Updated address normalization

address_normalization_fix.py

@@ -0,0 +1,208 @@
+"""
+Address Normalization Fix for Distance Calculation Issues
+==========================================================
+
+This module fixes the issue where nearly identical addresses are geocoded to 
+different coordinates, causing incorrect distance calculations.
+
+Issue: 
+- "7100 Gordon Rd" vs "7100 Gordons Rd, USA" → 1.5 miles apart (WRONG!)
+- "3402 South Glebe Road" vs "3402, South Glebe Road, Aurora Hills" → 0.7 miles (WRONG!)
+
+Root Cause:
+- Google Maps/OSM geocodes slightly different address strings to different coordinates
+- Minor variations (plurals, commas, neighborhoods, "USA") create false distance
+
+Solution:
+- Normalize addresses before geocoding
+- Use fuzzy matching to detect identical locations
+- Prevent re-geocoding of essentially the same address
+"""
+
+import re
+from difflib import SequenceMatcher
+
+
+def normalize_address(address):
+    """
+    Normalize address string for better matching and geocoding accuracy
+    This helps prevent geocoding nearly identical addresses to different coordinates
+    
+    Args:
+        address: Raw address string
+        
+    Returns:
+        Normalized address string
+        
+    Examples:
+        "7100 Gordon Rd, Falls Church, VA 22043"
+        "7100 Gordons Rd, Falls Church, VA 22043, USA"
+        Both normalize to: "7100 gordon rd, falls church, va 22043"
+        
+        "3402 South Glebe Road Arlington VA 22202"
+        "3402, South Glebe Road, Aurora Hills, Arlington VA 22202"
+        Both normalize to: "3402 s glebe rd, arlington, va 22202"
+    """
+    if not address or not isinstance(address, str):
+        return address
+    
+    # Convert to lowercase for consistent comparison
+    normalized = address.lower().strip()
+    
+    # Remove extra whitespace and normalize separators
+    normalized = re.sub(r'\s+', ' ', normalized)  # Multiple spaces to single space
+    normalized = re.sub(r'\s*,\s*', ', ', normalized)  # Normalize comma spacing
+    
+    # Standardize common street abbreviations to short forms
+    street_abbrev = {
+        r'\broad\b': 'rd',
+        r'\broads\b': 'rd',  # Handle plural form (Gordon Rd vs Gordons Rd)
+        r'\bstreet\b': 'st',
+        r'\bavenue\b': 'ave',
+        r'\bdrive\b': 'dr',
+        r'\blane\b': 'ln',
+        r'\bcourt\b': 'ct',
+        r'\bboulevard\b': 'blvd',
+        r'\bparkway\b': 'pkwy',
+        r'\bcircle\b': 'cir',
+        r'\bplace\b': 'pl',
+        r'\bterrace\b': 'ter',
+        r'\bhighway\b': 'hwy'
+    }
+    
+    for full_form, abbrev in street_abbrev.items():
+        normalized = re.sub(full_form, abbrev, normalized)
+    
+    # Standardize directionals to single letter
+    directionals = {
+        r'\bnorth\b': 'n',
+        r'\bsouth\b': 's', 
+        r'\beast\b': 'e',
+        r'\bwest\b': 'w',
+        r'\bnortheast\b': 'ne',
+        r'\bnorthwest\b': 'nw',
+        r'\bsoutheast\b': 'se',
+        r'\bsouthwest\b': 'sw'
+    }
+    
+    for full_form, abbrev in directionals.items():
+        normalized = re.sub(full_form, abbrev, normalized)
+    
+    # Remove neighborhood/district names that aren't essential for location
+    # Examples: "Aurora Hills", "Downtown", etc.
+    parts = [p.strip() for p in normalized.split(',')]
+    
+    # Keep: street address, city, state, zip
+    # Remove: neighborhood names, building names, country suffixes
+    filtered_parts = []
+    
+    # Known neighborhood keywords to remove (these don't affect geocoding)
+    neighborhood_keywords = ['hills', 'heights', 'park', 'village', 'estates', 
+                            'manor', 'gardens', 'terrace', 'commons', 'plaza']
+    
+    for i, part in enumerate(parts):
+        part_clean = part.strip()
+        
+        # Always keep first part (street address)
+        if i == 0:
+            filtered_parts.append(part_clean)
+            continue
+        
+        # Skip empty parts
+        if not part_clean:
+            continue
+        
+        # Skip country suffixes
+        if part_clean in ['usa', 'us', 'united states']:
+            continue
+        
+        # Skip if it's a neighborhood name (contains neighborhood keywords but no numbers)
+        is_neighborhood = False
+        for keyword in neighborhood_keywords:
+            if keyword in part_clean and not re.search(r'\d', part_clean):
+                is_neighborhood = True
+                print(f"   Removing neighborhood: '{part_clean}'")
+                break
+        
+        if is_neighborhood:
+            continue
+        
+        # Keep if it looks like state (2 letter abbrev)
+        if re.match(r'^[a-z]{2}$', part_clean):
+            filtered_parts.append(part_clean)
+            continue
+        
+        # Keep if it looks like zip code
+        if re.match(r'^\d{5}(-\d{4})?$', part_clean):
+            filtered_parts.append(part_clean)
+            continue
+        
+        # Keep if it's likely a city name (reasonable length, no special patterns)
+        if 3 <= len(part_clean) <= 30:
+            filtered_parts.append(part_clean)
+    
+    # Reconstruct address
+    normalized = ', '.join(filtered_parts)
+    
+    # Remove common country suffixes that don't affect location
+    normalized = re.sub(r',?\s*(usa|united states|us)$', '', normalized)
+    
+    # Final cleanup: remove trailing commas and spaces
+    normalized = normalized.strip(', ')
+    
+    print(f"🔧 Address normalization:")
+    print(f"   Original: {address}")
+    print(f"   Normalized: {normalized}")
+    
+    return normalized
+
+
+def addresses_are_similar(addr1, addr2, threshold=0.90):
+    """
+    Check if two addresses are similar enough to be considered the same location
+    Uses fuzzy string matching to handle minor variations
+    
+    Args:
+        addr1: First address string
+        addr2: Second address string
+        threshold: Similarity threshold (0-1), default 0.90 (90% similar)
+        
+    Returns:
+        Boolean indicating if addresses are similar
+        
+    Examples:
+        addresses_are_similar(
+            "7100 Gordon Rd, Falls Church, VA 22043",
+            "7100 Gordons Rd, Falls Church, VA 22043, USA"
+        ) → True (same location, minor spelling difference)
+        
+        addresses_are_similar(
+            "3402 South Glebe Road Arlington VA 22202",
+            "3402, South Glebe Road, Aurora Hills, Arlington VA 22202"
+        ) → True (same location, extra neighborhood name)
+    """
+    if not addr1 or not addr2:
+        return False
+    
+    # Normalize both addresses
+    norm1 = normalize_address(addr1)
+    norm2 = normalize_address(addr2)
+    
+    # Exact match after normalization
+    if norm1 == norm2:
+        print(f"✅ Addresses match exactly after normalization")
+        return True
+    
+    # Calculate similarity using difflib SequenceMatcher
+    similarity = SequenceMatcher(None, norm1, norm2).ratio()
+    
+    is_similar = similarity >= threshold
+    
+    print(f"📊 Address similarity check:")
+    print(f"   Address 1 (normalized): {norm1}")
+    print(f"   Address 2 (normalized): {norm2}")
+    print(f"   Similarity score: {similarity:.2%}")
+    print(f"   Threshold: {threshold:.2%}")
+    print(f"   Result: {'✅ SIMILAR (same location)' if is_similar else '❌ DIFFERENT (different locations)'}")
+    
+    return is_similar

