Add readme info, claude scripts, gitignore

.gitignore

@@ -1,2 +1,5 @@
 __pycache__
 desktop.ini
+*.geojson
+osm_cache/
+.claude

README.md

@@ -2,6 +2,9 @@
 
 See [https://wiki.openstreetmap.org/wiki/The_Villages_Road_and_Address_Import](https://wiki.openstreetmap.org/wiki/The_Villages_Road_and_Address_Import)
 
+See compare-addresses.py for an automated way of running the complete address diff toolchain in one step.
+- TODO: fails to split out units
+
 ## Data
 
 - Lake County Streets and Address Points: https://c.lakecountyfl.gov/ftp/GIS/GisDownloads/Shapefiles/
@@ -120,3 +123,22 @@ source:url=https://gitlab.com/zyphlar/the-villages-import
 
 
 * Review imported data in Achavi or Osmcha to ensure it looks proper.
+
+
+## Useful queries:
+
+```
+[timeout:60];
+area["name"="Florida"]->.state;
+area["name"="Lake County"](area.state)->.searchArea;nwr["addr:housenumber"](area.searchArea);
+(._;>;);
+out meta;
+```
+
+```
+[timeout:60];
+area["name"="Florida"]->.state;
+area["name"="Lake County"](area.state)->.searchArea;way["highway"](area.searchArea);
+(._;>;);
+out meta;
+```

