Briefing Document: Address Parsing and Geocoding Tools

This briefing document summarizes the main themes and important ideas from the provided sources, focusing on techniques and tools for address parsing, standardization, validation, and geocoding.

Main Themes:

• The Complexity of Address Data: Addresses are unstructured and prone to variations, abbreviations, misspellings, and inconsistencies, making accurate processing challenging.
• Need for Robust Parsing and Matching: Effective address management requires tools capable of breaking down addresses into components, standardizing formats, and matching records despite minor discrepancies.
• Availability of Specialized Libraries: Several open-source and commercial libraries exist in various programming languages to address these challenges. These libraries employ different techniques, from rule-based parsing to statistical NLP and fuzzy matching.
• Geocoding for Spatial Analysis: Converting addresses to geographic coordinates (latitude and longitude) enables location-based services, spatial analysis, and mapping.
• Importance of Data Quality: Accurate address processing is crucial for various applications, including logistics, customer relationship management, and data analysis.

Key Ideas and Facts from the Sources:

1. Fuzzy Logic for Address Matching (Placekey):

• Damerau-Levenshtein Distance: This method extends standard string distance calculations by including the operation of transposition of adjacent characters, allowing for more accurate matching that accounts for common typing errors.
• “The Damerau-Levenshtein distance goes a step further, enabling another operation for data matching: transposition of two adjacent characters. This allows for even more flexibility in data matching, as it can help account for input errors.”
• Customizable Comparisons: Matching can be tailored by specifying various comparison factors and setting thresholds to define acceptable results.
• “As you can see, you can specify your comparison based on a number of factors. You can use this to customize it to the task you are trying to perform, as well as refine your search for addresses in a number of generic ways. Set up thresholds yourself to define what results are returned.”
• Blocking: To improve efficiency and accuracy, comparisons can be restricted to records that share certain criteria, such as the same region (city or state), especially useful for deduplication.
• “You can also refine your comparisons using blockers, ensuring that for a match to occur, certain criteria has to match. For example, if you are trying to deduplicate addresses, you want to restrict your comparisons to addresses within the same region, such as a city or state.”

2. Geocoding using Google Sheets Script (Reddit):

• A user shared a Google Apps Script function (convertAddressToCoordinates()) that utilizes the Google Maps Geocoding API to convert addresses in a spreadsheet to latitude, longitude, and formatted address.
• The script iterates through a specified range of addresses in a Google Sheet, geocodes them, and outputs the coordinates and formatted address into new columns.
• The user sought information on where to run the script and the daily lookup quota for the Google Maps Geocoding API.
• This highlights a practical, albeit potentially limited by quotas, approach to geocoding a moderate number of addresses.

3. Address Parsing with Libpostal (Geoapify & GitHub):

• Libpostal: This is a C library focused on parsing and normalizing street addresses globally, leveraging statistical NLP and open geo data.
• “libpostal is a C library for parsing/normalizing street addresses around the world using statistical NLP and open data. The goal of this project is to understand location-based strings in every language, everywhere.” (GitHub)
• Multi-Language Support: Libpostal supports address parsing and normalization in over 60 languages.
• Language Bindings: Bindings are available for various programming languages, including Python, Go, Ruby, Java, and NodeJS.
• “The core library is written in pure C. Language bindings for Python, Ruby, Go, Java, PHP, and NodeJS are officially supported and it’s easy to write bindings in other languages.” (GitHub)
• Open Source: Libpostal is open source and distributed under the MIT license.
• Functionality: It can parse addresses into components like road, house number, postcode, city, state district, and country.
• Example Output:
• {
•   “road” : “franz-rennefeld-weg”,
•   “house_number” : “8”,
•   “postcode” : “40472”,
•   “city” : “düsseldorf”
• }
• Normalization: Libpostal can normalize address formats and expand abbreviations.
• Example: “Quatre-vingt-douze Ave des Champs-Élysées” can be expanded to “quatre-vingt-douze avenue des champs élysées”. (GitHub)
• Alternative Data Model (Senzing): An alternative data model from Senzing Inc. provides improved parsing for US, UK, and Singapore addresses, including better handling of US rural routes. (GitHub)
• Installation: Instructions are provided for installing the C library on various operating systems, including Linux, macOS, and Windows (using Msys2). (GitHub)
• Parser Training Data: Libpostal’s parser is trained on a large dataset of tagged addresses from various sources like OpenStreetMap and OpenAddresses. (GitHub)

4. Python Style Guide (PEP 8):

• While not directly about address processing, PEP 8 provides crucial guidelines for writing clean and consistent Python code, which is relevant when using Python libraries for address manipulation.
• Key recommendations include:
• Indentation: Use 4 spaces per indentation level.
• Maximum Line Length: Limit lines to 79 characters (72 for docstrings and comments).
• Imports: Organize imports into standard library, third-party, and local application/library imports, with blank lines separating groups. Use absolute imports generally.
• Naming Conventions: Follow consistent naming styles for variables, functions, classes, and constants (e.g., lowercase with underscores for functions and variables, CamelCase for classes, uppercase with underscores for constants).
• Whitespace: Use appropriate whitespace around operators, after commas, and in other syntactic elements for readability.
• Comments: Write clear and up-to-date comments, using block comments for larger explanations and inline comments sparingly.
• Adhering to PEP 8 enhances code readability and maintainability when working with address processing libraries in Python.

