534 lines
21 KiB
Python
534 lines
21 KiB
Python
"""
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Address Normalization Fix for Distance Calculation Issues
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==========================================================
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This module fixes the issue where nearly identical addresses are geocoded to
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different coordinates, causing incorrect distance calculations.
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Issue:
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- "7100 Gordon Rd" vs "7100 Gordons Rd, USA" → 1.5 miles apart (WRONG!)
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- "3402 South Glebe Road" vs "3402, South Glebe Road, Aurora Hills" → 0.7 miles (WRONG!)
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Root Cause:
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- Google Maps/OSM geocodes slightly different address strings to different coordinates
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- Minor variations (plurals, commas, neighborhoods, "USA") create false distance
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Solution:
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- Normalize addresses before geocoding
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- Use fuzzy matching to detect identical locations
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- Prevent re-geocoding of essentially the same address
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- Extract and compare street number + street name as primary identifier
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"""
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import re
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from difflib import SequenceMatcher
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# Try to import logger_handler for logging (optional - won't break if not available)
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try:
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from logger_handler import AppLogger
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logger_handler = AppLogger()
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LOGGING_ENABLED = True
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except ImportError:
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logger_handler = None
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LOGGING_ENABLED = False
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def _log_activity(action, message):
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"""Helper function to log activity if logger is available"""
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if LOGGING_ENABLED and logger_handler:
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try:
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logger_handler.log_user_activity(action, message)
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except Exception:
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pass # Ignore logging errors
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def extract_street_address(address):
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"""
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Extract the core street address (number + street name) from an address string.
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This is the most reliable identifier for location matching.
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Handles cases where:
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- Street number is at the beginning: "735 18th St S"
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- Building name comes first: "Aurora Hills Library, 735, 18th Street South"
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Args:
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address: Normalized address string
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Returns:
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Core street address string (e.g., "735 18th st s")
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"""
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if not address:
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return ""
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addr_lower = address.lower()
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# Pattern to match: street number + optional directional + street name + street type
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# This pattern searches ANYWHERE in the string, not just at the beginning
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# Examples: "735 18th st s", "3402 south glebe road", "7100 gordon rd"
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street_types = r'(?:rd|st|ave|dr|ln|ct|blvd|pkwy|cir|pl|ter|hwy|way|trail|pike|run|walk|path|loop|road|street|avenue|drive|lane|court|boulevard|parkway|circle|place|terrace|highway)'
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# Pattern: number + ordinal/street name + optional directional + street type
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# Handles: "735 18th st s", "735, 18th street south"
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street_pattern = rf'(\d+)[\s,]+(\d*(?:st|nd|rd|th)?\s*[\w\s]*?{street_types})(?:\s+([nsew]|north|south|east|west))?'
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match = re.search(street_pattern, addr_lower)
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if match:
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street_num = match.group(1).strip()
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street_name = match.group(2).strip()
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direction = match.group(3) if match.group(3) else ""
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# Clean up extra spaces and commas
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street_name = re.sub(r'[\s,]+', ' ', street_name).strip()
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# Normalize direction
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dir_map = {'north': 'n', 'south': 's', 'east': 'e', 'west': 'w'}
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if direction:
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direction = dir_map.get(direction, direction)
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result = f"{street_num} {street_name}"
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if direction:
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result += f" {direction}"
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return result
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# Fallback: try to find just a street number followed by some words
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simple_pattern = r'(\d+)[\s,]+([\w\s]+)'
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match = re.search(simple_pattern, addr_lower)
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if match:
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street_num = match.group(1).strip()
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# Take words until we hit something that looks like a city/state
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words = match.group(2).split()
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street_words = []
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for word in words:
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# Stop at state abbreviations or zip codes
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if re.match(r'^[a-z]{2}$', word) and word in ['va', 'md', 'dc', 'ca', 'ny', 'tx', 'fl', 'pa', 'il', 'oh', 'ga', 'nc', 'nj']:
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break
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if re.match(r'^\d{5}', word):
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break
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street_words.append(word)
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if street_words:
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return f"{street_num} {' '.join(street_words[:4])}" # Limit to 4 words
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return address
