613 lines
24 KiB
Python
613 lines
24 KiB
Python
#!/usr/bin/env python3
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"""
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Process Berlin green spaces from local OSM data file.
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Downloads Berlin OSM extract once, then processes locally without API dependencies.
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"""
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import json
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import requests
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import asyncio
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import xml.etree.ElementTree as ET
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from pathlib import Path
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from datetime import datetime
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from typing import List, Dict, Optional, Tuple
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import sys
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import gzip
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import math
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# Add the app directory to Python path to import services
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sys.path.append(str(Path(__file__).parent.parent))
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from app.services.street_tree_service import StreetTreeService
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from app.services.berlin_data_service import BerlinDataService
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class LocalOSMProcessor:
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def __init__(self, data_dir: str = "app/data"):
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self.data_dir = Path(data_dir)
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self.raw_dir = self.data_dir / "osm-raw"
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self.processed_dir = self.data_dir / "processed"
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# Create directories
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self.raw_dir.mkdir(parents=True, exist_ok=True)
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self.processed_dir.mkdir(parents=True, exist_ok=True)
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# Initialize existing services
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self.tree_service = StreetTreeService()
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self.berlin_data = BerlinDataService()
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# Berlin bounding box for filtering
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self.berlin_bbox = {
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'min_lat': 52.3370, 'max_lat': 52.6755,
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'min_lon': 13.0882, 'max_lon': 13.7611
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}
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def download_berlin_osm_extract(self):
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"""Download Berlin OSM extract from Geofabrik."""
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osm_file = self.raw_dir / "berlin-latest.osm.pbf"
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if osm_file.exists():
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print(f"✅ OSM file already exists: {osm_file}")
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return osm_file
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# Try PBF format first (smaller), fallback to XML
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urls = [
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"https://download.geofabrik.de/europe/germany/berlin-latest.osm.pbf",
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"https://download.geofabrik.de/europe/germany/berlin-latest.osm.bz2"
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]
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for url in urls:
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try:
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print(f"Downloading Berlin OSM data from {url}")
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print("This is a one-time download (~50MB)...")
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response = requests.get(url, stream=True, timeout=300)
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response.raise_for_status()
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filename = url.split('/')[-1]
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local_file = self.raw_dir / filename
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# Download with progress
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total_size = int(response.headers.get('content-length', 0))
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downloaded = 0
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with open(local_file, 'wb') as f:
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for chunk in response.iter_content(chunk_size=8192):
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if chunk:
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f.write(chunk)
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downloaded += len(chunk)
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if total_size > 0:
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percent = (downloaded / total_size) * 100
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print(f"\rDownload progress: {percent:.1f}%", end="")
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print(f"\n✅ Downloaded: {local_file}")
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return local_file
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except Exception as e:
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print(f"❌ Failed to download {url}: {e}")
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continue
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raise Exception("Could not download OSM data from any source")
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def download_simple_osm_extract(self):
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"""Download simpler XML format if PBF tools not available."""
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osm_file = self.raw_dir / "berlin_green_spaces.osm"
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if osm_file.exists():
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print(f"✅ OSM file already exists: {osm_file}")
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return osm_file
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# Use Overpass API to get a one-time export of green spaces
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print("Downloading Berlin green spaces extract...")
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overpass_url = "http://overpass-api.de/api/interpreter"
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# Query for all green spaces in Berlin (one-time download)
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query = f"""
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[out:xml][timeout:120];
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(
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way["leisure"~"^(park|garden|nature_reserve|recreation_ground|playground|common)$"]
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({self.berlin_bbox['min_lat']},{self.berlin_bbox['min_lon']},{self.berlin_bbox['max_lat']},{self.berlin_bbox['max_lon']});
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way["landuse"~"^(forest|grass|meadow|recreation_ground|village_green|allotments)$"]
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({self.berlin_bbox['min_lat']},{self.berlin_bbox['min_lon']},{self.berlin_bbox['max_lat']},{self.berlin_bbox['max_lon']});
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way["natural"~"^(forest|grass|meadow|scrub|heath|wood)$"]
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({self.berlin_bbox['min_lat']},{self.berlin_bbox['min_lon']},{self.berlin_bbox['max_lat']},{self.berlin_bbox['max_lon']});
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);
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out geom meta;
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"""
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try:
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response = requests.post(overpass_url, data=query, timeout=180)
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response.raise_for_status()
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with open(osm_file, 'w', encoding='utf-8') as f:
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f.write(response.text)
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print(f"✅ Downloaded green spaces extract: {osm_file}")
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return osm_file
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except Exception as e:
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print(f"❌ Failed to download OSM extract: {e}")
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raise
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def parse_osm_xml(self, osm_file: Path) -> List[Dict]:
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"""Parse OSM XML file to extract green spaces."""