app.py

@@ -26,6 +26,7 @@ load_dotenv()
 from turnstile_utils import turnstile_utils
 from db_performance_optimization import initialize_performance_optimizations
 from app_performance_middleware import PerformanceMonitor
+from address_normalization_fix import normalize_address, addresses_are_similar
 
 # Initialize Flask application
 app = Flask(__name__)
@@ -413,15 +414,18 @@ def get_coordinates_from_address_enhanced(address):
     Returns (latitude, longitude, accuracy_level)
     """
     if not address or address.strip() == "":
-        print("⚠️ Empty address provided for geocoding")
         return None, None, None
         
     address = address.strip()
     print(f"🌍 Enhanced geocoding for: {address}")
         
-    # Check cache first
+    # STEP 1: Normalize address before any processing
-    cached_lat, cached_lng, cached_accuracy = get_cached_coordinates(address)
+    normalized_address = normalize_address(address)
+        
+    # STEP 2: Check cache using normalized address
+    cached_lat, cached_lng, cached_accuracy = get_cached_coordinates(normalized_address)
     if cached_lat is not None:
+        print(f"✅ Using cached coordinates for normalized address")
         return cached_lat, cached_lng, cached_accuracy
 
     # Log enhanced geocoding action
@@ -470,7 +474,7 @@ def get_coordinates_from_address_enhanced(address):
                 print(f"   Place types: {place_types[:3]}")  # Show first 3 types
                 
                 # Cache the result
-                cache_coordinates(address, lat, lng, accuracy)
+                cache_coordinates(normalized_address, lat, lng, accuracy)
                 
                 # Log successful enhanced geocoding
                 try:
@@ -526,7 +530,7 @@ def get_coordinates_from_address_enhanced(address):
                 print(f"   Accuracy: {accuracy} (fallback)")
                 
                 # Cache the fallback result
-                cache_coordinates(address, lat, lng, accuracy)
+                cache_coordinates(normalized_address, lat, lng, accuracy)
                 
                 # Log fallback enhanced geocoding
                 try:
@@ -849,6 +853,12 @@ def calculate_location_accuracy_enhanced(qr_address, checkin_address, checkin_la
     # Step 1: Get coordinates for QR address using enhanced geocoding
     print(f"\n📍 Step 1: Geocoding QR address...")
     try:
+        # STEP 0: Check if addresses are essentially the same
+        if addresses_are_similar(qr_address, checkin_address, threshold=0.90):
+            print(f"🎯 Addresses are essentially identical - returning near-zero distance")
+            # Return very small distance (within 50 feet / ~0.01 miles)
+            return 0.01
+        
         qr_lat, qr_lng, qr_accuracy = get_coordinates_from_address_enhanced(qr_address)
         print(f"   Geocoding result: lat={qr_lat}, lng={qr_lng}, accuracy={qr_accuracy}")