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def normalize_address(address):
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"""
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Normalize address string for better matching and geocoding accuracy
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This helps prevent geocoding nearly identical addresses to different coordinates
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Args:
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address: Raw address string
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Returns:
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Normalized address string
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Examples:
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"7100 Gordon Rd, Falls Church, VA 22043"
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"7100 Gordons Rd, Falls Church, VA 22043, USA"
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Both normalize to: "7100 gordon rd, falls church, va 22043"
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"3402 South Glebe Road Arlington VA 22202"
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"3402, South Glebe Road, Aurora Hills, Arlington VA 22202"
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Both normalize to: "3402 s glebe rd, arlington, va 22202"
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"Aurora Hills Branch Library, 735, 18th Street South, Arlington, VA 22202"
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"735 18th St S, Arlington, VA 22202"
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Both normalize to: "735 18th st s, arlington, va 22202"
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"""
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if not address or not isinstance(address, str):
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return address
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# Convert to lowercase for consistent comparison
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normalized = address.lower().strip()
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# Remove extra whitespace and normalize separators
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normalized = re.sub(r'\s+', ' ', normalized) # Multiple spaces to single space
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normalized = re.sub(r'\s*,\s*', ', ', normalized) # Normalize comma spacing
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# Remove building/location names that come BEFORE the street number
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# Pattern: remove text before a street number if it looks like a building name
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# Examples: "Aurora Hills Branch Library, 735" → "735"
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# "Fire Station #7, 123 Main St" → "123 Main St"
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building_pattern = r'^[^,\d]*(?:library|station|center|building|plaza|tower|hall|office|school|church|hospital|clinic|bank|hotel|restaurant|store|shop|mall|complex|headquarters|hq|branch)[^,\d]*,\s*'
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normalized = re.sub(building_pattern, '', normalized, flags=re.IGNORECASE)
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# Standardize common street abbreviations to short forms
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street_abbrev = {
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r'\broad\b': 'rd',
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r'\broads\b': 'rd', # Handle plural form (Gordon Rd vs Gordons Rd)
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r'\bstreet\b': 'st',
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r'\bavenue\b': 'ave',
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r'\bdrive\b': 'dr',
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r'\blane\b': 'ln',
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r'\bcourt\b': 'ct',
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r'\bboulevard\b': 'blvd',
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r'\bparkway\b': 'pkwy',
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r'\bcircle\b': 'cir',
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r'\bplace\b': 'pl',
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r'\bterrace\b': 'ter',
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r'\bhighway\b': 'hwy'
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}
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for full_form, abbrev in street_abbrev.items():
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normalized = re.sub(full_form, abbrev, normalized)
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# Standardize directionals to single letter
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directionals = {
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r'\bnorth\b': 'n',
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r'\bsouth\b': 's',
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r'\beast\b': 'e',
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r'\bwest\b': 'w',
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r'\bnortheast\b': 'ne',
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r'\bnorthwest\b': 'nw',
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r'\bsoutheast\b': 'se',
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r'\bsouthwest\b': 'sw'
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}
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for full_form, abbrev in directionals.items():
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normalized = re.sub(full_form, abbrev, normalized)
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# Convert full state names to abbreviations
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state_names = {
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r'\bvirginia\b': 'va',
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r'\bmaryland\b': 'md',
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r'\bdistrict of columbia\b': 'dc',
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r'\bcalifornia\b': 'ca',
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r'\bnew york\b': 'ny',
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r'\btexas\b': 'tx',
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r'\bflorida\b': 'fl',
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r'\bpennsylvania\b': 'pa',
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r'\billinois\b': 'il',
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r'\bohio\b': 'oh',
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r'\bgeorgia\b': 'ga',
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r'\bnorth carolina\b': 'nc',
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r'\bnew jersey\b': 'nj',
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r'\bwashington\b': 'wa',
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r'\bmassachusetts\b': 'ma',
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r'\barizona\b': 'az',
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r'\bcolorado\b': 'co',
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r'\btennessee\b': 'tn',
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r'\bindiana\b': 'in',
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r'\bmissouri\b': 'mo',
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r'\bwisconsin\b': 'wi',
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r'\bminnesota\b': 'mn',
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r'\bsouth carolina\b': 'sc',
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r'\balabama\b': 'al',
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r'\blouisiana\b': 'la',
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r'\bkentucky\b': 'ky',
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r'\boregon\b': 'or',
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r'\boklahoma\b': 'ok',
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r'\bconnecticut\b': 'ct',
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r'\biowa\b': 'ia',
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r'\bmississippi\b': 'ms',
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r'\barkansas\b': 'ar',
