How to Parse Usable Street Address, City, State, and Zip Code from a Single String

When migrating data from an Access database to SQL Server 2005, a common challenge arises: parsing a single address field into its individual components. For instance, an address may be received from a user or existing database in one cluttered string, like this:

A. P. Croll & Son 2299 Lewes-Georgetown Hwy, Georgetown, DE 19947

With approximately 4,000 records to process, the task can become overwhelming. This blog post guides you through practical and efficient methods to break down an address string into usable parts: street address, city, state, and zip code.

Understanding the Problem

The Challenge

The main challenge lies in the unpredictability of address formats. Each one could include:

• Variations in presenting the street addresses (e.g., including addressees or suite numbers)
• Abbreviations for states
• Possible typos and formatting inconsistencies
• Standard 5-digit zip codes or extended zip+4 codes

Assumptions

When creating a parsing solution, we assume:

1. The addresses are within the U.S.
2. Some entries might contain addressees or secondary address lines (like “Suite B”).
3. Various abbreviations and potential typos exist.

Step-by-Step Parsing Strategy

1. Start with the Zip Code

Begin parsing from the end of the address string. The zip code is typically found near the end and generally appears in one of two known formats:

• XXXXX (5 digits)
• XXXXX-XXXX (zip+4)

If neither format is present, you’re likely still in the city or state section.

2. Extract the State

Immediately preceding the zip code, you will find the state. This can be either:

• A two-letter abbreviation (e.g., DE for Delaware)
• Written out as a full word, although that’s less common

Utilizing a reference list of U.S. state abbreviations can help normalize the results. Typographical errors can be mitigated by using a Soundex algorithm for spelling correction on state names.

3. Identify the City

Typically, the city name will appear right before the state. While parsing, you could cross-reference the extracted zip code against a zip-code database to confirm validity. This serves as a double-check mechanism for the city-state association.

4. Determine the Street Address

The street address is ordinarily found at the beginning of the string. If multiple lines are present, the second line often contains a suite number or a P.O. Box. Break down this section into components by identifying common patterns (e.g., characters like commas, and line breaks).

5. Address Line Naming

Identifying names or addressees can be tricky. A potential rule to apply:

• If a line is not prefixed by a number, or starts with terms like “attn:” or “attention to:”, consider it likely to be a name rather than an address.

Final Steps and Visual Check

After parsing, it’s wise to conduct a visual examination of results. Due to the inherent errors from source data and variations in formatting, a manual review can ensure no significant discrepancies exist.

Conclusion

While parsing a single string into accurate address components poses challenges due to inconsistencies and potential inaccuracies, following a structured approach can help significantly streamline the process. By working backward from the zip code and employing checks against known data, you can extract valuable address information efficiently.

Implementing these methods will allow you to maintain an organized, normalized table for your records in SQL Server, making future data handling much easier. Happy parsing!