compare-addresses.py

@@ -0,0 +1,627 @@
+#!/usr/bin/env python3
+"""
+Address Data Comparison Tool for US Counties
+
+Compares local government address data (from ZIP/shapefile) with OpenStreetMap address data.
+Downloads OSM data via Overpass API, converts local data to GeoJSON, and performs comprehensive
+comparison to identify new, existing, and removed addresses.
+
+Usage:
+    python compare-addresses.py "Lake" "Florida" --local-zip "original data/Lake/Addresspoints 2025-06.zip"
+    python compare-addresses.py "Sumter" "Florida" --local-zip "original data/Sumter/Address9_13_2024.zip"
+"""
+
+import argparse
+import json
+import os
+import sys
+import zipfile
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Tuple, Any, Optional
+import urllib.error
+import urllib.parse
+import urllib.request
+
+import geopandas as gpd
+import pandas as pd
+from shapely.geometry import Point
+from shapely.strtree import STRtree
+from shapely.ops import nearest_points
+import warnings
+
+# Import local modules
+import importlib
+qgis_functions = importlib.import_module("qgis-functions")
+
+# Suppress warnings for cleaner output
+warnings.filterwarnings('ignore')
+
+
+class AddressComparator:
+    def __init__(self, tolerance_meters: float = 50.0, cache_dir: str = "osm_cache"):
+        """
+        Initialize the address comparator.
+        
+        Args:
+            tolerance_meters: Distance tolerance for considering addresses as matching
+            cache_dir: Directory to cache OSM data
+        """
+        self.tolerance_meters = tolerance_meters
+        self.cache_dir = Path(cache_dir)
+        self.cache_dir.mkdir(exist_ok=True)
+        
+        # Convert meters to degrees (approximate)
+        # 1 degree latitude ≈ 111,000 meters
+        self.tolerance_deg = tolerance_meters / 111000.0
+        
+    def download_osm_addresses(self, county: str, state: str, output_file: str = None) -> str:
+        """Download address data from OpenStreetMap via Overpass API."""
+        if output_file is None:
+            timestamp = datetime.now().strftime("%Y%m%d")
+            output_file = self.cache_dir / f"osm_addresses_{county.lower()}_{timestamp}.geojson"
+        else:
+            output_file = Path(output_file)
+            
+        # Check if cached file exists and is recent (less than 7 days old)
+        if output_file.exists():
+            file_age = datetime.now().timestamp() - output_file.stat().st_mtime
+            if file_age < 7 * 24 * 3600:  # 7 days in seconds
+                print(f"Using cached OSM data: {output_file}")
+                return str(output_file)
+        
+        print(f"Downloading OSM addresses for {county} County, {state}...")
+        
+        # Build Overpass query for addresses
+        query = f"""[out:json][timeout:180];
+area["name"="{state}"]->.state;
+area["name"="{county} County"](area.state)->.searchArea;
+nwr["addr:housenumber"](area.searchArea);
+out geom;"""
+        
+        # Query Overpass API
+        osm_data = self._query_overpass(query)
+        
+        # Convert to GeoJSON
+        geojson = self._convert_osm_to_geojson(osm_data)
+        
+        # Save to file
+        output_file.parent.mkdir(parents=True, exist_ok=True)
+        with open(output_file, 'w', encoding='utf-8') as f:
+            json.dump(geojson, f, indent=2)
+        
+        print(f"Downloaded {len(geojson['features'])} OSM addresses to {output_file}")
+        return str(output_file)
+    
+    def _query_overpass(self, query: str) -> Dict[str, Any]:
+        """Send query to Overpass API and return JSON response."""
+        url = "https://overpass-api.de/api/interpreter"
+        data = urllib.parse.urlencode({"data": query}).encode("utf-8")
+        
+        try:
+            with urllib.request.urlopen(url, data=data, timeout=300) as response:
+                return json.loads(response.read().decode("utf-8"))
+        except urllib.error.HTTPError as e:
+            print(f"HTTP Error {e.code}: {e.reason}", file=sys.stderr)
+            try:
+                error_body = e.read().decode("utf-8")
+                print(f"Error response: {error_body}", file=sys.stderr)
+            except:
+                pass
+            sys.exit(1)
+        except Exception as e:
+            print(f"Error querying Overpass API: {e}", file=sys.stderr)
+            sys.exit(1)
+    
+    def _convert_osm_to_geojson(self, overpass_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Convert Overpass API response to GeoJSON format."""
+        features = []
+        
+        for element in overpass_data.get("elements", []):
+            properties = element.get("tags", {})
+            
+            # Extract coordinates based on element type
+            if element["type"] == "node":
+                coordinates = [element["lon"], element["lat"]]
+                geometry = {"type": "Point", "coordinates": coordinates}
+            elif element["type"] == "way" and "geometry" in element:
+                # For ways, use the centroid
+                coords = [[coord["lon"], coord["lat"]] for coord in element["geometry"]]
+                if len(coords) > 0:
+                    # Calculate centroid
+                    lon = sum(coord[0] for coord in coords) / len(coords)
+                    lat = sum(coord[1] for coord in coords) / len(coords)
+                    coordinates = [lon, lat]
+                    geometry = {"type": "Point", "coordinates": coordinates}
+                else:
+                    continue
+            else:
+                continue  # Skip relations and ways without geometry
+            
+            feature = {