5. Google Maps Address Validation API Client (Python):

• Google provides a Python client library for its Address Validation API.
• Installation: The library can be installed using pip within a Python virtual environment.
• python3 -m venv <your-env>
• source <your-env>/bin/activate
• pip install google-maps-addressvalidation
• Prerequisites: Using the API requires a Google Cloud Platform project with billing enabled and the Address Validation API activated. Authentication setup is also necessary.
• Supported Python Versions: The client library supports Python 3.7 and later.
• Concurrency: The client is thread-safe and recommends creating client instances after os.fork() in multiprocessing scenarios.
• The API and its client library offer a way to programmatically validate and standardize addresses using Google’s data and services.

6. GeoPy Library for Geocoding (Python):

• GeoPy: This Python library provides geocoding services for various providers (e.g., Nominatim, GoogleV3, Bing) and allows calculating distances between geographic points.
• Supported Python Versions: GeoPy is tested against various CPython versions (3.7 to 3.12) and PyPy3.
• Geocoders: It supports a wide range of geocoding services, each with its own configuration and potential rate limits.
• Examples include Nominatim, GoogleV3, HERE, MapBox, OpenCage, and many others.
• Specifying Parameters: The functools.partial() function can be used to set common parameters (e.g., language, user agent) for geocoding requests.
• from functools import partial
• from geopy.geocoders import Nominatim
• geolocator = Nominatim(user_agent=”specify_your_app_name_here”)
• geocode = partial(geolocator.geocode, language=”es”)
• Rate Limiting: GeoPy includes a RateLimiter utility to manage API call frequency and avoid exceeding provider limits.
• from geopy.extra.rate_limiter import RateLimiter
• geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)
• Pandas Integration: GeoPy can be easily integrated with the Pandas library to geocode addresses stored in DataFrames.
• df[‘location’] = df[‘name’].apply(geocode)
• Distance Calculation: The geopy.distance module allows calculating distances between points using different methods (e.g., geodesic, great-circle) and units.
• from geopy import distance
• newport_ri = (41.49008, -71.312796)
• cleveland_oh = (41.499498, -81.695391)
• print(distance.distance(newport_ri, cleveland_oh).miles)
• Point Class: GeoPy provides a Point class to represent geographic coordinates with latitude, longitude, and optional altitude, offering various formatting options.

7. usaddress Library for US Address Parsing (Python & GitHub):

• usaddress: This Python library is specifically designed for parsing unstructured United States address strings into their components.
• “🇺🇸 a python library for parsing unstructured United States address strings into address components” (GitHub)
• Parsing and Tagging: It offers two main methods:
• parse(): Splits the address string into components and labels each one.
• Example: usaddress.parse(‘123 Main St. Suite 100 Chicago, IL’) would output [(u’123′, ‘AddressNumber’), (u’Main’, ‘StreetName’), (u’St.’, ‘StreetNamePostType’), (u’Suite’, ‘OccupancyType’), (u’100′, ‘OccupancyIdentifier’), (u’Chicago,’, ‘PlaceName’), (u’IL’, ‘StateName’)]
• tag(): Attempts to be smarter by merging consecutive components, stripping commas, and returning an ordered dictionary of labeled components along with an address type.
• Example: usaddress.tag(‘123 Main St. Suite 100 Chicago, IL’) would output (OrderedDict([(‘AddressNumber’, u’123′), (‘StreetName’, u’Main’), (‘StreetNamePostType’, u’St.’), (‘OccupancyType’, u’Suite’), (‘OccupancyIdentifier’, u’100′), (‘PlaceName’, u’Chicago’), (‘StateName’, u’IL’)]), ‘Street Address’)
• Installation: It can be installed using pip install usaddress.
• Open Source: Released under the MIT License.
• Extensibility: Users can add new training data to improve the parser’s accuracy on specific address patterns.

Conclusion:

The provided sources highlight a range of tools and techniques for handling address data. From fuzzy matching algorithms that account for typographical errors to specialized libraries for parsing and geocoding, developers have access to sophisticated solutions. The choice of tool depends on the specific requirements, such as the geographic scope of the addresses, the need for parsing vs. geocoding, the volume of data, and the programming language being used. Furthermore, adhering to coding style guides like PEP 8 is essential for maintaining clean and effective code when implementing these solutions in Python.