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print(f"Parsing OSM data from {osm_file}...")
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green_spaces = []
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try:
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# Handle different file formats
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if osm_file.suffix == '.gz':
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with gzip.open(osm_file, 'rt', encoding='utf-8') as f:
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tree = ET.parse(f)
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else:
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tree = ET.parse(osm_file)
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root = tree.getroot()
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# Parse ways (areas)
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ways = root.findall('.//way')
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print(f"Found {len(ways)} ways in OSM data")
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for way in ways:
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try:
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processed_space = self._process_osm_way(way, root)
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if processed_space:
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green_spaces.append(processed_space)
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except Exception as e:
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continue
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print(f"✅ Extracted {len(green_spaces)} green spaces from OSM data")
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return green_spaces
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except Exception as e:
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print(f"❌ Error parsing OSM file: {e}")
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return []
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def _process_osm_way(self, way, root) -> Optional[Dict]:
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"""Process a single OSM way into green space format."""
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# Get tags
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tags = {}
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for tag in way.findall('tag'):
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tags[tag.get('k')] = tag.get('v')
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# Check if it's a green space
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green_space_type = self._get_green_space_type(tags)
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if not green_space_type:
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return None
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# Get node references
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nd_refs = [nd.get('ref') for nd in way.findall('nd')]
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if len(nd_refs) < 3: # Need at least 3 points for an area
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return None
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# Find node coordinates
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coordinates = []
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for nd_ref in nd_refs:
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node = root.find(f".//node[@id='{nd_ref}']")
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if node is not None:
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lat = float(node.get('lat'))
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lon = float(node.get('lon'))
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# Check if within Berlin bounds
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if (self.berlin_bbox['min_lat'] <= lat <= self.berlin_bbox['max_lat'] and
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self.berlin_bbox['min_lon'] <= lon <= self.berlin_bbox['max_lon']):
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coordinates.append((lat, lon))
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if len(coordinates) < 3:
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return None
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# Calculate centroid and area
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centroid_lat, centroid_lon = self._calculate_centroid(coordinates)
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area_sqm = self._calculate_area(coordinates)
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# Skip very small areas
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if area_sqm < 500:
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return None
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# Get name
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name = tags.get('name', f"{green_space_type.title()} near {centroid_lat:.3f}, {centroid_lon:.3f}")
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# Estimate district
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district = self._estimate_district(centroid_lat, centroid_lon)
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return {
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'id': f"osm_way_{way.get('id')}",
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'name': name,
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'fclass': green_space_type,
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'lat': centroid_lat,
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'lng': centroid_lon,
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'area_sqm': int(area_sqm),
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'district': district,
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'osm_tags': tags,
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'osm_id': way.get('id')
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}
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def _get_green_space_type(self, tags: Dict) -> Optional[str]:
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"""Determine if tags represent a green space and what type."""
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# Check leisure tags
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leisure = tags.get('leisure', '')
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if leisure in ['park', 'garden', 'nature_reserve', 'recreation_ground',
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'playground', 'common', 'golf_course']:
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return leisure
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# Check landuse tags
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landuse = tags.get('landuse', '')
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if landuse in ['forest', 'grass', 'meadow', 'recreation_ground',
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'village_green', 'allotments']:
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return landuse
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# Check natural tags
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natural = tags.get('natural', '')
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if natural in ['forest', 'grass', 'meadow', 'scrub', 'heath', 'wood']:
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return natural
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return None
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def _calculate_centroid(self, coordinates: List[Tuple[float, float]]) -> Tuple[float, float]:
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"""Calculate centroid of polygon."""
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lat_sum = sum(coord[0] for coord in coordinates)
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lon_sum = sum(coord[1] for coord in coordinates)
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count = len(coordinates)
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return lat_sum / count, lon_sum / count
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def _calculate_area(self, coordinates: List[Tuple[float, float]]) -> float:
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"""Calculate area of polygon using shoelace formula."""
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if len(coordinates) < 3:
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return 0
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# Convert to approximate meters for Berlin
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lat_to_m = 111000 # meters per degree latitude
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lon_to_m = 111000 * math.cos(math.radians(52.5)) # adjust for Berlin latitude
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# Convert coordinates to meters
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coords_m = [(lat * lat_to_m, lon * lon_to_m) for lat, lon in coordinates]
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# Shoelace formula
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area = 0
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n = len(coords_m)
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for i in range(n):
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j = (i + 1) % n
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area += coords_m[i][0] * coords_m[j][1]
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area -= coords_m[j][0] * coords_m[i][1]
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return abs(area) / 2
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def _estimate_district(self, lat: float, lng: float) -> str:
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"""Rough district estimation from coordinates."""