+                "type": "Feature",
+                "properties": properties,
+                "geometry": geometry
+            }
+            features.append(feature)
+        
+        return {
+            "type": "FeatureCollection",
+            "features": features
+        }
+    
+    def load_local_addresses(self, zip_path: str, output_geojson: str = None) -> str:
+        """Load and convert local address data from ZIP file."""
+        zip_path = Path(zip_path)
+        
+        if output_geojson is None:
+            output_geojson = zip_path.parent / f"{zip_path.stem}_converted.geojson"
+        else:
+            output_geojson = Path(output_geojson)
+        
+        # Check if conversion already exists and is newer than the ZIP
+        if (output_geojson.exists() and 
+            zip_path.exists() and 
+            output_geojson.stat().st_mtime > zip_path.stat().st_mtime):
+            print(f"Using existing converted data: {output_geojson}")
+            return str(output_geojson)
+        
+        print(f"Converting local address data from {zip_path}...")
+        
+        # Extract and find shapefile in ZIP
+        temp_dir = zip_path.parent / "temp_extract"
+        temp_dir.mkdir(exist_ok=True)
+        
+        try:
+            with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                zip_ref.extractall(temp_dir)
+            
+            # Find the shapefile
+            shp_files = list(temp_dir.glob("*.shp"))
+            if not shp_files:
+                raise FileNotFoundError("No shapefile (.shp) found in ZIP")
+            
+            shp_file = shp_files[0]
+            
+            # Load and process shapefile
+            gdf = gpd.read_file(shp_file)
+            
+            # Convert CRS to WGS84 if needed
+            if gdf.crs and gdf.crs != 'EPSG:4326':
+                print(f"Converting from {gdf.crs} to EPSG:4326")
+                gdf = gdf.to_crs('EPSG:4326')
+            
+            # Process address fields using existing logic from sumter-address-convert.py
+            gdf = self._process_address_fields(gdf)
+            
+            # Filter to only point geometries and valid addresses
+            gdf = gdf[gdf.geometry.type == 'Point'].copy()
+            gdf = gdf[gdf['addr:housenumber'].notna()].copy()
+            
+            # Clean output data - keep only OSM address fields
+            osm_fields = [
+                'addr:housenumber', 'addr:unit', 'addr:street', 
+                'addr:city', 'addr:postcode', 'addr:state'
+            ]
+            existing_fields = [field for field in osm_fields if field in gdf.columns]
+            gdf = gdf[existing_fields + ['geometry']]
+            
+            # Save to GeoJSON
+            output_geojson.parent.mkdir(parents=True, exist_ok=True)
+            gdf.to_file(output_geojson, driver='GeoJSON')
+            
+            print(f"Converted {len(gdf)} addresses to {output_geojson}")
+            
+        finally:
+            # Clean up temp directory
+            import shutil
+            if temp_dir.exists():
+                shutil.rmtree(temp_dir)
+        
+        return str(output_geojson)
+    
+    def _process_address_fields(self, gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
+        """Process address fields according to OSM schema (handles multiple formats)."""
+        processed_gdf = gdf.copy()
+        address_mapping = {}
+        
+        # House number - try multiple field names
+        house_number_fields = ['ADD_NUM', 'AddressNum', 'ADDRESS_NUM', 'HOUSE_NUM']
+        for field in house_number_fields:
+            if field in processed_gdf.columns:
+                add_num_series = processed_gdf[field].copy()
+                add_num_series = pd.to_numeric(add_num_series, errors='coerce')
+                address_mapping['addr:housenumber'] = add_num_series.round().astype('Int64')
+                break
+        
+        # Unit number - try multiple field names
+        unit_fields = ['UNIT', 'UnitNumber', 'UNIT_NUM', 'APT']
+        for field in unit_fields:
+            if field in processed_gdf.columns:
+                unit_series = processed_gdf[field].copy()
+                unit_series = unit_series.replace(['nan', 'None', '', None], None)
+                unit_series = unit_series.where(unit_series.notna(), None)
+                address_mapping['addr:unit'] = unit_series
+                break
+        
+        # Street name - try multiple approaches
+        if 'SADD' in processed_gdf.columns:
+            # Sumter County format - full address in SADD field
+            street_names = []
+            for sadd_value in processed_gdf['SADD']:
+                if pd.notna(sadd_value):
+                    street_from_addr = qgis_functions.getstreetfromaddress(str(sadd_value), None, None)
+                    street_titled = qgis_functions.title(street_from_addr)
+                    street_names.append(street_titled)
+                else:
+                    street_names.append(None)
+            address_mapping['addr:street'] = street_names
+        elif 'FullAddres' in processed_gdf.columns:
+            # Lake County format - full address in FullAddres field
+            street_names = []
+            for full_addr in processed_gdf['FullAddres']:
+                if pd.notna(full_addr):
+                    street_from_addr = qgis_functions.getstreetfromaddress(str(full_addr), None, None)
+                    street_titled = qgis_functions.title(street_from_addr)
+                    street_names.append(street_titled)
+                else:
+                    street_names.append(None)