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r'\bkansas\b': 'ks',
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r'\butah\b': 'ut',
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r'\bnevada\b': 'nv',
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r'\bnew mexico\b': 'nm',
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r'\bwest virginia\b': 'wv',
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r'\bnebraska\b': 'ne',
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r'\bidaho\b': 'id',
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r'\bhawaii\b': 'hi',
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r'\bmaine\b': 'me',
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r'\bnew hampshire\b': 'nh',
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r'\brhode island\b': 'ri',
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r'\bmontana\b': 'mt',
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r'\bdelaware\b': 'de',
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r'\bsouth dakota\b': 'sd',
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r'\bnorth dakota\b': 'nd',
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r'\balaska\b': 'ak',
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r'\bvermont\b': 'vt',
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r'\bwyoming\b': 'wy'
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}
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for full_name, abbrev in state_names.items():
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normalized = re.sub(full_name, abbrev, normalized)
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# Remove neighborhood/district names that aren't essential for location
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# Examples: "Aurora Hills", "Downtown", etc.
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parts = [p.strip() for p in normalized.split(',')]
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# Keep: street address, city, state, zip
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# Remove: neighborhood names, building names, country suffixes, county names
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filtered_parts = []
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# Known neighborhood keywords to remove (these don't affect geocoding)
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neighborhood_keywords = ['hills', 'heights', 'park', 'village', 'estates',
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'manor', 'gardens', 'terrace', 'commons', 'plaza',
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'downtown', 'midtown', 'uptown', 'district', 'center',
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'crossing', 'corner', 'square', 'point', 'landing',
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'aurora', 'crystal', 'forest', 'lake', 'river', 'creek',
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'meadow', 'valley', 'ridge', 'grove', 'glen', 'woods',
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'addison', 'colonial', 'fairfax', 'heritage', 'liberty']
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# Country names and suffixes to remove (English and other languages)
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country_suffixes = ['usa', 'us', 'united states', 'united states of america',
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'estados unidos', 'estados unidos de américa', 'estados unidos de america',
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'eeuu', 'e.u.', 'u.s.a.', 'u.s.', 'america', 'américas']
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for i, part in enumerate(parts):
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part_clean = part.strip()
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# Always keep first part (street address) - but only if it contains a number
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if i == 0:
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# Check if this looks like a building name (no street number)
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if re.search(r'\d', part_clean):
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filtered_parts.append(part_clean)
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else:
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print(f" Removing building name: '{part_clean}'")
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continue
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# Skip empty parts
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if not part_clean:
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continue
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# Skip country suffixes (multiple languages)
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if part_clean in country_suffixes:
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print(f" Removing country: '{part_clean}'")
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continue
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# Skip county names (e.g., "Arlington County", "Fairfax County")
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if 'county' in part_clean:
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print(f" Removing county: '{part_clean}'")
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continue
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# Skip if it's a neighborhood name (contains neighborhood keywords but no numbers)
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is_neighborhood = False
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for keyword in neighborhood_keywords:
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if keyword in part_clean and not re.search(r'\d', part_clean):
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is_neighborhood = True
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print(f" Removing neighborhood: '{part_clean}'")
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break
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if is_neighborhood:
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continue
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# Keep if it looks like state (2 letter abbrev)
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if re.match(r'^[a-z]{2}$', part_clean):
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filtered_parts.append(part_clean)
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continue
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# Keep if it looks like zip code
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if re.match(r'^\d{5}(-\d{4})?$', part_clean):
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filtered_parts.append(part_clean)
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continue
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# Keep if it's likely a city name (reasonable length, no special patterns)
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if 3 <= len(part_clean) <= 30:
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filtered_parts.append(part_clean)
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# Reconstruct address
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normalized = ', '.join(filtered_parts)
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# Remove common country suffixes that don't affect location (final cleanup)
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normalized = re.sub(r',?\s*(usa|united states|us|estados unidos.*?|eeuu|u\.s\.a?\.|america|américas?)$', '', normalized, flags=re.IGNORECASE)
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# Final cleanup: remove trailing commas and spaces
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normalized = normalized.strip(', ')
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print(f"🔧 Address normalization:")
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print(f" Original: {address}")
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print(f" Normalized: {normalized}")
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return normalized
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def extract_address_components(address):
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"""
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Extract key components from an address for comparison.