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# Very rough Berlin district boundaries
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if lat > 52.55:
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return "Pankow" if lng < 13.45 else "Lichtenberg"
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elif lat > 52.52:
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if lng < 13.25:
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return "Charlottenburg-Wilmersdorf"
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elif lng < 13.42:
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return "Mitte"
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else:
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return "Friedrichshain-Kreuzberg"
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elif lat > 52.45:
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if lng < 13.25:
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return "Steglitz-Zehlendorf"
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elif lng < 13.42:
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return "Tempelhof-Schöneberg"
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else:
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return "Neukölln"
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else:
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return "Treptow-Köpenick"
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async def enhance_green_space_with_real_data(self, space_data: Dict):
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"""Enhance green space with real tree and toilet data."""
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try:
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lat = space_data['lat']
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lng = space_data['lng']
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area_sqm = space_data['area_sqm']
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print(f"Enhancing {space_data['name']} ({space_data['district']})...")
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# Adaptive radius
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radius = min(350, max(100, int((area_sqm ** 0.5) * 0.7)))
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# Get real data using existing services
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tree_response = await self.tree_service.get_trees_near_location(
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lat, lng, radius_m=radius
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)
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nearby_toilets = await self.berlin_data.get_toilets_near_point(lat, lng, 600)
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# Calculate scores
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toilet_score = self._score_toilet_accessibility(nearby_toilets)
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space_type = self._map_to_space_type(space_data.get('fclass', ''))
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enhanced_space = {
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"id": space_data['id'],
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"name": space_data['name'],
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"description": f"Berlin {space_data.get('fclass', 'green space')} from local OSM data",
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"type": space_type,
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"coordinates": {
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"lat": float(lat),
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"lng": float(lng)
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},
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"neighborhood": space_data.get('district', 'Unknown'),
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"area_sqm": area_sqm,
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"perimeter_m": int(4 * (area_sqm ** 0.5)),
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# Environmental features from real tree data
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"environmental": {
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"tree_coverage_percent": max(5, int(tree_response.shade_analysis.estimated_shade_coverage)),
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"shade_quality": tree_response.shade_analysis.shade_quality_score,
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"noise_level": self._estimate_noise_level(space_data),
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"wildlife_diversity_score": tree_response.metrics.species_diversity_score,
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"water_features": self._detect_water_features(space_data),
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"natural_surface_percent": self._estimate_natural_surface(space_data.get('fclass', ''))
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},
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# Real tree metrics
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"tree_data": {
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"total_trees": tree_response.metrics.total_trees,
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"trees_per_hectare": tree_response.metrics.trees_per_hectare,
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"species_count": len(tree_response.metrics.dominant_species),
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"species_diversity_score": tree_response.metrics.species_diversity_score,
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"mature_trees_count": tree_response.metrics.mature_trees_count,
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"young_trees_count": tree_response.metrics.young_trees_count,
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"average_tree_age": tree_response.metrics.average_tree_age,
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"average_height": tree_response.metrics.average_height,
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"average_crown_diameter": tree_response.metrics.average_crown_diameter,
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"shade_coverage_percent": tree_response.metrics.shade_coverage_percent,
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"dominant_species": tree_response.metrics.dominant_species[:3]
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},
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# Real toilet accessibility
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"toilet_accessibility": {
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"nearby_toilets_count": len(nearby_toilets),
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"accessibility_score": toilet_score,
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"nearest_distance_m": nearby_toilets[0]['distance_meters'] if nearby_toilets else None,
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"free_toilets_count": len([t for t in nearby_toilets if t.get('is_free', False)]),
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"accessible_toilets_count": len([t for t in nearby_toilets if t.get('wheelchair_accessible', False)])
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},
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# Standard features
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"accessibility": {
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"wheelchair_accessible": True,
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"public_transport_score": self._estimate_transport_score(space_data.get('district', '')),
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"cycling_infrastructure": area_sqm > 4000,
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"parking_availability": 2 if area_sqm > 20000 else 1,
