Address parsing, cleanup, and normalization
When working with large amounts of address data, simply standardizing the addresses to make them workable can take longer than doing the actual analysis.
If you're pulling together data from multiple sources or user-inputted data, this can be even more time-consuming. Geocodio takes away this frustration by parsing addresses into their components, allowing you to quickly standardize a list of addresses.
We bought a list of addresses from the USPS that was incomplete. Using Geocodio, were able to get the missing information for thousands of addresses in a few minutes. It was simple and easy.
Sebastien Benoit, SAP Director, Avertra Corporation
Address Parsing
Split addresses up into their components: street, city, state, ZIP, and county.
Address Completion
Add missing components, including as city/state (if ZIP given), ZIP (if city/state given), and county.
Address Normalization
Get all of the addresses in a consistent format.
Example Input: "42370 ## Bob Hoppe RANCH MraGE"
Output
- number: 42370
- street: Bob Hope
- suffix: Dr
- formatted_street: Bob Hope Dr
- city: Rancho Mirage
- county: Riverside County
- state: CA
- zip: 92270
- lat: 33.739482744493 lng: -116.40828650441