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Handles addresses where the street number may not be at the beginning
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(e.g., "Aurora Hills Library, 735, 18th Street South")
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Args:
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address: Address string (raw or normalized)
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Returns:
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Dictionary with extracted components:
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- street_number: The street number (e.g., "735")
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- street_name: The street name with type (e.g., "18th st s")
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- city: City name if found
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- state: State abbreviation if found
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- zip_code: ZIP code if found
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"""
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if not address:
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return {}
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addr_lower = address.lower().strip()
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components = {
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'street_number': None,
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'street_name': None,
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'city': None,
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'state': None,
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'zip_code': None
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}
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# Extract ZIP code first (most reliable)
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zip_match = re.search(r'\b(\d{5})(?:-\d{4})?\b', addr_lower)
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if zip_match:
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components['zip_code'] = zip_match.group(1)
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# Extract state (2-letter abbreviation, typically before zip or at end)
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# Also handle full state names that might not have been normalized
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valid_states = ['al', 'ak', 'az', 'ar', 'ca', 'co', 'ct', 'de', 'fl', 'ga',
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'hi', 'id', 'il', 'in', 'ia', 'ks', 'ky', 'la', 'me', 'md',
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'ma', 'mi', 'mn', 'ms', 'mo', 'mt', 'ne', 'nv', 'nh', 'nj',
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'nm', 'ny', 'nc', 'nd', 'oh', 'ok', 'or', 'pa', 'ri', 'sc',
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'sd', 'tn', 'tx', 'ut', 'vt', 'va', 'wa', 'wv', 'wi', 'wy', 'dc']
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state_match = re.search(r'\b([a-z]{2})\s*(?:,?\s*\d{5}|,|$)', addr_lower)
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if state_match:
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potential_state = state_match.group(1)
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if potential_state in valid_states:
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components['state'] = potential_state
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# Extract street number - look for it ANYWHERE in the address
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# Pattern: standalone number that's likely a street number (not a zip code or ordinal in street name)
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# Match numbers like "735" or "3402" but not "22202" (zip) or "18th" (ordinal)
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# First, try to find a number followed by a street-like pattern
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street_num_pattern = r'(?:^|,\s*)(\d{1,5})(?:\s*,\s*|\s+)(\d*(?:st|nd|rd|th)?\s*[\w\s]*?(?:rd|st|ave|dr|ln|ct|blvd|pkwy|cir|pl|ter|hwy|way|street|road|avenue|drive|lane|court|boulevard))'
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match = re.search(street_num_pattern, addr_lower)
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if match:
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components['street_number'] = match.group(1)
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street_name_raw = match.group(2).strip()
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# Clean up the street name
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street_name_raw = re.sub(r'[\s,]+', ' ', street_name_raw)
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components['street_name'] = street_name_raw
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else:
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# Fallback: try simpler pattern - just find a number at the start or after comma
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simple_num_match = re.search(r'(?:^|,\s*)(\d{1,5})(?:\s*,|\s+)(?!\d{4,5}\b)', addr_lower)
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if simple_num_match:
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components['street_number'] = simple_num_match.group(1)
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# Try to extract street name after the number
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remainder = addr_lower[simple_num_match.end():]
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remainder = remainder.lstrip(', ')
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# Look for street type keywords
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street_types = ['rd', 'st', 'ave', 'dr', 'ln', 'ct', 'blvd', 'pkwy', 'cir',
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'pl', 'ter', 'hwy', 'way', 'trail', 'pike', 'run', 'walk',
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'path', 'loop', 'road', 'street', 'avenue', 'drive', 'lane',