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"lighting_quality": 3 if 'mitte' in space_data.get('district', '').lower() else 2
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},
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"recreation": {
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"playground_quality": self._estimate_playground_quality(space_data),
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"sports_facilities": self._estimate_sports_facilities(space_data),
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"running_paths": area_sqm > 6000,
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"cycling_paths": area_sqm > 12000,
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"dog_friendly": True,
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"bbq_allowed": self._allows_bbq(space_data)
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},
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# OSM metadata
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"osm_metadata": {
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"osm_id": space_data.get('osm_id'),
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"tags": space_data.get('osm_tags', {}),
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"source": "local_osm_extract"
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},
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"last_updated": datetime.now().isoformat(),
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"data_sources": ["local_osm_extract", "berlin_tree_cadastre", "berlin_toilets"],
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"confidence_score": 92
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}
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trees = tree_response.metrics.total_trees
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toilets = len(nearby_toilets)
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print(f"✅ {space_data['name']}: {trees} trees, {toilets} toilets")
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return enhanced_space
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except Exception as e:
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print(f"❌ Error enhancing {space_data['name']}: {e}")
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return None
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def _score_toilet_accessibility(self, nearby_toilets: List[Dict]) -> int:
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if not nearby_toilets:
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return 25
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nearest = nearby_toilets[0]['distance_meters']
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if nearest <= 200:
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score = 90
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elif nearest <= 400:
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score = 70
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else:
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score = 50
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# Quality bonuses
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free = len([t for t in nearby_toilets if t.get('is_free', False)])
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accessible = len([t for t in nearby_toilets if t.get('wheelchair_accessible', False)])
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score += min(10, free * 5 + accessible * 3)
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return min(100, score)
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def _map_to_space_type(self, fclass: str) -> str:
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mapping = {
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'park': 'PARK', 'forest': 'FOREST', 'garden': 'GARDEN', 'wood': 'FOREST',
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'nature_reserve': 'NATURE_RESERVE', 'playground': 'PLAYGROUND',
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'meadow': 'MEADOW', 'grass': 'GRASS', 'recreation_ground': 'PARK',
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'common': 'PARK', 'village_green': 'GRASS', 'allotments': 'GARDEN'
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}
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return mapping.get(fclass, 'PARK')
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def _detect_water_features(self, space_data: Dict) -> bool:
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name = space_data.get('name', '').lower()
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tags = space_data.get('osm_tags', {})
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water_keywords = ['see', 'teich', 'pond', 'lake', 'bach', 'spree', 'wasser']
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return any(keyword in name for keyword in water_keywords) or 'water' in tags.values()
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def _estimate_noise_level(self, space_data: Dict) -> int:
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fclass = space_data.get('fclass', '')
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district = space_data.get('district', '')
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base = {'forest': 1, 'wood': 1, 'nature_reserve': 1, 'meadow': 2,
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'park': 2, 'garden': 2, 'playground': 3}.get(fclass, 2)
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if any(busy in district.lower() for busy in ['mitte', 'kreuzberg', 'friedrichshain']):
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base += 1
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return min(5, base)
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def _estimate_natural_surface(self, fclass: str) -> int:
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return {'forest': 95, 'wood': 95, 'nature_reserve': 90, 'meadow': 95,
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'grass': 85, 'park': 75, 'garden': 65, 'playground': 40}.get(fclass, 70)
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def _estimate_transport_score(self, district: str) -> int:
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district_lower = district.lower()
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if 'mitte' in district_lower:
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return 5
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elif any(name in district_lower for name in ['charlottenburg', 'kreuzberg', 'friedrichshain']):
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return 4
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else:
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return 3
|
|
|
|
def _estimate_playground_quality(self, space_data: Dict) -> int:
|
|
fclass = space_data.get('fclass', '')
|
|
tags = space_data.get('osm_tags', {})
|
|
|
|
if fclass == 'playground':
|
|
return 80
|
|
elif 'playground' in tags.values():
|
|
return 75
|
|
elif fclass == 'park':
|
|
return 55
|
|
else:
|
|
return 30
|
|
|
|
def _estimate_sports_facilities(self, space_data: Dict) -> bool:
|
|
fclass = space_data.get('fclass', '')
|
|
tags = space_data.get('osm_tags', {})
|
|
name = space_data.get('name', '').lower()
|
|
|
|
return (fclass == 'recreation_ground' or
|
|
'sport' in str(tags.values()).lower() or
|
|
any(term in name for term in ['sport', 'football', 'tennis']))
|
|
|
|
def _allows_bbq(self, space_data: Dict) -> bool:
|
|
fclass = space_data.get('fclass', '')
|
|
area = space_data.get('area_sqm', 0)
|
|
tags = space_data.get('osm_tags', {})
|
|
|
|
# Check explicit BBQ tags
|
|
if tags.get('bbq') == 'yes':
|
|
return True
|
|
elif tags.get('bbq') == 'no':
|
|
return False
|
|
|
|
# Default based on type and size
|
|
return fclass in ['park', 'recreation_ground'] and area > 5000
|
|
|
|
async def process_all_green_spaces(self):
|
|
"""Main processing pipeline."""