+            address_mapping['addr:street'] = street_names
+        elif 'BaseStreet' in processed_gdf.columns:
+            # Lake County alternative - combine street components
+            street_names = []
+            for idx, row in processed_gdf.iterrows():
+                street_parts = []
+                
+                # Prefix direction
+                if 'PrefixDire' in row and pd.notna(row['PrefixDire']):
+                    street_parts.append(str(row['PrefixDire']).strip())
+                
+                # Prefix type
+                if 'PrefixType' in row and pd.notna(row['PrefixType']):
+                    street_parts.append(str(row['PrefixType']).strip())
+                
+                # Base street name
+                if pd.notna(row['BaseStreet']):
+                    street_parts.append(str(row['BaseStreet']).strip())
+                
+                # Suffix type
+                if 'SuffixType' in row and pd.notna(row['SuffixType']):
+                    street_parts.append(str(row['SuffixType']).strip())
+                
+                if street_parts:
+                    street_name = ' '.join(street_parts)
+                    street_titled = qgis_functions.title(street_name)
+                    street_names.append(street_titled)
+                else:
+                    street_names.append(None)
+            
+            address_mapping['addr:street'] = street_names
+        
+        # City - try multiple field names
+        city_fields = ['POST_COMM', 'PostalCity', 'CITY', 'Jurisdicti']
+        for field in city_fields:
+            if field in processed_gdf.columns:
+                city_names = []
+                for city_value in processed_gdf[field]:
+                    if pd.notna(city_value):
+                        city_titled = qgis_functions.title(str(city_value))
+                        city_names.append(city_titled)
+                    else:
+                        city_names.append(None)
+                address_mapping['addr:city'] = city_names
+                break
+        
+        # Postal code - try multiple field names
+        postcode_fields = ['POST_CODE', 'ZipCode', 'ZIP', 'POSTAL_CODE']
+        for field in postcode_fields:
+            if field in processed_gdf.columns:
+                post_code_series = processed_gdf[field].copy()
+                post_code_series = pd.to_numeric(post_code_series, errors='coerce')
+                address_mapping['addr:postcode'] = post_code_series.round().astype('Int64')
+                break
+        
+        # Manually add addr:state
+        address_mapping['addr:state'] = 'FL'
+        
+        # Add the new address columns to the GeoDataFrame
+        for key, value in address_mapping.items():
+            processed_gdf[key] = value
+        
+        return processed_gdf
+    
+    def compare_addresses(self, local_file: str, osm_file: str) -> Tuple[List[Dict], List[Dict], List[Dict]]:
+        """
+        Compare local and OSM address data.
+        
+        Returns:
+            Tuple of (new_addresses, existing_addresses, removed_addresses)
+        """
+        print(f"Comparing addresses: {local_file} vs {osm_file}")
+        
+        # Load data
+        local_gdf = gpd.read_file(local_file)
+        osm_gdf = gpd.read_file(osm_file)
+        
+        print(f"Loaded local addresses: {len(local_gdf)}")
+        print(f"Loaded OSM addresses: {len(osm_gdf)}")
+        
+        # Apply sampling if requested
+        if hasattr(self, 'sample_size') and self.sample_size:
+            if len(local_gdf) > self.sample_size:
+                local_gdf = local_gdf.sample(n=self.sample_size, random_state=42).reset_index(drop=True)
+                print(f"Sampled local addresses to: {len(local_gdf)}")
+        
+        if hasattr(self, 'max_osm') and self.max_osm:
+            if len(osm_gdf) > self.max_osm:
+                osm_gdf = osm_gdf.sample(n=self.max_osm, random_state=42).reset_index(drop=True)
+                print(f"Sampled OSM addresses to: {len(osm_gdf)}")
+        
+        print(f"Processing local addresses: {len(local_gdf)}")
+        print(f"Processing OSM addresses: {len(osm_gdf)}")
+        
+        # Ensure both are in the same CRS
+        if local_gdf.crs != osm_gdf.crs:
+            osm_gdf = osm_gdf.to_crs(local_gdf.crs)
+        
+        # Create spatial indexes
+        local_index = STRtree(local_gdf.geometry.tolist())
+        osm_index = STRtree(osm_gdf.geometry.tolist())
+        
+        # Find matches
+        existing_addresses = []
+        new_addresses = []
+        
+        # For each local address, find closest OSM address
+        for idx, local_row in local_gdf.iterrows():
+            local_point = local_row.geometry
+            
+            # Query nearby OSM addresses
+            nearby_indices = osm_index.query(local_point.buffer(self.tolerance_deg))
+            
+            best_match = None
+            min_distance = float('inf')
+            
+            for osm_idx in nearby_indices:
+                osm_row = osm_gdf.iloc[osm_idx]
+                osm_point = osm_row.geometry
+                distance = local_point.distance(osm_point)
+                
+                # Additional verification: check if house numbers match (handle type differences)
+                local_house_num = str(local_row.get('addr:housenumber', ''))
+                osm_house_num = str(osm_row.get('addr:housenumber', ''))
+                
+                # Only consider as potential match if house numbers match
+                if local_house_num == osm_house_num and distance < min_distance:
+                    min_distance = distance
+                    best_match = osm_idx
+            
+            # Convert distance to meters for comparison
+            distance_meters = min_distance * 111000.0
+            
+            if best_match is not None and distance_meters <= self.tolerance_meters:
+                # Found a match - this is an existing address
+                local_props = dict(local_row.drop('geometry'))
+                osm_props = dict(osm_gdf.iloc[best_match].drop('geometry'))
+                
+                existing_addresses.append({
+                    'geometry': local_point,
+                    'local_data': local_props,
+                    'osm_data': osm_props,
+                    'distance_meters': distance_meters
+                })
+            else:
+                # No match found - this is a new address to add to OSM
+                local_props = dict(local_row.drop('geometry'))
+                local_props['status'] = 'new'
+                new_addresses.append({
+                    'geometry': local_point,
+                    **local_props
+                })
+        
+        # Find OSM addresses that don't have local matches (potentially removed)
+        removed_addresses = []
+        
+        # Track which OSM addresses were matched during the first pass
+        # by storing their index during the matching process above
+        matched_osm_indices = set()
+        
+        # Re-do the matching to track OSM indices
+        for idx, local_row in local_gdf.iterrows():
+            local_point = local_row.geometry
+            nearby_indices = osm_index.query(local_point.buffer(self.tolerance_deg))
+            
+            for osm_idx in nearby_indices:
+                osm_row = osm_gdf.iloc[osm_idx]
+                osm_point = osm_row.geometry
+                distance_meters = local_point.distance(osm_point) * 111000.0
+                
+                # Check if house numbers match (handle type differences)
+                local_house_num = str(local_row.get('addr:housenumber', ''))
+                osm_house_num = str(osm_row.get('addr:housenumber', ''))
+                
+                if (distance_meters <= self.tolerance_meters and 
+                    local_house_num == osm_house_num):
+                    matched_osm_indices.add(osm_idx)
+                    break  # Only match to first OSM address found
+        
+        # Find unmatched OSM addresses
+        for idx, osm_row in osm_gdf.iterrows():
+            if idx not in matched_osm_indices:
+                osm_props = dict(osm_row.drop('geometry'))
+                osm_props['status'] = 'removed'
+                removed_addresses.append({
+                    'geometry': osm_row.geometry,
+                    **osm_props
+                })
+        
+        return new_addresses, existing_addresses, removed_addresses
+    
+    def save_results(self, new_addresses: List[Dict], existing_addresses: List[Dict], 
+                    removed_addresses: List[Dict], output_dir: str):
+        """Save comparison results to separate GeoJSON files."""
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+        
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        
+        # Save new addresses (to add to OSM)
+        if new_addresses:
+            new_gdf = gpd.GeoDataFrame(new_addresses)
+            new_file = output_dir / f"addresses_to_add_{timestamp}.geojson"
+            new_gdf.to_file(new_file, driver='GeoJSON')
+            print(f"Saved {len(new_addresses)} new addresses to {new_file}")
+        
+        # Save removed addresses (missing from local data)
+        if removed_addresses:
+            removed_gdf = gpd.GeoDataFrame(removed_addresses)
+            removed_file = output_dir / f"addresses_potentially_removed_{timestamp}.geojson"
+            removed_gdf.to_file(removed_file, driver='GeoJSON')
+            print(f"Saved {len(removed_addresses)} potentially removed addresses to {removed_file}")
+        
+        # Save existing addresses for reference
+        if existing_addresses:
+            # Create simplified format for existing addresses
+            existing_simple = []
+            for addr in existing_addresses:
+                existing_simple.append({
+                    'geometry': addr['geometry'],
+                    'distance_meters': addr['distance_meters'],
+                    'status': 'existing'
+                })
+            
+            existing_gdf = gpd.GeoDataFrame(existing_simple)
+            existing_file = output_dir / f"addresses_existing_{timestamp}.geojson"
+            existing_gdf.to_file(existing_file, driver='GeoJSON')
+            print(f"Saved {len(existing_addresses)} existing addresses to {existing_file}")
+    
+    def print_summary(self, new_addresses: List[Dict], existing_addresses: List[Dict], 
+                     removed_addresses: List[Dict]):
+        """Print a summary of the comparison results."""
+        print("\n" + "="*60)
+        print("ADDRESS COMPARISON SUMMARY")
+        print("="*60)
+        
+        print(f"\nTOTAL ADDRESSES ANALYZED:")
+        print(f"  • New addresses (to add to OSM): {len(new_addresses)}")
+        print(f"  • Existing addresses (matched): {len(existing_addresses)}")
+        print(f"  • Potentially removed addresses: {len(removed_addresses)}")
+        
+        if existing_addresses:
+            distances = [addr['distance_meters'] for addr in existing_addresses]
+            avg_distance = sum(distances) / len(distances)
+            print(f"\nMATCHING STATISTICS:")
+            print(f"  • Average distance of matches: {avg_distance:.1f} meters")