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'court', 'boulevard', 'parkway', 'circle', 'place', 'terrace',
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'highway']
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for st_type in street_types:
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pattern = rf'^([\w\s]+?\s*{st_type})\b'
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st_match = re.search(pattern, remainder)
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if st_match:
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components['street_name'] = st_match.group(1).strip()
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break
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return components
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def addresses_are_similar(addr1, addr2, threshold=0.85):
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"""
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Check if two addresses are similar enough to be considered the same location
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Uses multiple comparison strategies for robust matching:
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1. Direct street address comparison (highest priority)
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2. Component-based comparison
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3. Fuzzy string matching on normalized addresses
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Args:
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addr1: First address string
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addr2: Second address string
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threshold: Similarity threshold (0-1), default 0.85 (85% similar)
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Returns:
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Boolean indicating if addresses are similar
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Examples:
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addresses_are_similar(
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"7100 Gordon Rd, Falls Church, VA 22043",
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"7100 Gordons Rd, Falls Church, VA 22043, USA"
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) → True (same location, minor spelling difference)
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addresses_are_similar(
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"3402 South Glebe Road Arlington VA 22202",
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"3402, South Glebe Road, Aurora Hills, Arlington VA 22202"
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) → True (same location, extra neighborhood name)
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"""
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if not addr1 or not addr2:
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return False
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print(f"\n🔍 ADDRESS SIMILARITY CHECK:")
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print(f" Address 1: {addr1}")
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print(f" Address 2: {addr2}")
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# Normalize both addresses
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norm1 = normalize_address(addr1)
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norm2 = normalize_address(addr2)
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|
|
|
# Exact match after normalization
|
|
if norm1 == norm2:
|
|
print(f"✅ Addresses match exactly after normalization")
|
|
return True
|
|
|
|
# STRATEGY 1: Extract and compare core street addresses
|
|
# This is the most reliable method for catching cases like:
|
|
# "3402 South Glebe Road Arlington VA 22202" vs
|
|
# "3402, South Glebe Road, Aurora Hills, Arlington VA 22202"
|
|
street1 = extract_street_address(norm1)
|
|
street2 = extract_street_address(norm2)
|
|
|
|
print(f" Street Address 1: '{street1}'")
|
|
print(f" Street Address 2: '{street2}'")
|
|
|
|
if street1 and street2:
|
|
street_similarity = SequenceMatcher(None, street1, street2).ratio()
|
|
print(f" Street similarity: {street_similarity:.2%}")
|
|
|
|
# If street addresses are very similar (>92%), addresses are the same
|
|
if street_similarity >= 0.92:
|
|
print(f"✅ SIMILAR - Street addresses match ({street_similarity:.2%})")
|
|
return True
|
|
|
|
# STRATEGY 2: Component-based comparison
|
|
comp1 = extract_address_components(addr1)
|
|
comp2 = extract_address_components(addr2)
|
|
|
|
print(f" Components 1: {comp1}")
|
|
print(f" Components 2: {comp2}")
|
|
|
|
# If street numbers match exactly and street names are similar
|
|
if comp1.get('street_number') and comp2.get('street_number'):
|
|
if comp1['street_number'] == comp2['street_number']:
|
|
# Same street number - check street name similarity
|
|
if comp1.get('street_name') and comp2.get('street_name'):
|
|
name_sim = SequenceMatcher(None,
|
|
comp1['street_name'],
|
|
comp2['street_name']).ratio()
|
|
print(f" Street name similarity: {name_sim:.2%}")
|
|
|
|
if name_sim >= 0.85:
|
|
# Also check if zip codes match (if both have them)
|
|
if comp1.get('zip_code') and comp2.get('zip_code'):
|
|
if comp1['zip_code'] == comp2['zip_code']:
|
|
print(f"✅ SIMILAR - Same street number, similar name, same ZIP")
|
|
return True
|
|
else:
|
|
# No zip to compare, but street info matches
|
|
print(f"✅ SIMILAR - Same street number, similar street name")
|
|
return True
|
|
|
|
# STRATEGY 3: Full normalized address fuzzy matching
|
|
similarity = SequenceMatcher(None, norm1, norm2).ratio()
|
|
is_similar = similarity >= threshold
|
|
|
|
print(f"📊 Full address similarity:")
|
|
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)'}")
|
|
|
|
# Log the address similarity check result
|
|
_log_activity(
|
|
'address_similarity_check',
|
|
f"Compared addresses: similarity={similarity:.2%}, result={'SIMILAR' if is_similar else 'DIFFERENT'}"
|
|
)
|
|
|
|
return is_similar |