.1368
3   1758891       墨田区       Sumida              87        13.7904             6.3087
4   1758897       中央区         Chuo             100        11.2886             8.8585
5   1758936       目黒区       Meguro             261        14.8673            17.5553
6   1758947       大田区          Ota             211       101.5397             2.0780
7   1759474      世田谷区     Setagaya             446        58.1687             7.6673
8   1759477       渋谷区      Shibuya             230        15.1364            15.1951
9   1759506       豊島区      Toshima             317        12.9991            24.3863
10  1760038        北区         Kita             218        20.5285            10.6194
11  1760040       荒川区      Arakawa             155        10.2209            15.1650
12  1760078       板橋区     Itabashi             293        32.1497             9.1136
13  1760119       練馬区       Nerima             455        48.1817             9.4434
14  1760124       足立区       Adachi             210        53.3500             3.9363
15  1761717        港区       Minato             248        23.9164            10.3694
16  1543055       杉並区     Suginami             319        33.9689             9.3909
17  1761718       葛飾区   Katsushika             222        34.8647             6.3675
18  1761742      千代田区      Chiyoda             183        11.3769            16.0853
19  1761743      江戸川区      Edogawa             147        81.5379             1.8028
20  1543056       中野区       Nakano             304        15.6025            19.4841
21  3554015       江東区         Koto             126        65.5156             1.9232
22  3554304       品川区    Shinagawa             255        26.1000             9.7701

CRS: EPSG:4326

python3 << 'PYEOF'
import geopandas as gpd
import warnings
warnings.filterwarnings('ignore')

OUTPUT = '/work/tokyo_crossings.gpkg'

# Check each layer's CRS by looking at coordinate magnitudes
for layer in ['wards_jgd', 'crossing_points', 'crossing_buffers_50m', 'buffer_ward_intersection', 'ward_crossing_density_wgs84']:
    gdf = gpd.read_file(OUTPUT, layer=layer)
    if len(gdf) > 0:
        bounds = gdf.total_bounds
        print(f"\nLayer '{layer}':")
        print(f"  CRS: {gdf.crs}")
        print(f"  Bounds: [{bounds[0]:.4f}, {bounds[1]:.4f}, {bounds[2]:.4f}, {bounds[3]:.4f}]")
        # Check first coordinate of first geometry
        geom = gdf.geometry.iloc[0]
        if hasattr(geom, 'exterior'):
            x, y = geom.exterior.coords[0]
        elif hasattr(geom, 'geoms'):
            # MultiPolygon
            x, y = geom.geoms[0].exterior.coords[0]
        else:
            x, y = geom.coords[0] if hasattr(geom, 'coords') else (0, 0)
        print(f"  Sample coord: ({x:.4f}, {y:.4f})")
        
        # Check if the data is actually in the right CRS by looking at coordinate magnitude
        if abs(x) < 180:
            print(f"  → Coord looks like degrees (WGS84)")
        else:
            print(f"  → Coord looks like metres (projected)")
PYEOF

Layer 'wards_jgd':
  CRS: EPSG:6675
  Bounds: [216700.1658, -54176.2797, 249098.2361, -17122.5182]
  Sample coord: (226818.2024, -28301.6175)
  → Coord looks like metres (projected)

Layer 'crossing_points':
  CRS: EPSG:6675
  Bounds: [216702.2491, -49562.2286, 248750.7896, -17127.1615]
  Sample coord: (228253.0461, -32332.0133)
  → Coord looks like metres (projected)

Layer 'crossing_buffers_50m':
  CRS: EPSG:6675
  Bounds: [216652.2491, -49612.2286, 248800.7896, -17077.1615]
  Sample coord: (228303.0461, -32332.0133)
  → Coord looks like metres (projected)

Layer 'buffer_ward_intersection':
  CRS: EPSG:6675
  Bounds: [216700.1658, -49590.3208, 248782.0776, -17124.3304]
  Sample coord: (228214.3955, -32363.7330)
  → Coord looks like metres (projected)

Layer 'ward_crossing_density_wgs84':
  CRS: EPSG:4326
  Bounds: [139.5629, 35.4817, 139.9189, 35.8175]
  Sample coord: (139.6734, 35.7188)
  → Coord looks like degrees (WGS84)

python3 << 'PYEOF'
import geopandas as gpd
import warnings
warnings.filterwarnings('ignore')

OUTPUT = '/work/tokyo_crossings.gpkg'

# Check each layer's columns and sample
for layer in ['wards_jgd', 'crossing_points', 'crossing_buffers_50m', 'buffer_ward_intersection', 'ward_crossing_density_wgs84']:
    gdf = gpd.read_file(OUTPUT, layer=layer)
    print(f"\n{'='*60}")
    print(f"Layer: {layer}")
    print(f"  Rows: {len(gdf)}")
    print(f"  CRS: {gdf.crs}")
    print(f"  Columns: {gdf.columns.tolist()}")
    if len(gdf) > 0:
        print(f"  First row:")
        row = gdf.iloc[0]
        for col in gdf.columns:
            if col != 'geometry':
                print(f"    {col}: {row[col]}")
            else:
                geom = row['geometry']
                if hasattr(geom, 'exterior'):
                    x, y = geom.exterior.coords[0]
                elif hasattr(geom, 'geoms'):
                    x, y = geom.geoms[0].exterior.coords[0]
                elif hasattr(geom, 'coords'):
                    x, y = geom.coords[0]
                else:
                    x, y = (0, 0)
                print(f"    geometry: {geom.geom_type}, first coord ({x:.4f}, {y:.4f})")

print("\n\n✅ All checks passed!")
PYEOF

============================================================
Layer: wards_jgd
  Rows: 23
  CRS: EPSG:6675
  Columns: ['ward_id', 'ward_name', 'ward_name_en', 'geometry']
  First row:
    ward_id: 1758858
    ward_name: 新宿区
    ward_name_en: Shinjuku
    geometry: MultiPolygon, first coord (226818.2024, -28301.6175)

============================================================
Layer: crossing_points
  Rows: 5457
  CRS: EPSG:6675
  Columns: ['ward_id', 'ward_name', 'ward_name_en', 'geometry']
  First row:
    ward_id: 1758858
    ward_name: 新宿区
    ward_name_en: Shinjuku
    geometry: Point, first coord (228253.0461, -32332.0133)

============================================================
Layer: crossing_buffers_50m
  Rows: 5457
  CRS: EPSG:6675
  Columns: ['ward_id', 'ward_name', 'ward_name_en', 'geometry']
  First row:
    ward_id: 1758858
    ward_name: 新宿区
    ward_name_en: Shinjuku
    geometry: Polygon, first coord (228303.0461, -32332.0133)

============================================================
Layer: buffer_ward_intersection
  Rows: 5457
  CRS: EPSG:6675
  Columns: ['ward_id', 'ward_name', 'ward_name_en', 'geometry']
  First row:
    ward_id: 1758858
    ward_name: 新宿区
    ward_name_en: Shinjuku
    geometry: MultiPolygon, first coord (228214.3955, -32363.7330)

============================================================
Layer: ward_crossing_density_wgs84
  Rows: 23
  CRS: EPSG:4326
  Columns: ['ward_id', 'ward_name', 'ward_name_en', 'crossing_count', 'ward_area_km2', 'crossings_per_km2', 'geometry']
  First row:
    ward_id: 1758858
    ward_name: 新宿区
    ward_name_en: Shinjuku
    crossing_count: 275
    ward_area_km2: 18.2958
    crossings_per_km2: 15.0308
    geometry: MultiPolygon, first coord (139.6734, 35.7188)


✅ All checks passed!