+            print(f"  • Max distance of matches: {max(distances):.1f} meters")
+        
+        if new_addresses:
+            print(f"\nNEW ADDRESSES TO ADD:")
+            print(f"  These addresses exist in local data but not in OSM")
+            
+            # Group by street name
+            streets = {}
+            for addr in new_addresses[:10]:  # Show first 10
+                street = addr.get('addr:street', 'Unknown Street')
+                if street not in streets:
+                    streets[street] = 0
+                streets[street] += 1
+            
+            for street, count in sorted(streets.items()):
+                print(f"  • {street}: {count} address(es)")
+            
+            if len(new_addresses) > 10:
+                print(f"  • ... and {len(new_addresses) - 10} more")
+        
+        if removed_addresses:
+            print(f"\nPOTENTIALLY REMOVED ADDRESSES:")
+            print(f"  These addresses exist in OSM but not in local data")
+            print(f"  (May indicate addresses that were removed or demolished)")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Compare local government address data with OpenStreetMap addresses",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  python compare-addresses.py "Lake" "Florida" --local-zip "original data/Lake/Addresspoints 2025-06.zip"
+  python compare-addresses.py "Sumter" "Florida" --local-zip "original data/Sumter/Address9_13_2024.zip" --tolerance 30
+  python compare-addresses.py "Orange" "Florida" --local-zip "addresses.zip" --output-dir "results/orange"
+        """
+    )
+    
+    parser.add_argument('county', help='County name (e.g., "Lake", "Sumter")')
+    parser.add_argument('state', help='State name (e.g., "Florida")')
+    parser.add_argument('--local-zip', required=True, help='Path to local address data ZIP file')
+    parser.add_argument('--tolerance', '-t', type=float, default=50.0,
+                       help='Distance tolerance in meters for matching addresses (default: 50)')
+    parser.add_argument('--output-dir', '-o', help='Output directory for results (default: processed data/[County])')
+    parser.add_argument('--cache-dir', default='osm_cache', 
+                       help='Directory to cache OSM downloads (default: osm_cache)')
+    parser.add_argument('--force-download', action='store_true',
+                       help='Force re-download of OSM data (ignore cache)')
+    parser.add_argument('--sample', '-s', type=int, 
+                       help='Process only a sample of N addresses for testing')
+    parser.add_argument('--max-osm', type=int, default=50000,
+                       help='Maximum number of OSM addresses to process (default: 50000)')
+    
+    args = parser.parse_args()
+    
+    # Validate input file
+    local_zip = Path(args.local_zip)
+    if not local_zip.exists():
+        print(f"Error: Local ZIP file {args.local_zip} does not exist")
+        return 1
+    
+    # Set output directory
+    if args.output_dir:
+        output_dir = Path(args.output_dir)
+    else:
+        output_dir = Path("processed data") / args.county
+    
+    try:
+        # Create comparator
+        comparator = AddressComparator(
+            tolerance_meters=args.tolerance,
+            cache_dir=args.cache_dir
+        )
+        
+        # Set sampling parameters
+        if args.sample:
+            comparator.sample_size = args.sample
+        if args.max_osm:
+            comparator.max_osm = args.max_osm
+        
+        # Download/load OSM data
+        if args.force_download:
+            # Remove existing cache for this county
+            for cache_file in Path(args.cache_dir).glob(f"osm_addresses_{args.county.lower()}_*.geojson"):
+                cache_file.unlink()
+        
+        osm_file = comparator.download_osm_addresses(args.county, args.state)
+        
+        # Convert local data
+        local_file = comparator.load_local_addresses(args.local_zip)
+        
+        # Perform comparison
+        new_addresses, existing_addresses, removed_addresses = comparator.compare_addresses(
+            local_file, osm_file
+        )
+        
+        # Save results
+        comparator.save_results(new_addresses, existing_addresses, removed_addresses, output_dir)
+        
+        # Print summary
+        comparator.print_summary(new_addresses, existing_addresses, removed_addresses)
+        
+        return 0
+        
+    except Exception as e:
+        print(f"Error: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

run-lake-addresses.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python3
+"""
+Simple wrapper script for comparing Lake County addresses
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+
+def main():
+    # Change to script directory
+    script_dir = Path(__file__).parent
+    
+    # Define the command
+    cmd = [
+        sys.executable, 
+        "compare-addresses.py",
+        "Lake", 
+        "Florida",
+        "--local-zip", "original data/Lake/Addresspoints 2025-06.zip",
+        "--tolerance", "50",
+        "--output-dir", "processed data/Lake"
+    ]
+    
+    print("Running Lake County address comparison...")
+    print("Command:", " ".join(cmd))
+    print()
+    
+    # Run the command
+    result = subprocess.run(cmd, cwd=script_dir)
+    
+    if result.returncode == 0:
+        print("\nAddress comparison completed successfully!")
+        print("Results saved in: processed data/Lake/")
+    else:
+        print(f"\nError: Script failed with return code {result.returncode}")
+    
+    return result.returncode
+
+if __name__ == "__main__":
+    sys.exit(main())