|
|
print("🌳 Processing Berlin green spaces from local OSM data...")
|
|
|
|
# Step 1: Get OSM data
|
|
try:
|
|
osm_file = self.download_simple_osm_extract() # More reliable than PBF
|
|
except:
|
|
print("❌ Could not download OSM data")
|
|
return []
|
|
|
|
# Step 2: Parse green spaces
|
|
green_spaces = self.parse_osm_xml(osm_file)
|
|
|
|
if not green_spaces:
|
|
print("❌ No green spaces found in OSM data")
|
|
return []
|
|
|
|
print(f"📊 Found {len(green_spaces)} green spaces to enhance")
|
|
|
|
# Step 3: Enhance with real data
|
|
enhanced_spaces = []
|
|
|
|
for i, space_data in enumerate(green_spaces, 1):
|
|
print(f"[{i}/{len(green_spaces)}]", end=" ")
|
|
|
|
result = await self.enhance_green_space_with_real_data(space_data)
|
|
if result:
|
|
enhanced_spaces.append(result)
|
|
|
|
if i % 20 == 0:
|
|
print(f"\n Progress: {len(enhanced_spaces)} enhanced so far...")
|
|
|
|
await asyncio.sleep(0.1)
|
|
|
|
print(f"\n✅ Enhanced {len(enhanced_spaces)} spaces with real data!")
|
|
return enhanced_spaces
|
|
|
|
def save_enhanced_data(self, enhanced_spaces: List[Dict]):
|
|
"""Save the final dataset."""
|
|
output_file = self.processed_dir / "osm_berlin_green_spaces_enhanced.json"
|
|
|
|
# Calculate statistics
|
|
with_trees = len([s for s in enhanced_spaces if s["tree_data"]["total_trees"] > 0])
|
|
with_toilets = len([s for s in enhanced_spaces if s["toilet_accessibility"]["nearby_toilets_count"] > 0])
|
|
total_trees = sum(s["tree_data"]["total_trees"] for s in enhanced_spaces)
|
|
|
|
data = {
|
|
"green_spaces": enhanced_spaces,
|
|
"total_count": len(enhanced_spaces),
|
|
"last_updated": datetime.now().isoformat(),
|
|
"data_sources": [
|
|
"local_osm_extract_processed_offline",
|
|
"berlin_tree_cadastre",
|
|
"berlin_toilets"
|
|
],
|
|
"processing_info": {
|
|
"method": "local_osm_processing_no_api_dependency",
|
|
"includes_all_osm_green_spaces": True,
|
|
"enhanced_with_real_berlin_data": True
|
|
},
|
|
"summary_stats": {
|
|
"total_spaces": len(enhanced_spaces),
|
|
"spaces_with_tree_data": with_trees,
|
|
"spaces_with_toilet_data": with_toilets,
|
|
"total_trees_analyzed": total_trees,
|
|
"tree_coverage": f"{round((with_trees/len(enhanced_spaces))*100, 1)}%",
|
|
"toilet_coverage": f"{round((with_toilets/len(enhanced_spaces))*100, 1)}%"
|
|
}
|
|
}
|
|
|
|
with open(output_file, 'w', encoding='utf-8') as f:
|
|
json.dump(data, f, indent=2, ensure_ascii=False)
|
|
|
|
print(f"\n🎉 Saved comprehensive dataset: {output_file}")
|
|
print(f"📊 {len(enhanced_spaces)} total green spaces")
|
|
print(f"🌲 {with_trees} with tree data, 🚻 {with_toilets} with toilet data")
|
|
print(f"🌿 {total_trees} total trees analyzed")
|
|
print(f"\n✨ Ready to replace mock data in your API!")
|
|
|
|
return output_file
|
|
|
|
|
|
async def main():
|
|
processor = LocalOSMProcessor()
|
|
|
|
try:
|
|
print("🚀 Berlin Green Spaces: Local OSM Processing")
|
|
print("=" * 50)
|
|
print("• Downloads OSM data once (no API dependency)")
|
|
print("• Processes locally for all green spaces")
|
|
print("• Enhances with real Berlin tree + toilet data")
|
|
print("=" * 50)
|
|
|
|
enhanced_spaces = await processor.process_all_green_spaces()
|
|
|
|
if enhanced_spaces:
|
|
processor.save_enhanced_data(enhanced_spaces)
|
|
|
|
except KeyboardInterrupt:
|
|
print("\n⚠️ Interrupted")
|
|
except Exception as e:
|
|
print(f"❌ Error: {e}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main()) |